diff --git a/.gitattributes b/.gitattributes
index 528e6c467718c150e70de8996d16816761318448..d988010853b0f749807be4a2a8f1980c82fe3d4b 100644
--- a/.gitattributes
+++ b/.gitattributes
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wandb/run-20250721_155939-ib4az6kg/run-ib4az6kg.wandb filter=lfs diff=lfs merge=lfs -text
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diff --git a/simson_modeling/.create_augmented_dataset.py.swp b/simson_modeling/.create_augmented_dataset.py.swp
new file mode 100644
index 0000000000000000000000000000000000000000..42debcd3a5ae55f67e05723de7b206989c2b2a98
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diff --git a/simson_modeling/.create_splits.py.swp b/simson_modeling/.create_splits.py.swp
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diff --git a/simson_modeling/.ipynb_checkpoints/create_augmented_dataset-checkpoint.py b/simson_modeling/.ipynb_checkpoints/create_augmented_dataset-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd992af2f0eb19b2fd9653d1030e0f9b5336a701
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/create_augmented_dataset-checkpoint.py
@@ -0,0 +1,83 @@
+import pandas as pd
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset
+from multiprocessing import Pool, cpu_count
+import os
+
+# Suppress RDKit console output for cleaner logs
+RDLogger.DisableLog('rdApp.*')
+
+class SmilesEnumerator:
+ """
+ A simple class to encapsulate the SMILES randomization logic.
+ Needed for multiprocessing to work correctly with instance methods.
+ """
+ def randomize_smiles(self, smiles):
+ """Generates a randomized SMILES string."""
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ # Return a randomized, non-canonical SMILES string
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ # If RDKit fails, return the original smiles string
+ return smiles
+
+def create_augmented_pair(smiles_string):
+ """
+ Worker function: takes one SMILES string and returns a tuple
+ containing two different randomized versions of it.
+ """
+ enumerator = SmilesEnumerator()
+ smiles_1 = enumerator.randomize_smiles(smiles_string)
+ smiles_2 = enumerator.randomize_smiles(smiles_string)
+ return smiles_1, smiles_2
+
+def main():
+ """
+ Main function to run the parallel data preprocessing.
+ """
+ # --- Configuration ---
+ # Load your desired dataset from Hugging Face
+ dataset_name = 'jablonkagroup/pubchem-smiles-molecular-formula'
+ # Specify the column containing the SMILES strings
+ smiles_column_name = 'smiles'
+ # Set the output file path
+ output_path = 'data/pubchem_2_epoch'
+
+ # --- Data Loading ---
+ print(f"Loading dataset '{dataset_name}'...")
+ # Use streaming to avoid downloading the whole dataset if you only need a subset
+ #dataset = pd.read_csv('/home/jovyan/simson_training_bolgov/data/PI1M_v2.csv')
+ dataset = load_dataset(dataset_name)['train']
+ # Take the desired number of samples
+ smiles_list = dataset[smiles_column_name].to_list()
+ print(f"Successfully fetched {len(smiles_list)} SMILES strings.")
+
+ # --- Parallel Processing ---
+ # Use all available CPU cores for maximum speed
+ num_workers = cpu_count()
+ print(f"Starting SMILES augmentation with {num_workers} worker processes...")
+
+ # A Pool of processes will run the `create_augmented_pair` function in parallel
+ with Pool(num_workers) as p:
+ # Use tqdm to create a progress bar for the mapping operation
+ results = list(tqdm(p.imap(create_augmented_pair, smiles_list), total=len(smiles_list), desc="Augmenting Pairs"))
+
+ # --- Saving Data ---
+ print("Processing complete. Converting to DataFrame...")
+ # Convert the list of tuples into a pandas DataFrame
+ df = pd.DataFrame(results, columns=['smiles_1', 'smiles_2'])
+
+ # Ensure the output directory exists
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+ print(f"Saving augmented pairs to '{output_path}'...")
+ # Save the DataFrame to a Parquet file for efficient storage and loading
+ df.to_parquet(output_path)
+
+ print("All done. Your pre-computed dataset is ready!")
+
+if __name__ == '__main__':
+ main()
+
diff --git a/simson_modeling/.ipynb_checkpoints/create_augmented_dataset.py-checkpoint.save b/simson_modeling/.ipynb_checkpoints/create_augmented_dataset.py-checkpoint.save
new file mode 100644
index 0000000000000000000000000000000000000000..26e348bc61c9db901521026a44df21ccb7260c8f
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/create_augmented_dataset.py-checkpoint.save
@@ -0,0 +1,83 @@
+import pandas as pd
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset
+from multiprocessing import Pool, cpu_count
+import os
+
+# Suppress RDKit console output for cleaner logs
+RDLogger.DisableLog('rdApp.*')
+
+class SmilesEnumerator:
+ """
+ A simple class to encapsulate the SMILES randomization logic.
+ Needed for multiprocessing to work correctly with instance methods.
+ """
+ def randomize_smiles(self, smiles):
+ """Generates a randomized SMILES string."""
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ # Return a randomized, non-canonical SMILES string
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ # If RDKit fails, return the original smiles string
+ return smiles
+
+def create_augmented_pair(smiles_string):
+ """
+ Worker function: takes one SMILES string and returns a tuple
+ containing two different randomized versions of it.
+ """
+ enumerator = SmilesEnumerator()
+ smiles_1 = enumerator.randomize_smiles(smiles_string)
+ smiles_2 = enumerator.randomize_smiles(smiles_string)
+ return smiles_1, smiles_2
+
+def main():
+ """
+ Main function to run the parallel data preprocessing.
+ """
+ # --- Configuration ---
+ # Load your desired dataset from Hugging Face
+ dataset_name = 'jablonkagroup/pubchem-smiles-molecular-formula'
+ # Specify the column containing the SMILES strings
+ smiles_column_name = 'smiles'
+ # Set the output file path
+ output_path = 'data/pubchem_computed_110_end_M.parquet'
+
+ # --- Data Loading ---
+ print(f"Loading dataset '{dataset_name}'...")
+ # Use streaming to avoid downloading the whole dataset if you only need a subset
+ dataset = load_dataset(dataset_name, split='train').select(range(110_000_000, ))
+
+ # Take the desired number of samples
+ smiles_list = dataset[smiles_column_name]
+ print(f"Successfully fetched {len(smiles_list)} SMILES strings.")
+
+ # --- Parallel Processing ---
+ # Use all available CPU cores for maximum speed
+ num_workers = cpu_count()
+ print(f"Starting SMILES augmentation with {num_workers} worker processes...")
+
+ # A Pool of processes will run the `create_augmented_pair` function in parallel
+ with Pool(num_workers) as p:
+ # Use tqdm to create a progress bar for the mapping operation
+ results = list(tqdm(p.imap(create_augmented_pair, smiles_list), total=len(smiles_list), desc="Augmenting Pairs"))
+
+ # --- Saving Data ---
+ print("Processing complete. Converting to DataFrame...")
+ # Convert the list of tuples into a pandas DataFrame
+ df = pd.DataFrame(results, columns=['smiles_1', 'smiles_2'])
+
+ # Ensure the output directory exists
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+ print(f"Saving augmented pairs to '{output_path}'...")
+ # Save the DataFrame to a Parquet file for efficient storage and loading
+ df.to_parquet(output_path)
+
+ print("All done. Your pre-computed dataset is ready!")
+
+if __name__ == '__main__':
+ main()
+
diff --git a/simson_modeling/.ipynb_checkpoints/create_splits-checkpoint.py b/simson_modeling/.ipynb_checkpoints/create_splits-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..c05553f7f90fbfda614cc9f6be4a0dbc74c7f57a
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/create_splits-checkpoint.py
@@ -0,0 +1,200 @@
+import os
+import pandas as pd
+from pathlib import Path
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+def concatenate_and_split_parquet(
+ input_dir: str,
+ output_dir: str,
+ val_size: int = 10000,
+ test_size: int = 5000,
+ random_state: int = 42
+):
+ """
+ Concatenate all parquet files in a directory and split into train/val/test sets.
+
+ Args:
+ input_dir: Path to directory containing parquet files
+ output_dir: Path to directory where split files will be saved
+ val_size: Number of samples for validation set (default: 10000)
+ test_size: Number of samples for test set (default: 5000)
+ random_state: Random seed for reproducibility
+ """
+
+ # Create output directory if it doesn't exist
+ Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+ # Find all parquet files in the input directory
+ input_path = Path(input_dir)
+ parquet_files = list(input_path.glob("*.parquet"))
+
+ if not parquet_files:
+ raise ValueError(f"No parquet files found in {input_dir}")
+
+ print(f"Found {len(parquet_files)} parquet files")
+
+ # Read and concatenate all parquet files
+ print("Reading and concatenating parquet files...")
+ dataframes = []
+
+ for file_path in parquet_files:
+ print(f"Reading {file_path.name}...")
+ df = pd.read_parquet(file_path)
+ dataframes.append(df)
+
+ # Concatenate all dataframes
+ combined_df = pd.concat(dataframes, ignore_index=True)
+ print(f"Combined dataset shape: {combined_df.shape}")
+
+ # Check if we have enough samples
+ total_samples = len(combined_df)
+ required_samples = val_size + test_size
+
+ if total_samples < required_samples:
+ raise ValueError(
+ f"Not enough samples. Required: {required_samples}, Available: {total_samples}"
+ )
+
+ # Shuffle the data
+ combined_df = combined_df.sample(frac=1, random_state=random_state).reset_index(drop=True)
+
+ # Split the data
+ print("Splitting data...")
+
+ # First split: separate test set
+ temp_df, test_df = train_test_split(
+ combined_df,
+ test_size=test_size,
+ random_state=random_state
+ )
+
+ # Second split: separate validation from remaining data
+ train_df, val_df = train_test_split(
+ temp_df,
+ test_size=val_size,
+ random_state=random_state
+ )
+
+ print(f"Training set shape: {train_df.shape}")
+ print(f"Validation set shape: {val_df.shape}")
+ print(f"Test set shape: {test_df.shape}")
+
+ # Save the splits as parquet files
+ output_path = Path(output_dir)
+
+ train_path = output_path / "train.parquet"
+ val_path = output_path / "validation.parquet"
+ test_path = output_path / "test.parquet"
+
+ print("Saving split datasets...")
+ train_df.to_parquet(train_path, index=False)
+ val_df.to_parquet(val_path, index=False)
+ test_df.to_parquet(test_path, index=False)
+
+ print(f"Files saved to:")
+ print(f" Training: {train_path}")
+ print(f" Validation: {val_path}")
+ print(f" Test: {test_path}")
+
+ return train_df, val_df, test_df
+
+# Alternative version using PyArrow for better performance with large files
+def concatenate_and_split_parquet_arrow(
+ input_dir: str,
+ output_dir: str,
+ val_size: int = 10000,
+ test_size: int = 5000,
+ random_state: int = 42
+):
+ """
+ Same functionality as above but using PyArrow for better performance.
+ """
+ import pyarrow as pa
+ import pyarrow.parquet as pq
+
+ # Create output directory if it doesn't exist
+ Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+ # Find all parquet files
+ input_path = Path(input_dir)
+ parquet_files = list(input_path.glob("*.parquet"))
+
+ if not parquet_files:
+ raise ValueError(f"No parquet files found in {input_dir}")
+
+ print(f"Found {len(parquet_files)} parquet files")
+
+ # Read and concatenate using PyArrow
+ print("Reading and concatenating parquet files...")
+ tables = []
+
+ for file_path in parquet_files:
+ print(f"Reading {file_path.name}...")
+ table = pq.read_table(file_path)
+ tables.append(table)
+
+ # Concatenate tables
+ combined_table = pa.concat_tables(tables)
+ combined_df = combined_table.to_pandas()
+
+ print(f"Combined dataset shape: {combined_df.shape}")
+
+ # Rest of the function is the same as above
+ total_samples = len(combined_df)
+ required_samples = val_size + test_size
+
+ if total_samples < required_samples:
+ raise ValueError(
+ f"Not enough samples. Required: {required_samples}, Available: {total_samples}"
+ )
+
+ # Shuffle and split
+ combined_df = combined_df.sample(frac=1, random_state=random_state).reset_index(drop=True)
+
+ temp_df, test_df = train_test_split(
+ combined_df, test_size=test_size, random_state=random_state
+ )
+
+ train_df, val_df = train_test_split(
+ temp_df, test_size=val_size, random_state=random_state
+ )
+
+ print(f"Training set shape: {train_df.shape}")
+ print(f"Validation set shape: {val_df.shape}")
+ print(f"Test set shape: {test_df.shape}")
+
+ # Save using PyArrow
+ output_path = Path(output_dir)
+
+ pq.write_table(pa.Table.from_pandas(train_df), output_path / "train.parquet")
+ pq.write_table(pa.Table.from_pandas(val_df), output_path / "validation.parquet")
+ pq.write_table(pa.Table.from_pandas(test_df), output_path / "test.parquet")
+
+ print(f"Files saved to {output_dir}")
+
+ return train_df, val_df, test_df
+
+# Example usage
+if __name__ == "__main__":
+ # Example usage
+ input_directory = "data"
+ output_directory = "data/polymer_splits"
+
+ # Using pandas version
+ train_df, val_df, test_df = concatenate_and_split_parquet(
+ input_dir=input_directory,
+ output_dir=output_directory,
+ val_size=10000,
+ test_size=5000,
+ random_state=42
+ )
+
+ # Or using PyArrow version for better performance
+ # train_df, val_df, test_df = concatenate_and_split_parquet_arrow(
+ # input_dir=input_directory,
+ # output_dir=output_directory,
+ # val_size=10000,
+ # test_size=5000,
+ # random_state=42
+ # )
diff --git a/simson_modeling/.ipynb_checkpoints/fingerprint_training-checkpoint.ipynb b/simson_modeling/.ipynb_checkpoints/fingerprint_training-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7617d1cdce2ccb95b586f44a79f7cd334bc4ab6e
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/fingerprint_training-checkpoint.ipynb
@@ -0,0 +1,1550 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3d5d52d1-4874-44b5-b532-ef03da47644a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import Descriptors, rdMolDescriptors, Crippen, Lipinski\n",
+ "from tqdm import tqdm\n",
+ "import warnings\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import random\n",
+ "from concurrent.futures import ProcessPoolExecutor\n",
+ "import multiprocessing\n",
+ "\n",
+ "def analyze_polymer_features_rdkit(smiles):\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return None\n",
+ " \n",
+ " features = {}\n",
+ " \n",
+ " # Basic molecular properties\n",
+ " features['mol_weight'] = Descriptors.MolWt(mol)\n",
+ " features['exact_mol_weight'] = Descriptors.ExactMolWt(mol)\n",
+ " features['num_heavy_atoms'] = mol.GetNumHeavyAtoms()\n",
+ " features['num_atoms'] = mol.GetNumAtoms()\n",
+ " features['num_bonds'] = mol.GetNumBonds()\n",
+ " \n",
+ " # Hydrogen bonding features\n",
+ " features['num_hbond_donors'] = Descriptors.NumHDonors(mol)\n",
+ " features['num_hbond_acceptors'] = Descriptors.NumHAcceptors(mol)\n",
+ " features['num_heteroatoms'] = Descriptors.NumHeteroatoms(mol)\n",
+ " \n",
+ " # Structural complexity\n",
+ " features['num_rotatable_bonds'] = Descriptors.NumRotatableBonds(mol)\n",
+ " features['num_saturated_rings'] = Descriptors.NumSaturatedRings(mol)\n",
+ " features['num_aromatic_rings'] = Descriptors.NumAromaticRings(mol)\n",
+ " features['num_aliphatic_rings'] = Descriptors.NumAliphaticRings(mol)\n",
+ " features['ring_count'] = Descriptors.RingCount(mol)\n",
+ " features['fraction_csp3'] = Descriptors.FractionCSP3(mol)\n",
+ " \n",
+ " # Surface area and polarity\n",
+ " features['tpsa'] = Descriptors.TPSA(mol)\n",
+ " features['polar_surface_area'] = rdMolDescriptors.CalcTPSA(mol)\n",
+ " \n",
+ " # Lipophilicity and solubility\n",
+ " features['logp'] = Descriptors.MolLogP(mol)\n",
+ " features['crippen_logp'] = Crippen.MolLogP(mol)\n",
+ " features['crippen_mr'] = Crippen.MolMR(mol) # Molar refractivity\n",
+ " \n",
+ " # Flexibility and rigidity\n",
+ " features['kappa1'] = Descriptors.Kappa1(mol) # Molecular shape index\n",
+ " features['kappa2'] = Descriptors.Kappa2(mol)\n",
+ " features['kappa3'] = Descriptors.Kappa3(mol)\n",
+ " features['chi0v'] = Descriptors.Chi0v(mol) # Connectivity indices\n",
+ " features['chi1v'] = Descriptors.Chi1v(mol)\n",
+ " features['chi2v'] = Descriptors.Chi2v(mol)\n",
+ " \n",
+ " # Electronic properties\n",
+ " features['balaban_j'] = Descriptors.BalabanJ(mol)\n",
+ " features['bertz_ct'] = Descriptors.BertzCT(mol) # Complexity index\n",
+ " \n",
+ " # Polymer-specific features\n",
+ " features['num_radical_electrons'] = Descriptors.NumRadicalElectrons(mol)\n",
+ " features['num_valence_electrons'] = Descriptors.NumValenceElectrons(mol)\n",
+ " \n",
+ " # Atom type counts\n",
+ " atom_counts = {}\n",
+ " for atom in mol.GetAtoms():\n",
+ " symbol = atom.GetSymbol()\n",
+ " atom_counts[symbol] = atom_counts.get(symbol, 0) + 1\n",
+ " \n",
+ " # Add individual atom counts as features\n",
+ " for element in ['C', 'N', 'O', 'S', 'P', 'F', 'Cl', 'Br', 'I']:\n",
+ " features[f'count_{element}'] = atom_counts.get(element, 0)\n",
+ " features[f'ratio_{element}'] = atom_counts.get(element, 0) / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Bond type analysis\n",
+ " bond_types = {'SINGLE': 0, 'DOUBLE': 0, 'TRIPLE': 0, 'AROMATIC': 0}\n",
+ " for bond in mol.GetBonds():\n",
+ " bond_type = str(bond.GetBondType())\n",
+ " if bond_type in bond_types:\n",
+ " bond_types[bond_type] += 1\n",
+ " \n",
+ " for bond_type, count in bond_types.items():\n",
+ " features[f'num_{bond_type.lower()}_bonds'] = count\n",
+ " features[f'ratio_{bond_type.lower()}_bonds'] = count / features['num_bonds'] if features['num_bonds'] > 0 else 0\n",
+ " \n",
+ " # Hybridization analysis\n",
+ " hybridization_counts = {'SP': 0, 'SP2': 0, 'SP3': 0, 'SP3D': 0, 'SP3D2': 0}\n",
+ " for atom in mol.GetAtoms():\n",
+ " hyb = str(atom.GetHybridization())\n",
+ " if hyb in hybridization_counts:\n",
+ " hybridization_counts[hyb] += 1\n",
+ " \n",
+ " for hyb_type, count in hybridization_counts.items():\n",
+ " features[f'num_{hyb_type.lower()}_carbons'] = count\n",
+ " features[f'ratio_{hyb_type.lower()}_carbons'] = count / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Formal charge analysis\n",
+ " formal_charges = [atom.GetFormalCharge() for atom in mol.GetAtoms()]\n",
+ " features['total_formal_charge'] = sum(formal_charges)\n",
+ " features['abs_total_formal_charge'] = sum(abs(charge) for charge in formal_charges)\n",
+ " features['max_formal_charge'] = max(formal_charges) if formal_charges else 0\n",
+ " features['min_formal_charge'] = min(formal_charges) if formal_charges else 0\n",
+ " \n",
+ " # Aromaticity features\n",
+ " aromatic_atoms = sum(1 for atom in mol.GetAtoms() if atom.GetIsAromatic())\n",
+ " features['num_aromatic_atoms'] = aromatic_atoms\n",
+ " features['aromatic_ratio'] = aromatic_atoms / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Ring size analysis\n",
+ " ring_info = mol.GetRingInfo()\n",
+ " ring_sizes = [len(ring) for ring in ring_info.AtomRings()]\n",
+ " if ring_sizes:\n",
+ " features['avg_ring_size'] = sum(ring_sizes) / len(ring_sizes)\n",
+ " features['max_ring_size'] = max(ring_sizes)\n",
+ " features['min_ring_size'] = min(ring_sizes)\n",
+ " features['num_3_rings'] = sum(1 for size in ring_sizes if size == 3)\n",
+ " features['num_4_rings'] = sum(1 for size in ring_sizes if size == 4)\n",
+ " features['num_5_rings'] = sum(1 for size in ring_sizes if size == 5)\n",
+ " features['num_6_rings'] = sum(1 for size in ring_sizes if size == 6)\n",
+ " features['num_7_rings'] = sum(1 for size in ring_sizes if size == 7)\n",
+ " features['num_large_rings'] = sum(1 for size in ring_sizes if size > 7)\n",
+ " else:\n",
+ " features.update({\n",
+ " 'avg_ring_size': 0, 'max_ring_size': 0, 'min_ring_size': 0,\n",
+ " 'num_3_rings': 0, 'num_4_rings': 0, 'num_5_rings': 0,\n",
+ " 'num_6_rings': 0, 'num_7_rings': 0, 'num_large_rings': 0\n",
+ " })\n",
+ " \n",
+ " # Polymer-specific structural features\n",
+ " features['has_polymer_notation'] = '*' in smiles\n",
+ " features['smiles_length'] = len(smiles)\n",
+ " features['branch_count'] = smiles.count('(')\n",
+ " features['branch_ratio'] = smiles.count('(') / len(smiles) if len(smiles) > 0 else 0\n",
+ " \n",
+ " return features\n",
+ "\n",
+ "def add_features(df, num_workers=None):\n",
+ " \"\"\"\n",
+ " Improved version using multiprocessing to calculate RDKit descriptors efficiently.\n",
+ " \n",
+ " Parameters:\n",
+ " df: pandas DataFrame with 'Smiles' column\n",
+ " num_workers: Number of worker processes (defaults to number of CPU cores)\n",
+ " \"\"\"\n",
+ " if num_workers is None:\n",
+ " num_workers = multiprocessing.cpu_count()\n",
+ " \n",
+ " smiles_list = df['Smiles'].tolist()\n",
+ " \n",
+ " with ProcessPoolExecutor(max_workers=num_workers) as executor:\n",
+ " # Use tqdm with executor.map for progress tracking\n",
+ " features_list = list(tqdm(executor.map(analyze_polymer_features_rdkit, smiles_list), \n",
+ " total=len(smiles_list), \n",
+ " desc=\"Computing RDKit descriptors\"))\n",
+ " \n",
+ " # Convert results to DataFrame\n",
+ " features_df = pd.DataFrame(features_list)\n",
+ " \n",
+ " # Concatenate with original DataFrame\n",
+ " df_result = pd.concat([df, features_df], axis=1)\n",
+ " \n",
+ " return df_result\n",
+ "\n",
+ "def get_list_dif(l1, l2):\n",
+ " return list(set(l1) - set(l2))\n",
+ "\n",
+ "# Usage example:\n",
+ "# df_with_features = add_features(df, num_workers=4)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "155598af-79f3-4933-8b5c-1fd11f64b870",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv').drop(columns=['Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c69cc497-9fb6-4f74-96eb-257d7aa4a91a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/kaggle_comp/train.csv')\n",
+ "df['Smiles'] = df['SMILES']\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b076c55-d6ef-4780-af97-5fccd5062661",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_df = df.iloc[:10_000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96313883-c2ca-4eb8-9ec7-9aaca8dba077",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "features_df = add_features(sample_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "41c7f85a-ea65-42e5-b315-ef304ba311c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "selected_features = ['mol_weight', 'exact_mol_weight', 'num_heavy_atoms', 'num_atoms',\n",
+ " 'num_bonds', 'num_hbond_donors', 'num_hbond_acceptors',\n",
+ " 'num_heteroatoms', 'num_rotatable_bonds', 'num_saturated_rings',\n",
+ " 'num_aromatic_rings', 'num_aliphatic_rings', 'ring_count',\n",
+ " 'fraction_csp3', 'tpsa', 'polar_surface_area', 'logp', 'crippen_logp',\n",
+ " 'crippen_mr', 'kappa1', 'kappa2', 'kappa3', 'chi0v', 'chi1v', 'chi2v',\n",
+ " 'balaban_j', 'bertz_ct', 'num_radical_electrons',\n",
+ " 'num_valence_electrons',\n",
+ " 'count_O', 'ratio_O', 'count_S', 'ratio_S', 'count_P', 'ratio_P',\n",
+ " 'count_F', 'ratio_F', 'count_Cl', 'ratio_Cl', 'count_Br', 'ratio_Br',\n",
+ " 'count_I', 'ratio_I', 'num_single_bonds', 'ratio_single_bonds',\n",
+ " 'num_double_bonds', 'ratio_double_bonds', 'num_triple_bonds',\n",
+ " 'ratio_triple_bonds', 'num_aromatic_bonds', 'ratio_aromatic_bonds',\n",
+ " 'num_sp_carbons', 'ratio_sp_carbons', 'num_sp2_carbons',\n",
+ " 'ratio_sp2_carbons', 'num_sp3_carbons', 'ratio_sp3_carbons',\n",
+ " 'num_sp3d_carbons', 'ratio_sp3d_carbons', 'num_sp3d2_carbons',\n",
+ " 'ratio_sp3d2_carbons', 'total_formal_charge', 'abs_total_formal_charge',\n",
+ " 'max_formal_charge', 'min_formal_charge', 'num_aromatic_atoms',\n",
+ " 'aromatic_ratio', 'avg_ring_size', 'max_ring_size', 'min_ring_size',\n",
+ " 'num_3_rings', 'num_4_rings', 'num_5_rings', 'num_6_rings',\n",
+ " 'num_7_rings', 'num_large_rings', 'has_polymer_notation',\n",
+ " 'branch_count', 'branch_ratio']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc31605d-cc21-4533-b04e-f8acdaef1a65",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "for col in selected_features:\n",
+ " scaler = StandardScaler()\n",
+ " features_df[col] = scaler.fit_transform(features_df[col].to_numpy().reshape(-1, 1)).flatten()\n",
+ " scalers.append(scaler)\n",
+ " \n",
+ "features_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f2f1a614-0ba7-4a01-9731-532afc1d14e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['mol_weight', 'exact_mol_weight', 'fraction_csp3', 'tpsa', 'polar_surface_area', 'logp', 'crippen_logp', 'crippen_mr', 'kappa1', 'kappa2', 'kappa3', 'chi0v', 'chi1v', 'chi2v', 'balaban_j', 'bertz_ct', 'ratio_O', 'ratio_single_bonds', 'ratio_double_bonds', 'ratio_aromatic_bonds', 'ratio_sp2_carbons', 'ratio_sp3_carbons', 'aromatic_ratio', 'branch_ratio', 'Smiles']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(25, 79)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_features = []\n",
+ "\n",
+ "for feature in selected_features:\n",
+ " unique_list = features_df[feature].unique()\n",
+ " if len(unique_list) > 300:\n",
+ " new_features.append(feature)\n",
+ "new_features.append('Smiles')\n",
+ "print(new_features)\n",
+ "len(new_features), len(selected_features)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28cbac75-8a9f-4292-aedb-11f33f5a6056",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c065d950-7a63-4424-9923-1072d2e2268c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "features_df.to_csv('7k_w_descriptors.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "069a9021-d440-4bf1-9882-a2af25f2e801",
+ "metadata": {},
+ "outputs": [
+ {
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+ " 0.387324 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)... | \n",
+ " 0.417716 | \n",
+ " 0.417722 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " -0.626991 | \n",
+ " 1.118291 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 79 | \n",
+ " 0.556606 | \n",
+ " 0.132247 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 539187 | \n",
+ " *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... | \n",
+ " NaN | \n",
+ " 0.355470 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... | \n",
+ " 2.178003 | \n",
+ " 2.178499 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 1.501149 | \n",
+ " 0.355413 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 118 | \n",
+ " 0.556606 | \n",
+ " -0.830501 | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 7968 | \n",
+ " 2146592435 | \n",
+ " *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 | \n",
+ " NaN | \n",
+ " 0.367498 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 | \n",
+ " -0.375261 | \n",
+ " -0.375084 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " -0.626991 | \n",
+ " -0.407465 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 44 | \n",
+ " -0.324438 | \n",
+ " 0.124891 | \n",
+ "
\n",
+ " \n",
+ " | 7969 | \n",
+ " 2146810552 | \n",
+ " *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... | \n",
+ " NaN | \n",
+ " 0.353280 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... | \n",
+ " 1.284275 | \n",
+ " 1.284737 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 1.501149 | \n",
+ " 0.736852 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 110 | \n",
+ " 1.217388 | \n",
+ " 0.008668 | \n",
+ "
\n",
+ " \n",
+ " | 7970 | \n",
+ " 2147191531 | \n",
+ " *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... | \n",
+ " NaN | \n",
+ " 0.369411 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... | \n",
+ " 0.329570 | \n",
+ " 0.329823 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 1.501149 | \n",
+ " -0.026026 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 73 | \n",
+ " 0.336345 | \n",
+ " 0.021405 | \n",
+ "
\n",
+ " \n",
+ " | 7971 | \n",
+ " 2147435020 | \n",
+ " *C=C(*)c1ccccc1C | \n",
+ " 261.662355 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *C=C(*)c1ccccc1C | \n",
+ " -1.359802 | \n",
+ " -1.359728 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " -0.626991 | \n",
+ " -0.788904 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 16 | \n",
+ " -1.205481 | \n",
+ " -1.182617 | \n",
+ "
\n",
+ " \n",
+ " | 7972 | \n",
+ " 2147438299 | \n",
+ " *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... | \n",
+ " NaN | \n",
+ " 0.374049 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... | \n",
+ " 1.160667 | \n",
+ " 1.160653 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 0.437079 | \n",
+ " -0.407465 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 72 | \n",
+ " -0.324438 | \n",
+ " -1.005054 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
7973 rows × 92 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id SMILES \\\n",
+ "0 87817 *CC(*)c1ccccc1C(=O)OCCCCCC \n",
+ "1 106919 *Nc1ccc([C@H](CCC)c2ccc(C3(c4ccc([C@@H](CCC)c5... \n",
+ "2 388772 *Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(... \n",
+ "3 519416 *Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)... \n",
+ "4 539187 *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... \n",
+ "... ... ... \n",
+ "7968 2146592435 *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 \n",
+ "7969 2146810552 *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... \n",
+ "7970 2147191531 *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... \n",
+ "7971 2147435020 *C=C(*)c1ccccc1C \n",
+ "7972 2147438299 *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... \n",
+ "\n",
+ " Tg FFV Tc Density Rg \\\n",
+ "0 NaN 0.374645 0.205667 NaN NaN \n",
+ "1 NaN 0.370410 NaN NaN NaN \n",
+ "2 NaN 0.378860 NaN NaN NaN \n",
+ "3 NaN 0.387324 NaN NaN NaN \n",
+ "4 NaN 0.355470 NaN NaN NaN \n",
+ "... ... ... ... ... .. \n",
+ "7968 NaN 0.367498 NaN NaN NaN \n",
+ "7969 NaN 0.353280 NaN NaN NaN \n",
+ "7970 NaN 0.369411 NaN NaN NaN \n",
+ "7971 261.662355 NaN NaN NaN NaN \n",
+ "7972 NaN 0.374049 NaN NaN NaN \n",
+ "\n",
+ " Smiles mol_weight \\\n",
+ "0 *CC(*)c1ccccc1C(=O)OCCCCCC -0.875755 \n",
+ "1 *Nc1ccc([C@H](CCC)c2ccc(C3(c4ccc([C@@H](CCC)c5... 0.651876 \n",
+ "2 *Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(... 2.336573 \n",
+ "3 *Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)... 0.417716 \n",
+ "4 *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... 2.178003 \n",
+ "... ... ... \n",
+ "7968 *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 -0.375261 \n",
+ "7969 *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... 1.284275 \n",
+ "7970 *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... 0.329570 \n",
+ "7971 *C=C(*)c1ccccc1C -1.359802 \n",
+ "7972 *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... 1.160667 \n",
+ "\n",
+ " exact_mol_weight ... num_3_rings num_4_rings num_5_rings \\\n",
+ "0 -0.875617 ... -0.048476 -0.069289 -0.626991 \n",
+ "1 0.651916 ... -0.048476 -0.069289 -0.626991 \n",
+ "2 2.336165 ... -0.048476 -0.069289 -0.626991 \n",
+ "3 0.417722 ... -0.048476 -0.069289 -0.626991 \n",
+ "4 2.178499 ... -0.048476 -0.069289 1.501149 \n",
+ "... ... ... ... ... ... \n",
+ "7968 -0.375084 ... -0.048476 -0.069289 -0.626991 \n",
+ "7969 1.284737 ... -0.048476 -0.069289 1.501149 \n",
+ "7970 0.329823 ... -0.048476 -0.069289 1.501149 \n",
+ "7971 -1.359728 ... -0.048476 -0.069289 -0.626991 \n",
+ "7972 1.160653 ... -0.048476 -0.069289 0.437079 \n",
+ "\n",
+ " num_6_rings num_7_rings num_large_rings has_polymer_notation \\\n",
+ "0 -0.788904 -0.051542 -0.047917 0.0 \n",
+ "1 0.736852 -0.051542 -0.047917 0.0 \n",
+ "2 2.644047 -0.051542 -0.047917 0.0 \n",
+ "3 1.118291 -0.051542 -0.047917 0.0 \n",
+ "4 0.355413 -0.051542 -0.047917 0.0 \n",
+ "... ... ... ... ... \n",
+ "7968 -0.407465 -0.051542 -0.047917 0.0 \n",
+ "7969 0.736852 -0.051542 -0.047917 0.0 \n",
+ "7970 -0.026026 -0.051542 -0.047917 0.0 \n",
+ "7971 -0.788904 -0.051542 -0.047917 0.0 \n",
+ "7972 -0.407465 -0.051542 -0.047917 0.0 \n",
+ "\n",
+ " smiles_length branch_count branch_ratio \n",
+ "0 26 -0.985221 -0.813832 \n",
+ "1 82 0.336345 -0.286141 \n",
+ "2 134 1.657910 -0.109289 \n",
+ "3 79 0.556606 0.132247 \n",
+ "4 118 0.556606 -0.830501 \n",
+ "... ... ... ... \n",
+ "7968 44 -0.324438 0.124891 \n",
+ "7969 110 1.217388 0.008668 \n",
+ "7970 73 0.336345 0.021405 \n",
+ "7971 16 -1.205481 -1.182617 \n",
+ "7972 72 -0.324438 -1.005054 \n",
+ "\n",
+ "[7973 rows x 92 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "features_df = pd.read_csv('7k_w_descriptors.csv')\n",
+ "features_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "49998b8a-3925-4383-917a-116f70187d46",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n"
+ ]
+ }
+ ],
+ "source": [
+ "old_len = len(features_df)\n",
+ "new_len = len(features_df.drop_duplicates())\n",
+ "print(new_len - old_len)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c2f08ca9-21f6-4a79-ab94-80556b8dab1d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████| 6378/6378 [00:01<00:00, 3382.49it/s]\n",
+ "100%|█████████████████████████████████████| 1595/1595 [00:00<00:00, 3554.96it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "import copy\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "def create_splits(df):\n",
+ " train, test = train_test_split(df, test_size=0.2)\n",
+ " return train, test\n",
+ "\n",
+ "def create_samples(df, features):\n",
+ " samples = []\n",
+ " features_without_smiles = copy.deepcopy(features)\n",
+ " features_without_smiles.remove('Smiles')\n",
+ " for i, row in tqdm(df.iterrows(), total=len(df)):\n",
+ " properties = torch.Tensor(row[features_without_smiles].to_list())\n",
+ " sample = {'Smiles': row['Smiles'], 'property_tensor': properties}\n",
+ " samples.append(sample)\n",
+ " return samples\n",
+ "\n",
+ "train, val = create_splits(features_df.reset_index(drop=True))\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "val = val.reset_index(drop=True)\n",
+ "\n",
+ "train_list = create_samples(train, new_features)\n",
+ "val_list = create_samples(val, new_features)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2fdb3171-deda-4c1f-ae4b-853d781ffdd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|████████████████████████████████████████| 20/20 [00:00<00:00, 74764.78it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "prop_vectors = [el['property_tensor'] for el in train_list[:20]]\n",
+ "\n",
+ "sim_matrix = cosine_similarity(prop_vectors)\n",
+ " \n",
+ "n = len(prop_vectors)\n",
+ "positive_pairs, negative_candidates = [], []\n",
+ "sims = []\n",
+ "\n",
+ "positive_threshold = 0.9\n",
+ "negative_threshold = 0.2\n",
+ "\n",
+ "for i in tqdm(range(n)):\n",
+ " for j in range(i + 1, n):\n",
+ " sim = sim_matrix[i, j]\n",
+ "\n",
+ " if sim > positive_threshold:\n",
+ " positive_pairs.append((i, j, sim))\n",
+ " elif sim < negative_threshold:\n",
+ " negative_candidates.append((i, j, sim))\n",
+ " sims.append(float(sim))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54f29e98-7c32-441c-bb1b-cdaf3fd1df49",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(positive_pairs), len(negative_candidates)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22e0f46e-2673-4840-95fd-f98914e57b78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(sims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "79e7e873-7950-4123-ab13-299360ae19ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from transformers import BertConfig, BertModel, AutoTokenizer\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "def initialize_model_and_tokenizer():\n",
+ " \"\"\"Initialize BERT model from config and ChemBERTa tokenizer\"\"\"\n",
+ " \n",
+ " \n",
+ " tokenizer = AutoTokenizer.from_pretrained(\"DeepChem/ChemBERTa-77M-MTR\")\n",
+ " config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512,\n",
+ " )\n",
+ " model = SimSonEncoder(config=config, max_len=512).cuda()\n",
+ " return model, tokenizer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8a3adaff-da65-46b4-b9ee-95851d786a67",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "\n",
+ "class MolecularContrastiveDataset(Dataset):\n",
+ " def __init__(self, data_list, tokenizer, positive_threshold=0.9, cache_path=None, split_type='train'):\n",
+ " \"\"\"\n",
+ " Dataset that only contains positive pairs for NT-Xent contrastive learning\n",
+ " \"\"\"\n",
+ " self.data_list = data_list\n",
+ " self.tokenizer = tokenizer\n",
+ " self.positive_threshold = positive_threshold\n",
+ " self.cache_path = cache_path\n",
+ " self.split_type = split_type\n",
+ "\n",
+ " # Load or compute pairs\n",
+ " if cache_path and os.path.exists(cache_path) and os.path.getsize(cache_path) > 0:\n",
+ " print(f\"Loading cached pairs from {cache_path}\")\n",
+ " self._load_pairs()\n",
+ " else:\n",
+ " print(\"Computing positive pairs only...\")\n",
+ " self._compute_positive_pairs()\n",
+ " if cache_path:\n",
+ " self._save_pairs()\n",
+ " \n",
+ " def _compute_positive_pairs(self):\n",
+ " \"\"\"\n",
+ " Compute ONLY positive pairs based on descriptor similarity\n",
+ " \"\"\"\n",
+ " # --- 1. Cosine-similarity matrix ---------------------------------------\n",
+ " prop_vectors = torch.stack(\n",
+ " [item['property_tensor'] for item in self.data_list]\n",
+ " ).numpy()\n",
+ " sim_matrix = cosine_similarity(prop_vectors)\n",
+ "\n",
+ " n = len(self.data_list)\n",
+ " positive_pairs = []\n",
+ " pairs_per_molecule = 1 # STRICTLY ONE FOR CREATING PROPER NEGATIVE PAIRS\n",
+ " current_pairs_per_molecule = 0\n",
+ " # --- 2. Collect only positive pairs ------------------------------------\n",
+ " print(f'Collecting positive pairs with similarity threshold {self.positive_threshold}')\n",
+ " for i in tqdm(range(n)):\n",
+ " for j in range(i + 1, n):\n",
+ " sim = sim_matrix[i, j]\n",
+ " if sim > self.positive_threshold:\n",
+ " positive_pairs.append((i, j, sim))\n",
+ " current_pairs_per_molecule += 1\n",
+ " if current_pairs_per_molecule > pairs_per_molecule:\n",
+ " current_pairs_per_molecule = 0\n",
+ " break\n",
+ "\n",
+ " # --- 3. Store only positive pairs --------------------------------------\n",
+ " if len(positive_pairs) == 0:\n",
+ " raise ValueError(\"No positive pairs found – lower the positive_threshold.\")\n",
+ "\n",
+ " # No shuffling - we want consistent positive pairs\n",
+ " self.pairs = [(i, j) for i, j, _ in positive_pairs]\n",
+ " self.descriptor_similarities = [sim for _, _, sim in positive_pairs]\n",
+ "\n",
+ " print(f\"Generated {len(self.pairs)} positive pairs\")\n",
+ "\n",
+ " def _save_pairs(self):\n",
+ " \"\"\"Save computed pairs to cache file\"\"\"\n",
+ " cache_data = {\n",
+ " 'pairs': self.pairs,\n",
+ " 'descriptor_similarities': self.descriptor_similarities\n",
+ " }\n",
+ " with open(self.cache_path, 'wb') as f:\n",
+ " pickle.dump(cache_data, f)\n",
+ " print(f\"Cached pairs saved to {self.cache_path}\")\n",
+ " \n",
+ " def _load_pairs(self):\n",
+ " \"\"\"Load pairs from cache file\"\"\"\n",
+ " with open(self.cache_path, 'rb') as f:\n",
+ " cache_data = pickle.load(f)\n",
+ " \n",
+ " self.pairs = cache_data['pairs']\n",
+ " self.descriptor_similarities = cache_data['descriptor_similarities']\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.pairs)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " i, j = self.pairs[idx]\n",
+ " desc_sim = self.descriptor_similarities[idx]\n",
+ " \n",
+ " # Get SMILES for both molecules\n",
+ " smiles_i = self.data_list[i]['Smiles']\n",
+ " smiles_j = self.data_list[j]['Smiles']\n",
+ " if self.split_type == 'val':\n",
+ " print(f'POSITIVE PAIR SMILES: \\n{smiles_i} \\n {smiles_j}')\n",
+ " # Tokenize SMILES\n",
+ " tokens_i = self.tokenizer(\n",
+ " smiles_i, \n",
+ " return_tensors='pt', \n",
+ " padding='max_length', \n",
+ " truncation=True, \n",
+ " max_length=256\n",
+ " )\n",
+ " tokens_j = self.tokenizer(\n",
+ " smiles_j, \n",
+ " return_tensors='pt', \n",
+ " padding='max_length', \n",
+ " truncation=True, \n",
+ " max_length=256\n",
+ " )\n",
+ " \n",
+ " # Remove batch dimension\n",
+ " tokens_i = {key: val.squeeze(0) for key, val in tokens_i.items()}\n",
+ " tokens_j = {key: val.squeeze(0) for key, val in tokens_j.items()}\n",
+ " \n",
+ " # Get property vectors\n",
+ " prop_vec_i = self.data_list[i]['property_tensor']\n",
+ " prop_vec_j = self.data_list[j]['property_tensor']\n",
+ " \n",
+ " return {\n",
+ " 'tokens_i': tokens_i,\n",
+ " 'tokens_j': tokens_j,\n",
+ " 'descriptor_similarity': torch.tensor(desc_sim, dtype=torch.float32),\n",
+ " 'property_tensor_i': prop_vec_i,\n",
+ " 'property_tensor_j': prop_vec_j\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def contrastive_collate_fn(batch):\n",
+ " \"\"\"\n",
+ " Collate function that creates proper NT-Xent batches:\n",
+ " - Element 0 and 1 are positive pairs\n",
+ " - Element 2 and 3 are positive pairs \n",
+ " - etc.\n",
+ " \"\"\"\n",
+ " batch_size = len(batch)\n",
+ " \n",
+ " # Ensure even batch size for proper pairing\n",
+ " if batch_size % 2 != 0:\n",
+ " batch = batch[:-1] # Drop last element if odd\n",
+ " batch_size = len(batch)\n",
+ " \n",
+ " # Interleave: [sample1_i, sample1_j, sample2_i, sample2_j, ...]\n",
+ " tokens_list = []\n",
+ " desc_similarities = []\n",
+ " \n",
+ " for i in range(0, batch_size, 1):\n",
+ " # Add first molecule of pair i\n",
+ " tokens_list.append(batch[i]['tokens_i'])\n",
+ " desc_similarities.append(batch[i]['descriptor_similarity'])\n",
+ " \n",
+ " # Add second molecule of pair i (positive pair)\n",
+ " tokens_list.append(batch[i]['tokens_j'])\n",
+ " desc_similarities.append(batch[i]['descriptor_similarity']) # Same similarity for both elements in pair\n",
+ " \n",
+ " # Stack all tokens\n",
+ " tokens = {}\n",
+ " for key in tokens_list[0].keys():\n",
+ " tokens[key] = torch.stack([item[key] for item in tokens_list])\n",
+ " \n",
+ " desc_similarities_tensor = torch.stack(desc_similarities)\n",
+ " \n",
+ " return {\n",
+ " 'tokens': tokens,\n",
+ " 'descriptor_similarities': desc_similarities_tensor,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def create_dataloaders(train_list, val_list, tokenizer, batch_size=32, \n",
+ " positive_threshold=0.85, cache_dir=\"cache\"):\n",
+ " \"\"\"Create train and validation dataloaders for NT-Xent\"\"\"\n",
+ " os.makedirs(cache_dir, exist_ok=True)\n",
+ " \n",
+ " # Ensure even batch size for proper pairing\n",
+ " if batch_size % 2 != 0:\n",
+ " batch_size += 1\n",
+ " print(f\"Adjusted batch_size to {batch_size} (must be even for NT-Xent)\")\n",
+ " \n",
+ " train_cache = os.path.join(cache_dir, 'train_positive_pairs.pkl')\n",
+ " val_cache = os.path.join(cache_dir, 'val_positive_pairs.pkl')\n",
+ " \n",
+ " train_dataset = MolecularContrastiveDataset(\n",
+ " train_list, tokenizer, positive_threshold=positive_threshold, cache_path=train_cache\n",
+ " )\n",
+ " val_dataset = MolecularContrastiveDataset(\n",
+ " val_list, tokenizer, positive_threshold=positive_threshold, cache_path=val_cache, split_type='val',\n",
+ " )\n",
+ " \n",
+ " train_loader = DataLoader(\n",
+ " train_dataset, batch_size=batch_size, shuffle=True, collate_fn=contrastive_collate_fn, drop_last=True, pin_memory=True\n",
+ " )\n",
+ " val_loader = DataLoader(\n",
+ " val_dataset, batch_size=batch_size, shuffle=False, collate_fn=contrastive_collate_fn, drop_last=True, pin_memory=True\n",
+ " )\n",
+ " \n",
+ " return train_loader, val_loader\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "f956a50b-85a5-49df-b7c6-6e40dce160e1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized with 23,299,840 trainable parameters\n"
+ ]
+ }
+ ],
+ "source": [
+ "def nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, descriptor_similarity, base_temp=0.02):\n",
+ " batch_size = embeddings1.shape[0]\n",
+ " device = embeddings1.device\n",
+ " #individual_temperatures = sigmoid_temp_scaling(descriptor_similarity, base_temp)\n",
+ " #temperature = individual_temperatures.mean() # Single temperature for the whole batch\n",
+ " temperature = base_temp\n",
+ " # Normalize projections\n",
+ " z_i = F.normalize(embeddings1, p=2, dim=1)\n",
+ " z_j = F.normalize(embeddings2, p=2, dim=1)\n",
+ " \n",
+ " # Concatenate for similarity matrix calculation\n",
+ " representations = torch.cat([z_i, z_j], dim=0)\n",
+ " # Calculate cosine similarity between all pairs\n",
+ " similarity_matrix = F.cosine_similarity(representations.unsqueeze(1), representations.unsqueeze(0), dim=2)\n",
+ " #similarity_matrix = torch.clamp(similarity_matrix, min=-0.999, max=0.999)\n",
+ " sim_ij = torch.diag(similarity_matrix, batch_size)\n",
+ " sim_ji = torch.diag(similarity_matrix, -batch_size)\n",
+ " positives = torch.cat([sim_ij, sim_ji], dim=0)\n",
+ " \n",
+ " # Create a mask to exclude self-comparisons\n",
+ " nominator = torch.exp(positives / temperature)\n",
+ " mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool, device=device)).float()\n",
+ " denominator = mask * torch.exp(similarity_matrix / temperature)\n",
+ " \n",
+ " # Calculate the final loss\n",
+ " loss = -torch.log(nominator / torch.sum(denominator, dim=1))\n",
+ " if torch.isnan(loss).any():\n",
+ " print(similarity_matrix)\n",
+ " print(f\"Temperature: {temperature}\")\n",
+ " print(f\"Nominator range: {nominator.min().item():.6f} to {nominator.max().item():.6f}\")\n",
+ " \n",
+ " return torch.sum(loss) / (2 * batch_size)\n",
+ "\n",
+ "\n",
+ "def sigmoid_temp_scaling(descriptor_similarity, base_temp=0.05, steepness=10.0, midpoint=0.5):\n",
+ " \"\"\"Smooth sigmoid-based temperature scaling\"\"\"\n",
+ " sigmoid_factor = torch.sigmoid(steepness * (descriptor_similarity - midpoint))\n",
+ " temperature = base_temp * (2.0 - sigmoid_factor)\n",
+ " return temperature\n",
+ "\n",
+ "\n",
+ "def train_step(batch, model, optimizer, device, scheduler, base_temp=0.1):\n",
+ " \"\"\"Single training step for NT-Xent\"\"\"\n",
+ " model.train()\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Move batch to device\n",
+ " tokens = {k: v.to(device) for k, v in batch['tokens'].items()}\n",
+ " desc_similarities = batch['descriptor_similarities'].to(device)\n",
+ " \n",
+ " # Forward pass - get embeddings for all samples\n",
+ " outputs = model(**tokens) # i1, j1, i2, j2 ...\n",
+ " embeddings = outputs\n",
+ " \n",
+ " # Split embeddings: even indices are embeddings1, odd indices are embeddings2\n",
+ " embeddings1 = embeddings[::2] # [0, 2, 4, ...]\n",
+ " embeddings2 = embeddings[1::2] # [1, 3, 5, ...]\n",
+ " \n",
+ " # Get descriptor similarities for each pair (take every other one since they're duplicated)\n",
+ " pair_desc_similarities = desc_similarities[::2]\n",
+ " #print(f'FIRST TRAIN EMBED: {embeddings1}')\n",
+ " #print(f'SECOND TRAIN EMBED: {embeddings2}')\n",
+ " #print(f'COSINE SIM BETWEEN THEM TRAIN: {F.cosine_similarity(embeddings1, embeddings2, dim=1)}')\n",
+ " # Calculate NT-Xent loss\n",
+ " loss = nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, pair_desc_similarities, base_temp=base_temp)\n",
+ " \n",
+ " # Backward pass\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " scheduler.step()\n",
+ " return loss.item()\n",
+ "\n",
+ "def val_step(batch, model, device, base_temp=0.1):\n",
+ " \"\"\"Single validation step for NT-Xent\"\"\"\n",
+ " model.eval()\n",
+ " with torch.no_grad():\n",
+ " # Move batch to device\n",
+ " tokens = {k: v.to(device) for k, v in batch['tokens'].items()}\n",
+ " desc_similarities = batch['descriptor_similarities'].to(device)\n",
+ " \n",
+ " # Forward pass\n",
+ " outputs = model(**tokens)\n",
+ " embeddings = outputs\n",
+ " \n",
+ " # Split embeddings\n",
+ " embeddings1 = embeddings[::2]\n",
+ " embeddings2 = embeddings[1::2]\n",
+ " \n",
+ " # Get descriptor similarities for pairs\n",
+ " pair_desc_similarities = desc_similarities[::2]\n",
+ " \n",
+ " print(f'FIRST VAL EMBED: {embeddings1}')\n",
+ " print(f'SECOND VAL EMBED: {embeddings2}')\n",
+ " print(f'COSINE SIM BETWEEN THEM: {F.cosine_similarity(embeddings1, embeddings2, dim=1)}')\n",
+ " #print(f'SECOND VAL EMBED: {embeddings2}')\n",
+ " loss = nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, pair_desc_similarities, base_temp=base_temp)\n",
+ " print(f'VAL LOSS: {loss}')\n",
+ " \n",
+ " return loss.item()\n",
+ "\n",
+ "def train_epoch(train_loader, model, optimizer, scheduler, base_temp=0.01):\n",
+ " \"\"\"Train for one epoch\"\"\"\n",
+ " total_loss = 0\n",
+ " num_batches = 0\n",
+ " \n",
+ " progress_bar = tqdm(train_loader, desc=\"Training\")\n",
+ " \n",
+ " for batch in progress_bar:\n",
+ " loss = train_step(batch, model, optimizer, 'cuda', scheduler, base_temp=base_temp)\n",
+ " total_loss += loss\n",
+ " num_batches += 1\n",
+ " \n",
+ " # Calculate running average loss\n",
+ " avg_loss = total_loss / num_batches\n",
+ " \n",
+ " # Update progress bar with current loss info\n",
+ " progress_bar.set_postfix({\n",
+ " 'Loss': f'{loss:.4f}',\n",
+ " 'Avg Loss': f'{avg_loss:.4f}'\n",
+ " })\n",
+ " \n",
+ " return total_loss / num_batches if num_batches > 0 else 0\n",
+ "\n",
+ "\n",
+ "def validate_epoch(val_loader, model, base_temp=0.01):\n",
+ " \"\"\"Validate for one epoch\"\"\"\n",
+ " total_loss = 0\n",
+ " num_batches = 0\n",
+ " print('nah twin')\n",
+ " return 0\n",
+ " for batch in val_loader:\n",
+ " loss = val_step(batch, model, 'cuda', base_temp=base_temp)\n",
+ " total_loss += loss\n",
+ " num_batches += 1\n",
+ " \n",
+ " return total_loss / num_batches if num_batches > 0 else 0\n",
+ "\n",
+ "def training_loop(train_loader, val_loader, model, tokenizer, epochs=50, patience=5, lr=1e-4, base_temp=0.02,\n",
+ " device_name='cuda', save_path='best_model.pt'):\n",
+ " \"\"\"Main training loop with early stopping\"\"\"\n",
+ " device = torch.device(device_name if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " \n",
+ " # Initialize model and optimizer\n",
+ " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " total_steps = epochs * len(train_loader)\n",
+ " scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_mult=1, T_0=total_steps)\n",
+ " # Early stopping variables\n",
+ " best_val_loss = float('inf')\n",
+ " no_improve_epochs = 0\n",
+ " \n",
+ " print(\"Starting training...\")\n",
+ " \n",
+ " for epoch in range(epochs):\n",
+ " # Training\n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " train_loss = train_epoch(train_loader, model, optimizer, scheduler, base_temp=base_temp)\n",
+ " print('END TRAIN')\n",
+ " # Validation\n",
+ " val_loss = validate_epoch(val_loader, model)\n",
+ " \n",
+ " print(f\"Epoch {epoch + 1}/{epochs}: Train Loss = {train_loss:.4f}, Val Loss = {val_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check\n",
+ " if val_loss < best_val_loss:\n",
+ " best_val_loss = val_loss\n",
+ " no_improve_epochs = 0\n",
+ " # Save best model\n",
+ " torch.save(model.state_dict(), save_path)\n",
+ " print(f\"New best model saved with val loss: {val_loss:.4f}\")\n",
+ " else:\n",
+ " no_improve_epochs += 1\n",
+ " print(f\"No improvement for {no_improve_epochs} epochs\")\n",
+ " \n",
+ " if no_improve_epochs >= patience:\n",
+ " print(f\"Early stopping triggered after {epoch + 1} epochs\")\n",
+ " break\n",
+ " \n",
+ " # Load best model\n",
+ " print(f\"Loading best model from {save_path}\")\n",
+ " model.load_state_dict(torch.load(save_path))\n",
+ " model.eval()\n",
+ " \n",
+ " print(f\"Training completed. Best validation loss: {best_val_loss:.4f}\")\n",
+ "\n",
+ "\n",
+ "model, tokenizer = initialize_model_and_tokenizer()\n",
+ "#model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl', weights_only=False))\n",
+ "print(f\"Model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad):,} trainable parameters\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "c73e2bba-59c1-4b41-b2ff-235526dd2912",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!rm -rf cache"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0072c8f5-c5e9-4590-9544-c73cf1fac1e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing positive pairs only...\n",
+ "Collecting positive pairs with similarity threshold 0.8\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|████████████████████████████████████| 6378/6378 [00:00<00:00, 54740.22it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated 12534 positive pairs\n",
+ "Cached pairs saved to cache/train_positive_pairs.pkl\n",
+ "Computing positive pairs only...\n",
+ "Collecting positive pairs with similarity threshold 0.8\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████| 100/100 [00:00<00:00, 200780.47it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated 138 positive pairs\n",
+ "Cached pairs saved to cache/val_positive_pairs.pkl\n",
+ "Using device: cuda\n",
+ "Starting training...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1566/1566 [00:31<00:00, 50.05it/s, Loss=1.1129, Avg Loss=1.528\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 1/10: Train Loss = 1.5288, Val Loss = 0.0000\n",
+ "New best model saved with val loss: 0.0000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1566/1566 [00:30<00:00, 50.76it/s, Loss=2.1831, Avg Loss=2.190\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 2/10: Train Loss = 2.1905, Val Loss = 0.0000\n",
+ "No improvement for 1 epochs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1566/1566 [00:30<00:00, 50.69it/s, Loss=2.7081, Avg Loss=2.708\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 3/10: Train Loss = 2.7081, Val Loss = 0.0000\n",
+ "No improvement for 2 epochs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1566/1566 [00:31<00:00, 50.37it/s, Loss=2.7081, Avg Loss=2.708\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 4/10: Train Loss = 2.7081, Val Loss = 0.0000\n",
+ "No improvement for 3 epochs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1566/1566 [00:31<00:00, 50.40it/s, Loss=2.7081, Avg Loss=2.708\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 5/10: Train Loss = 2.7081, Val Loss = 0.0000\n",
+ "No improvement for 4 epochs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 13%|▏| 198/1566 [00:04<00:27, 50.58it/s, Loss=2.7081, Avg Loss=2.7081"
+ ]
+ }
+ ],
+ "source": [
+ "train_loader, val_loader = create_dataloaders(\n",
+ " train_list, val_list[:100], tokenizer, \n",
+ " batch_size=8, positive_threshold=0.8\n",
+ ")\n",
+ "\n",
+ "training_loop(\n",
+ " train_loader, val_loader, model, tokenizer,\n",
+ " epochs=10, patience=5, lr=1e-5, \n",
+ " device_name='cuda', base_temp=0.1\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "58343b16-1bdb-4476-ac61-e797fbc661d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(train_list[:5], '\\n\\n', val_list[:5])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47561022-5f57-4b7b-b903-ef1f8773f903",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5fcef978-3630-4201-9301-6963a8560517",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/.ipynb_checkpoints/simson_ddp_train-checkpoint.py b/simson_modeling/.ipynb_checkpoints/simson_ddp_train-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..98dfe1ad417cf32019dabf850d4b54eacc79cdbf
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/simson_ddp_train-checkpoint.py
@@ -0,0 +1,545 @@
+# ==============================================================================
+# 1. IMPORTS
+# ==============================================================================
+import os
+import warnings
+import wandb
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+import numpy as np
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset, load_from_disk
+from transformers import AutoTokenizer, BertModel, BertConfig
+import pandas as pd
+
+# ==============================================================================
+# 2. INITIAL SETUP
+# ==============================================================================
+# Suppress RDKit console output
+RDLogger.DisableLog('rdApp.*')
+# Ignore warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# ==============================================================================
+# 3. MODEL AND LOSS FUNCTION
+# ==============================================================================
+def global_average_pooling(x):
+ """Global Average Pooling: from [B, max_len, hid_dim] to [B, hid_dim]"""
+ return torch.mean(x, dim=1)
+
+class SimSonEncoder(nn.Module):
+ """The main encoder model based on BERT."""
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.bert.config.pad_token_id)
+
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled_output = global_average_pooling(hidden_states)
+ return self.linear(pooled_output)
+
+class ContrastiveLoss(nn.Module):
+ """Calculates the contrastive loss for the SimSon model."""
+ def __init__(self, temperature=0.2):
+ super(ContrastiveLoss, self).__init__()
+ self.temperature = temperature
+ self.similarity_fn = F.cosine_similarity
+
+ def forward(self, proj_1, proj_2):
+ batch_size = proj_1.shape[0]
+ device = proj_1.device
+
+ # Normalize projections
+ z_i = F.normalize(proj_1, p=2, dim=1)
+ z_j = F.normalize(proj_2, p=2, dim=1)
+
+ # Concatenate for similarity matrix calculation
+ representations = torch.cat([z_i, z_j], dim=0)
+
+ # Calculate cosine similarity between all pairs
+ similarity_matrix = self.similarity_fn(representations.unsqueeze(1), representations.unsqueeze(0), dim=2)
+
+ # Identify positive pairs (original and its augmentation)
+ sim_ij = torch.diag(similarity_matrix, batch_size)
+ sim_ji = torch.diag(similarity_matrix, -batch_size)
+ positives = torch.cat([sim_ij, sim_ji], dim=0)
+
+ # Create a mask to exclude self-comparisons
+ nominator = torch.exp(positives / self.temperature)
+ mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool, device=device)).float()
+ denominator = mask * torch.exp(similarity_matrix / self.temperature)
+
+ # Calculate the final loss
+ loss = -torch.log(nominator / torch.sum(denominator, dim=1))
+ return torch.sum(loss) / (2 * batch_size)
+
+# ==============================================================================
+# 4. DATA HANDLING (Keeping your existing classes unchanged)
+# ==============================================================================
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+class ContrastiveSmilesDataset(Dataset):
+ """Dataset for creating pairs of augmented SMILES for contrastive learning."""
+ def __init__(self, smiles_list, tokenizer, max_length=512):
+ self.smiles_list = smiles_list
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.enumerator = SmilesEnumerator()
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ original_smiles = self.smiles_list[idx]
+
+ # Create two different augmentations of the same SMILES
+ smiles_1 = self.enumerator.randomize_smiles(original_smiles)
+ smiles_2 = self.enumerator.randomize_smiles(original_smiles)
+
+ # Tokenize and do pad. Padding will be handled by the collate_fn.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PrecomputedContrastiveSmilesDataset(Dataset):
+ """
+ A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.
+ This is significantly faster as it offloads the expensive SMILES randomization
+ to a one-time preprocessing step.
+ """
+ def __init__(self, tokenizer, file_path: str, max_length: int = 512):
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ # Load the entire dataset from the Parquet file into memory.
+ # This is fast and efficient for subsequent access.
+ print(f"Loading pre-computed data from {file_path}...")
+ self.data = pd.read_parquet(file_path)
+ print("Data loaded successfully.")
+
+ def __len__(self):
+ """Returns the total number of pairs in the dataset."""
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ """
+ Retrieves a pre-augmented pair, tokenizes it, and returns it
+ in the format expected by the DataCollator.
+ """
+ # Retrieve the pre-augmented pair from the DataFrame
+ row = self.data.iloc[idx]
+ smiles_1 = row['smiles_1']
+ smiles_2 = row['smiles_2']
+
+ # Tokenize the pair. This operation is fast and remains in the data loader.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PreTokenizedSmilesDataset(Dataset):
+ """
+ A Dataset that loads a pre-tokenized and pre-padded dataset created
+ by the preprocessing script. It uses memory-mapping for instant loads
+ and high efficiency.
+ """
+ def __init__(self, dataset_path: str):
+ # Load the dataset from disk. This is very fast due to memory-mapping.
+ self.dataset = load_from_disk(dataset_path)
+ # Set the format to PyTorch tensors for direct use in the model
+ self.dataset.set_format(type='torch', columns=[
+ 'input_ids_1', 'attention_mask_1', 'input_ids_2', 'attention_mask_2'
+ ])
+ print(f"Successfully loaded pre-tokenized dataset from {dataset_path}.")
+
+ def __len__(self):
+ """Returns the total number of items in the dataset."""
+ return len(self.dataset)
+
+ def __getitem__(self, idx):
+ """Retrieves a single pre-processed item."""
+ return self.dataset[idx]
+
+class DataCollatorWithPadding:
+ """
+ A collate function that dynamically pads inputs to the longest sequence
+ across both augmented views in the batch, ensuring consistent tensor shapes.
+ """
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def __call__(self, features):
+ # Create a combined list of features for both views to find the global max length
+ combined_features = []
+ for feature in features:
+ combined_features.append({'input_ids': feature['input_ids_1'], 'attention_mask': feature['attention_mask_1']})
+ combined_features.append({'input_ids': feature['input_ids_2'], 'attention_mask': feature['attention_mask_2']})
+
+ # Pad the combined batch. This ensures all sequences are padded to the same length.
+ padded_combined = self.tokenizer.pad(combined_features, padding='longest', return_tensors='pt')
+
+ # Split the padded tensors back into two views
+ batch_size = len(features)
+ input_ids_1, input_ids_2 = torch.split(padded_combined['input_ids'], batch_size, dim=0)
+ attention_mask_1, attention_mask_2 = torch.split(padded_combined['attention_mask'], batch_size, dim=0)
+
+ return {
+ 'input_ids_1': input_ids_1,
+ 'attention_mask_1': attention_mask_1,
+ 'input_ids_2': input_ids_2,
+ 'attention_mask_2': attention_mask_2,
+ }
+
+# ==============================================================================
+# 5. CHECKPOINT UTILITIES
+# ==============================================================================
+def save_checkpoint(model, optimizer, scheduler, global_step, save_path):
+ """Save complete checkpoint with model, optimizer, scheduler states and step count."""
+ checkpoint = {
+ 'model_state_dict': model.state_dict(),
+ 'optimizer_state_dict': optimizer.state_dict(),
+ 'scheduler_state_dict': scheduler.state_dict(),
+ 'global_step': global_step,
+ }
+ torch.save(checkpoint, save_path)
+ print(f"Full checkpoint saved at step {global_step}")
+
+def load_checkpoint(checkpoint_path, model, optimizer, scheduler):
+ """Load checkpoint and return the global step to resume from."""
+ checkpoint = torch.load(checkpoint_path, map_location='cpu')
+ model.load_state_dict(checkpoint['model_state_dict'])
+ optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+ scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+ global_step = checkpoint['global_step']
+ print(f"Checkpoint loaded from step {global_step}")
+ return global_step
+
+# ==============================================================================
+# 6. TRAINING AND EVALUATION LOOPS - MODIFIED
+# ==============================================================================
+def evaluation_step(model, batch, criterion, device):
+ """Performs a single evaluation step on a batch of data."""
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ with torch.no_grad():
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+ return proj_1, proj_2, loss
+
+def train_with_step_based_validation(model, train_loader, val_loader, optimizer, criterion, device,
+ scheduler, checkpoint_path, save_steps, validation_steps,
+ start_step=0, max_steps=None):
+ """
+ Modified training function with step-based validation and checkpointing.
+ """
+ model.train()
+ global_step = start_step
+ best_val_loss = float('inf')
+
+ # Calculate total steps if max_steps is not provided
+ if max_steps is None:
+ max_steps = len(train_loader)
+
+ progress_bar = tqdm(total=max_steps - start_step, desc="Training Steps", initial=start_step)
+
+ # Create iterator that can be resumed from any point
+ train_iterator = iter(train_loader)
+
+ # Skip batches if resuming from checkpoint
+ if start_step > 0:
+ batches_to_skip = start_step % len(train_loader)
+ for _ in range(batches_to_skip):
+ try:
+ next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+
+ while global_step < max_steps:
+ try:
+ batch = next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+ batch = next(train_iterator)
+
+ # Training step
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ optimizer.zero_grad()
+ with torch.autocast(dtype=torch.float16, device_type="cuda"):
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+
+ loss.backward()
+ optimizer.step()
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+ scheduler.step()
+
+ global_step += 1
+
+ progress_bar.update(1)
+ progress_bar.set_postfix(loss=f"{loss.item():.4f}", step=global_step)
+
+ wandb.log({
+ "train_batch_loss": loss.item(),
+ "learning_rate": scheduler.get_last_lr()[0],
+ "global_step": global_step
+ })
+
+ # Step-based validation
+ if global_step % validation_steps == 0:
+ val_loss = validate_epoch(model, val_loader, criterion, device)
+ wandb.log({
+ "val_loss": val_loss,
+ "global_step": global_step
+ })
+
+ # Save best model (model state only for best checkpoint)
+ if val_loss < best_val_loss:
+ best_val_loss = val_loss
+ model_save_path = checkpoint_path.replace('.pt', '_best_model.bin')
+ torch.save(model.state_dict(), model_save_path)
+ progress_bar.write(f"Step {global_step}: New best model saved with val loss {val_loss:.4f}")
+
+ model.train() # Resume training mode after validation
+
+ # Step-based checkpointing (full checkpoint)
+ if global_step % save_steps == 0:
+ save_checkpoint(model, optimizer, scheduler, global_step, checkpoint_path)
+
+ progress_bar.close()
+ return global_step
+
+def validate_epoch(model, val_loader, criterion, device):
+ """Validation function - unchanged from original."""
+ model.eval()
+ total_loss = 0
+ progress_bar = tqdm(val_loader, desc="Validating", leave=False)
+
+ for batch in progress_bar:
+ _, _, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+
+ avg_loss = total_loss / len(val_loader)
+ print(f'Validation loss: {avg_loss:.4f}')
+ return avg_loss
+
+def test_model(model, test_loader, criterion, device):
+ """Test function - unchanged from original."""
+ model.eval()
+ total_loss = 0
+ all_similarities = []
+ progress_bar = tqdm(test_loader, desc="Testing", leave=False)
+
+ for batch in progress_bar:
+ proj_1, proj_2, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+
+ proj_1_norm = F.normalize(proj_1, p=2, dim=1)
+ proj_2_norm = F.normalize(proj_2, p=2, dim=1)
+ batch_similarities = F.cosine_similarity(proj_1_norm, proj_2_norm, dim=1)
+ all_similarities.extend(batch_similarities.cpu().numpy())
+
+ avg_loss = total_loss / len(test_loader)
+ avg_sim = np.mean(all_similarities)
+ std_sim = np.std(all_similarities)
+
+ return avg_loss, avg_sim, std_sim
+
+# ==============================================================================
+# 7. MODIFIED SINGLE-GPU TRAINING
+# ==============================================================================
+def run_training(model_config, hparams, data_splits):
+ """The main function to run the training and evaluation process with step-based validation."""
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {device}")
+
+ wandb_key = os.getenv("WANDB_API_KEY")
+ if wandb_key:
+ wandb.login(key=wandb_key)
+ wandb.init(
+ #project="simson-contrastive-learning-single-gpu",
+ #name=f"run-{wandb.util.generate_id()}",
+ #config=hparams
+ )
+
+ train_smiles, val_smiles, test_smiles = data_splits
+
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+
+ precomputed_train_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/train.parquet'
+ precomputed_test_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/test.parquet'
+ precomputed_val_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/validation.parquet'
+
+ train_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_train_path, max_length=hparams['max_length'])
+ test_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_test_path, max_length=hparams['max_length'])
+ val_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_val_path, max_length=hparams['max_length'])
+
+ train_loader = DataLoader(train_dataset, batch_size=hparams['batch_size'], shuffle=True, num_workers=8, prefetch_factor=128, pin_memory=True)
+ val_loader = DataLoader(val_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+ test_loader = DataLoader(test_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+
+ print('Initialized all data. Compiling the model...')
+ model = SimSonEncoder(config=model_config, max_len=hparams['max_embeddings']).to(device)
+ model = torch.compile(model)
+ model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints/checkpoint_best_model.bin'))
+ print(model)
+
+ total_params = sum(p.numel() for p in model.parameters())
+
+ print(f"Total number of parameters: {total_params // 1_000_000} M")
+ wandb.config.update({"total_params_M": total_params // 1_000_000})
+
+ criterion = ContrastiveLoss(temperature=hparams['temperature']).to(device)
+ optimizer = optim.AdamW(model.parameters(), lr=hparams['lr'], weight_decay=1e-5, fused=True)
+
+ total_steps = hparams['epochs'] * len(train_loader)
+ scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_mult=1, T_0=total_steps)
+
+ print("Starting training...")
+ wandb.watch(model, log='all', log_freq=5000)
+
+ start_step = 0
+ checkpoint_path = hparams['checkpoint_path']
+
+ # Resume from checkpoint if provided
+ if hparams.get('resume_checkpoint') and os.path.exists(hparams['resume_checkpoint']):
+ print(f"Resuming from checkpoint: {hparams['resume_checkpoint']}")
+ start_step = load_checkpoint(hparams['resume_checkpoint'], model, optimizer, scheduler)
+
+ # Train with step-based validation
+ final_step = train_with_step_based_validation(
+ model, train_loader, val_loader, optimizer, criterion, device,
+ scheduler, checkpoint_path, hparams['save_steps'], hparams['validation_steps'],
+ start_step=start_step, max_steps=total_steps
+ )
+
+ print("Training complete. Starting final testing...")
+
+ # Load the best model for testing (model state only)
+ best_model_path = checkpoint_path.replace('.pt', '_best_model.bin')
+ if os.path.exists(best_model_path):
+ model.load_state_dict(torch.load(best_model_path))
+ print("Loaded best model for testing")
+
+ test_loss, avg_sim, std_sim = test_model(model, test_loader, criterion, device)
+
+ print("\n--- Test Results ---")
+ print(f"Test Loss: {test_loss:.4f}")
+ print(f"Average Cosine Similarity: {avg_sim:.4f} ± {std_sim:.4f}")
+ print("--------------------")
+
+ wandb.log({
+ "test_loss": test_loss,
+ "avg_cosine_similarity": avg_sim,
+ "std_cosine_similarity": std_sim
+ })
+
+ # Save final model state only
+ final_model_path = hparams['save_path']
+ torch.save(model.state_dict(), final_model_path)
+ print(f"Final model saved to {final_model_path}")
+
+ wandb.finish()
+
+# ==============================================================================
+# 8. MAIN EXECUTION
+# ==============================================================================
+def main():
+ """Main function to configure and run the training process."""
+ hparams = {
+ 'epochs': 2,
+ 'lr': 6e-6,
+ 'temperature': 0.05,
+ 'batch_size': 64,
+ 'max_length': 256,
+ 'save_path': "simson_checkpoints_more_epochs/simson_model_single_gpu.bin",
+ 'checkpoint_path': "simson_checkpoints_more_epochs/checkpoint.pt", # Full checkpoint
+ 'save_steps': 50000, # Save checkpoint every 10k steps
+ 'validation_steps': 5000, # Validate every 5k steps
+ 'max_embeddings': 512,
+ 'resume_checkpoint': None, # Set to checkpoint path to resume
+ }
+
+ dataset = load_dataset('HoangHa/SMILES-250M')['train']
+ smiles_column_name = 'SMILES'
+
+ total_size = len(dataset)
+ test_size = int(0.1 * total_size)
+ val_size = int(0.1 * (total_size - test_size))
+
+ test_smiles = dataset.select(range(test_size))[smiles_column_name]
+ val_smiles = dataset.select(range(test_size, test_size + val_size))[smiles_column_name]
+ train_smiles = dataset.select(range(test_size + val_size, total_size))[smiles_column_name]
+ data_splits = (train_smiles, val_smiles, test_smiles)
+
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ model_config = BertConfig(
+ vocab_size=tokenizer.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ # Create directories
+ save_dir = os.path.dirname(hparams['save_path'])
+ checkpoint_dir = os.path.dirname(hparams['checkpoint_path'])
+ for directory in [save_dir, checkpoint_dir]:
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+ # Directly call the training function for a single-GPU run
+ run_training(model_config, hparams, data_splits)
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/.ipynb_checkpoints/upload_state_to_hf-checkpoint.py b/simson_modeling/.ipynb_checkpoints/upload_state_to_hf-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb3b4950595fe7fdce983fc7e61a07622a406734
--- /dev/null
+++ b/simson_modeling/.ipynb_checkpoints/upload_state_to_hf-checkpoint.py
@@ -0,0 +1,23 @@
+from huggingface_hub import HfApi
+
+state_path = '/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin'
+
+from huggingface_hub import HfApi
+api = HfApi()
+upload_folder = True
+if not upload_folder:
+
+ api.upload_file(
+ path_or_fileobj=state_path,
+ path_in_repo="polymer_1M_weights.bin",
+ repo_id="Defetya/simson_base",
+ repo_type="model",
+ )
+else:
+
+ api.upload_folder(
+ folder_path="/home/jovyan/simson_training_bolgov",
+ repo_id="Defetya/simson_base",
+ path_in_repo="simson_modeling",
+ repo_type="model",
+ )
\ No newline at end of file
diff --git a/simson_modeling/.simson_ddp_train.py.swp b/simson_modeling/.simson_ddp_train.py.swp
new file mode 100644
index 0000000000000000000000000000000000000000..cce89841baaf62ef267495aea5f3569b3cd6808b
Binary files /dev/null and b/simson_modeling/.simson_ddp_train.py.swp differ
diff --git a/simson_modeling/.upload_state_to_hf.py.swp b/simson_modeling/.upload_state_to_hf.py.swp
new file mode 100644
index 0000000000000000000000000000000000000000..75d9bf4ae9b16619615f20b2172e39d74ceef701
Binary files /dev/null and b/simson_modeling/.upload_state_to_hf.py.swp differ
diff --git a/simson_modeling/__pycache__/create_augmented_dataset.cpython-312.pyc b/simson_modeling/__pycache__/create_augmented_dataset.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0b7c8f7d88a18af7303457459062a7695de1ab87
Binary files /dev/null and b/simson_modeling/__pycache__/create_augmented_dataset.cpython-312.pyc differ
diff --git a/simson_modeling/__pycache__/create_splits.cpython-312.pyc b/simson_modeling/__pycache__/create_splits.cpython-312.pyc
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diff --git a/simson_modeling/create_augmented_dataset.py b/simson_modeling/create_augmented_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf190b9ee2c0ede4f05e7afb7311cccaf96e9372
--- /dev/null
+++ b/simson_modeling/create_augmented_dataset.py
@@ -0,0 +1,83 @@
+import pandas as pd
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset
+from multiprocessing import Pool, cpu_count
+import os
+
+# Suppress RDKit console output for cleaner logs
+RDLogger.DisableLog('rdApp.*')
+
+class SmilesEnumerator:
+ """
+ A simple class to encapsulate the SMILES randomization logic.
+ Needed for multiprocessing to work correctly with instance methods.
+ """
+ def randomize_smiles(self, smiles):
+ """Generates a randomized SMILES string."""
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ # Return a randomized, non-canonical SMILES string
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ # If RDKit fails, return the original smiles string
+ return smiles
+
+def create_augmented_pair(smiles_string):
+ """
+ Worker function: takes one SMILES string and returns a tuple
+ containing two different randomized versions of it.
+ """
+ enumerator = SmilesEnumerator()
+ smiles_1 = enumerator.randomize_smiles(smiles_string)
+ smiles_2 = enumerator.randomize_smiles(smiles_string)
+ return smiles_1, smiles_2
+
+def main():
+ """
+ Main function to run the parallel data preprocessing.
+ """
+ # --- Configuration ---
+ # Load your desired dataset from Hugging Face
+ dataset_name = 'jablonkagroup/pubchem-smiles-molecular-formula'
+ # Specify the column containing the SMILES strings
+ smiles_column_name = 'smiles'
+ # Set the output file path
+ output_path = 'data/pubchem_2_epoch_50M'
+
+ # --- Data Loading ---
+ print(f"Loading dataset '{dataset_name}'...")
+ # Use streaming to avoid downloading the whole dataset if you only need a subset
+ #dataset = pd.read_csv('/home/jovyan/simson_training_bolgov/data/PI1M_v2.csv')
+ dataset = load_dataset(dataset_name)['train'].select(range(50_000_000))
+ # Take the desired number of samples
+ smiles_list = dataset[smiles_column_name]
+ print(f"Successfully fetched {len(smiles_list)} SMILES strings.")
+
+ # --- Parallel Processing ---
+ # Use all available CPU cores for maximum speed
+ num_workers = cpu_count()
+ print(f"Starting SMILES augmentation with {num_workers} worker processes...")
+
+ # A Pool of processes will run the `create_augmented_pair` function in parallel
+ with Pool(num_workers) as p:
+ # Use tqdm to create a progress bar for the mapping operation
+ results = list(tqdm(p.imap(create_augmented_pair, smiles_list), total=len(smiles_list), desc="Augmenting Pairs"))
+
+ # --- Saving Data ---
+ print("Processing complete. Converting to DataFrame...")
+ # Convert the list of tuples into a pandas DataFrame
+ df = pd.DataFrame(results, columns=['smiles_1', 'smiles_2'])
+
+ # Ensure the output directory exists
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+ print(f"Saving augmented pairs to '{output_path}'...")
+ # Save the DataFrame to a Parquet file for efficient storage and loading
+ df.to_parquet(output_path)
+
+ print("All done. Your pre-computed dataset is ready!")
+
+if __name__ == '__main__':
+ main()
+
diff --git a/simson_modeling/create_augmented_dataset.py.save b/simson_modeling/create_augmented_dataset.py.save
new file mode 100644
index 0000000000000000000000000000000000000000..26e348bc61c9db901521026a44df21ccb7260c8f
--- /dev/null
+++ b/simson_modeling/create_augmented_dataset.py.save
@@ -0,0 +1,83 @@
+import pandas as pd
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset
+from multiprocessing import Pool, cpu_count
+import os
+
+# Suppress RDKit console output for cleaner logs
+RDLogger.DisableLog('rdApp.*')
+
+class SmilesEnumerator:
+ """
+ A simple class to encapsulate the SMILES randomization logic.
+ Needed for multiprocessing to work correctly with instance methods.
+ """
+ def randomize_smiles(self, smiles):
+ """Generates a randomized SMILES string."""
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ # Return a randomized, non-canonical SMILES string
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ # If RDKit fails, return the original smiles string
+ return smiles
+
+def create_augmented_pair(smiles_string):
+ """
+ Worker function: takes one SMILES string and returns a tuple
+ containing two different randomized versions of it.
+ """
+ enumerator = SmilesEnumerator()
+ smiles_1 = enumerator.randomize_smiles(smiles_string)
+ smiles_2 = enumerator.randomize_smiles(smiles_string)
+ return smiles_1, smiles_2
+
+def main():
+ """
+ Main function to run the parallel data preprocessing.
+ """
+ # --- Configuration ---
+ # Load your desired dataset from Hugging Face
+ dataset_name = 'jablonkagroup/pubchem-smiles-molecular-formula'
+ # Specify the column containing the SMILES strings
+ smiles_column_name = 'smiles'
+ # Set the output file path
+ output_path = 'data/pubchem_computed_110_end_M.parquet'
+
+ # --- Data Loading ---
+ print(f"Loading dataset '{dataset_name}'...")
+ # Use streaming to avoid downloading the whole dataset if you only need a subset
+ dataset = load_dataset(dataset_name, split='train').select(range(110_000_000, ))
+
+ # Take the desired number of samples
+ smiles_list = dataset[smiles_column_name]
+ print(f"Successfully fetched {len(smiles_list)} SMILES strings.")
+
+ # --- Parallel Processing ---
+ # Use all available CPU cores for maximum speed
+ num_workers = cpu_count()
+ print(f"Starting SMILES augmentation with {num_workers} worker processes...")
+
+ # A Pool of processes will run the `create_augmented_pair` function in parallel
+ with Pool(num_workers) as p:
+ # Use tqdm to create a progress bar for the mapping operation
+ results = list(tqdm(p.imap(create_augmented_pair, smiles_list), total=len(smiles_list), desc="Augmenting Pairs"))
+
+ # --- Saving Data ---
+ print("Processing complete. Converting to DataFrame...")
+ # Convert the list of tuples into a pandas DataFrame
+ df = pd.DataFrame(results, columns=['smiles_1', 'smiles_2'])
+
+ # Ensure the output directory exists
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+ print(f"Saving augmented pairs to '{output_path}'...")
+ # Save the DataFrame to a Parquet file for efficient storage and loading
+ df.to_parquet(output_path)
+
+ print("All done. Your pre-computed dataset is ready!")
+
+if __name__ == '__main__':
+ main()
+
diff --git a/simson_modeling/create_splits.py b/simson_modeling/create_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..c05553f7f90fbfda614cc9f6be4a0dbc74c7f57a
--- /dev/null
+++ b/simson_modeling/create_splits.py
@@ -0,0 +1,200 @@
+import os
+import pandas as pd
+from pathlib import Path
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+def concatenate_and_split_parquet(
+ input_dir: str,
+ output_dir: str,
+ val_size: int = 10000,
+ test_size: int = 5000,
+ random_state: int = 42
+):
+ """
+ Concatenate all parquet files in a directory and split into train/val/test sets.
+
+ Args:
+ input_dir: Path to directory containing parquet files
+ output_dir: Path to directory where split files will be saved
+ val_size: Number of samples for validation set (default: 10000)
+ test_size: Number of samples for test set (default: 5000)
+ random_state: Random seed for reproducibility
+ """
+
+ # Create output directory if it doesn't exist
+ Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+ # Find all parquet files in the input directory
+ input_path = Path(input_dir)
+ parquet_files = list(input_path.glob("*.parquet"))
+
+ if not parquet_files:
+ raise ValueError(f"No parquet files found in {input_dir}")
+
+ print(f"Found {len(parquet_files)} parquet files")
+
+ # Read and concatenate all parquet files
+ print("Reading and concatenating parquet files...")
+ dataframes = []
+
+ for file_path in parquet_files:
+ print(f"Reading {file_path.name}...")
+ df = pd.read_parquet(file_path)
+ dataframes.append(df)
+
+ # Concatenate all dataframes
+ combined_df = pd.concat(dataframes, ignore_index=True)
+ print(f"Combined dataset shape: {combined_df.shape}")
+
+ # Check if we have enough samples
+ total_samples = len(combined_df)
+ required_samples = val_size + test_size
+
+ if total_samples < required_samples:
+ raise ValueError(
+ f"Not enough samples. Required: {required_samples}, Available: {total_samples}"
+ )
+
+ # Shuffle the data
+ combined_df = combined_df.sample(frac=1, random_state=random_state).reset_index(drop=True)
+
+ # Split the data
+ print("Splitting data...")
+
+ # First split: separate test set
+ temp_df, test_df = train_test_split(
+ combined_df,
+ test_size=test_size,
+ random_state=random_state
+ )
+
+ # Second split: separate validation from remaining data
+ train_df, val_df = train_test_split(
+ temp_df,
+ test_size=val_size,
+ random_state=random_state
+ )
+
+ print(f"Training set shape: {train_df.shape}")
+ print(f"Validation set shape: {val_df.shape}")
+ print(f"Test set shape: {test_df.shape}")
+
+ # Save the splits as parquet files
+ output_path = Path(output_dir)
+
+ train_path = output_path / "train.parquet"
+ val_path = output_path / "validation.parquet"
+ test_path = output_path / "test.parquet"
+
+ print("Saving split datasets...")
+ train_df.to_parquet(train_path, index=False)
+ val_df.to_parquet(val_path, index=False)
+ test_df.to_parquet(test_path, index=False)
+
+ print(f"Files saved to:")
+ print(f" Training: {train_path}")
+ print(f" Validation: {val_path}")
+ print(f" Test: {test_path}")
+
+ return train_df, val_df, test_df
+
+# Alternative version using PyArrow for better performance with large files
+def concatenate_and_split_parquet_arrow(
+ input_dir: str,
+ output_dir: str,
+ val_size: int = 10000,
+ test_size: int = 5000,
+ random_state: int = 42
+):
+ """
+ Same functionality as above but using PyArrow for better performance.
+ """
+ import pyarrow as pa
+ import pyarrow.parquet as pq
+
+ # Create output directory if it doesn't exist
+ Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+ # Find all parquet files
+ input_path = Path(input_dir)
+ parquet_files = list(input_path.glob("*.parquet"))
+
+ if not parquet_files:
+ raise ValueError(f"No parquet files found in {input_dir}")
+
+ print(f"Found {len(parquet_files)} parquet files")
+
+ # Read and concatenate using PyArrow
+ print("Reading and concatenating parquet files...")
+ tables = []
+
+ for file_path in parquet_files:
+ print(f"Reading {file_path.name}...")
+ table = pq.read_table(file_path)
+ tables.append(table)
+
+ # Concatenate tables
+ combined_table = pa.concat_tables(tables)
+ combined_df = combined_table.to_pandas()
+
+ print(f"Combined dataset shape: {combined_df.shape}")
+
+ # Rest of the function is the same as above
+ total_samples = len(combined_df)
+ required_samples = val_size + test_size
+
+ if total_samples < required_samples:
+ raise ValueError(
+ f"Not enough samples. Required: {required_samples}, Available: {total_samples}"
+ )
+
+ # Shuffle and split
+ combined_df = combined_df.sample(frac=1, random_state=random_state).reset_index(drop=True)
+
+ temp_df, test_df = train_test_split(
+ combined_df, test_size=test_size, random_state=random_state
+ )
+
+ train_df, val_df = train_test_split(
+ temp_df, test_size=val_size, random_state=random_state
+ )
+
+ print(f"Training set shape: {train_df.shape}")
+ print(f"Validation set shape: {val_df.shape}")
+ print(f"Test set shape: {test_df.shape}")
+
+ # Save using PyArrow
+ output_path = Path(output_dir)
+
+ pq.write_table(pa.Table.from_pandas(train_df), output_path / "train.parquet")
+ pq.write_table(pa.Table.from_pandas(val_df), output_path / "validation.parquet")
+ pq.write_table(pa.Table.from_pandas(test_df), output_path / "test.parquet")
+
+ print(f"Files saved to {output_dir}")
+
+ return train_df, val_df, test_df
+
+# Example usage
+if __name__ == "__main__":
+ # Example usage
+ input_directory = "data"
+ output_directory = "data/polymer_splits"
+
+ # Using pandas version
+ train_df, val_df, test_df = concatenate_and_split_parquet(
+ input_dir=input_directory,
+ output_dir=output_directory,
+ val_size=10000,
+ test_size=5000,
+ random_state=42
+ )
+
+ # Or using PyArrow version for better performance
+ # train_df, val_df, test_df = concatenate_and_split_parquet_arrow(
+ # input_dir=input_directory,
+ # output_dir=output_directory,
+ # val_size=10000,
+ # test_size=5000,
+ # random_state=42
+ # )
diff --git a/simson_modeling/data/polymer_1M.parquet b/simson_modeling/data/polymer_1M.parquet
new file mode 100644
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--- /dev/null
+++ b/simson_modeling/data/polymer_1M.parquet
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+version https://git-lfs.github.com/spec/v1
+oid sha256:db6b4f85cfbbb110e31a910db1b7160d6f4732b9420e0cef824df581e2802c97
+size 50029214
diff --git a/simson_modeling/data/polymer_splits/test.parquet b/simson_modeling/data/polymer_splits/test.parquet
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--- /dev/null
+++ b/simson_modeling/data/polymer_splits/test.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba93d4addaef6c9074da8eed669e6921c5bd25c205d79d8e8fb3f01b081ca03f
+size 268419
diff --git a/simson_modeling/data/polymer_splits/train.parquet b/simson_modeling/data/polymer_splits/train.parquet
new file mode 100644
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--- /dev/null
+++ b/simson_modeling/data/polymer_splits/train.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c4eef5324339903c17386034a870095bd2ceb790166c86a693ff2e39b070448
+size 49317149
diff --git a/simson_modeling/data/polymer_splits/validation.parquet b/simson_modeling/data/polymer_splits/validation.parquet
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+++ b/simson_modeling/data/polymer_splits/validation.parquet
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+version https://git-lfs.github.com/spec/v1
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+size 536944
diff --git a/simson_modeling/data/pubchem_119m_splits/test.parquet b/simson_modeling/data/pubchem_119m_splits/test.parquet
new file mode 100644
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/simson_modeling/data/pubchem_2_epoch_50M b/simson_modeling/data/pubchem_2_epoch_50M
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diff --git a/simson_modeling/fingerprint_training.ipynb b/simson_modeling/fingerprint_training.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1dbb60f292acd87ba55cbde04ac163a79a51cb95
--- /dev/null
+++ b/simson_modeling/fingerprint_training.ipynb
@@ -0,0 +1,1559 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3d5d52d1-4874-44b5-b532-ef03da47644a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import Descriptors, rdMolDescriptors, Crippen, Lipinski\n",
+ "from tqdm import tqdm\n",
+ "import warnings\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import random\n",
+ "from concurrent.futures import ProcessPoolExecutor\n",
+ "import multiprocessing\n",
+ "\n",
+ "def analyze_polymer_features_rdkit(smiles):\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return None\n",
+ " \n",
+ " features = {}\n",
+ " \n",
+ " # Basic molecular properties\n",
+ " features['mol_weight'] = Descriptors.MolWt(mol)\n",
+ " features['exact_mol_weight'] = Descriptors.ExactMolWt(mol)\n",
+ " features['num_heavy_atoms'] = mol.GetNumHeavyAtoms()\n",
+ " features['num_atoms'] = mol.GetNumAtoms()\n",
+ " features['num_bonds'] = mol.GetNumBonds()\n",
+ " \n",
+ " # Hydrogen bonding features\n",
+ " features['num_hbond_donors'] = Descriptors.NumHDonors(mol)\n",
+ " features['num_hbond_acceptors'] = Descriptors.NumHAcceptors(mol)\n",
+ " features['num_heteroatoms'] = Descriptors.NumHeteroatoms(mol)\n",
+ " \n",
+ " # Structural complexity\n",
+ " features['num_rotatable_bonds'] = Descriptors.NumRotatableBonds(mol)\n",
+ " features['num_saturated_rings'] = Descriptors.NumSaturatedRings(mol)\n",
+ " features['num_aromatic_rings'] = Descriptors.NumAromaticRings(mol)\n",
+ " features['num_aliphatic_rings'] = Descriptors.NumAliphaticRings(mol)\n",
+ " features['ring_count'] = Descriptors.RingCount(mol)\n",
+ " features['fraction_csp3'] = Descriptors.FractionCSP3(mol)\n",
+ " \n",
+ " # Surface area and polarity\n",
+ " features['tpsa'] = Descriptors.TPSA(mol)\n",
+ " features['polar_surface_area'] = rdMolDescriptors.CalcTPSA(mol)\n",
+ " \n",
+ " # Lipophilicity and solubility\n",
+ " features['logp'] = Descriptors.MolLogP(mol)\n",
+ " features['crippen_logp'] = Crippen.MolLogP(mol)\n",
+ " features['crippen_mr'] = Crippen.MolMR(mol) # Molar refractivity\n",
+ " \n",
+ " # Flexibility and rigidity\n",
+ " features['kappa1'] = Descriptors.Kappa1(mol) # Molecular shape index\n",
+ " features['kappa2'] = Descriptors.Kappa2(mol)\n",
+ " features['kappa3'] = Descriptors.Kappa3(mol)\n",
+ " features['chi0v'] = Descriptors.Chi0v(mol) # Connectivity indices\n",
+ " features['chi1v'] = Descriptors.Chi1v(mol)\n",
+ " features['chi2v'] = Descriptors.Chi2v(mol)\n",
+ " \n",
+ " # Electronic properties\n",
+ " features['balaban_j'] = Descriptors.BalabanJ(mol)\n",
+ " features['bertz_ct'] = Descriptors.BertzCT(mol) # Complexity index\n",
+ " \n",
+ " # Polymer-specific features\n",
+ " features['num_radical_electrons'] = Descriptors.NumRadicalElectrons(mol)\n",
+ " features['num_valence_electrons'] = Descriptors.NumValenceElectrons(mol)\n",
+ " \n",
+ " # Atom type counts\n",
+ " atom_counts = {}\n",
+ " for atom in mol.GetAtoms():\n",
+ " symbol = atom.GetSymbol()\n",
+ " atom_counts[symbol] = atom_counts.get(symbol, 0) + 1\n",
+ " \n",
+ " # Add individual atom counts as features\n",
+ " for element in ['C', 'N', 'O', 'S', 'P', 'F', 'Cl', 'Br', 'I']:\n",
+ " features[f'count_{element}'] = atom_counts.get(element, 0)\n",
+ " features[f'ratio_{element}'] = atom_counts.get(element, 0) / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Bond type analysis\n",
+ " bond_types = {'SINGLE': 0, 'DOUBLE': 0, 'TRIPLE': 0, 'AROMATIC': 0}\n",
+ " for bond in mol.GetBonds():\n",
+ " bond_type = str(bond.GetBondType())\n",
+ " if bond_type in bond_types:\n",
+ " bond_types[bond_type] += 1\n",
+ " \n",
+ " for bond_type, count in bond_types.items():\n",
+ " features[f'num_{bond_type.lower()}_bonds'] = count\n",
+ " features[f'ratio_{bond_type.lower()}_bonds'] = count / features['num_bonds'] if features['num_bonds'] > 0 else 0\n",
+ " \n",
+ " # Hybridization analysis\n",
+ " hybridization_counts = {'SP': 0, 'SP2': 0, 'SP3': 0, 'SP3D': 0, 'SP3D2': 0}\n",
+ " for atom in mol.GetAtoms():\n",
+ " hyb = str(atom.GetHybridization())\n",
+ " if hyb in hybridization_counts:\n",
+ " hybridization_counts[hyb] += 1\n",
+ " \n",
+ " for hyb_type, count in hybridization_counts.items():\n",
+ " features[f'num_{hyb_type.lower()}_carbons'] = count\n",
+ " features[f'ratio_{hyb_type.lower()}_carbons'] = count / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Formal charge analysis\n",
+ " formal_charges = [atom.GetFormalCharge() for atom in mol.GetAtoms()]\n",
+ " features['total_formal_charge'] = sum(formal_charges)\n",
+ " features['abs_total_formal_charge'] = sum(abs(charge) for charge in formal_charges)\n",
+ " features['max_formal_charge'] = max(formal_charges) if formal_charges else 0\n",
+ " features['min_formal_charge'] = min(formal_charges) if formal_charges else 0\n",
+ " \n",
+ " # Aromaticity features\n",
+ " aromatic_atoms = sum(1 for atom in mol.GetAtoms() if atom.GetIsAromatic())\n",
+ " features['num_aromatic_atoms'] = aromatic_atoms\n",
+ " features['aromatic_ratio'] = aromatic_atoms / features['num_atoms'] if features['num_atoms'] > 0 else 0\n",
+ " \n",
+ " # Ring size analysis\n",
+ " ring_info = mol.GetRingInfo()\n",
+ " ring_sizes = [len(ring) for ring in ring_info.AtomRings()]\n",
+ " if ring_sizes:\n",
+ " features['avg_ring_size'] = sum(ring_sizes) / len(ring_sizes)\n",
+ " features['max_ring_size'] = max(ring_sizes)\n",
+ " features['min_ring_size'] = min(ring_sizes)\n",
+ " features['num_3_rings'] = sum(1 for size in ring_sizes if size == 3)\n",
+ " features['num_4_rings'] = sum(1 for size in ring_sizes if size == 4)\n",
+ " features['num_5_rings'] = sum(1 for size in ring_sizes if size == 5)\n",
+ " features['num_6_rings'] = sum(1 for size in ring_sizes if size == 6)\n",
+ " features['num_7_rings'] = sum(1 for size in ring_sizes if size == 7)\n",
+ " features['num_large_rings'] = sum(1 for size in ring_sizes if size > 7)\n",
+ " else:\n",
+ " features.update({\n",
+ " 'avg_ring_size': 0, 'max_ring_size': 0, 'min_ring_size': 0,\n",
+ " 'num_3_rings': 0, 'num_4_rings': 0, 'num_5_rings': 0,\n",
+ " 'num_6_rings': 0, 'num_7_rings': 0, 'num_large_rings': 0\n",
+ " })\n",
+ " \n",
+ " # Polymer-specific structural features\n",
+ " features['has_polymer_notation'] = '*' in smiles\n",
+ " features['smiles_length'] = len(smiles)\n",
+ " features['branch_count'] = smiles.count('(')\n",
+ " features['branch_ratio'] = smiles.count('(') / len(smiles) if len(smiles) > 0 else 0\n",
+ " \n",
+ " return features\n",
+ "\n",
+ "def add_features(df, num_workers=None):\n",
+ " \"\"\"\n",
+ " Improved version using multiprocessing to calculate RDKit descriptors efficiently.\n",
+ " \n",
+ " Parameters:\n",
+ " df: pandas DataFrame with 'Smiles' column\n",
+ " num_workers: Number of worker processes (defaults to number of CPU cores)\n",
+ " \"\"\"\n",
+ " if num_workers is None:\n",
+ " num_workers = multiprocessing.cpu_count()\n",
+ " \n",
+ " smiles_list = df['Smiles'].tolist()\n",
+ " \n",
+ " with ProcessPoolExecutor(max_workers=num_workers) as executor:\n",
+ " # Use tqdm with executor.map for progress tracking\n",
+ " features_list = list(tqdm(executor.map(analyze_polymer_features_rdkit, smiles_list), \n",
+ " total=len(smiles_list), \n",
+ " desc=\"Computing RDKit descriptors\"))\n",
+ " \n",
+ " # Convert results to DataFrame\n",
+ " features_df = pd.DataFrame(features_list)\n",
+ " \n",
+ " # Concatenate with original DataFrame\n",
+ " df_result = pd.concat([df, features_df], axis=1)\n",
+ " \n",
+ " return df_result\n",
+ "\n",
+ "def get_list_dif(l1, l2):\n",
+ " return list(set(l1) - set(l2))\n",
+ "\n",
+ "# Usage example:\n",
+ "# df_with_features = add_features(df, num_workers=4)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "155598af-79f3-4933-8b5c-1fd11f64b870",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv').drop(columns=['Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c69cc497-9fb6-4f74-96eb-257d7aa4a91a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/kaggle_comp/train.csv')\n",
+ "df['Smiles'] = df['SMILES']\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b076c55-d6ef-4780-af97-5fccd5062661",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_df = df.iloc[:10_000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96313883-c2ca-4eb8-9ec7-9aaca8dba077",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "features_df = add_features(sample_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "41c7f85a-ea65-42e5-b315-ef304ba311c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "selected_features = ['mol_weight', 'exact_mol_weight', 'num_heavy_atoms', 'num_atoms',\n",
+ " 'num_bonds', 'num_hbond_donors', 'num_hbond_acceptors',\n",
+ " 'num_heteroatoms', 'num_rotatable_bonds', 'num_saturated_rings',\n",
+ " 'num_aromatic_rings', 'num_aliphatic_rings', 'ring_count',\n",
+ " 'fraction_csp3', 'tpsa', 'polar_surface_area', 'logp', 'crippen_logp',\n",
+ " 'crippen_mr', 'kappa1', 'kappa2', 'kappa3', 'chi0v', 'chi1v', 'chi2v',\n",
+ " 'balaban_j', 'bertz_ct', 'num_radical_electrons',\n",
+ " 'num_valence_electrons',\n",
+ " 'count_O', 'ratio_O', 'count_S', 'ratio_S', 'count_P', 'ratio_P',\n",
+ " 'count_F', 'ratio_F', 'count_Cl', 'ratio_Cl', 'count_Br', 'ratio_Br',\n",
+ " 'count_I', 'ratio_I', 'num_single_bonds', 'ratio_single_bonds',\n",
+ " 'num_double_bonds', 'ratio_double_bonds', 'num_triple_bonds',\n",
+ " 'ratio_triple_bonds', 'num_aromatic_bonds', 'ratio_aromatic_bonds',\n",
+ " 'num_sp_carbons', 'ratio_sp_carbons', 'num_sp2_carbons',\n",
+ " 'ratio_sp2_carbons', 'num_sp3_carbons', 'ratio_sp3_carbons',\n",
+ " 'num_sp3d_carbons', 'ratio_sp3d_carbons', 'num_sp3d2_carbons',\n",
+ " 'ratio_sp3d2_carbons', 'total_formal_charge', 'abs_total_formal_charge',\n",
+ " 'max_formal_charge', 'min_formal_charge', 'num_aromatic_atoms',\n",
+ " 'aromatic_ratio', 'avg_ring_size', 'max_ring_size', 'min_ring_size',\n",
+ " 'num_3_rings', 'num_4_rings', 'num_5_rings', 'num_6_rings',\n",
+ " 'num_7_rings', 'num_large_rings', 'has_polymer_notation',\n",
+ " 'branch_count', 'branch_ratio']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "fc31605d-cc21-4533-b04e-f8acdaef1a65",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['id', 'SMILES', 'Tg', 'FFV', 'Tc', 'Density', 'Rg', 'Smiles',\n",
+ " 'mol_weight', 'exact_mol_weight', 'num_heavy_atoms', 'num_atoms',\n",
+ " 'num_bonds', 'num_hbond_donors', 'num_hbond_acceptors',\n",
+ " 'num_heteroatoms', 'num_rotatable_bonds', 'num_saturated_rings',\n",
+ " 'num_aromatic_rings', 'num_aliphatic_rings', 'ring_count',\n",
+ " 'fraction_csp3', 'tpsa', 'polar_surface_area', 'logp', 'crippen_logp',\n",
+ " 'crippen_mr', 'kappa1', 'kappa2', 'kappa3', 'chi0v', 'chi1v', 'chi2v',\n",
+ " 'balaban_j', 'bertz_ct', 'num_radical_electrons',\n",
+ " 'num_valence_electrons', 'count_C', 'ratio_C', 'count_N', 'ratio_N',\n",
+ " 'count_O', 'ratio_O', 'count_S', 'ratio_S', 'count_P', 'ratio_P',\n",
+ " 'count_F', 'ratio_F', 'count_Cl', 'ratio_Cl', 'count_Br', 'ratio_Br',\n",
+ " 'count_I', 'ratio_I', 'num_single_bonds', 'ratio_single_bonds',\n",
+ " 'num_double_bonds', 'ratio_double_bonds', 'num_triple_bonds',\n",
+ " 'ratio_triple_bonds', 'num_aromatic_bonds', 'ratio_aromatic_bonds',\n",
+ " 'num_sp_carbons', 'ratio_sp_carbons', 'num_sp2_carbons',\n",
+ " 'ratio_sp2_carbons', 'num_sp3_carbons', 'ratio_sp3_carbons',\n",
+ " 'num_sp3d_carbons', 'ratio_sp3d_carbons', 'num_sp3d2_carbons',\n",
+ " 'ratio_sp3d2_carbons', 'total_formal_charge', 'abs_total_formal_charge',\n",
+ " 'max_formal_charge', 'min_formal_charge', 'num_aromatic_atoms',\n",
+ " 'aromatic_ratio', 'avg_ring_size', 'max_ring_size', 'min_ring_size',\n",
+ " 'num_3_rings', 'num_4_rings', 'num_5_rings', 'num_6_rings',\n",
+ " 'num_7_rings', 'num_large_rings', 'has_polymer_notation',\n",
+ " 'smiles_length', 'branch_count', 'branch_ratio'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scalers = []\n",
+ "for col in selected_features:\n",
+ " scaler = StandardScaler()\n",
+ " features_df[col] = scaler.fit_transform(features_df[col].to_numpy().reshape(-1, 1)).flatten()\n",
+ " scalers.append(scaler)\n",
+ " \n",
+ "features_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f2f1a614-0ba7-4a01-9731-532afc1d14e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'features_df' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m new_features = []\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m selected_features:\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m unique_list = \u001b[43mfeatures_df\u001b[49m[feature].unique()\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_list) > \u001b[32m300\u001b[39m:\n\u001b[32m 6\u001b[39m new_features.append(feature)\n",
+ "\u001b[31mNameError\u001b[39m: name 'features_df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "new_features = []\n",
+ "\n",
+ "for feature in selected_features:\n",
+ " unique_list = features_df[feature].unique()\n",
+ " if len(unique_list) > 300:\n",
+ " new_features.append(feature)\n",
+ "new_features.append('Smiles')\n",
+ "print(new_features)\n",
+ "len(new_features), len(selected_features)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28cbac75-8a9f-4292-aedb-11f33f5a6056",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c065d950-7a63-4424-9923-1072d2e2268c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "features_df.to_csv('7k_w_descriptors.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "069a9021-d440-4bf1-9882-a2af25f2e801",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... | \n",
+ " 1.284275 | \n",
+ " 1.284737 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 1.501149 | \n",
+ " 0.736852 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 110 | \n",
+ " 1.217388 | \n",
+ " 0.008668 | \n",
+ "
\n",
+ " \n",
+ " | 7970 | \n",
+ " 2147191531 | \n",
+ " *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... | \n",
+ " NaN | \n",
+ " 0.369411 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... | \n",
+ " 0.329570 | \n",
+ " 0.329823 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 1.501149 | \n",
+ " -0.026026 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 73 | \n",
+ " 0.336345 | \n",
+ " 0.021405 | \n",
+ "
\n",
+ " \n",
+ " | 7971 | \n",
+ " 2147435020 | \n",
+ " *C=C(*)c1ccccc1C | \n",
+ " 261.662355 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *C=C(*)c1ccccc1C | \n",
+ " -1.359802 | \n",
+ " -1.359728 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " -0.626991 | \n",
+ " -0.788904 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 16 | \n",
+ " -1.205481 | \n",
+ " -1.182617 | \n",
+ "
\n",
+ " \n",
+ " | 7972 | \n",
+ " 2147438299 | \n",
+ " *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... | \n",
+ " NaN | \n",
+ " 0.374049 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... | \n",
+ " 1.160667 | \n",
+ " 1.160653 | \n",
+ " ... | \n",
+ " -0.048476 | \n",
+ " -0.069289 | \n",
+ " 0.437079 | \n",
+ " -0.407465 | \n",
+ " -0.051542 | \n",
+ " -0.047917 | \n",
+ " 0.0 | \n",
+ " 72 | \n",
+ " -0.324438 | \n",
+ " -1.005054 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
7973 rows × 92 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id SMILES \\\n",
+ "0 87817 *CC(*)c1ccccc1C(=O)OCCCCCC \n",
+ "1 106919 *Nc1ccc([C@H](CCC)c2ccc(C3(c4ccc([C@@H](CCC)c5... \n",
+ "2 388772 *Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(... \n",
+ "3 519416 *Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)... \n",
+ "4 539187 *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... \n",
+ "... ... ... \n",
+ "7968 2146592435 *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 \n",
+ "7969 2146810552 *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... \n",
+ "7970 2147191531 *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... \n",
+ "7971 2147435020 *C=C(*)c1ccccc1C \n",
+ "7972 2147438299 *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... \n",
+ "\n",
+ " Tg FFV Tc Density Rg \\\n",
+ "0 NaN 0.374645 0.205667 NaN NaN \n",
+ "1 NaN 0.370410 NaN NaN NaN \n",
+ "2 NaN 0.378860 NaN NaN NaN \n",
+ "3 NaN 0.387324 NaN NaN NaN \n",
+ "4 NaN 0.355470 NaN NaN NaN \n",
+ "... ... ... ... ... .. \n",
+ "7968 NaN 0.367498 NaN NaN NaN \n",
+ "7969 NaN 0.353280 NaN NaN NaN \n",
+ "7970 NaN 0.369411 NaN NaN NaN \n",
+ "7971 261.662355 NaN NaN NaN NaN \n",
+ "7972 NaN 0.374049 NaN NaN NaN \n",
+ "\n",
+ " Smiles mol_weight \\\n",
+ "0 *CC(*)c1ccccc1C(=O)OCCCCCC -0.875755 \n",
+ "1 *Nc1ccc([C@H](CCC)c2ccc(C3(c4ccc([C@@H](CCC)c5... 0.651876 \n",
+ "2 *Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(... 2.336573 \n",
+ "3 *Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)... 0.417716 \n",
+ "4 *Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N... 2.178003 \n",
+ "... ... ... \n",
+ "7968 *Oc1cc(CCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1 -0.375261 \n",
+ "7969 *C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3... 1.284275 \n",
+ "7970 *c1cc(C(=O)NCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(... 0.329570 \n",
+ "7971 *C=C(*)c1ccccc1C -1.359802 \n",
+ "7972 *c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCC... 1.160667 \n",
+ "\n",
+ " exact_mol_weight ... num_3_rings num_4_rings num_5_rings \\\n",
+ "0 -0.875617 ... -0.048476 -0.069289 -0.626991 \n",
+ "1 0.651916 ... -0.048476 -0.069289 -0.626991 \n",
+ "2 2.336165 ... -0.048476 -0.069289 -0.626991 \n",
+ "3 0.417722 ... -0.048476 -0.069289 -0.626991 \n",
+ "4 2.178499 ... -0.048476 -0.069289 1.501149 \n",
+ "... ... ... ... ... ... \n",
+ "7968 -0.375084 ... -0.048476 -0.069289 -0.626991 \n",
+ "7969 1.284737 ... -0.048476 -0.069289 1.501149 \n",
+ "7970 0.329823 ... -0.048476 -0.069289 1.501149 \n",
+ "7971 -1.359728 ... -0.048476 -0.069289 -0.626991 \n",
+ "7972 1.160653 ... -0.048476 -0.069289 0.437079 \n",
+ "\n",
+ " num_6_rings num_7_rings num_large_rings has_polymer_notation \\\n",
+ "0 -0.788904 -0.051542 -0.047917 0.0 \n",
+ "1 0.736852 -0.051542 -0.047917 0.0 \n",
+ "2 2.644047 -0.051542 -0.047917 0.0 \n",
+ "3 1.118291 -0.051542 -0.047917 0.0 \n",
+ "4 0.355413 -0.051542 -0.047917 0.0 \n",
+ "... ... ... ... ... \n",
+ "7968 -0.407465 -0.051542 -0.047917 0.0 \n",
+ "7969 0.736852 -0.051542 -0.047917 0.0 \n",
+ "7970 -0.026026 -0.051542 -0.047917 0.0 \n",
+ "7971 -0.788904 -0.051542 -0.047917 0.0 \n",
+ "7972 -0.407465 -0.051542 -0.047917 0.0 \n",
+ "\n",
+ " smiles_length branch_count branch_ratio \n",
+ "0 26 -0.985221 -0.813832 \n",
+ "1 82 0.336345 -0.286141 \n",
+ "2 134 1.657910 -0.109289 \n",
+ "3 79 0.556606 0.132247 \n",
+ "4 118 0.556606 -0.830501 \n",
+ "... ... ... ... \n",
+ "7968 44 -0.324438 0.124891 \n",
+ "7969 110 1.217388 0.008668 \n",
+ "7970 73 0.336345 0.021405 \n",
+ "7971 16 -1.205481 -1.182617 \n",
+ "7972 72 -0.324438 -1.005054 \n",
+ "\n",
+ "[7973 rows x 92 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "features_df = pd.read_csv('7k_w_descriptors.csv')\n",
+ "features_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "49998b8a-3925-4383-917a-116f70187d46",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n"
+ ]
+ }
+ ],
+ "source": [
+ "old_len = len(features_df)\n",
+ "new_len = len(features_df.drop_duplicates())\n",
+ "print(new_len - old_len)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "c2f08ca9-21f6-4a79-ab94-80556b8dab1d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████| 6378/6378 [00:01<00:00, 3492.45it/s]\n",
+ "100%|█████████████████████████████████████| 1595/1595 [00:00<00:00, 3576.37it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "import copy\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "def create_splits(df):\n",
+ " train, test = train_test_split(df, test_size=0.2)\n",
+ " return train, test\n",
+ "\n",
+ "def create_samples(df, features):\n",
+ " samples = []\n",
+ " features_without_smiles = copy.deepcopy(features)\n",
+ " features_without_smiles.remove('Smiles')\n",
+ " for i, row in tqdm(df.iterrows(), total=len(df)):\n",
+ " properties = torch.Tensor(row[features_without_smiles].to_list())\n",
+ " sample = {'Smiles': row['Smiles'], 'property_tensor': properties}\n",
+ " samples.append(sample)\n",
+ " return samples\n",
+ "\n",
+ "train, val = create_splits(features_df.reset_index(drop=True))\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "val = val.reset_index(drop=True)\n",
+ "\n",
+ "train_list = create_samples(train, new_features)\n",
+ "val_list = create_samples(val, new_features)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "2fdb3171-deda-4c1f-ae4b-853d781ffdd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|███████████████████████████████████████| 20/20 [00:00<00:00, 106050.67it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "prop_vectors = [el['property_tensor'] for el in train_list[:20]]\n",
+ "\n",
+ "sim_matrix = cosine_similarity(prop_vectors)\n",
+ " \n",
+ "n = len(prop_vectors)\n",
+ "positive_pairs, negative_candidates = [], []\n",
+ "sims = []\n",
+ "\n",
+ "positive_threshold = 0.9\n",
+ "negative_threshold = 0.2\n",
+ "\n",
+ "for i in tqdm(range(n)):\n",
+ " for j in range(i + 1, n):\n",
+ " sim = sim_matrix[i, j]\n",
+ "\n",
+ " if sim > positive_threshold:\n",
+ " positive_pairs.append((i, j, sim))\n",
+ " elif sim < negative_threshold:\n",
+ " negative_candidates.append((i, j, sim))\n",
+ " sims.append(float(sim))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "54f29e98-7c32-441c-bb1b-cdaf3fd1df49",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3, 126)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(positive_pairs), len(negative_candidates)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "22e0f46e-2673-4840-95fd-f98914e57b78",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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2m5BVVKNSJj5KqcO+V1Fde+21+MAHPoCPfOQjeOCBB/Da174Ws7OzuOqqqwAAV155Ja677rrE6z74wQ/isssuw3HHHWc8PjMzg9e//vW488478dhjj+G2227Dy1/+cjz96U/HxRdf3O+Ps2zAo/2Ds4sjc3MMAypFZdXgjM7NWmDlgRaLejNCsxUZDE6n1+bBmdiGY/3kGMbKYft9uhzoAKE1OEMeSAZ4ANqPsVKKqhQGAEajTPwtn7kfL3rX7ZheqA97KAAGoMG5/PLLsX//frz1rW/Fnj17cN555+GWW25RwuOdO3ciDM0466GHHsJXvvIVfOELX0gcr1Qq4dvf/jY+8pGP4MiRI9i2bRte8pKX4B3veEfhhZMDUqB2cGYRW9eOD2k0w0VqN/GCwikwRPA0VK3RFAxOZ8ekzcxYKUQQxIvjUrrOtQ/OaI+53xslSlGtGitheqExEhqcW767B/uP1fDtJ47i+c/YOOzhDEZkfM011+Caa66x/u32229PPHb66ac7aa6JiQl8/vOf7+XwViRkieH+Y7UVG+BEMI3+DIOu0Z5DCyxzcJnFQr1lBDidLpoUwJfCAO3N/5K6zmlt6FSDNCj0vVVDe56aHCuPTIBDDOOuo/NDHkmMohfVCoVU4O+fWbntGtKN/kZ7Ei2wvMGvv4V6Ews8RdVhVEKBQRgCITE4S+g6p489yCF/8CuP4or334G5Rf/GxFwE3A8FAM3hk9XYGmXYIuMoitT52X1kNNaTIsAZEGZqDbz8xq/ixi/aK70GDWIpJirxzSF70yxlHJip4f/e8Zh3HpjrHID+O5AWKOALfvkt1JuY61EVFQCUAs3gjJIwNAvDEBn/w907cecPD+G+J454v6Zh+OD0Q2RMKao4ETNsBqfWaKngc3fB4KwsfPvJI/jWE0fwz/c+OeyhANAsxfHr4rTUcqqkev+Xf4i3/Mt38f++7mcHIDU4BYNTYFRgMjitnlRR0evCMNAanCV0mQ/DB4eCQsl8p6HvrRrax58cazM4Qw5wuAD+qSNFgLOiQAr3URHz0c1wwroJAMvL7I9urul5PwYntdnmaHxdBVYojACn0TScjDtdM5sGg7P0UlTD8MEhtqyeI4io91nLRwzO6mqbwRlyimqOXZu7jxYpqhUF1T9mRCYSWsy3rW0HOCPK4HRC7R6diwMb30klweBwcWAR4RQYImSKyjD661KDs1RFxsNo1SA3QT7oN4PTUBqc0UhRzdW0Pmn3kfmRSHsWAc6AsKgYnPTn3f3oIbz+H7+Fw7OLfR0P3RyUoto3gADnrh8exI1ffNj7wr/uU9/Bjnd/yZjUfXB4Lj53vsEk1+BEUaQcjYGltbMtsPzAr7+aTFF1WUUVBgHCdoQzCouRL7QGZ4Dv2Z4SFnOkqHizzb744LSPv2psNETGXB82u9jE9IK/ILtfKAKcAUF3AE6/0j/w3z/EP97zJP7rwX19HQ/Rp9vWDYbBabUivO4fvok/+/xDuPOHh7xe84Xv7sEj+2fx8L6ZXO91RDE4frMKf14rKpptFhgd8OtP+uB0yi62GIMTBOZjSwHD8MGhAFD6h6Wh3xslVSgyIhqcObERHQWhcRHgDAgUUGTtukir029XSlrEeYqqn7u47+6aViyR74VPNyzZ1PuCGBzfj8PXiUarVVRRFRgZSJHxQk8YnPhnnKJaeiLjaBgMTvu98ji+1/vsg0Ms/KhUUc3XTcZmFErFiwBnQCD6MGvXRX/vt1aHbtRN7V408/VmrgqBvLjtQd1w9YCnoJnOGZ/Us1Br6FJa3x0un3yarchkcJbSzF9g2YFff7JMvNNL0270t3Suc+2DM3gNTudVVD0fkupWPio+OJLBGQWzvyLAGRB8RcaqBLKPC2sURepGXdW+OYD+3iA85eaTDouiSI2nVvcfF6WngDwaHP17vRkVTsYFRgb8+pMi465TVKyKagnFN8MpE++AwTHmkT5MJKPG4CQCnBEoFS8CnAGh5qnBoUW50woJH/Bj080BADVPpuSexw/hkf3+uph9xxbw7SePqv8fmMkWUDdbkZp086SoKD0F5ElRpTA4S2nmL7DsYJaJ99oHB8oHp5/zTa8xjBSV1uD4v2m/5xHdqmFENDi1IkW1YrHo6YPTGsDuhN94lXKISime5HwYnH3TC/ilv70TV33o697vd/uD+43/+zA4fCwLHTI4nYiMCw1OgVGCZHAWetCLiouMl3KKapBjpo1nHpbb7EXV8yGpAGdVu0y80YqGmlInH5xqu0N9kaJaQVj0FBlT8NHPHRWnWcthgGrZfwfwyP5ZNFuRt44G0Pqb85+2HoCfBoePpZaDwTnCGBzfc8ifltTgeL91gQI9B9eZ9NzJOFiaImNVJj7AQdN7dcrgAL3XDNHxqYoKGK4Oh9Knp2xcBWA0zP6KAGdA8PXBoZu3nyJjLpSrlEKMtSNun8otyqv6Tq61RhNf+cEBAMDlz94OwDPA6ZDBOWwwOH6v4RNPoxkVDA6AJw7N4bc+fm+u3jsFeg/ZbHO+TyLjpeWDY/4cBOj0dKrBAXo/XqnBAYYb4JAG57TNqwHEAc6wr6siwBkQRklkTDdeGMST3FgpNMaYBgpwfBf+7++ZwexiE+smK3jhGZsAAAdnFzP9JPhY8lRRmRqc/AxOQzA4o+I8PWj8+3d249++vRsf/Mqjwx7Kiob0weHBftcpqoD3oko/1ld+cGAkRKMA1+AMoYoqB6UrGZxej1dqcIDh6nBUgLNxFYIgHsvBPhvWZqEIcAYEbvSXtvA2VYqqf2MhB8xyO7CpVnIwOEfzMTi0o5gar+C4VVWEQbwbOjSXfuFzlimPJ5BRRdVRmXjLMOhaofGNOuePH5wd8khWNqQPztyiFnJ2nqKKf4aePjgP7TmGV37wLvzuJ+/r6P16Dd2qYZDvGf+sN/KkqCSD058U1RjXUQ4xwJlvX5tTExVsWh3bjwxbaFwEOAPComfpsUpR9VH8QexJpc1PE4Pjo3V5qn3BtiI/hoQLGkthgA2r4gs/S2hsaHDyMDhsx+Az/0dRZEyUjVYkxIErM8Kha+Txg3NDHsnKBr/8pJNx5wFO/N3GZeLxY2nXOaWUR6Vf3TBExp30okpqcHo6JMXglHOy8P0CMTgTYyUc33bIH7bQuAhwBgR+4aVNTIrBGYAGRzI4eVJUgN8EqwWN8f/JWDCrVLze7DRFpRkcnwBMPiWpwfF+62UFmpyPztdV89ICgwe/FucXe5OiMjU42T44naRn+olBVJpKaA1ODgZHPLfnDE77+FxHOQoanMmxEratjXscDjutWQQ4AwJPs6Rd6ANJURGDU5IMTvqbRlFkBjg5GByaSDeuHgOQvRusGVVU/ifj6DyrosoxPoLU4KxUJ2N+Dh4/VKSphgV+fR6Zrzv/lge07oaevag6Sc/0E0NptqmcjDvrJh4fo6dDUmxSuRToAGeoDE6copqolHF8uwXQsCupigBnQOCRdSqDMwABHY/8AXjfHEfn66ZVvMe9RM8ptSkczeD4p6g6ZXB8JhT5nGbhgwPAnMiLNNXwwC+/I4JJ63QTREF7KYCXBkeVSI8Kg8PGOqgqHZqX8zTblMFQL60/uBt9ORxBBmddzOAc9DB17SfK2U8p0AvUeYoqbbfUflpffXBY5A/A2wfnycMm3ZiHIaFqDRKfZTE49Y6N/rgGxyNFBcHgNCPjvVdqgMOvv52HigBnWODX32EhzO+cwaEUVeilwemkD1M/wcfaiuJArZ/gOr1Oe1HRcXoFfuxKKVCb1aGKjOs6wLnigpNwxQUnYXV1uCFGEeAMCIbI2EeD09cy8TaDE5oMTlYqSOZTvTQ4ajKN/98Rg+Np9BdFkbHL7USD02yZGpwh968bGvhEXlRSDQ98MT86LxmcbjU4cZoKSL9X6G3ysBf9BB9qK4pQQn8jHLNXXWfdxIHepqj4sculcKRExpNj5aEHNoQiRTUgeIuMB5KiMhkcnaJKDyQ6CXBo4tQaHL8Ah08kvs02j9UapoeNx/jkea4LDc6wjaoGhe88eRQH2XfCq/iKFNXwYKZj5N86uzZViipkPjgptxjdR/U+brrywGRw+j8m/h75NDj9KxPngu/YjX74Ac48S1GNCooAZ0BY9ExRDYLBWVTlhe0qKs/87S4hGPOroop/ygAns0y8mZ/BOTIrRZjZryk0OMCjB2bxs3/1FfzWx+9Vj/EKkCJFNRxkBdedCuCbbNPhk6LSjSZHg8HhYx3E7SmNQH3RT6O/hsONflganCiKMNsWGRcBzgqEmaJyP085GQ9EZEwanHaKKoMpeUowOD5j5D44gH+ZuCky9rtpj8ybx8zj00NoNGU3ca+3XtLY0w5cecUDPwd7phdyCb0L9AZZ116nVhKcwfESGUf65yhUFfIhDJrBycOQyDLxXg414UY/ZAan1mipzzdRBDgrD74MTmsADA5VQ6gqqpIng9NBioo+D+0UqUz80OxiKt3Lx+LbbPNwosrEI4UmhhBrcFaWyFiZmLEJuSHcnJ88XLA4g0bWtdfpZt3G4KS6q/MUzQhUUrUGvAGRRqC+6CeDI93oh63B4dW1k2Ojob8BigBnYDB8cDw0OHlupLzQRn9tBqfiV0XVjciYdorrJ8cUm3MopU+J6WTsyeAkqkyyX5OlwVkJAY7SWLAVU+4+Cx3O4JF17XWcomLWDT69qGQz2mHDbK0yuhqcZKuGng0p6UZPLPyQUlTkgTNWDtX8PgooApwBgQt4fZyMe00F3/+UFpG6GJy0KqrFRgv7hG7GT8Qb/6QAJwwDHLcq2+yvEyfjw7P5y2jlcxIanOFvWPsOW1AtA+wiwBk8sq69rptteqeoRi3A0b8PogjAYLC6KBPv5Zwu3ehpLq8PicEhgfGqEUpPAUWAMzAYRn8enhO9nEd2HpzDz7zvK3jtx2IRKTmSlmX0n3Jz7J1eQBTFz10zXm6P0T9FxaN60uHsT6mkWuzAyZhSVFSi6BfgmP9vNM1eVCuhm7gycuMMTvuxLVPxd1UIjQePzBRVl60afEXGnBQYiRSV8MHpN3gaO1+ZuPncnmpw1CZVVMIOjcHRJeKjhCLAGRAWPVNUjT4wOLvbDc+ebC9S2uhPVFGlBBIkMD5h3YQKjHzGqI3+9GM+lVSLLMjwZXAoRXVcW+fjMxfLHaD0wVkJZeLK54QzOO2J8tSNqwEAjxVeOAMHX8gDC+vfeYqKNh3I1YsKGA0GR/rg9Bvm5++8VUMvN0sN5mIM+M3h/QRvtDlKKAKcAaDRbBk7DdeFzh0ze5lbpmMRE0I3x5ho1ZAm5iX9zbZ144qN8blhmykMTpoXDr9R4w7f2TcuMTgb2imwThicWIPj1/l9uaCpGJzkQnba5lUAYhawwGDBr72JSnLh6DTWUPdk4NeLKjJSNKPG4Axag+P/fkmjv16mqISX2dBFxqNXIg4UAc5AIGlDV/BiOOj2MtpvH5eYkMTN4RH9qwBn7YTa9fkZ/cU/Q7YFVWZ/x/xExoBfmoqaER6XK8ARu6xmK7dZ4FKHrVs0BXnE4DxxeG5FsFmjBH6++cJBv3ftg2NocPxSVP0sfvDFMH1wumm22cv7h76HRD/BIaeobIH4MFEEOAOAXKxdqRMe1PQyRZVgcKjEUNGb7SqqlJvj2EIcoa+brGgGp4MqKkAHIIdm/UTGgF+ailJUmsHJfIm1m3izySfQ4U/o/QZ9j1HE2Jz2z7UTFQDxbnQlBHujBIPBMQIcf42Z9biMwaF72V9kPAIMDhvCIBicThks+dyetmpokFmr/ya1nxhFF2NgQAHOjTfeiJNPPhnj4+O48MILcffddzuf++EPfxhBEBj/xsfHjedEUYS3vvWtOP744zExMYEdO3bgBz/4Qb8/RsdIMDiOm5LfuP1gcCjVQzeHFKillWOroKgU5mJweL6fQO+XZv0ub9QFjxv3sApwYobIq9mmeEozUSaeeYglD1sZLKWoxtmObCUIrkcJ/HvhO+PV1fj3rntRlfx8cKIOUzT9wqBFxoaTcTdVVH31wYmvCd+CjF5Dp6hWmMj4k5/8JK699lq87W1vw7333otzzz0XF198Mfbt2+d8zdTUFHbv3q3+Pf7448bf//RP/xTvfe97cdNNN+Guu+7CqlWrcPHFF2NhYcFxxOFCLtbOFFWf/B24cV2t0WI3h8jfpuxOmor1CdTr/AKIJINDr0/bDXbE4MzKFFXmS+wMzgr1wQF4MByffwpGgZVRMj9K4AJ9HmiuHu+OwVENcAPug5PyfOP6GP5F0OoT0+3zfnlSQAmjvx6eOuWD055LK+X457A0UnP1FcrgvPvd78bVV1+Nq666CmeeeSZuuukmTE5O4uabb3a+JggCbN26Vf3bsmWL+lsURXjPe96DN7/5zXj5y1+Oc845Bx/96Eexa9cufOYzn+n3x+kIiRSVY2LiqZGe9i1hN9pCvclujnaKqpJNb9LEVgoDlBSDk/3eqiSViYypi3nabkgaVvmY/dFNphaAHD49hLhVAyvpXwEUjrGAEYPTfqzKApyVzuAcna/jF2/6Gj781UcH8n5cvzZe1gvHqvYuudNr096qwX0sU4My/GvA9MHp//vZNgA+SBr99VJkLDQ4QxYZr8gU1eLiIu655x7s2LFDv2EYYseOHbjjjjucr5uZmcHTnvY0bN++HS9/+cvx3e9+V/3t0UcfxZ49e4xjrl27FhdeeKHzmLVaDdPT08a/QcJbZNynckz+frVGKyFQq5ayq6h0WWKggpVOjP4AzeCkpaikYVVWw01e3j3eDtg6ERnXRcXbSljTI8sCRt+3EeCMwOI2TNy78zC+/thhfOhrjw3k/bRfjd6EANrnqeMqKnZPah8c9/NHSYMjU2mD0eDo35utyJs16kcvqpZgWEmDM+wy8dkalYmvoBTVgQMH0Gw2DQYGALZs2YI9e/ZYX3P66afj5ptvxr/8y7/g7//+79FqtfC85z0PTz75JACo1+U55g033IC1a9eqf9u3b+/2o+VCUmTsCHD6lBppCE8ZVUWVQ6DWULu+UDE4uZptMh8PEjamTZYyKMxKUXFqlna7PvOQnDDl+66IFBVfwNoTJ12L1UKDo0D30VOH5wey0NO1FwaBkaKabAc4nQrgbQxO2rGMAGfIjKZ8+0GXiQP+Zoe97kV1787DOPf6L+Bjdz3OGiaPRhXVfL0oE/fCRRddhCuvvBLnnXceXvCCF+BTn/oUNm3ahL/927/t+JjXXXcdjh49qv498cQTPRxxNhIaHJfIuG8aHJPB0WXioooqJcBpKtZHMzg+kx2fpAl0U6axVHnLxPlYaLfbSYpKBlIrYVE3U1Tx73WR45fPW4ngFWa883q/YKSo+iAyDkM/Hxz+NsNaQAlynIMRGUuW15fB6e1m6cvf349jCw3c/tD+jqw++om5lZii2rhxI0qlEvbu3Ws8vnfvXmzdutXrGJVKBc961rPw8MMPA4B6XZ5jVqtVTE1NGf8GCW+RseGD07v3T2pw2sFKjlYNDbbry+NkTPc41+DQ69N2QlIsV8ticNjYq4rByZ+iklqfFRDfWHfoOqANc9kCLGfwzz+I3lx6cwCMs1RhtxocLjJWGpyUdXGUnIyTAc4gGBzz/77sXa8ZnD3toProfD1h9UFVVMMOcFaUk/HY2BjOP/983HbbbeqxVquF2267DRdddJHXMZrNJr7zne/g+OOPBwCccsop2Lp1q3HM6elp3HXXXd7HHDSkYNYpMuYpqn5WUVGAUxb0ZiqDo9Na+TQ4epImEIOT9vpEmXiGyJgCojDQAZRXgCMOK7U+KyJFZREZ8zSmEpWvgHORBi4affxQ/1tXcP1aL6uodIoKfiJjy/UxLMj7ddA+OIA/i8V1i0D3bBOxhkfn6okqqm66iV//2e/h6o9+o6s1RzfbXEEaHAC49tpr8YEPfAAf+chH8MADD+C1r30tZmdncdVVVwEArrzySlx33XXq+ddffz2+8IUv4Ic//CHuvfdevPKVr8Tjjz+OX//1XwcQV1j9zu/8Dv7wD/8Q//qv/4rvfOc7uPLKK7Ft2zZcdtll/f44HSHJ4NifZzA4PQxwEgyOQ6CWdnNQUBRrcNpj9AogNPNDUCLjtBQVtZPwaCMRP19XhoU5JpQsBqeXE+gj+2dwyXu+jM9+a1fPjtkL8GuNvpMm2yG2N4kDKckdZfDzNIjWFWaZeFJk3OnXwc03tQ9O2jj072mFAYNAgsEZQLxlq7T0AWdBge7vH2JwjswvWrqJt+fUDhicj931OG793t6u+s2RD86oMTh9D7cuv/xy7N+/H29961uxZ88enHfeebjllluUSHjnzp0IQ33zHj58GFdffTX27NmD9evX4/zzz8fXvvY1nHnmmeo5b3jDGzA7O4vXvOY1OHLkCJ7//OfjlltuSRgCjgo68cHp5cKaqKJyCdQaLURRpLwxbMcoh8z91CdFZfPBUWXiKSLjdkAzNV7GgZlFDwanHRCVQq8OyS4kGJweTqBfffgAHtxzDJ/79i787LnbenfgLmGmqNoMDvNKKuUwdlzOGHSKSnlIhYFKuwLaTK1ro7+Q++B4ioyHzeAMJUUlNTh+56DOvKTm680eMDhxu5w4RdXe0LUnu1IO1joxzvbnOdpuddMJRlWDMxA+6ZprrsE111xj/dvtt99u/P8v/uIv8Bd/8RepxwuCANdffz2uv/76Xg2xr/D1wWkNiMFxCdSAmAnhk6k8Bq+88BMZxz9NkXH26ylgWTNeaQc4flVUlXLoRbvr8Q2OwZHsyKjA5nPS4CmqHKLy5QyDwTk0CAYn/hmnqJgGp+qvMbOhadzL9F6+KaphMzjm/wftgwP4iYxbLd04mebXbtq+zNYamG63y1motzDT/p3m8DyVrXKc9PGOeAY4kWIW9Zw+qgHOyFVRLUd04oPTS72Dq4qqonpRsQDHQXEqDU4pn5OxLUVFv6fthGgca9p6g6wqKl714yOcVOMTH0Gmwnpbrk/6ltEKFKQGh096ZSYyXgl6pDQ0RIDT7z5lhsjYqKLqDYMTBlpP55+ict9UTx6ewye/vrOvQtdh++AAfm7O/DyRCV83+4M902bV3v6ZuC0NpaiCDllWvs4cncsOcKIowq984C5c/v47je9CN9scLQ3OaI1mmcLbybhPImO+66rVm6yvlNmqAYgDiTUpxyiH+XpRcR0BwatMvB0MTI3HzR6zGZzk+DpicDz1Up1AViiNCowdesvsxVViDM6ojXvQ4NfKTK2BQ7OLOG51tW/vR+c7CALlR1QpBYoR6NjJmKqofBkczyqqP/mPB/G5b+/G1HgFLz37+I7GloWR8MFpeKTm2UDp++pmrHuELcHBmbhRcTJFle+4/PukZsVpOFZr4I4fHgQATC80VDPe+cXCB2fFopMy8V6mA5JVVGarhiAIMq2+eYqqk27iJa7BUSmqFFGzYHB8q6jGyqGXtwdB7ghlINXLXbr0mBkV8F1cvdkyvtdKqQhwCHJxf7zPaSr6WkpBoMrExysldS91emnaNTgpzxfXhwsH2ovugdnshbJTjIQPjg+Dw64VzeB0Pljpu0TnmuZw2qPmvUf5HOyTopqr6fmR1oooilZuL6oCOVJUGQzO/mM13PjFh7H/WC3X+/NgyRQZ66Ajy+pb+y4EufK9yqzM8MEJE+OSoIouFeBkVFHxDul5djPJFFX/NDjSJXhUILVffAIvM+fqURv3oCE/f78rqWwpqsmxkk5HdKrBYYGTD9vJ/5R2z9ImZGExuzFup5DzYr/ThEByjvCpVDI2CeXuAlIA2NMWGBMOOFJUeecrPs4jHimq2TZTA+h1rdZoqc9GLtujgiLAGQC8RcYZGpz33vYD/NnnH8L/vfPxxN/SwC/ihXpTV8iw6rUssz9igUo5nYx5vp+gRMYOujuKIrVTXNNOUWU12zTKxJUGx18jREimqHoZ4LQZnBELFMwdemT0nDJ8j1a4Bkd+/n4LjekyCZgPzkSllKuK0Xpci8g4VYPj6YND7GdWOrkbJFNUfXsrBRlE+cx7tJkJg9haA+gtg6NSVFJknPOE1HOmqOYXkwzOHHtsolIwOCsOSQbH/jz+uHxOFEX4rwf3AYgV9XkgGRzagZQZg5Nl9qfdj8NcTsY2oz/adbjo7garQFAanCwGh5W+5ykTTzA4iRRV5iG8QYtDcxDmHTnAv4ZGq6UYnDCImbc83/dyhgx2+10qru6dENiwKr4P1q8a0+mILquoQlYRmRbIywDYBQps5vsa4MgU1eAZHB+jP1OzGD/WzWaJNDi0T5xtBxW0SS11uAkxGByPFBVfe2j+pseq5dAoJhkFFAHOAODbiyqt2eYj+2fw1JH5xPN8IBkc5aHAxMUqRdW0T05GaWkeJ2NLFVUlgwHi50tVUWVMmtzZM4/RX0KD09CLe3yM3k2gslP3qICfg0ZTd2WnyTMPY7ec0VDWBfE1ubPPbsbKBycI8Kzt6/EHP/ejuP7nzvIKSqYX6vhfn/gmvvjQvsTfdANcv15UZooqjcFpGT/7gVHwwfG5f9U9xKs6uxgqMTgnbZg0HqdNaqfvYWhwPFJUcxYGZ35E9TdAEeAMBL7dxNOabd7+0H7n37LAL+JaPWn0B7AUlWNy4pVX2ro/+71tRn9ctGrLoXNmx7dMvOMUldydNbRY2fb3bsCbNQ4K/3zPk7jy5rsxveCevEwnY319JDw2VniAQ8zbqZtWAxgEgxP/pHLuX3veyTj7xLWsA7j7tf/9/QP4l/t24f1f+mHib5rB8VsYfauoiGXtJ4MjP/MgsqbyuvcpEqDnmGnAblJU8eb2jK1mjSulqDp1G+ffp4/RH9fg1ESKanLE2jQARYAzEPhWUfGFT16oX/o+C3C6oCEXGk2VgrClqFztGihI4lVUeQIIw8mYBVY2ypvOVxBozw/fMvH8KSr7c1S/rD6IjLtxg/2P7+zGFe+/A3un/bpZf+SOx/Dl7+/HnY8cdD6Hf0ZeJk7fc6f093IDff7TNq4CAOw7VjM0Cb0G3V/SWNynqo2s8+cs942t2Wba4mtr5WED3aNZbGs3GIUUlU+Aw9s0+FSqpWGh3sThNrtyxtYp428qRdWh8JyvOT4aHFsV1ai2aQCKAGcgkDlbLydjw0Spgbt+eEj/LWeKw/TBaRl6GgK5F7sYnKbKKed0MlaLpX6MV2/ZKG86X2OlUIkrfcvEDQbH4zS5vgsq7exHmXg3DM7H796JO394CP/9gwNez59p58fTdtVSRNpg5xJA4WTcBn3+DavGMNVmFvvZdNO2OeD/T1vMaHdtCzZsIuN0Hxz9uytFFUWRTlFl6OW6gbwEB1HZl2zVkP2eundfvqpTG0h/M1EpJVJUmsHpjGXl5+/ofD3z9ZzBqbMqKsDslzYqGL0RLUN04oPDf7/zhweNIKlrBqdpYXDIBydF+Au0y4aJDs3BkHBbb169lcbgjJVC3Qg0U2RMqaV8VT+up/QjRUXnsBsNDgWgvkJlYhjSUnxSRMonZ/5zxaeo6LyUApzeThV8b9d0395Pa3DMx32+D7qHbN+7Sht7+uBE4vqwgb9PX1ktccMOJEWV0ODkYHDCQKePOhws6W+OXzuOdZMV42/EhneqweFsVCuKjfzSYNPg0JzEDWNHBaM3omWIROmxS2TMHuc3A+lvOq1mMaqo6i2WzvGvouKiOSp79DL6ax/OEBmz97UdQzXOLIfKwdW32SZPUfmwL5kpqh4u6jSZdMOE0O7Y9xg0IaWlDWQVFZ+cAXiJWlcCeGrnrBPWAgDuf6p/AY5NvwbAq4qK5hxbalcFaoEfgyNbedjA36evImNxDQ6jVYOXBofbanRpzLhnOtbfbLUFOLQJ6bRVg3h+VruGOYsPDv209TAcNooAZwDgKRcgRWTMJxL2+1fa6YjnnLwh8TcfSAZHpiAAZDIlppNx8rgu2HahQcAaOFomCwqyKqVQ0Z6+zTa5MZ1fisr+OA/CepWm0iLjzhcAOg++QS7tptMWHaOKqhUZkzOgJ9EVH+AoFjPAWdvaAc6uo317P1eKKvAQfdN9nMbg8Ma5qT44RorK/kR+ffW3TDz9//15z/wpKr1JCDs24SMQg7N17bhqjUCgObxTlkh+n0fm03U4sxYNjizMGCWM3oiWIRbbkw0t1k4fHHZxRpFeePa3TZ1+ZMvqxPN8IKuolNGfpYrK7YNDAUS+nLJrF0qLps30jkrVx8qh1gZlNdtUN1mQq/GcU4PDdiO9mkRVN/FuUlSNdBZo3zEtPm40Wyq4TgsQzR16ZEzOAJa10d93dx31dgbXQX6oGJzv7ZruW+qO++Bw+ATwi2kMTvtWCr2djHmKyn4fzhsMTiEytlVRdXqZkAZn29oJrJ0YM/5WSaSocq4N4rNklYrbGBwKpqtFgLMyQZMNqcxd+gm5INP/6Sela7oRkhkMTuifouIMTi4fnPZTpAFUOZXB0SmqvAwOT1EB2ewL/Z0zNgAwxv7fq0mUAk2fXjYu0Hmwnft/uHsnLvij2/CP33gCgFlBkyb8NKqomi1jcgY6p79HHU8dmcfPvO8r+J//9xtez2+y8vnTNq3CeCXETK2Bxw72R2gcOVNU2d9HLUWDw5tt+vjgmNeHi8EZToAzmFYNQoPjlZrXm8hOgw9CGoOjrBw6ZFnl87PM/mYtGpyCwVnhoEiXbKx9jP7482hSoQg5b4qKP39+samCjrKRokpnSnjZY54Fj4KxBIOj3IxtDA5PUelxpU1mi4YGhwcnGeNr/70iBHL8/71a2GXA2gkoFWCbLB/cHetBvtf+ycWeaa0ueMBcb0XG5Bz/7G6CHlXsObqAKEra4LvA2chyKcQzj49Ldu/vk9CY4uBA3Ds+jBrtqputKLGJsJl2pvvg6N9d7MWgNDjyIw8i5pbzjmsTyNEwqk7jxzpl+sgSYuvUOMbKoWGoV7EY/eUJ+uRacjSjVHyOiZClkL0IcFYo6m1GghZrd6sGQb+2n0c7/ywNjwv8uDPsAi17NtuMItMbhbQZfiJjotnNSVr1o7KwGXV2w3DaMy1NZTA4oT/70op0YMTBb9Zeres0xnrTbnDog1qKyJiCPPKq4BUP6QyO/r3RTDZjVbYAI+bA3C3yBpxcgwNA63Ce6o8Ox9bmBADrJp4S8LN7ZUHcN/ZeVO5j8XvI1UdtcBqcwaeo5Hzto6Hjxqjd+uDMLMRz9lSbvVnHWBzZqgHIN1/Jz5KdomIMTtNkcAqR8QqFYnDakbdPs00g3qG1WpG6Maqk4cmdZ9XP571ExmwaHEv0xW9MrsHxGYdrklYdxVMYnLGSbjIIpLMQDfYa/l5Zi5eLweEBTq8mUbMVh9/z7915WAU13GvEFuTSRDNDJm8sX562qzYYnKbb6K8X5+Ez33wKf5+zWWy/kLe7uzwvZ53QZnD6FuDEP5M+OPFPnxQVkKygsxn9pTI4uauoCpEx1yx22/aFziel69dOah2ObtWgn59nfZDz75H5OurNFn77H76JD37l0cTzeYBTb8gAZ/TCidEb0TIEXQDjZWJwXCkq+f/IuFjHOixddi2s5TDJ4NjKiTktnd/JWE+mHGXF4CSPoT1twjgl1n6/NBZiUekjzBRV1r1OO9exkmSYWIqqRwEOnxh9hIr/fO+T+Pm//hr++ouPADAXLTuDE/+dglieovIWGbeY0R+JjFVKMnPIqYiiCG/452/jzZ+5H4dns11T+428rTNkXzVdKn60L1oQlw8OTyu53jeNweGtGnw0OD6tGvj1VWu0vOaGxUYLTx7O1+5Czn1p5/2G/3gAr/i7O73utTQkA5wcDE4YwsctOg30/dFmb+2Ebomgq6jSrTdcSGhw5ur4+qOH8K/f2oUbv/hw4vmzhci4gEQtITJ2BDiSfmV6CECLjLvpRcXBac00oz/+fvyG9WNw4p9yF0o3pm1HSOeLnkM3TtoizVNUpRwpKvqzzB9zdivqkaSAfw8+3+GudnPVJw7FiwAPcOwMTjs11U5RzS766SLkAiaZCl0m3t2JqDcjtfD6dC7uN2ix9mdwTPH1MzavwVgpxPRCA08enu/5+GhYUoNT8tCYpTE4XGTMy8RdC7ChwXFcA3LzkVX1CACv/6dv4fnv/CIe3OOvYZJjTLu/P/n1J/DVhw/ikf0z3se3v6f5f78Ap83g5Gz+a4NicNob5HWskkr64NjGmwaZcjw6v6g0fIdmFxPzs61VQyEyXuGghYdExq6bsinFgEz7AuiFvlulPBDrK/jEqXpRWSYmPgbO4HSjwVFl4hlOxgAMobELOsAJjN49WUGYjwanZ1VUzcj6e9bz5y09fqwMDqWoFIPDG+OltGoQTrUN0aus0woNCR7gzWY4pg4CylnaM3CTGpyxcqgcjb/D0lQP7zuGm7/yqJcYNfX9MhgcPiYJ/n3L4FaJjFmKCnAvjGaKKluDA/jpcB47MNv+6c/iJFJUKaeYqt66/R466SZOz+FBZCfzSJyWNlNU3OxPlokD+RhnuWk5Mlc33LkPCdGxrdlmTczXo4TRG9EyBLEi4xkMjLxvWq3I8EwZ6zDAsS2GZWGuUU0JcEwGJ1+A4xRKqv5GFpFx09wRjOdgcMbKIkXVPvy7b/0+XvBnX8QhkRpxaXDKOVggX/DvIY9QUQU47LuxTWJ0nalGi55VVGaLEO10TedAV+1kDjkVdYcWbFigyd2XmJLMFqDTVN9lhn9//O8P4vrPfc9okNsJoij5fvL/rmuTL+o82IkirekLQ1Ov5qMN9Kmisv3fBroe8hhf5hEZ0/fVfYrK/L+rnY3tvWPG234cH9SbTINZphQVExmLbuJAvvWBAjEKno7M1xWDAwAHjun5MooiU4MjRcZFL6qVCe2DQ/2NHBOJuDC5qyzQeYdr2wVflr4v7ZvHttuhCSgM4klRBzjZ791yTNI6RZXN4Pi0ayDvHF7Gzt//C9/dg8cPzuHbTx6xjq8i6NVyKVRMUK80OJzu9fPSiJ+v3Yj15JIqMu6iispaJt6jXlT88/Od4LDQKYPDr+XtGyYAAPumtVkglfV2qzPSmwO7yJg/R4IHw/y+4V9hKTBZXNfXyx93XbeSsfFhcJQGKkfknCfAofvWJ12W5z29GBzG9nWjweH3bVWJjC1VVAYTlyPAaY9z4+oqAGDf9AIe3qdTegdn9XW92GwZa4kqExdO/aOE0RvRMkOj2VITxEQmg2M+3uSLTRj0pBcVQV6MaUZ/2tPBvJm8mm22D5f0waEUVTaD49NwU6VVQnuKis6jyyisamFwuu0hkxxjZP3dBdrhUmDDF6q0FFVekbGrm7hicHJ0j08Df/1MLXsB7Df0NeF3T9kCnKnxeLGZXtCaIvq9267aTh8cno5wpajYtcLvG/78ThgcdxVVS/w/+7Mr48scDEseHxz6TrtNUcmAwUuDwxoa+wi5XaDzGAR6HuQaHGnlAORkcNrPPa4d4EwvNIz79OCMDtLnxD2rRMZ1YnCKMvEVB05nTuT1wRH+M2HY2ULjw+CkBRFyYs/jZOxq1UAVOmkLdSUHg8ODoiBITio60DFfp1JU5WQaIE8g5wNTg5M9SdL51SmqDAaHaXaaLZNOTjt3slWD9vAgj432e3Z5HvhCMwopKv59+LB0PO1AIG+S6Xn9eej3bsuls9K7gDu9xucd/t3z75rrQ4AUDY7QaNkghcw+nz1vFZsci+3/xvGj3gQ48lbN04uKb5Q6yZSp4KGse1rxFJW1iiqPBqc9qI2rxqx/PzCjGRzJuiqRccHgrFzwm2tiLC7vc4qMxY3eZBoczuD0oopKanBSGRwhrqTYKI+Tsbz208rEa51ocFiKCkCCfaFx2IJI/jo1PsYE9crJOC+DQ9/bvDeDo8/P3GIDc3Xug+PXqqHeigwXVgC5usengY95FAIcU3vU2bU8NR7f00fbVWGtVoRjxOB06ehLX0uiVYOHoLRmlG0zBoc9X4qM3alz/bsrnZfU4PjrVPIFOOn/V4+3InX+fDQz6e+Zn8FROrYS1+Dkv39qqo+hZke4yJhvVCnwzWf0Fz959XjZWgV1gDM4i4LBoRRVXfcOHDWM3oiWGegiCIJskbCdwdGlqZ0yCrbmjoneSylGf01ZVZPDj4fGKmn2ckqZeJ31ogL8qqh4ewcg2T+JJvbkDhDG6wilUj9SVCxAyVNFtRi/ztDgpIiMgbjrr9GqIeXcGd3Em61kQBvS+LsMcNj4ZkYgwKnnLNvnzTYJisFpBzWziw11TfHvq9Fs5WZ0XAwOv5WcImN2rg1xupGi8juWTy+qhAZnMQeDkyMAkcylS2/Cx9zrFJWPZqvJUuZdaXDageI4cwk2GBx2LXbSM46zktwh+dRNqwAABzmDI+5Z5WTc1CzTqGH0RrTMwEvoFPPhkesGYkqT94DqNEVFz+eTWVks6Nroz83glIQGx4cKpflQGv1Ro0+7k3E8OXbmgxMfV6aoaE5yGYXJgK8Saj+dvqSovCbJ+PkLliqqNHE2EC+0c50Y/TUjQz8AMM1VlwHO6FVR5WPUZJk4oBeb6TaDM71gd4++/P134gV/9sVcQQ4NSW4OgiDZ3+ibOw/jB3uPqecYGhyHOD3J4NjH4dNNPKHB8dAfKQanG5Gxx2ax+zJx8/9eTsYqzdtdqwZZIg7Yq6gAdMQ482uaM0M/+YxNAICDs9kMTuGDs4KxyNItWQ7AthQV3UylLkTGdNxVY0kHTEI1hcFJpizgPQ7lxiquNDoXNuMwYnCqgsHxKhN3pKhctvy8GShfuEp9SVHlY3DIhGu+3jT8MABHmbjQuEgGx73b5e/ZSmhNfJo7+oB//lEQGRsanFwMTlJkfKzWQKsVqUAH0It8FMUtN/ZO17D/WA2+cDE4fAzNKML0Qh2X/+2deMXf3aX+bhj9OewFeC8qGqd9HPp3VyAo7808DI7LPDBrLLb/68ezgzL/9zTnIz+Rsb6HutGwKQaHpaiOWz2GSilo9+rTj3eyIauzzQyJl8dKIS48ZQMAocFxMDh0fY1iL6py9lMKdIM6o++yxLkJJ+NI55HLXYiM6fmTYyWVGkikqEppZeLmxJ7HybjJAgiO1DJxwcbQ7iUtzdJgOW8+ViUydjA49N8wiMvfbeWdPYpvBIPjc+50UFZvRsau2HYNmQFO0+hFBcTnb9xS6SCN3KRjb6/KxOtNMwAbNmSLiizIVg0AsKatwYmiuAeYEeAw5o1ulXwVQ/Z7Rz8WV1kenatjsdnCvmM1NJotlMJAiIyTDE4YxEwQ3+e4vl7J8Nkg20HI/9tATGE/ysT5mLsvE49/jpVD1Botv/Qym0daUfcMDq9Qmhwr469fcT4CmKxJqYP5ijM4VH7+jC2rsXXtOACzikqmIZcCg1MEOH0GrwjK0tDIBaTZirSPTCnoOFVAE8nqahn72jvIstgWVlOCiIQGJ5fRX/wzT5m4VOXTzsDWJ0u+RqaoZHm4LYiMxxefE9qvlEp+nZZ9wTuyA366A06Fz9ebRtrBGuCIAEJSygv1pjXAkakaGqcqQe0wsJbgn0cGX8MA/zx+DI4Z+AHxzrraXviOztWNFBV9X7UMcbgLLhdwPoZWy5xPFhqtxL1tY3Do9aYPjn1sRorKJTJuX2tjpRCLzVbqvarG0oEGR96LrlvTSFF1yeAoK4lyCcfQyFUmXioFCFvd++CMi+Dhp8/cknhuJ4wzlx+QBueZx08pX5wDMzVEUYQgCDDbZl1XV8uYqenzUPSiWsHg0W0WgyMnv2bEfXBCg5bOA8XgVPXiJjU4qhdVqg9OBwGOZdcbHyu7TJzMB4nBSdsV8l5UABLsiwp0EhocqOfzMXLH5l4wOLb0Y57XLNSbBoNj0yIYGpfFRmLH5drJSp+TBBvWgXjRBr7zHQWRcTOnyNimwQFMoTFncGji599DHgbHtTngj/FCBCAOHOX3bKQ2LYxqVpUPPzVRZD9XdG2SjsMnRdWbKqpsBqdXrRpypahokxCGXfrgJFNULnSSomqwjeHzn7ERE5USXnrWVhy3Ok5X1Rotda/SpoS+Y8ngFAHOCgR35dXiXPtzbQwOTw912hPIrsFxVFGltGpQRn+98MEpuY+hWa/4OZPtcactirS40+fQJZNmYCMnU17lxXVJpVD7TvRCgyPfVza5y3rN/KLJ4EiaXO6s4xSVnzeJZHDkQl7uYOIE4gqMrz1yQH0HfIyzo6DBycngqGtZBjjtNNX0fMMw/KPFad6opsqfjrFpcOgx2a9uYbGVuIfNJq3xTx7MZ1ULynNjW+Dp2lo/GS+MuUTGuQIcef/an9fLKir6uMRy+4mMNdvXTarbJjJ2oaMAh60vLz/vBNz/Bxfjxc/cgsmxMibbzaEpTUX3rCvAGcUU1UBGdOONN+Lkk0/G+Pg4LrzwQtx9993O537gAx/AT/zET2D9+vVYv349duzYkXj+q171qraZm/53ySWX9PtjdATu6ZIpMs5wMu4kwOGpkVVVP5GxrWUEoG+gXE7Gjkk6j5PxxjUxXZom0Kw3JIMTP64M/jLKxMMACQanG/8KiQQ759OLip2b+QwGR07iUmQMuL1JpCBTCQ+FyDhviur3P/0d/MoH7sI9jx9ufx7GMI0Ag5NbEyWYTMJag8FJeg/x7yFP3yWXDw4AYy7hn2Ou3kiYddrE6aUgGeD4OBkD9nNF15ZmcNI/Z8TY6U5YLdfY1OPskL1icMZSrC0kdPVrd/OI0uB4CHg72ZDJtix8DiQWh9o1EINDQawUGa/IAOeTn/wkrr32WrztbW/Dvffei3PPPRcXX3wx9u3bZ33+7bffjl/+5V/GF7/4Rdxxxx3Yvn07XvKSl+Cpp54ynnfJJZdg9+7d6t8//MM/9PujdIQ8KaqEy24rMlTunQQ4/Kk8wHEZ/QHJnLXsLp3HyTgzReXRi2rT6uwAZ1Etyqa2QJaHu4z+ggCJKqpSxs42D+SkmKfUFEhqcOQiIyfxmVoj4TzqYnD4oeIycZr0uisT39Puz7Sn3ZuJL2SjkKLKr8GxX8tTrFTcYHCsKar8bIUlvjHS1ZLpS2NwbLoenUKxj0Ne/7YFPi+D4yNctsHXB4cHkj3T4LTTRIse49XVr7r5byciffruvBgcMef5QDbW5dio5t02g9OeTyigX2y0jCzDKFZR9T3Aefe7342rr74aV111Fc4880zcdNNNmJycxM0332x9/sc+9jH85m/+Js477zycccYZ+Lu/+zu0Wi3cdtttxvOq1Sq2bt2q/q1fv77fH6Uj2FNUrl2H2OWzXU4pDDs0ctJX+6oxfQG6UlSAJcBpmhN7VqqNg4aaMPpLKxMXDM7mqewAh24ylaISu1JdTWWfIMMgQImdk9jJuDfl0UByYcutwVlsOnsKAcnvbI754GT5CMlqooTRX0o6MQ20ENbFT2A0GJxmzrJ9uobk5kD3o5JVVK32z05TVPHPVA1OK8n0SQ1OzWi2mQzSshZgef3bAgYK4ojBWcjQ4DTENecLm1eY9Xns8V51E6d+dXk8rCo988HpjwanaRHOE45bFc+7msExg9jFppkOXXEMzuLiIu655x7s2LFDv2EYYseOHbjjjju8jjE3N4d6vY4NGzYYj99+++3YvHkzTj/9dLz2ta/FwYMHnceo1WqYnp42/g0KnMHJ8o+xpaj4YtOJyJgvSgaD4xAZA0mzP3WzJjQ4/jd6gsFJKROviXTTZpaisu3YeCpPpqhkYONKUQVBYLqChoHy7ulFispHxyCRSFGlVFHJXfv0fEM9tmGVFgxmja1uKRPvVGSsUhBtXyMjRbXY7LrsvFvwRdbnO9YMjvn41IRu12BqcJIpqnzpGHt6N36Mi4xNBichMrYExjaRsesU+HTTrqkUlZ3BOTpXxyfu3qlaWnTM4HimqPgc2atu4kqD43E8+p6511A3KSqfAIfmqzzrg6yY5NhIKaq2Boeaba5nGhw+76w4kfGBAwfQbDaxZYtZ0rZlyxbs2bPH6xhvfOMbsW3bNiNIuuSSS/DRj34Ut912G975znfiS1/6El760pei2bTvGm644QasXbtW/du+fXvnHyonuI11ln+MLX3CAwReGuoLPvkZDI6YNYMgcLZrSGhwVICT/f6RY5KmG8raqkEwOJvaAc5is4Ujc3Xn8/lx+a4piiI1MbpExgkNTqk7i/W0MQL5UiIABTgpDI6YdLlBl0obOFNUfLFpsUlPBLQ5zwN9Zrqe5M53rstmlN0it5MxSztwKAZnXmpw2gxOo7MAJ80Hh6ereYAwX7ekqCyBMQ/SsjQ4yRRVciNG3/F6RxXVh772KN70qe/g7+98PD5GznNPkGN0mleyY/bKyZg2gT4FAlzb0k3LF92qIXup7iQV1nBc0wDT4LTnkllVRRU/3oqg+t3JFP+oYPRCLoY/+ZM/wSc+8Ql8+tOfxvj4uHr8iiuuwM/93M/h7LPPxmWXXYbPfe5z+PrXv47bb7/depzrrrsOR48eVf+eeOKJAX0CyeBkUMEJISqsDE4eSpf3oUoTGQOagpUTgr5ZzQDH50ZyVVERzW+bLBZFgFMtl1Ted/9MMk1lBjgkjI3/32LBjW3MPA1giozDjoyz7njkIH7mff+N+544Yjwuv9s8vaiA5M5cTvRyl0rnKQg0w+AqszcYnJZu1ZAwdsybohIiUqldGHaaytTgeDBqInVHMETGjMGpWUXG+dkKmd4FzN26WSbeTIqMLQwOFxlnlTEn2Edxrvj76WDafA6xAIfb1v8ma5g/6CO4Tif/LL3qRaWrqHwYHH2tkN6pm2abVZ8UVZdGfxLaC6fN4CyaaUgAmGn7Po2VQut1Omz0NcDZuHEjSqUS9u7dazy+d+9ebN26NfW173rXu/Anf/In+MIXvoBzzjkn9bmnnnoqNm7ciIcfftj692q1iqmpKePfoMA1OFmW9/YqKnu5oS+rwCfUyTHug5O8GLXZnzlB1l0LnlcVFYzXyPe3NQKVImNAp6n2TdsCHH0M2WyzxdJXQJJ14gxTOaHBodf4zxi33L8b9z81jX+9b5fxuAxK83QTB9o+OHX3Qikn3QNtvdJkpZTZ6oIfykz3xSeAzkveCZoCNBqbZOuGHeDwa88n4HSWiSuRcUOlYAAdWCx06IOj00nJv/Hrm499QYjRAcHgWD6DXoDt48hKUfEAjs5F0oMp/r8uDc+nf9JjSR+b7Zi96iZOIlqXFxCH2WwzfaxpyOODk6f4gyALSDiOY2Z/gL5fjQCn/dgopqeAPgc4Y2NjOP/88w2BMAmGL7roIufr/vRP/xTveMc7cMstt+DZz3525vs8+eSTOHjwII4//viejLuXMHpRqZ2w/blJdkGbt1VKoRFl+17DPELnuwAbg+My+5NRfh4Gx1VFRYunXWQcv4aL1khovO/YQuL5tHDyNBMPBvnEIoMy+m8QBIZ41Awo/ScM+r73TpvjlCLjPEJFICkeledefmfE4ExWy6oTsctd1jg/rUgxLURb03nIsxAB+jPSZ5evH7YXTt5u4k6jv3G70V+9GQeLnfrgUPBtE4Dy6ztRRdW+BilAN+wFLPdjVipWPiyDtAW2IVlVtQfTdH3Sa/O2yVDjHwKDo3xw2HyUFajyZpuD8sHpJJCSJq4cG1dRmbjJ4EyNV9S1dYwYnBGsoAIGkKK69tpr8YEPfAAf+chH8MADD+C1r30tZmdncdVVVwEArrzySlx33XXq+e985zvxlre8BTfffDNOPvlk7NmzB3v27MHMzAwAYGZmBq9//etx55134rHHHsNtt92Gl7/85Xj605+Oiy++uN8fJze4YLaTZptcg8N3Xb6TAheM8hvUdkG7zP6SGhzz8TRoa3jzcZ8y8YrB4MQpyn2WSirdpkE/n9PuJoNjfjatwTGbbfKy/DwbL/q+dx+dNx7vJEVltGpYbKUyOHKXSq+dHCtl9vKSY6P3qciUZIcMjlrcxLkfdqm4cV1kfDbu25IsE49TgEfmFnGslizN554w+Rbz+Ke1ioprcBwpqtXtlLRdg5MUGbtu58S167hexiuhky2ka0+3Z+ABTg4GRzx3EBoceo+xPAEOa7apRdwdMDhUJu4RQHSSSuatGiTIf4wYHApwVlXLajM86gxO33tRXX755di/fz/e+ta3Ys+ePTjvvPNwyy23KOHxzp07EbKT+zd/8zdYXFzE//gf/8M4ztve9ja8/e1vR6lUwre//W185CMfwZEjR7Bt2za85CUvwTve8Q5Uq9V+f5zc4ILZvCJjVxUV4C805rtOTnPKKiqA9XxyanBIdBpax2sDTUiJMnESGVs+iNTgAFpobCsVV4xPyWRggHaAYzAUYnxqETEn/U6djGkse45KBkcGjfkZHB+RcTkMjAVjwidFJY5FTE9SVJ5vgpbl4UkGZ3Q0OFmLLP+zi8HZfWQhEQwv1Jv98cFh7KJs6UHXwtR4BccWTOM/m9FfkMFUJlNUgsFhlT4T6lqTOh2TzRtkFVWvuonz+SXre2ywFJX05MqD/peJU4bAViYeMzhH5uqoN1vK6G/VWBzg1BotpcFZsQEOAFxzzTW45pprrH+TwuDHHnss9VgTExP4/Oc/36OR9R+5RMY2DQ7Tv/CJ1beihbMv/CK0XdBZDI5KUeVyMobxGoISGYuJIoqidA2ONcBpMzjs83GfEH6+XVUYYRgkNDidUL5UQrrvWA3NVsSE4em7YBukBsdm2Eagc7ZucsyooIoZHPuio44lPh8tyKo1R2D/DFmQTrVyoZFGhIMG1+DYtGAc/LtwaXCIvRkrh0AUB+oLDcG89aoXFQs6zSam+jqZmqjgqSPzpg9O+1c7g+MKcMz/y3uWL8LEFro6TxODys9nJ6XzrrER+P3Rq27icUVUOy3onaIKO0p1E2q5UlT530fqKznWTY6pz3twZlExOJPVUnyN18Q1P4IYzVEtI6hGZCXtg+PN4EQmg8MnuqwJWR6zXAoNp0lrFVXZLjJOVNXQ5/BhcFxVVI4ycT558gBnkxIZJzU4sncVYO5KzRSVnCD1LrkkNDidpKh0SXSkyiuB5C7Vq4pKaCsWLGJR+b7rmQAQiPt45TH6i59nns9OU1R1FeCYPwnDTlHlYXD4OXJVURGmxiv6XsoQh6fBZbEAwJhLeNqVO15Tj6wFC4Nj+uCkMwzSHFCyj1oIm5aiiv9P10TnZeL2sUkYKaoeMThxIYLdSkOCa1vo++vEMLQzkbH/8dOqqEphoDy0njg8px6fHCupgGbUGZzRHNUygtGqISPlYesQzZ2MO2JwmAsx3wXYVPNjKsDJYHByLHjaGt58XPngpFQDGSLjtgbHnqIiOpgHKFBjNFNU9h1gGASGN5DhZJwrRaXHv5ulqTqpouJBbLxwJcWiBPrO1rcnJMLEWEmJy132+fJ7nBcpqk6qMwAdvKqgb9SqqHKIjPn3JXe7a8ZNInztRFmf83qr6xRVmpNxFJkMznxdi4wp8CKxM5AuMs5KUeleTHYGh6dDaw2zpx3Ngw1LujIPqyV1LE5zwp5qcOKfYRA4z4GETWTckQ8OlYl7BBA0pXfSbFO6cxOoVPzB3bE5bhDEeiCam4+1bREKBmeFggtgc4uMGYNTKZkiY9/Fxqii4gyO5YJ2pah4kAXkS1nwyYHDJTLm780ZmbR2DbJNA3+/VhQZO1MXg5PU4HSWouLjNwMcyeBkT7p1uTNnx3b1olo7UTE0G4bI2Nls0/w/2ewTy6e6iedYJ1ot7T9EaTs55pkhV1EZPjgZ3zG/Z2W6tVIKDQuGqYmKOucLjabpg9NBisrqg8MqMg2t1qIOhKcYs6RTRDolS8jywaHP7vKBIZahyjQ4gLlRkiJj6Z7ti2SKyjGX9rCKymRw2tWfmQyOZr2zzm8acjkZd2L0R0Uolg0vADz31OMAAJ/6ZtwLcrJSQhgGam7QIuMVWkW10lFnAU6WyFguINzjolPBJ6+iymRwHPSrtPPOs6NvCnqbUHGIjOl8cToY0CmqY5Yu2fW0FFXLPN+uMvEwkBocnjvP/JgK/NzxUnGb+2sW+HNmaw3j2HISo/NWLYeYZJPh5FhJVWD4GP3FzxMMTmD/rtLQMBYwhwZn2CkqrsHJ+GxpDA6ghcb0O0/VGAxOrnRMWoqKa3BYIMw0OJxZooVSi4z1sbKuc3pcsRfiifOGBqeUeBxIiozzNjqVY9H/H1yKKgj0wp4VlPHWMd2Viev0XxayfNZsSCsTB4CfPTe2XvnmziMAYtsJQF8LhQZnhYMuoIrRDdzx3Ja5UHOjP5ke8r2IXT44tiqqioN+lQ3ZyhlMFIdTg+MQGcs+VIQ11bK6yaUXjq1MnNO1hsjYqcEJDA1OucS7ifcgRZWzm7hMPcgWFS4GZ6wcGo7VE5VyahWV7Tuss2sW4Ndc6pDF+LiINGr/bLXHFI9nbtgi4xyVPLy82saoUKl4/HvFYM06FRm72E/AXUU1x5i+VWNlda/SY/YUFb1fRoqq7GJw2gFOu5CCFj/+uVWKqmUyOYDdC8uFTkTGvfLBKYU6jZ3F4NTZnNmphg3Q59CHIenOydgeCjxr+3psW6u7CFC7H6nBGbOsJ6OA0RzVMoJ2igwzL3SaY2mhbkZJn4KSBw15eHZRBQG8ior3MxmzMDgu+lVG+b5OxlEUsUna/Fspg8GRO4IgCJxeONwMkRA6FoC0FFVSg9N+Ta4qKv3cPcwLRwYkWYyB/Hq5Qy4fN0GJ2UWAMzlWShUZ888mK+vomssT0BJ4cLYodBekDRl+ispfg2NrccBhMjjaXFEyOHkEtbZ0EoEL/fkxFxiDM1ZOin5TRcZOBqedoiqna3DovaqWSirlZKwYHJZuzZOikvevR7q/2zJxLvamSs0sNrPJ5sxOfXCiKFLfZa4y8VwpqvY4HSmqMAxw6TnaQHdyrM3gUIBDKSoPhmkYGM1RLSMYPUmyRMZCS8JFxrIPlGuijKIIP/O+r2DHn38JC/WmQZUaDI4lYnexKokgy1OTwT9nIkXl0uCwVIvEZocXjgqKrAEOvEXGSQ1OdymqPdNukXFWqkI+//Dcovi7/bxxN1kgFhkr4adFg8PPh9wlqoA245qzjp+dB5Wiar+erN6HnaLK02zTZfJH4JVUMYOjhd28+q0v3cSlGJ0JU6uicMD2ObI1OPFPcquV1yYdm5i5CQtjSNcenWeTPcuvS9L/T2edgHg+66ZzPWd56Z5YbLiPF0W6+WiZ+Wnl9cHhGiafFFUnrWUagp234WfO2aZ+p7lFGf0VDM7Khk47+YuMxxSDExkMDP/puogbrQhPHZnH9EIDR+bqTgbHXkXVXsgcna9lmixrF8OH6DL6kxO+zcWY4CoVV1VUJfukzc93UoOjFxGpwdFl4v4TBqfD9xxN0eBkVWGIv88J3ZHLB2esHGLVmGZwVhlOxpYUFftsMqhUQXUH4kXbDpquKwpwRqlMPCt94Gq0SeCC3liDQ6xZS4iMc7AVaSkqxgZLJ2PO5iUYnG6qqMr2DZBsJ2DzXaqJa8DQaHWgS9L/tz9Pxkzd6HD496DS+Clz3707D+PAzCLGyiFOWDfRsQ8O35DkYXDyMM40D9mKTgjnnLgWJ22YBABMFAxOAQ6aDEqh1nQ4RcaRmWrhrRp8S7Rl7plreHiazBZAuDp8y4Zsvk0/+RjdImPz9a4UFeA2+7OlqHggmMrgtOeQQDI4pc6abUoNDp2fxOfMIWq1wVUmXinJFFU51ejPZHBEgCMYu1ypOr6AtXe79D2tm4hL2V0MThRFuP+po11rJ7KQT4OTXm0yNc41OLxMXPrg5NHgZDM4zZZ5rXBDyLFyycng2FJUzpLrRIrK/AwUwI07GBxu3qkYnKb7nkyDL4Mjz3N3AQ5LUSmRsft4N3/lMQDAZedtw9rJSmYrDBe42N82X0t0pBnMYCaBeG6kNNW6diBPm/BpKhMvjWYV1UCcjFcyaDKIy7zjx1w3tGJwyjrAkR4vWY0P+Y232GwmqrDGyyFmF5vWnajLfE8GWbLpp2PONyYf+XauMnGXyBgANk+5NDjJ1/BJm59vV5lpKJpt8pRipymqWqOFI3N1rF815jynLrho+yCIP5MzRSVFxozBsfng8MPIoDIhbO8wRUVjqwsGxxXgfO7bu/G6f/gmzj1xLT74qucoL45eI08ljxKaujQ4ksEp66By3hDb5mcrbKJmzgbzcz232FD3ULUcqu+Ugg1p2hcf33w/jijS5f4qwBHnSnm1tAMbzV7Fj/N7QmtwkgyfD7x9cBwatU7Axd6qSbBj/n3y8Bz+4/7dAID/3/NPUa+Lj5MvwuHibR8ELOj1hZRAuPDaF56GRrOFXzj/RABJBqeoolqh4EZKvuyLWUVlXoDljGPwgGGh3kqo5GkSqlguyIqjDFSzUO0gy9OPh//N6WQsd1qWNg0EVz8qzfq4UlT2McV/p/GZgVunPjh1MZGSDifpg5O1oNr/Tukn1wQep6jMMvFq2a+KKhHgKN1X+phssC1g9F2vnUwXGT92YBYA8K0nj+IX/uZr6v+9Bg8MshizLK2CITLmPjgJkXF+vUma0V8rikSqDZhh5mvceA8wK4KSx0qOgV9mY4q9kCkqU4ND70mfm2tJiLnsWGTs64MjU1RdBDjc7bzsqDQlfPSOx9GKgB9/+nE4Y+uUeh2Q38k4j4sxYLpb+6KhNtDpAc7UeAX/59Iz1WeitYLeqnAyXqFQImNWduxkcESKik9evhocnvpYbLYSr6fdgC3nShe53FG50mQ0Rhf4EOUk7SpJp2ohXnZL2ORIUdl0OzqYTBcZR2zyKhkanKAjDQ5932RxTjqcRKuGDlNUE2Ml69+50NrJ4NhSVOyzJRkckzXMx+AkAxzfFBUPCB4/OIdf+9DdXYlEnWM0GJyMqpgMDY4hMh7XacHphboRJHSymNtTVO1xCZExABxp30NcZJz0wWEBTmi+n20MQJrRX7oGx9bN3BR4dxL0mf9PPM+hUesEPNAcS0lRzdYa+Ie7dwIAXt1mb+h1aWN1IY/JH4CO5qssJ2MX5FxRMDgrFA2WPgnZomu7CG0pKjmx0nXoWgD5ZBdrcEwGiIyabKp81+4kEWSxyTFt52u4v4pZuuQIpg7PxtVCG0TLAYBXUUmRsRkYAqarp1+ZeJBgcPJSvrx6gkR55IUjNTeZVu/tv8udEbEzUWSeX3eZeNmLwQmCZFpQs4Y64PYF/16VuZvolzXPqvw4KMC5/NnbEQZxkMMbiPYK5nXh91xbyTbg9sE5KvyL8qVj4p+pIuNWlLgH6T2ronUCPZ+/nh/fOifxAFjND/Gx9hxdQKsVJRbiCcHgWFNURgAceS/KLbEg+zgZyzHkBT9naU7GX3/sEI4tNHDi+gm88Ec2q8dpzu40ReUr4O2ktYya2zNSVBJyXioYnBUKHqDwwMB2EdKNVEmposq6sWWAI1//uzt+BL98wUk4d/u6xGvJB0buqJqqEswMslyfQ32eFA2OKhMXrz+UEuDQLvnYgrnz1xocPmnrMfBxyLiC3j5h9BeGuVNUnLrf3g5wKEVFosoxh45Bgr6Dajk00nWTrELKZkdvS1HxRU5OsnxHL1m9ikpJ0pg6S1FJgek61hDUZvZHotXtGyawta27evLIfOJ53aKXDI50MqagMqu8Pw08NSLB3cSlXosYnLESLxM3q6jMVg0ULCXfx0hRURVVK8Lnvr0Lz73hNvzNlx5JuO1qk8Om8ROwG/3Z/u+C1BC5ggaXEWYnMFo1MCuNJw7NYRe7LmleOnH9hHF+O62iWlCbFk8Gp4MAR8kiMlJUEpKxKQKcFYq6qkAKTe1Kym6JFrSYfTBzpIqa9klRiSoqALj0nONxw8+fba+iolYNQgjZEBocTmempQ6abIJ2lYlLJuOgCnCSwlKXRoizZATDB4drFJwMjjD6K+VPUfFd3UkbJgBosz+qVhhXAY7ngloKDbaNe9zY7OiTTsYl4/WykSpf8KTQsCS8l/KViXMGJ/6dFplJ5rA7a9HhcOv/E9bH5/Gpw70PcGw+OLVGE5/79i4VaMu/OzU4LEW1Zly7bh/ugsFJ897RFZnuYKFqdPduMzgqoNXPTwvkTRuBdnq02cJ3d00DAL7w3T1Gs02AaXAW/UTGtv+7QE/L6o+WSFH1oIoqCAKl8zsyv4iXvfe/8XN/9RV1vum65ZsQILtbuwsy9ZcFPV/5HT+KotRrLA3SFLRIUa1Q2Fo1APaLne5BLTLmGh5RsuuTorJocNLg6g+VSJOxQ6UJ2ugwtsoTRfU6DO02sF0+gX92HnQspqSomsIHR342ngbgNG0pyN9NnO8Sn7ZhFQCdoqIFnyb/rBQVN4icYIwMD1744qNKg0slIwjiDA6QNPvj35Fs3yGr5nKViVs0ONw1lZpT2rxwaGGcGCvhhHXtAKcfDA5b9Og7/uy3duOaj38Tf/mf3zeeK1MjEsetjhnHNW39DZ3zI5LByaHBSUtRcXdcl4+MafTnZnDSNCL82uc+ONQ65P5d02pTUhUBDlVX1SxGh5Ix8w9w9DXE/582bqB3Ghz6/u/8YZyOOjCzqFhIdd0KzYwPg/OGf/oWfvWDdxlzla6i8mNwfB3mCfyc59bgiLLwUW22WZSJ9xl6oQrNFJVVg2MyES1LhE0sitvoTzI46RMzh0v4K70SgiCuMGpF6bt6Vx8qQKc/qIybjn1wph3gWEqDeQql0YpYyaaFwWF5b36uEw1NDfpZB3BhB2XivFHotnXE4JgiYxXgZBw02UMs1qBwEz9bGqhSClApmRocsotvRfGisxY6eOSUv6SpE605cizOUmMRP6aF0KurZUwvNKxCY70THjyDQ1qfAzN2BselwTl+7QT+8LKzlE5sXKWoZA+xPIJafW1K8BSVK71mGv21AwubyJgFS8kx6N/HGPs43U6DNVsRHm1XudFn1gxOW2TcSAaSLhY2CzTEkkrV25/X2zJxxvK255ivP3pI/X1+sYk14xV13fINCb2Oj912/H+850lEEbB7ekEF9bUcjTb5+/huyPjzssrEJZaKyLgIcPoM3kAzS7tiExlLB9Wskt2EBkf44KTBJaBTaS7OcIQBWs0odbegd4vu9wLic1QKTc3CcRYNDmdYmq0ItFHSFUQ2XYEQGTt8NLgGR1cPxX/zzZ3zpp8b14wZn6euAhw/DY5q1lcKDAZmkk2eNpHxGNPsBEH8fkEQH2NusZkQGjd5gJfoRWWmqPIZ/ZnVfIDJRhITZQ1w2E74hHWxlqkfDA5nPtTC6+h8nqXBAYBXPvdp6ncShpIRmnrPDqqorD44bLfuOmbVMPpr++BYGN0gJZDnQQ/vRXVkfjHxXLq2JwSDs2iUicfHk/OX73lRDM5ANTjxT+6Dwyv9yGV8zsHgBBkMTiyyjn/neiU6f/2qouLXeN4UVSLAKVo1rEw02KTOd0025oMe4kJU2ck7S0jGd4i1ZstrYia4hL+2ICnLcBBIp9g528KPQdqH9ZPJAId/BrNKR+ucCFyjYIiMnRocPXnJc51XZDxWDg2jt/h9ZYrKT4NTCUNMsB0cD3AMBodpcKhSbrJSUpOrrKYhtFQQmjQ6pNd2YvTXFCmqKNItBcphoAIca4rKosHZ1YcAx1Zdp9gmh0bEdyGg8y0vnV754HBdlOt74QxOTV2H7WMa9zK9n3vTFR8vPla9GSWavwKauVC2BIvkg5Pspp48v37nRaaoXEF3IkXVEw2O3YCUApv5dqpq0sHguOYRfn74/Zm3TFyzel5PNxmcvAGO2AyNaquGgsHpM7QTsanBsaeo2gskExk3WIoLyN5N851Qrd7MNTG7GZxkmqscBqghffG30eEEPh76jK1WpCh90jQY4+Pnr8UX0HYwYGhw4p+xkE4fw9XLJgx4pZpOxfHnZIGbFMoeQFpkXEqM3wYeVHLKe7xSQikM0GyZ2iL6zqrlENvWjaMUBjhx/aR+naOjOP+OjF5cgq0D8lWB8AWL0pD8e1pNDI6tioqJNTesitNpvU5RcYFlPF4KcNIZnLwBjkQ+DY5PisotXB6zanDaGyZrN/H0FBXX6B2ZSwY4dG3T9ao0OGzRprS2vP59z0tCZOxKUfVLg2NJ5czX2xocR0CSleo2enYZAU6HKaoONDjLlcEpApw+gy6iSilOFZDVvo3BkUZ/TasGJ5/IWIkjPXKsWT44PMAIM8YBpJe5GmxMe9KdXqir461LERkDdhGrWSauJ+20nkMRY3BonJQKy18mrlNUPBXVaLbUztVllibBnXM55V0tx0xgE5FVgzNWKmHzmnH8y2/9uFFq7+pHZVRRiTJ5Ai2GeUqcZcqh3oyM74mE0DY3Y56iIi3TsVoDR+frhqFeN0hWHpnMQqI5at4ARywAE5US5uvNnN3E459pImN+fdPcQqiWS+p6S2hwrD44yTHY9CcNxuBQsA3oa4wCHVVFJYKLRitpTugtMhbfgysd0y8fHNtCLlNUCQYnxUgREAwO78CuusLnKxP3rXbUm+fAmgZNQ6JM3JNlGjRGM+xaJuC7RNmZOY3BqTAGp64eE4JPx0Usy8RzVVGFdganwfQgBJ9dvS3fTwiYsR7daFSNsaZatt7U/DUmg6PTMwS+w+VjdFm9BwEvgzeDSV/igqeJ+PgX2PdAwUomg8OuBb4jrLYZHHkMrsEBgLNOWKuCA/64ZHB4FVXFweCE7Dz45vdtzQ4bjMFJ1eAosWbYZnHiQK2XLI4rBSXbSui/m3YLWZC7+DXtZpy98sHh6VM6r6ur5n7VbNVADE78Nx40pfWi4gERXRO1RlN5vlxw8gb1XOWDMyZbNZjXXKPVSp5fzwBEa3DS/cB6y+Do78G2UaTAZqFuD3CyNDictbEzOPlSVL4bsqz2I2mQVVSjyuCM5qiWCfguVpumuQMUFeCUdRAkNThZ5ZFOJ+McVVSJbuIs0idoLZD7eLbdIodMiSmTP0t6yvUaQPv22FJUksFx96JKanA6LROvlALD+KpW101PabLKElUaKapKMkVFn02+t6uaQabMCC3LAgaY37UrNegzfkK9qRe1cilQ7RpsqQ7ZnbofpeKJ60AwN/L7sTWpTIMzwMklMo5/WhkcNo/QeeVmg5QSVykq6YPDLpO0FAo9FjAjyIPMI2jHmVvU70kn4/g9rQxOxyJjtMdPbIX9ef0x+jO7elPF3LxgcJwpKscQas4UVT4fnLxl4nJDnQeFD04BY5eSaJYpy5XZDUnRcNOiwckS9xpdnHMyOFndxA1am9iXFHEg/clFf0pRc5rAWI3RIoRO7yYunYztC1cYJt2iu0lRhWGgWRMWaNJkld29WgdLXINTLeumrfwc1CwsFodyl5VGf+zzu1JUXJDqy0DYFhhumUDamsPCUK/VitQYyTBNBTiH51BrNPHOWx7EHY8c9BqHc3yOFImTwclRjQgkF6U17eCjk87Z6UZ/OligIArQFU/Sk0bfy/bNgIQSoTP2gqwcVlfL+PGnH6fGSO9JTtokupXXXKNp0eDkFBlXMjZ6PU1RGZug+DOun6zgnBPXAuAiY7vRX1axgikyZlVUuZtt5ktR1XNe0xxLxcm40OD0EXxXkpWisvV8sfngZJmuycqaULwuDS4fHFukX8rYldD4+XMlVCVEy2RwbCXirtcADg0OEyGmMTi80kumpnjDTh9woS8Q6zAWGy0sMO2F9sHJ0uB4MDjt50RRZLBHNjgZHLWAmSkqPukZ1X++9LdYUHhZbaUUKKfqgyLA4c+jz628cI7M45/veQp/c/sjuPvRQ/jn1z7PayzW8TnMLLVnj/0eGGSKimtrJIxeVM1kgEMLkGRw9Hyij5XWi4rfw6TRI6+gtRMVnL5lDf7nC07FuokxtZGZVOnHpMgYaKeoLBotH2gfnPT0cS9TVFyHRMHbBadsUGloZfTHUqscuuDBfnwjRcXYHFUm7hk85PXtyntNcxQBTgFjks9KUfH/V2zNNj1t86XRn6y+SoPqJu5wGTUWvYxAC0g3KgO0qJkmN8XgpAU4ofkaAIa2g6AqClp+zTYNHxyhd/LdEcmu5uOVEqYXGlhg1Wy+Rn+cuRs3ApxQs3hiUQaAasm+21Nl645WDdLJ2BXseKeoxPNodxsfmzE4c+4AhyZNYnB2HVnAg3uOAYAymusUSQahHdi07FVUucvEhYZsqgMGJ80ok1f40Zi5BocW36pgcGybjrRqQYO9aH92YizWTlQQBAGue+kzjddQEDCXyuDYr8Ms+Gpw5GnuRRVVEAT4ufNOwJ7pGv7H+Sfi7/77hwCSKaqJirmsdqrBIcGxr4A3yyNNgqeM8yLZbHM0RcZFgNNHKPfTQAc2LnEu/7+qomolJ1a5uEnUhQanWvYXRzqrqJrJY/h4o2R1YJYiYy8GxyawzZGiSutFJdOIaeJLG2TLCF7BogKcsv0cS/C2BmaKqpQ4B3zRdKWoqqIBIkGnqJLd1G2/+9q4yM83xwKcMmNwZM8nrb/RvdtILP293dN44tAcAL1gdwp5/2ijv8j697waHOkLQuxKr6qo+GKmU1Rag0Pv72JwQuP7pfdzMzixwNb8TK6KNsXgWHxwgPgcd+pknNDgOAOcpMi9U/A5YsOqMbzppWcA0OXwc3UzReVyMnaNlbOqi9YUVV4Gx5dlNYPFPJC6nVHV4BQBTh9hM6BzVUHxG54uFp6iUn2BcoiMa80WJiw5dxcqDg1OGoOTWkWVMkEDyX5UaZ3EE6+xGP3Zy8TN3VxSg6Ofv7G96NL7527VIIS+tIuvNZqsTNwzRcWCygnB4Mjgku9OnRqcsitFFf8sBWaZuC0d6TNugmQBedfwCtPgJAIcS8PCE9spKmoJEH+OzhcswM2g0kKYDPL97yMgucPtRGSc5oNjq6IyUlSMRQTSGZxUkTG79+VO32blAGgGJ9ZdtSwi41bi/Lv6abnGo+dB+/MSGpwe+eBwTCqtUTvAcVRR8SpEG9xVVJ31ovKvosoXtHMUrRoK6NSJZbfkquIAtEtko6W9Q6TIOHcvKh8fnPZ7LDr1B8kUUNqEnbXrVSLjXCmq5OfXrRosgWRktpOQGzm+iJx1whTe/6vn45nHTxnj9i2NlkwSd5FN9KLyZXDC0KnBoc9F71sKA+e5zhYZu43+wlD7N3mbiInPR4sAsZnE4Bydr6PebKlzZmtYeAIrdyfIQC0v3AyOPUWVV68QBLHols43sSuNVtwo1sd3hKdGJMxeVN0xOOkaHP2cSujJ4LDgdG6xadHgdMPgmHOK0wenfbhyGKDRinrmg8NBnzO72aY5dgnT+8aiwckpMs7rZNxJiorPtdLEdpQwmmHXMoHOcVrEuVJkzDU4zAfHpcFx+uBwBievD46jm3jDciPk8cFxzeWyastPZJzU4MiO64AoEze6RpufjS8iQRDgJT+6Fds3TBrj9s1paz+e+IXKsr7eVOd0gmlw0gInpY0pBcpXBDCrqOj8cgdlF7LLxKXuxjyWj6icQy5glK6g7yjWb8R/4zqceUtp7LrJSmJXLLui50WymzVVT9lTVFmWBzbwhYmzK3k7Z2cxOHTdmVVUorN33fTBMXtRtd/PMi6uUZML4VoHgzNWDlUgOLfYSHxX9aaFwemwF5XrVNJnofutVz44HHTsucUmWq3I2WwzyxHdZHCSKSrfNghhyvdog0164AvO2IwqewMUAU5foa3pzd0wYKHIDcM5/RwZoGQ7GXMGp5lr55lVRWVqNLLLndNaNcTHM313vFJUNg2OpYKI08L846T1opLIm6KyiYyBeCemRcb6lks7d3Vniqqk2bP262siNWaDEpuKxUbtTmWKSlwvPrYAHIkqqvYul4KwUhgoO4DDs1owbEtRBUGgWJyNbY+kRcsiyXFkbhFff+yQM4iUC6pM9zlbNeRwfOXfNWdX8rYlSPPBabX02KYsKSrdqqHVPmZ2iuqpI/NK68SrrmSFHnkZ2UAB6WytmWBP+LzGH/MBPS1LZKzut7FeBDjxz7QUFdeEJXtRpc/ZziqqDntR5U1RdaLB4ZupUa2gAooAp6+wibicImOmheApCBlcpDkhA6JMvEMfHDm511kKRH8OpI7D+EyuFJWjTNxLg8MWWt2x3a514jsaOcfoySv5XnmbbS6KVFmVNdyUKap43NkMTiJFVS7pyV0yOGkBjqsXlWLZ3GXiQPcMzpxicPRx17cZgIOzNfWYi+YnVu1nz92mHqPd7lNH5vEHn/0udh6cU3/7/U9/B7940x24+9FD1vG5q6goVSVTKJpR84WLwZH6JBdU+tTytfIUrGZwbCkq3WSVO6u7mm22WhFe/ldfwaXv/W8sNlrCRsEvRQVAOVXPLzYTwva6pYrKN3Cmc5KlwaF7thcMTuTYBCmR8WLTqBKUmhldJu4KcFzNNlvW47mQtTZIdJWiKhicAnVLGZ7LAZhrIXhPEWmnrYIfp9EfC3CaLTWR5GJwHLsrW3+iNDqU09s28HTT/GJT7d7TAhzFHFlSVEarBp6iMjQ45ngjB/0sj2FDFEV4YPe0mtzq5KhMHiSscom+R76rTwtweP8v7qtRZdVF9BwZWNmg2SSZotMBsFkmbh4ry39JQgbJKsBh19BxbR2OweBQFZXYBb/up56OX77gJLzup56hHqMF4JN378SHvvoYPvy1x9TffrB3BgCw89AcbEhUSQkNTtInJz+dzxcmI0WVMx1ju39U5ZOhwbGJjPX5rjFNHl/TeC+qxWYLB2YWMb3QwGytYTCcviJjgDE4i40Eg9NothIMWl4nY22Yan8dfU4V4PSoiopDaXDqTeZiHCaqRrOYYNPJONmXKncVVc6Uerci4xUf4Nx44404+eSTMT4+jgsvvBB333136vP/8R//EWeccQbGx8dx9tln49///d+Nv0dRhLe+9a04/vjjMTExgR07duAHP/hBPz9CR7D6s7hSVLRDDAL9HIPBMf1saKGJogjHFvQCIUXGeao/ePonYse3sUBykbVB6xbsf1eOvM0Ih9o6jEopSPTU4aio92VVVA03gxNF6UZ/aYtIln/FNx4/jJf+5X/j/3z6O/E4RKDBvWcUg8MWPVeQCjD2rxSYPji8TJxExh4MDtcDcShNRuAuEwf4deu3UMjzTMErZ4nWq0qqWuJ5E2JSf9ZJ63HDz5+NDavG1DHos0y3+yLtPqpbOexvm9HNWHpd2T6H9BSqN02NVNOxi08DX5hWjZWZMN/3HMY/bWkxg8Fpj93wwWlfM9yfxAhwDA2Ovs75/cwF+mGYDHp9GBybBkd6UwGdiIwzfHCIwelhikrOEZPMsXmhbmcegezqpgUHg0O/501R+RbqKQPXrlNUo+mBAwwgwPnkJz+Ja6+9Fm9729tw77334txzz8XFF1+Mffv2WZ//ta99Db/8y7+MV7/61fjmN7+Jyy67DJdddhnuv/9+9Zw//dM/xXvf+17cdNNNuOuuu7Bq1SpcfPHFWFhY6PfHyQWrf4xLZMx20pwdqbOFjv5OfwOAt/zL/fixd9yKh/fFO1bpg5NHg8N38HQcPg9VLExU2m7BRe3K4zVaLRya0emptAoTJUzmqbgUJ2OZokqUibfnk040OI+1y5YfOzhrjEOViRsi4/ggVSNF5Z50OWNgdBNnO8SmWoyzGRytr3JVUZkLmNRb5K3QkDtyaqrJWQDthZNkcKTdPYcseacqln3H4qCm1miqHlczC/YAx6Uz48yT0YU+x31EqArtlGIsPXfYafcPvzYbthRV+xqslAIVWNXqTYMp1seCOpbcDPAxyM+eFuBwDY70walbNDjeZeKSwXEEDQmRcR8YHJ6imku5bnU3cfvxDQaHVbvRmL2rqDIYZ4luysRL7cpKYHQbbQIDCHDe/e534+qrr8ZVV12FM888EzfddBMmJydx8803W5//l3/5l7jkkkvw+te/Hs985jPxjne8Az/2Yz+Gv/qrvwIQ3/Tvec978OY3vxkvf/nLcc455+CjH/0odu3ahc985jP9/ji5QDdt2YfBYRcbXXC8XJtuaGn0952nplFvRvj+3tjhtZteVHxR01Ul+nh5nYxtnYs5uCsx6TBo0XOhLErL49fbGJz4Z1aKKrVSpX04V+58Xi2w8U8pdh5nwt4GC8JsvaQk6up7C9WkGQTxwtUJg1OxBIaAWQLrMvfj//dvtilFxsTg6DFuSGFw0iZ1KZimpo772wEO9UoCgJlFF4MjGAShwZG/N7vU4ExUSpp9zMlW2K9NzajRMG2tGuJyda3D4aJyAmcYZFDHBba5GBxWQp0UGbcSDFrT95y0zO/BNf1IUX93Gpz4Z7JMXIuMbdV/hLQyfMBeRcWDQt8UVd5eVN04GQdBoAKbFZuiWlxcxD333IMdO3boNwxD7NixA3fccYf1NXfccYfxfAC4+OKL1fMfffRR7Nmzx3jO2rVrceGFFzqPWavVMD09bfwbBBoWZsElzuVaCBXgsIvc1YuqLqo+JLPRiQ9OfNz4dXzCs4ml0xa8rFYN3FiQSoVp0XOOUQihm2wStqWoWpHFvZj9n4soJbK6iVNgQ0yCDLRsDE4p1DthH5FxpRRgy1QVP/+sE3D1T5wat5QQ7JlPFZUOJu0aHNmLSrrW5hVcy3NG54rT4YrBmUtWUdmofoI6rw3TQXbfsQVEUaQCHSCFwXFcEzYDSSC7ItA6TvZ9jI+FVouDNLhSI4DeKPGF29ZsEzCvQyuDwwJ5vqFpsqaYYZCcQ9I0OBOcwUmUiWtzwjGH7s+FZJl4BoPTgxQVF+JzTFYoiGumMo+ZPjiWFBWvdvRNAWXNVxK6CCZ/gAMk+52NIvo6sgMHDqDZbGLLli3G41u2bMGePXusr9mzZ0/q8+lnnmPecMMNWLt2rfq3ffv2jj5PXtQtF5ArtaMnEs7g6ItcanDkhGwrb419cPyFZHyBI4E0n4xzOxlnsEe8TPygSlFlMTjmTcw/r60KKBIMDmAGly6PCyA7RUWLtmRw9I2vd84NFbBoBiZtJ8+7VwdBgHdffh5+/2Vxzx9akPKIjHUw6WYOzW7idgbHdyGSKQeyszdTVBYGR9ndewim603xs4VjtYYZ4Dg0OC6vJ3498XPVSWPCBIPj8JlywccHh+/+K6VQLTZ8UeTXoe1zcJ8Wk8FpmSJjscFJ08pxBofGyFuz0PuQED+vD05WI1ylwWkHIf1MUc3Xm5htM4XSAwfI64NDAY7WrPmmkPI2B7Z1ls8DutZWLIMzKrjuuutw9OhR9e+JJ54YyPvajP6yU1R6YeU7H9WLSiw0cpFrdKHBCVienY5jMjhJWjtt3uD0tg2qMzhjcNJM/uIxmDoGM8DR55mb9CUq1thnSvPBSevRA+hJaF4wOLKCpca6iZdZtVJ6FZW7akd6gPikqOR5I7iqZGRJcPcpKtLgWBicvBocdV7j9+BtIPYfqymBMaC1P8nxmfeF7EUFSCuC/IsBTy2MV0rW9GoaFLtouQboNPLFsVwKVNpkzMHg2NzFOcMggzrOcPINBDXadGGyqvUpdH2S8LjRYr3ZlLO3b9AX/1RmqE4nY2JwepeicvngAMDhtsVFJyJjWxVV3jYNQH6Wtd5B0M6heu6tVJHxxo0bUSqVsHfvXuPxvXv3YuvWrdbXbN26NfX59DPPMavVKqampox/g4CuorIwOA4n45KTwTFTVNIDRaWqWuZuoN7MNzHLFBAttEEgG/RpDYALWZUnvCxdtWmYTA9wSiwoisepz6MzReU41wAQiedzZJVdSot2bexIAY42+uPVcDKItEG7R1ty+uL1XgGOo88Y10nx8+dicLwnz4TIOD5HY5zBaX/XeTU4UmQ8zxaIfdM1g8E55khRKQaBmp9auojz70f2QPIBfYZKKT63Lp8pF9IYHAou+MLNPZN42sDG4PDrnV/nUoPDfXP4tZimvwE4g6NFxtSjijM4FHzlFV5rhtb+vESZeA+cjBM+OOwaPdiev6TJX/w6pI7VLA03U1S+ncSB/M7rNId2osEBihQVxsbGcP755+O2225Tj7VaLdx222246KKLrK+56KKLjOcDwK233qqef8opp2Dr1q3Gc6anp3HXXXc5jzksyD5SAGdwzOcaPjjtp9NNGbLggpeHAnxiTjIugF4EfKN01R+qZR5PlhL6VNVkpajo8XqTpahWpwc4ukzcZHDCQKTQmLDP5Rod/x3q9RJZ1DKlpqhnmNTCjFuM/sqlgFWCpaWo3KnFpMiYgofsKqqEQy8r5efXiJz0dImz5+QpWlPMq+uQMTirtZMxLVx+GhyzgSQvfd93bMEzRUUpklJ7vCYjCpiftWEJDLJQLZuBrstnyoVUHxwR4JADOqVIXBocm/mmUZHFhdWtyAiy+PWRFeAQgzNba6gxTloYHAq+8jI4WRqcfvjgyK8hDAN1bmn+SmNwnE7GBoNDwvnsVK1E3k1IJ5WBHEtBZNz3ZpvXXnstfu3Xfg3PfvazccEFF+A973kPZmdncdVVVwEArrzySpxwwgm44YYbAAD/63/9L7zgBS/An//5n+PSSy/FJz7xCXzjG9/A+9//fgDxzf47v/M7+MM//EM84xnPwCmnnIK3vOUt2LZtGy677LJ+f5xc0LqL5MKbLFfWF5tKUTWSAZJMFVBg4+qCTDeKbx5X7vS5FoTDxzUzTd8COETGWQyOCMBkewSC2s1YAhzT2di9iGSJA/nCOrfYTIiMq2xhqbOUk0+qwnbt6HGZ14AsT7fB1YbD7BYdJp5P6HTynBwrYb7eVGxX2cLgLDZbmKk1sGa8wlJUHiJjqqJiLrL7j5kMjitFJRkcm6aLs6HdaHBo0SurgN63Yij+aU+f0hxhbmBsAY7B4FjYiIBd58kycT2GSscMjkxRaSdj+i79WzWY85FTZNx+nAwj610xOPFP2/cwOVbGQn1RMdB2DY45JglTZGwK51elpGolOr1HO9XgLAUGp+8BzuWXX479+/fjrW99K/bs2YPzzjsPt9xyixIJ79y5EyE7wc973vPw8Y9/HG9+85vx+7//+3jGM56Bz3zmMzjrrLPUc97whjdgdnYWr3nNa3DkyBE8//nPxy233ILx8fF+f5xcaFgYHFcpH6eCZRBjLc+mCbnhFhkD3EHWN8AxRX+uKN+nJNE2fuO9uMjYo00DYAZF8TjtAtu0FFXDEuCkleK6Jow5trAu1HmAE7+OFpb5elMtFGWWqvCporJNPkmhdfwzjcGxtbjgx5Aai2SZeL6FiM7FxFgJmGVVVGyME2MlTFTiAOjwbD0OcHKViZsaKCCpwclkcMo6YI6iyEit2RicTppt0s+yI8h0QaVj0qqoxByjU1SmdxLQrqKyfA6TwTF1R3JeCoKY9UyroAL0Qj+9UFfnjqeolAanPc6OnYw9U1S1LhictJYZdPwD7WvOxuDIdNrD+2bwtUcO4FcuOAnlUmgVGaeJll3IW0XFKzU7QaVgcGJcc801uOaaa6x/u/322xOP/eIv/iJ+8Rd/0Xm8IAhw/fXX4/rrr+/VEPsCWxfurGabpSBI7BRsDrN0v9aFdkAumrQT8J2YK2KXSTst6f/h+hwcaSXYgMkWqU7iGSkqWc2jmI6yDHDin3JXCggGJ2WXrFJUjrmRBzhcTKlTE/FPziKUuQ+ORxWVLTCVgZdPmXjFwRrZ7AlonOZ7xj99WzXQ+6gUlSPQ3rBqDE8dmcfB2RpOOm6SVVH5aHCSDM4+weBkORlTIGBz1+Wbhax0qw10HUwwLQ7gX0XVTGFAVRVV3RSjn7h+El9/7DCOX6c3e4YPjsVdXGtEkgyO3ABUSiEWGy1vBucIswAg4Xi9qVO29D17V5aJwN/lLUOXOdfgRFGUKox2vmf7WLZAk5jGdA2Oeb/e8O8P4LYH92H7+km86IzN1mabHTE4GSl1CRc774uCwVnhsLVqoHUjUbrcsi80gBlc6F5WpvbGzeAkUwNp0BU+ZsCUYHDoc6SxEJ4i41qjhaPz8USYJTKWqRZbJ3HAZJhcrtFAllts/NPF4PCFdd6SoqKdO/diKYeBCjbSzl2aCVc3ImO5UzZ9cFJExnTdee60eYqK/18GohTgUIoyzfKeYPgLNVuGvkJqcBbqLdSbrUTKjc4DsRuNVtJd1zT987db0ONsMzhjZopqsZGPrUi7NhWD0/5+3/5zP4rLn7MdF5y8QT2X90RLFRlHUeIzyzFUwgCLANZ5anDoewWA1dVkMFnNzeCYrIObwWEMYhv1ZoSxcv7F3OWDA7AAhxgcS0AiU1RH2nMdXae8GWmt0UQURYrBSUvVSvD+ZD5Iq9T0QVEmvsJRt0yKmSmqIBngWBmcyKx6qDs0OGr34ZlnrYiF0NYRHeB9T9w3U5bRH32uAzM1xfasz6C+pUmezcUYMPvruEry47/T85PvlbUj4qmR+XpDOU8nAhzO4IShFlenBTgpDI4UWOZzMnZXURkiY5eo3Du/n1xgAM0QEta3U5Ik0pzz0uBokbFsHvrYgTnjewHsOhy6Biitx63x1WewtG3oRINDhn9y85CF1G7iKliiOSZ+0tqJCp576nFGxSPviZbei8rdqoHuBfoMUzkZnFKoHZXjYJKqhGjDkk9knGVbQIfjqc5OhcZp8xhd3xS0yB5q8evMeYQ+K80LnMEhofdcLfs+kFApqpR79DtPHsW7Pv8Q5hebLMPQoQaHUlSlFVomvtJhKxOXVVAEV6qAHpO/t1qRoadYVJoZ+03sXUUlGBKXjkbqQGzINPprv9e+6Xgns3aiknmzlRIanCRLBpiTipzX+ClK88HJ8q8wGZyWrmYSKSozwNFal7QSe82cuSdM7YGUXUXlEjbzlIVXmbjj+3543zG87h++iYf3UcsQYnDMHa38fsn3iFKUXmXizAeHfwcA8NSRuOHm6mpZfQ+2NJWuotIBjjw3nFXI0pPZ8NxTN+C0Tavws+duA8Bce3PqTawiY1FFlaajqLLzxe0oCJyplLojmSaj91mXwbTSwkznvloO1b1bZw7r4yxF6AMKuOg68u1FBXReKp6Waqfrm55jdzLWr4uiSM3Vs5YAh/6vAv0UM0UJn/6Af37rQ/irLz6MLz60r6OgnUMWU4wiihRVH5FPZKz/LnO9tjLzRqsF2VgTcFO9/ikqYhdMH5ykJiP7ZlITtFNkHD++71jcJDXL5A9IlrFLYa8eH9T4kiJjc8cUjzH5XlnVD9xgbm6xkQi2aMeqFypTRJ5Gy6e12JDBZZ4UVbIvkF64DaM/R8DoEkb/4z1P4rPf2oVta8dx3cueqUXGIlCR3xOlJKmbvC6P9fPBkQEOYdOaKqbn6zjYWLQGOFKDA5jVLEBScAvkC3BOXD+J237vher/vfTBkVq0tHFpBsfVbDP+PYrMa73ZjBJBVpkxRWlYJRbmsXKo2LsmSwfm9cGRrRqcPjiRvhdLYRAzdB0GOGmbIMmwjKf44MTHYgxOe/5YEIxjjVUdTubwwfFxMj7cZtQOzS6q67BTDQ7pvI5fO1rFPRxFgNNH1C2LVJ5mmwTTYZZeb5Y+8t5MNnhXUYmdvkuI5mKiOGz5fuO92p+LGJysCio+DmnMlidFxSdx347NNpgpKq3BkU7GBAoayiwt4kLa5COvIRXgpDA4mj1wV1H5tGpwBns1s22FdpI1J2jJSJGo/NDMorEIpU3svFUDfQcbVo3h2EJdBY2bVlfRiuLqPFs/KrnAAkgES7ZWDZ0uBgC7t3wX8xTth9w0yOufgzM4NlbVYHAMDU6UMNajY2WlkleJ771aDlUard6MlJarUyfjrOuRf19jpRDzraZ3YJl8T5PF4pABju265d8fP8eztQYazVbielhsdsbg+BR+zLWD/bnFBttEdcbAXPvTP4IXnb4ZzzvtuI5ePwgUAU4f0bAsvto/xnyuShUEQWLyshnYtSIzRVVvmgu+RF4NTkMETK4Fz6fZpos8IjbmWPumW+/D4IieSl4pqoSgOznG1DJxy2dstSKjId48q6KSRn8E6UadNuFy5+PkuMzn+PjgaEo/HnuodsB6ITCbbeb7vqXFfF2lqCSDY46RGJzDc4tGwJjK4DAfHG4MOF4OsetozAZuWlNVzI2VwSGRMfuOFhJNIZMMju378IW8t7KQlhqR12tuBsfwweGbAc5u6s0BPf23Xvh03PnoQZy3fV3q2OX3N1YOjdRsgsHJKTL2NforhfF7z9ebiVSQL9KYaJmSsl23/GXNVqSuq7la02BUiWmq1VsdiYyziiIAvQGZqTENTodB+5rxCn7yRzZ19NpBYXSTZ8sA1mabjkVTOaWGyXJE2+tj91x9DFeZuO0YaZA9i1wTe5Y7J5BO7QLJRdQnRSWN/lw+OLyPlEvQHf89/mnbJaelqKSQdW5RT1Y0kUsdiQpwfBiclMkn2Ysq/umTooqPzcSz7Dsqe2hwXNcXLR70U4mMM1JUxNodnF00GJS00tMqExnzsvJNU5oq37SmitXt7tppGhx+3SRTVOw6YQtmp3B1E1+oN/HEobnE832abapjp9zfJoOTfL7hgyM0ODJF9UvP2Y53/9J5mbt+ufBXyyWjkk9qcPKXiaezq9xHiu6LzjU47u9BBjT2AIdrcPQ5nqk1DBdj6gZfa7RYmXhve1HRvUDsEdAdKznqKAKcPsLabDPDybgUBgk9SMmh4eEpKrWwsNYF5jFyanAagsEp2SfU9BRV/NOpwRGTpE+KymX0l2wt4E5R+TbbTBMZz4l0xnzd4mQsFml63KcXVTOtTDwhMs5OUfFWG2bqRR/TbNUgAsYMAaPuoZORopJBbTtFdWCmZpSIp/mV8BQVf83mNboT/aY1Vaxp0/v2FJVZxRMfL5vB6dT1FeBtRsz3+e1/+CZ+4k+/iEf2zxiPp4mM5T2VprGjKi7eE82Wokr64HDfnHyLYCkMjPQfT1HFrRpkFZUvgxP/pCDf5YPDGXG6LzqvooI6loRMSdkYF37uWpFmcGZZp/VKKVDHqjWamPVoOivhlaJaZAFOi9jvIsAp0AFUFZXBwMQ/XYtuGNhExkkND58kAJ6iio8jDaJ8GRyl1RAaF1dlV7rI2L3zAZIlw/k0OEJ/IoIJbtKX7oMD5xjTdolSGBj74FDaIx5LGAZG0EHHk6XuNrjK8wG9mOmGq2b1lg18ATSaSLIqKhtTKP/vCmhVikqI3eWELz/PCesmAAC7jyyoxphZtLxasOtaqzAxVsImHuCsriqhaxqDw1Nm8ju1aXA6pfMBtxfRYwdnAQA7BYtjEwQTklWN2YxXrW5v1RCG+l6RvbharfR7OA18DuIi43pDM0NKg+PL4CRaNdifpx2YkcrgLNSbeP+XH0kEl7Zj2YLuBINj1eCY46c5daamG5FWyyX9PTVaSiuTL0WVfk4WWfPlmVrDYLmWK5bvJxsBqBSVZZFLpE1YbtlHZNxqmYZhurVCW6RZNW+M3AyOKBOXE7uraSiHb5k4wSfAkXb3ixadE2DStT4MTgC3zsEWxEkGh08YfCycIVAMjk+zzZRzl2BwfKqo2HFsPZZCcd2lGSfaoFJU7SCBGDZpfCaPu3VqHGOlEI1WpBaZtBJx/ncuMrYxOGkpqqa6NzVzlQhwLOepK5Gxo0xcCvoJaakR+Vhqiqp9XdQaTes9mdaLKi2FmwU+B1XLofr8PBWoUlTe7Svin/4aHMbgWAKcW7+3F3/87w/i3V/4vuP99PFtp9hPg8MZHH3fztY0g1Mth/p7qndYJs7S8jbwqs84RdV90D7qKAKcPoImSC8fHCNFlcLgsNdzCn1RpahcDI7fV60nYVPT42y2mbJIZ02O8sbyERknjP4cFURccOcSdPPfbUMMUlNU5qI5Pa8t6blbL1+sNYOTTcun9YlJlIl7iIwD1muKXzfcF4U/x2XsmKXBUQwOpagSGpzkcU/cELM4D+2JPXSy+u+Ms52uGeCYGpz0FFVyQ7EgNDjd+uBIuFJUi0rQbz6emqKSLG9aisrC4JitGpgGR1RR8VRPXvA5qFousUBSf07VDNO3skz54MTHiiJ7mopvGBWD00xaCpBB3/RCPfG3+P307z5l4mndxOPxaq+h2VpDBdVGgNNgZeIdMDiuFNUs25TN1pqpbunLBUWA00fYIuQsBseWorIZ/XE3UCBZRZVgcDwv4opYyGysBODnZNzMmBw7ERmXxSKhGk26UlSR2zWa/g440gAplK8sKeYTJA+2uA6hrIKHbAan12Xi8fsmAytJ+dNzZPCZZexIu/Ka0OAkUlSWMT5twyQA4EEKcDIZHEpRNbHgSlGt0Smq2UW3kzF3lpYanIahwelekOkSGcuKQEJa8J0rRcUWTnurhvhnJKqojF5UHawUPFAdK+smswsGg2O3L3BBV1HpAdmmIP45dYoq+cRFoTV0vR8dS8InRZWoomqfY4PBqZSMnmE+jt7J90lnWecYkznLy8QLBqdAJ9AC2KTIWO6EU31wLP4krVZk3LCLgnFJOMh6p6jMSdjF4HA/Hhekh0bivcSs6ScyNiuQZOUSwWBwUgKc1DRACuUrq6iOcgaHjYWXipPQN083cZu/SUmwgD4pKv6+RhNJoclQQVhKys8GCg5q7aaGLpGxjZF62nGrAAAP7Z22vkbCmqIa0ymqIIivpdXtAOeYL4OTSFElGZxeaHDkYk7Bk7wu08vE8zM4C04fHP3dJhicFP1JFkwGR6eo+HnWPji+DA4S47ddk/xzpomMFfPtEeAEltuLByBjpdAawPNzF3sLxb/PLjZNBqeiA9G5TpptZuiSTAZHG5MWGpwCHcGmUneJNV1aCP4a+js935qialGKqjMNTtIHpx2kdSAylh4arvci5BEZ02d3Le58fK5zDaSnAdJTVPYAp1IKjAnNlqIqeaSo0jQ4SZGxH4Oj2nAYC3f8UzVTFJVehOwUlWZwOBMhq0xsTMNJbQbniUNxm4VMBod1E1ci40oJp25ahanxMs4+YS0qpVBpcOy9qNqMjKHBkVVU7DylCH59IV24CbZGufySs7ZqSEljSxgMTqoPjrg3mAankxTVpGRwLOeZ7tu8/bn43GG7JJXtBmNwavUmPvDlH+KW+/eo52UxOFnfA/+MaYE5fT1cB9RsRZie160sqkw835kPTjqrzhmcmVoTaWnw5YLC6K+PUAyOV6sGnc5J9cFheVZbiqqhUlTmV+s7QSkfHFGV1ZGTcWaKykzl+JREarMwyeDYfXqsKSpPDY6mfJN/kykqCnBkkMFLxWnsFQ8GR7f5cO/edZm4PU0nYTMYlP4u2qtHBLS+ZeINs7ovq0wc0AGOeo1viqrRxHx7IZiolLBmvIL/fuNPqXO+Oq2KiqWPKeBMVlGxxagHgkxXqwaZDgZkaiR5rASDk7IL5wxOWpm4rRcVvU1HVVRVuwaH0jK8L5uvu7Nt/FYGxzCwjM/NF763F7d+by82rh7DJWdtbY/FFMW7jgM4fHAqZfZ7WoAToBVFCbPBQ7OxiztPUU3P13Vvq1wi4/R7VDI4vUi7jjoKBqeHuOX+3bjsxq/ihv94AACbRG0iY3E/cYfONI8LzgDJKiq+4+IMThj47zx1Lypz0k12l26PO2ViUhS74715mfhxq6rW50hwu3fALTI2KkMcgm5jjCkNDdNSVHSeFYMjggzO4KhWDYrBya6islHeZTGR+ZSJA8lGqkCSmaDnJFjEkv26JfAqKr5YyR2ojWV62nFmgJO1a6Vy2igCptvpJwqk1k5U1Dn3SVGVQs4sCJGxSNfEz++CwXFUUanNhIVZBOzpIXka0zR2Ng2OLUUVCQaH++C4zDrTwL/HajlUn590WqUwgI/gnoOGx+cj2/3JU4r0+f/zgb0A9DUD+KSo9O9ZDE7adUuvlZVcB9tNZjmDQ33ZgOxg33yP+Kdr08mZzPm6dl4vNDgFvHB4ro77njiCR/bFvha6ioozOPHPhDeLMMXjE5CLAZIpKl7+y9mQPPbysmeRa2LPar4I+PSi0uNavyq9t41+Db2vSeu7UlTNVtLJmMZsloDamJL4Z1qKakPbqI5od8kkcZGxEvIKFsqGtBJOmS7yMfrj72s0G2XMYTz+wHosqfuR4D44fLGSZeI2Bme7YHBsDQuNv7Nzeri9GNgWApWiShUZ65Sw3F03LFqlblo1KAaNnf8o0o7kTcv7AX4MjvSU4jA0OJaA3qw4FBqcjE1KGkwGJ0yc53IYOFktF2zGg/I2iiJz3DQ30EdbbOvEAF7BZr+u+ePdpKjopdIt+5AKcEpKg3NkNt4sjVfCXAF1KDY+EvI+oE1ZN9f0qGP5frIhgFd3AI5WDY5SPqUJaP+dp3XMKqr4p6yiWmyaC8uqalL74YOkD469lNDHyTjL6I+Pa4MngyNdgLNSVFHkLsk3d2fJ9+LaBAlKjchxJ1JUbOHVJdjmObYhrSyZn/uI0d7VSvrtTOfIpi2h93nV807Gi07fhLNOWGu8VgeMyYWIj6HZitT1HwRJN2fbZDpeKWEra7OQtWsdK4VqwaDuyLbFZbWHk3G55Ccy1oF+6tBSYauisr0HYAY4tmsgqdPLrqLiwnjTB0czldIHR29SnId3IsngmOeZp498U1R0WkwNjri/2X+5kzEHzRuUWnW9f5YPDr/u0q5bVxCtGJxKqFJUxODkERgD6VWfgG6ISzja1v/4VtguRRQBTg8xwao7ADuD4yqvlgwOn6/MMnNdRVQ3UlRmgGMyODkCHGanHv+0MwleTsYZtD6fpDZkdCdOjo/SM1H7WCkpKrEm07k3KiRShJxpDM5GIYyWTBKvoqLvrpwSLBDSPCrUNdSMjKqMaik9MLBpcCTL9qofPwUfuuqChNmeDnCSx603I0OMSVR4JQwT34tL0Mh1OFkBThAE6rwenk1hcFI0OJzBofMiK+PqFg1OV60aLFVUUvNCyEqNJBictBSV5dyUDAZHL4xyPLrKsMsUVaWU0DqVS6H1mkyDjcGJxEv5xo8zOBwUaGQxONkpKqbB6SBFdWgmmaJS13QOgTF/D7cPjmRw4vcpUlQFvMCbAAJ2DY5LrCkNuJwMDtttmVS36S/BJ5c8Ebrs1t1wTOw+fU/SKpQAczfvzeCI9I4zRcXOk0vQnZ0GiH/agjhaDKmXEkEuNIaTsUpRtXfyrkmVpQas3cRZuohPmJ1ocOgUZLF8acGepN0poCiXkrtnW9k7AJzEdDg+EzuxpZSisrkfU4qq3oycjTRLhg9OSqsGppHrFOUwyeDwe9iWEgMcAviUSksJW+PSkqELjH/afXDs7+cDvvjHJdQmi8E1OGnzCIctVZhgcFgMwY3+qC0IoAMNrcHJFhm7/Ijo+GmBOb02EeDwFFXZvKbzMjhpthZAsvJTZxiWbxiwfD/ZEEAXOFXY2KqoXIGB1ELwCcUQKbMUVV3cLLRzLotdSy4Gx+GDk2BwMnxRAFhLUjl4MCADBef4xI5Pl0ib76Hy0SxFReeExpxVApqeomprcERgltDgMAaHvscSY2Bs4Dt5G4PDRca1HAGOTe/gm4JQmivLmGV59WybCudVMnIMEk/LweAAOqBJ61/FFwiZprJpcBJGfzz4oPPUlcg4SByX38P8e+esRJoAXh87O0Xler3TybjZXYqKp8mrlTBRJs81OLl9cFJSVJwdL4UBfuyk9QgC4H+9+Bm6ZJxE8WIzmny/+PEgcHsB0bXnJTIWTJUhMm5f02lp1zSkbUIAu10CUDgZF/AEF/MBOX1wFIOTrGIxjf7ai3QrSmg45hX1a2/y6AMZQDQdpYRZvig0xvi1jvfiIuNJ3wDH3PG5GBwunKRxjAkGI8ul1EdkLN2XEymqSvK7Uy0THLtGHvxmiYx5JUTW92zzYfH1d0nr/ZNkcNrixVLMjvBT2zsGRzjIOro406Ij01TcKdrF4NhaNXTD4NgYNBlQEDKvTVlFlTKuIAgSQQ5/vZnONcfQXYrKZHBs/l7ljHtBQm4E43Gaz5HC4EvPOR4PXH8Jfuk521m/p7ZvU0YVVVqVJYG8nvL64AAmA0ljO0IMTrW3KSrJ4BCKMvECXpAiY52iSrIpMu0hg4EskXFTpKgAzSpUwtDIu+ehILXozx2k8fGlp6ho9+NKUenHfUz+gKQI2iUyNoSTwhyMxmWW4ibfK01nRMHkOqEdSlZR2RicdFqef69pIuMWS1FlsTf8/a0+OBkLWFpKUgonZxiDE/e3ymYT82hwgCQr4XqNq6O4TYOz4Kii4s7M3bVqSJ7/RQeDk5U+TXhlZezC5fmyzS8JBqdlr7ryheFkXAkTc0ilpFkdWaLuAi9c4Kk1Dn4c+mx0H/J2CIB/q4a0r50CG+6JI0HnT24G6H25BoeGkna8tPdwnUYXg1MpUlQFfCBFxjpFlaSDJSMqKXCTwbG8vmmKjAFt5FTqhsGRVVQODU5ZBAs20Dzu7iaeP8DRRn/mBOXsJt5Ktj2gcWUzONkpqlXVsrG4Ss2JrUxc6pwkeOrKNvkoBqcZqQaCfgFOkkHwZXDSysRriRRVW2Tcfj9+TlwMDrVrADpjcFwdyF0NN7n9gapwcfjg8O+/FxocV+WUqcHRr0sTwPuOyyUaB3jFoaWKqhsGx+gmXkq2/2AMDuAnNKbhBUHgvD9NBsf8GwURi0Jk7Db6o+OkMDjtQC4tRUXfoa2jOWBWURHyMjhG6Xwrwvd2TePyv70D33jsEICCwSnQJVSKqu2z0BALK+DnZMyfB5j5ZpWiiaLEhECly+UwNHZseSZlTaOnV1Fl0aFAep8nGifBN8CRbQ7cKSodgKkUVVmfO0DqHJLvxal7ibm6NpiTlvQcVUsvKt0w1T7ZEYMTBPbAgwcblA616SwkxiwaEHnduaCYQ0tQliYyBkz2zxXgrJ+sqGDET4NjHse1uLi8cHjQS9chbUzo2PoeMKtyOoWtiorfw4YGJ+PekUFPFkvLxe5ST8Kvc0N31Iy8GAwXVkmRsSUo4+POYnBkyTa/xzl4pZU8T1WpwalnMDit7ABPMTgeKSrJdupxlRI2Dz7O7hz8Hm5GEW65fzfuevQQ/umeJwHo+3LNuHnc5dyqoQhweggKcJptfYxutsl2Sy6RsSh/5DeUweDQQtNKpqgoQq+UTJFxnghdWqe77Lx1oOY+Vi6RsW+KSmh/XCZ3Nidjeg4ZqmUxOLJ53a4j8zg4E1ur8x5IfHcsJwtbN/GKCNIk1OLrWLS4ieGiI8CzgRaTxY6qqMzgkCOZomoYxzRSVI7JNAgCPPvk9SiFAU7ZuMr6HI6EBseVohqzuxnr6sCkyJiORc/J0kT5wuaDYwY4SQbHde/kZnC4XYE4Jm9JIhkc3+vDBrNMPEx895LByRIaG/42TNuVNE11B+1aZBzfv7JJcfI9swO8SZWiyi8yJvAUlTyuL3gz0FYUodZ+rwPtUvS5dpBPTWkJBYNTwAt8MVtoNNUNyxcqF9Uv/UhM3U0yWJE+OIBOUZW7CHBkLyqXuFI7GbcwW2vgnbc8iO88edR4TtYkvWa8gqdvXo0ztq7B2okOnYwbJjtD4L4tNKeoFFVE49Pnz1qKy3aIs7UGfvrdX8L/99dfQxRFWGif60nB4KRqcET60TWp8sXXBiPA8Wy0CbBzZ6miyspApLXmkAEO98EBzHOSlu+/6VfPxx1v+qmEs7EN44LOd+2eicFJ0+CoAKdB32m7vLyVDHC6atVgYe4aFiEzwBZW1zWQ0OD4MzjymEY3cSGA1tdHdwHOGGPK1JiZ/gnIFhpL3yr6LmTMrRsXJ49B2kRibnxbNaQxOKdvXQMA+JEta5zPUQxOPS3AESmqvFVUXHjd0nMj9bui6sbNa8aN1y3nMvGi2WYPQQ6rUQQsLDatZm2uhYL3ooqfZ2dweBWWK0VVCUNjwctTBujywbHlz+NxALd+by/+5vZH8PC+GXzgymfrz5QhzCyFAf7jf/0EAvhT/9xLJIoiDyfjCHSmK2UzPajz+fYJnFdRHZipYXaxidlDc5ieb2CurgOcCc8UlepFZUkVcbjSguo4TGRcUyLj7MkwrRdV1sKd1ppDaldkisqwLEjtmVTC5im/SX1cpFxcKTpKe0mBZZ0xk7KKSqaojACng4WeYNNA1Y10VTLYcX0tck3qjsGJf0qhb+yD00WKirdqqJQSY6QUUjkMYmf2TAbHL0WVxuBUS8RimmXiMVsVIQgC7Doyj3+5bxd+5YKTUpvxEt548Rl41fNOxvFrJ5zPofnNmaJiVVQE2eYkC/webrJ0I3ntKAZnymRwlnOZeBHg9BBBEGCiUsLcYhPz9aY2UrI123QxOCpFpf9mMwqMoqRgbc7J4PhH6CqAELtXW/4ciIMFuoHkIpK1CwXS/Tvs42O7lIiLjO2TdivSZmUqRaV8cNJTaKoSS/jNPHlkTqeoxvxFxvQ9ZjUYdLXHUJ+Ni4ypTYNXiip+Hd8p+1ZRpbXmkNVHJOilBZ1/N72aTDkzNlEpORmGVQ6RsbquS6yKilJUY2aKqtEjBsdWEm0LaoDs8uREN/GsKiqL2J3AKw5lFVU3KapqOUQYxPdptZxMUdF9UC7FAU6WyFj6VtEpSLS9SQna6TyoFFXDZDPLpQDv//IP8eGvPYZqOcRP/sjG+P1SPn8YBqnBDY1Xvh/HeDlMaHDyioz5JUEyCUB77RDDL1NUhZNxAW/QxDvL+n5UHCkmDtnU0sXgcDpxQYg7qbKnHIZdGP21J+H2jVh3sDBcS0SBjVywuzEJyxofEO9+nSJjNj5axHUVlcnguMbHe7twavmJQ3NqopqomAyODNgMBqf9RprBsQc4dZWist+enZeJd1FF5RDHA0kGZ3ZRG04C5jnxSaX5QAY4LlCK6ljNpcFJ+rPQ8SgQ4SXinaRqCDbtVd0hOM7q4p1IUeVgcORT+WaAB7BNpsnp5HMHQYB1bX+r1dVystKRrg9PN2Ppb+OqokpjjrUPjpmiAvT9OL1QVz+7KZPnoKFQ1WPS8byUSFF1IzKOGMN/bKGBucWG+qwyRbWcNTgFg9Nj0OTIqzbMFJWdVk04GRs+FTx/rl8j87mGyLjDMnHpg+NiE7iTMaUkZA5dCae7nBw4ZNWFS2TMm23SxKWqqFSAkz55cwqcVwo9sn9W/S41OD5Gf1IoLaErfNLH1WjpMnEfBsdWxZPXB8eaokrxwYnfl7NYvQlw+G7XVSIO6H5Ukl20aXAIlBqQIuNur2NpwQDYK9oAZKZGZECaR4OT2KywQKFpMEqtrlJUAPDH/9/ZeOLQHLZvmMSBtkBfj7m9mctI2RKkZk6n1nIwOMIHp9ZInn+enu/28xMkg7N2Ysw4H9VymJg78oqMjRRVKzLu8ycPz6vfN0kGp0f35CiiCHB6DJpIjrV3AYBotukor6Z5JcsHhz8uGRwKcEphYExonZWJmzd7ksGJfzZakdodSwanV7sfDll1UXcwGLZdaZLBaS8ijvfiVRrcxv/hfTPq79VyKKqoskXGmklJ1+BkiYxbrUgFuX4Bjpl+BPw1OC6DSiBFZGzxwekVHc5Zm7TyXFfDTaVzKlkCnIpdaN/tTtfWqmHRaJjLA5z4Z9q9UwoD77FVjaav9ns5SqSodHq30+DukrO2qt+lwJzGYevRZQO/9NIYHJqHbOeO5onFRgutVvLzxj+18Ji+qm7nMHo53SvrJiuJAKfrKipRJs7P5xOH5gDE16A0Jy1SVB3i0KFDeMUrXoGpqSmsW7cOr371qzEzM5P6/Ne97nU4/fTTMTExgZNOOgm//du/jaNHjxrPC4Ig8e8Tn/hEPz+KN4gK5mWptgBFblb0Ttp8Xtrv84sywNHaB95ZuiOjP0HPJyYnpk8hfYPMoUtdUS/Az2Wj1VI3sUtk3IrAUlQme5alc+DfFWdwfrDvGIDYoj0IAlEtYh7LdDI2GRwXJU+Bj2vi4VVYucrERfoR0Ndh1nfk0o4BFh+cBVEmXtbHzqu5coGf17SFgDw/js7XjccVMykqeQBWJq4WPLsOLS9s2iuTwdG/Z/ngAGbQkeVlYjOcJPCea4lmmz28h2XTX83wEbMY4RN378QHv/Ko9fXSB4drhzhsHccJVVYmLku2le+R2ty1MlOFvpAMzjpRNRo32+wuRQXozxxF5ny8sx3gTI6VVdAvX7Mc0VcG5xWveAV2796NW2+9FfV6HVdddRVe85rX4OMf/7j1+bt27cKuXbvwrne9C2eeeSYef/xx/MZv/AZ27dqFf/qnfzKe+6EPfQiXXHKJ+v+6dev6+VG8QbtJHuDYXENdIuNSewFwMjjsRpM7Z5cPTi4GR0zCLjZBdfVmKSqZvugVvctB5aEkonNWUTFRrG62qX2KfMZnpqiSDM6Exf8iLUVFE7ns9yWh2QUPH5wcZeK2XlS+35FLOwakORmbKaog6N1kOs7Oc1qKivQGe6fN9IjZTdxevSKrqOQCnRe2Vg2uvlQ+DA7/U1YhQdXHB8fK4KSnyvIgWahgVhXOLjbwfz5zP5qtCP/jx07EWsE08EuvFAbOXnFprBY3+pPzZ1MEtPzz9zpFtU703oudjLtjcID4u20ias+PXDcYp6hWjZWM6jagd5uOUUTfApwHHngAt9xyC77+9a/j2c+OS4ff97734WUvexne9a53Ydu2bYnXnHXWWfjnf/5n9f/TTjsNf/RHf4RXvvKVaDQaKJf1cNetW4etW7cmjjFs0II2wyZ5Th1miozJB4cb/Tn0NLI5oEtknKuKSrZqYFQ+B0+10Y5dplzSdlLdoNwOcPjnd6aoWtqsTLd5oACHnmsfH59AOUshq214OWeayFhR8iV3sACw0nzHebOVicvdn/V1Kd3Es52MUwIcqcFhjtqAPie9nEh9RcZb18Z6g71HF4zH+Tl2MTjyHuhWg6NShLwXlSPYydKHAeZ9Vcm4x9J9cOKfzVayVUO3KSqOhMhYGF/uObqg3n9msWEJcLgGR6eoJKmYyuBUtAZHMo80B9P3U2/qXlzdiMvj18c/6fuemjCX3qq1iir/8hyGAJptDQ77LhWDU00yOMuYwOlfiuqOO+7AunXrVHADADt27EAYhrjrrru8j3P06FFMTU0ZwQ0A/NZv/RY2btyICy64ADfffHNCaDYs0OSoSmUTeef4p5tWjf/PJyE+Acc3dvy7DHCofUClrSvQOe4cDI400mNdl83PoVNUpMGROXRiu7udHCTo8/DeKi6RMb/Jx1waHMfwOHVvM+iarCRbCyQ1OEmBbZbmwOUeTeCfrZajiirNB8e3isoe4DTb44r/T5e28sGhAKeHM6lvgLNlKmZwjtUahtCY7/LlZ58YM4X2Wd+HL3RgqtOmrnSVnA9scDXktSFVg2MR5MfjiXqWoomPYf5fVhXuOqqFsHJuA5Jso4vBUbpBmwaHfHAarUTJdqrIuMuVUjXbrGvGlRv5jVdKiTmsEwaHf5f88z15OA5wJINDDXGXK/rG4OzZswebN28236xcxoYNG7Bnzx6vYxw4cADveMc78JrXvMZ4/Prrr8dP/dRPYXJyEl/4whfwm7/5m5iZmcFv//ZvW49Tq9VQq2mKenp6Ouen8QftEEhknMZ8cCScjFMmr1IYoNXUi9vkWOy9M1fTDA4Q30TzrWYual0tvqLLrrMXVRRhphZ/VlkFoQSsvQ5wSvE2Zb6uF6ykD07yPbWTMWlw0hd3Q+9kmXCJwUmtorKUiacFC/xxV4pKNTpt5SwTp9SYzQcnK8Bx6B0AzWhNTVRwZE5rXaTGopfVGjxwTNfgVLBqrITZxSb2TC/gtE2rAehrNVWDI1nMbgMc9vnrrRaqYcnZi8pH3Mqv2yx2zNDgiGNyMb3J4LR6mqKKO8sHzAaB7od4bLuPaJbNFuBIzRzfgHCkBe2GBkd2j7eIjLO8snyhGro2tFfZqmpZedPEPkGhMj0EOk9RAWT0l2RwVlXLxnGXs8kf0AGD86Y3vckq8uX/Hnzwwa4HNj09jUsvvRRnnnkm3v72txt/e8tb3oIf//Efx7Oe9Sy88Y1vxBve8Ab82Z/9mfNYN9xwA9auXav+bd++vevxuaBExqKShOBa3CSt6tLgAPpmW1BuunGcSouwdJDtyAdH5KNlmot/Dp2iMj9T5LEL7QSSwQmD5OJp+8h0PmhRzzZT07/bOvHSQphm9BeGumRflk07m21mpKiMMvFe+eB0VSYenxvZbqMsUlO9bOpXZed8PGMh2LK2rcNpp6laPPUSusvEZRVVt0Jbfm3IZrH8MSDbBwdwFx/YwBmc1FYNRoAD4zz1ApzRlgHwboPBSd4bchNIh0pabpjH51BGf/WkBkc2GG60WplpbF/QUOg9y2FopIro++E6nE5Extz/i19bc4t6raiWdePT5dymAeggwPm93/s9PPDAA6n/Tj31VGzduhX79u0zXttoNHDo0KFM7cyxY8dwySWXYM2aNfj0pz+NSiW9T9GFF16IJ5980mBpOK677jocPXpU/XviiSfyfegcIHr7mKgkIbh2wlIYF6ZMXnRxUkBDjpdaZNxmcMpJwXIWZDdxlwaHVwIpHxxHFVXPU1QlM8Cx7V59GBw9ednfh4/btqNUTfa40V/ZPanS+2f1ospiDIwy8Ya/D46tmzj92osy8alx8z5VIuNyHzQ4Zb8UFQBsbaep9kzHAQ4X+NOumUNWUfWOwdGv1wGOS2SczZzw4WTtxNMYHJ7WaIqqLp9u2nlgVpSagf8uxuBI80ggeU54OxaOtKBd+eA0kykqFdjwFFUr+3vwAc0lvOpxsj1vB4G+Vyhwr5aTBpQ+0GniyJoCX1WNKz8pTbWcK6iADlJUmzZtwqZNmzKfd9FFF+HIkSO45557cP755wMA/uu//gutVgsXXnih83XT09O4+OKLUa1W8a//+q8YHx93Ppdw3333Yf369ahWq9a/V6tV5996DZp4idWQugNXN3EZ4JRSJi/Z14Qi/TnhICuZAx/QIkQ6AVeJbMiCrLqouFKfqYcCRQ7adZCo2sZe2HbbYwmRcXoAxm9+K4Njq6IqJRfb8UoJxxYaumxa9PuSsPUw4+BGgZ0wONx7xdeMkVelSZCuIMHgsFRp/P69uw74gp07wGHXaTkMEilcpcHJqCTMC1tjyYbB4HANTvwzNUXFCxFyVFG5RMa2Zpu9roTk14D0hTI0OI3sFBV3GudIS7uqbuL1VqJMnK4Lm8i42zmMhrKoGJxAdbqvlkM1B9FGpROBMcCYdUuvQkCvFaurZRydr/eUVR1F9E2D88xnPhOXXHIJrr76atx0002o1+u45pprcMUVV6gKqqeeegovfvGL8dGPfhQXXHABpqen8ZKXvARzc3P4+7//e0xPTyu9zKZNm1AqlfDZz34We/fuxXOf+1yMj4/j1ltvxR//8R/jf//v/92vj5ILtOjphoP21E4ibyyrqEL35CW76JJYTfa+IuagkyoqIJ6EGy6RsUqT2Sn2eHy9Eei5xkhBh61E2jYhu4z+XJM3f5zYMm6uNmnR4NgmjKpiMMzv1tlsM6NVAw82XE7ONpQt7+vdTVxpx5J/IxZJVobIgC6tk3hejHsa/QHJFJXsLSUXL9mqoaWC/O7Gb2ssaZgust+9fHD4HJHLByc5LoB8cMzx+PSTywNbRShdH1y/ZUtRyTS+0g45KlKzNDiycED6HlEDTqD3Pjjlkk5R8eCTxpcVtGe9j3QyJtBaQax/weB0gY997GO45ppr8OIXvxhhGOIXfuEX8N73vlf9vV6v46GHHsLcXCyAuvfee1WF1dOf/nTjWI8++ihOPvlkVCoV3Hjjjfjd3/1dRFGEpz/96Xj3u9+Nq6++up8fxRvjnYqMxURi7s7s6SHCpIj2EzvnPAwOm8QbzYjR8/Ygi0O2apA5816B3psYK98UVVkFOPH/8zQ0XGgHUydtmMSjB+JWDTQJjRspquRY6JqQZdPuZpvx466qI54uUk7GHhOi7X19S/l1SjI5aRKTKBkc6YPTWwYnf4qKvHD4xG8VGbd3uVFkltv2YpGXjSWNXlStbhic9LGl++DQe5q9qOL7P3sceWD21WszOJbA0V5FFf+UKSr3ZjH5/rxVA7U50a8zWbV6S5+Pbj++qqKi5sChThPx9DKNL2+jTfk+rZa9SpPWCmJylrsGp68BzoYNG5ymfgBw8sknG/nTF77whZnl3pdccolh8DdqoIlX+eCkiHM5ZE+gNAGhnGxWiR2sYnA60OBInYCLnrdN9rQgSJaq1wFOxSNFlTxnpn8M/+kT4BBbdOrGVTrAGaPJgu3ALMHWhlWxqRf5enANThRFiRRZPUeZODE4tveVSPXBybhGXKlVgGlwMkTGvZxMjRRVFoMjUlQJBkca/bGAqd5s9UyDA8TX7gJaTO/BvgurD477WGksrwRfRF0iY7p/1XgMBiP18N4w+vIJ40sOKQAGkvcrF0fbnmc7J3QebGXiMiXZaPZOZKx8cBiDQwEOD9aJdZ/oQGAM8AyBPUVFawWxR8u9iqroRdVj0MQ7TT44KU0qOWRPoDT6WS5GUm0vRca5qqiETsDV+NGVk643WyiF8U3kW4KcFypFVbd35gWSCwP3O6EJTO4IJfiw6b1O3bQKt7WLBCctGhwbg/OOl5+Fe3cexvknrY+fIxqGyu9Xn3P7omUze/QrE6fqrWRaJGsCd6VWAS0ITWpwAmNstnPTKXKJjClFJTQ41B1cnn9+vEYr8g4CfaA6ySsGR59Qnq7yqaLKJzL2dDJmpEavfXAA89qn321jt5eJm8EWL2/n0FVvyfdXGhyLk7FVZNxjHxztvB5gdTVZOaU0OB2UiAN6nE4NTjuwKVJUBToCTbw8UudQF2BCZEx/tzE46ekh6ZcgF5Y8/iNcJ1BvtpwmZ64bI28lSCdQVWSKwUlOBnJCDoNAUdYtFeD4MziUotq+YVLpcKxVVJZzffrWNTh96xr1fy5qbbQiyOG7Gpyq11vEz35Gf+biCviXAWvWKDlpLjqqqHRKrp2q6uFkmkeDQymqfcdqRsrJtpmQx2s0Wz0TGQP6XqxbysRNDQ4y39NlBmpDGoPDAwUeKzRbLd0EuEc3sY2Zts1P9jJxcyyZKaosDU6mD06rdxocMe9XGIPDHYwpRdWJBw7AU1Ra51Uth+qzag0OpaiWd4CzvBNwQ4CcbOWkzilEDi1kbE+6Kfn1RIBTlSmqzjU48etpIYyYJ0u2Bid+DZuw+2r0xwIcyw5QvmcpDFSfL5pksuh3foh55jlECyYtspxByyP2BexUPDehs8HWcDVXN3Hug9Py+4607if5twUXg9NHDQ7/vFkMzsbVYwiD+LMenKmpVFDFcq/JY9ebvWVw6D2lU7j83atVQ44qKltXewIPFCS71+tCAR7MKB8cy3lNczKmc+LywUljJVWrhrrNydhk1Xg38W6tLuRYyqUgVWTciQcOYFaWEVtEKVp+XJWiWuYanOX96YaAcdFPxJWiSvaiii9GbWKV3OnIYxBWyRSVYHDyTsy0IC02W6oLs1y83CmqfLvQTqBExnW3D04iRRUEhssnkK0R4m0xSNBcLYc4acMkAK05MZttZn/WajnE8e20yf1PHU38XTfbtB+LL1DzeVJUNh8cT5bNpxdVlsi4lz44YagbymYxOOVSiE1rYpuIPdMLCVZSXp+VUmi0LMkKOPNAMjgNS7DJf+9VFRUP2pJ6uvhnFEXJbuI9TlEZPjgpDte2MvFkq4ZAjZujqTQ4bgZn0eaDw7qIA/F31Otmm4RKGKpgw0hRVahMvNMUlb5PaR7ZMqUtUui4SmS8zDU4RYDTY8jOxs4u11JkLIIBHkAk9C9ZKSqlwaHqnc4CnCNzdTXOdaLxnSvwt5cg9/YmovMx3w46bIt77Kqt/x+Gge4DRikqjxJpmpg4U/J7L/kRvOp5J+NFp8d+UKUwUJOUzQfHNrbnnnocAODOHx5M/D2zTNwifvbqJm5hcHyrqEIRHHK4RMY0fj2pdjZpu0AdxX1KapUXDmvoWHYwS5VSoPVKbJHriwaHzQMNy+YgvZu4f4qKz0u29C2QZHC4/qhnAY7NB8cmMrakqJytGsRT/XxwkimqRDfxPvjgEMqlAD+6bQphAPzotin1OLE5E5XuGBzeONXO4BQanAIdQAY4ifRSZt44/r/J4KSnh6QpFL3nZHssPrt72+v3H4vLascrYeJzuVNUlvRHzxmc+POkORkD8UTIxduyAad/KW6kmJLxSgnPPnkDnn3yBuN5//MnT8WjB+ewfcOE12e46NTj8OlvPmUPcBzCboLB4BCz5LHIq15UHVRRpXVAd7VqoPHveOYWvOYnT8XPnrMtc4x5cOGpx+G+J47g5I2rMp8bT/JHsXd6ASeujxk4F4NTLoXx563H5ypLE5UHFSH0rrNF1saspTM45pjTkMrgBPo9Xd3E+yEyLuUUGctAk4+bI93JWIuM3b2o9M9e++AQKqUQ525fh2++5SWGf5ROUXW2GaC34Z3St7IAZ7USGcc/e+lNNYooApweY7xsZ1MILqo/0WyTT16OnDkhyeDEf//Vi56GWqOJS885Ps9HUAHD/pk4wFk/OZZ4jmuy54tnr0tM1fgoRZUhsC0FAZrQ51U1qZTNNtMCnBBAU6eCXFqXa19yeq7PQAzOfU8cwfxi00izNLPKxG0pqhwMDi3YUeS/gKU1iSVGaM243Y9pzXgFv/+yZ2aOLy/e/6vno96MvAJ4qqTaM80YHAeDUCkFrF9YlHh+N5Cl+g3hfdNqRQjDgFX4ud8zTzfxNAZHMyFRgsFpiY1Xt7D12OM6kDCIz0NPfHCsKSrtg1MTaTASFWsmp9UzHxz5PVLwv1Yw4y9+5hZ89ZEDeNEZ2d0CbNBNPfV1Rdc+oNeKolVDgY6QEBk72ixIqj/RbDNl8kora43/Hk8YZ52wFu+54lm5xs+PTwzOOluA47jjDZFij/P36r1lFZVj9uVvWwqTi3RWmTigX7OQw1DPB9s3TGDb2nHsOrqAex4/jOc/Y6P6W6OZvaBSJRd9hk40OHxhyOwmHupFkIPvgicqJYyVQ1ZB2N/JMwgCL80TwLxwjtYSGpyEADQMDbZLWjh0A9nwdFGYsTVaEcbCwIvBMbuJp4/NZHDEcRw+OIBOofUqzWz44JDImD22bd0Enjw8n+pkrKuo0B63Y7NoC3CYRnK27VVG4EUV9P9eMViJFJWDOfnpM7fgp8/c0vH7yK7lALCZMTgU2Jz/tPVYO1Ex5p3liOXNTw0BCZGxTC8F9oUirdlmGoMzVgoTi1u35bgyRbVuItns1IfB6TW9TaDd9VydnIwdTAcPEoMgwZ75eY2Yf/OpVvJBmg5Hi4zd7yXPv8+46FqkSZwvZlkaA/q77DfGJ9JqOTTGMUolqNrNWDM4ruoug8HpUxWVYnCaMk0S/9+HXczD4PCu9q4UVVOkqACdQutZispSRcWv8+3t9KG9F5UZ9AUZDI7t+uObIWqIzF/HGbV6s3dVZLYqqn6AzgnXMG1eo0XGxOCctmk1vvmWn8ZvvcjsGLDcUAQ4PUaSTZEMTvxTMjjJZpspDI6onpABTh7fGxtUiqod4KxflQxwpIiXYLQB6JsGRzA4rhQVe98wDBIVbD4VRPJvvQpwALgDnGZ21U5aabMLY2rRJgZHf1dZEzhVvMjrtsaaB5ZLplar2+uwl+ApqqQPjrnocvO/RquVKfrOA5WiIg1OIsDJqw+L4aOloGvEJTK2tQ6h8fUjRaUYHPYYadhsImPpg+MyTU1rIMvvk2kR4DQSKbpWz6rI5NfTy4pCDlpu6L4cK4XYuDpm4HkxRDym0dmA9AtFiqrHSFRRORicKIJh0y89Y7gZXNIskFPTYSJF022HWJqED7Q1OGsnkikqGmtDTC42oWSv72XZbNM1WZgpqiCRHvSpVJHBmfx+u8FFp8UBzreePIK5xYaqcNAd3N0nTgY/eVJUdRHgAR5Oxg7mcUFok/gE2ktjv25BKaq9R9M1OHSOdIqqtxqciggyZb8gCjJ8gm+6PILAb7GqVkIcqyWvaXoP2V07Hl+r/ZxeMTjJc80DxzQGR54Tlw9OWu+wOK0Zp1GpXyCh2TK7qTebvfPBka/vF7tJ3y3dl+VSgJOPW4XnnroBJ22Y7HlF66ijCHB6DLmTTmuzwG36Zd441ejPKCEPEwt8t+ZNCQZnMsngAPFnkSkLm4lcz8vEqRdVig8OkExR8SaVgF+lSr9SVABw4voJnLBuAk8dmceXv38Al5y1FQCj2FMCVTl55wpw2ouWkaLKTHHEP5MpKlObNOoMzrFaA9NtbydbFZX07Gk0o95qcEKTLZHO0FIflfaemgHxO88ksHW1arCB7ufe+eCEid/5dX5im8Hx0+DozaLxvAzzyqoKcCwMjmh+qtmgjA+WgUQVVQ/nEQ6VoqKmnqUQ5VKIT7zmor6836hjdGagZYIgCAwdjssHBxBtDcRNmdZsk08IFWuKqru7kSbMAylVVLZxAXYvj147GRO7Rcd3BR3yHEoGx6dSRX7EXomM6X1f0PbS+e1PfBMfv2snokgLHdMWtwSD41NF1f5eSUzK19asBYwvTJzFUd3MLQzOKGlwVlfLqspr56E5AHbXcKnL4f3YenEdKy8ics1tOBgcD/+Z0DJXpIEEtq5mmzYQq9Ore9jWTbxS0p9j6xSlqGwaHHO8iv1O6BnjnyXHPEjX6LGayeA0mq2EL5EPy+sDOZR+lWeXVIDj7tO3klAwOH3AeKWkdiBp7AunVuUuMUxhcMIMBqdXKSrlUOticNg41k5UcHS+riZuoH9VVDL14WZw9O8lQ4MTPzZsBgcA3njJGdh7dAG3PbgPv//p72D30XlVJp4WIEinax+2hAe+vDqIjpEG/l03owghzImUzovJ4IzW5Hri+kk8sHsajx2MAxzF4FjN5zSDozQ4Pfg8slS/LhicPPow3cvJb1zjTgYn+dwgiAOKRSUy9nqLTNiqqOhcb1w9pkSwaT44oZojzccJWS1iiMkiBod670kGp9GKWJl4dydgUCJjVUVV1wzOSsbK/vR9wkQKTS9TVPJ3G22e1uiyUgoSu/duxZByzC4Ghy+y5HRsM/rr9WZFfj5XeoZPSqFRReVfqRIax+g9K7F2ooIPXPlsXPvTPwIA+NhdO9Uu0ldk7MPeAOZkx/1dgOwFjJ9y/jqVoionOyOPWp+bE9fH7MDjB2cBsBSJNUVFqaRWqvV/Xsg0oRQZ15tmisrn2vQdl4vBkYt3EOhrqtcaHKMXlWrlEf/cvGZcBcgLlh5t+pzQz4wUleO8KAanHeBQUMU9nQj0+bv96l0+OL0GfbekYRq1TcagMVoz0DIB38UmfHA4g8PuYVeAU2pXdXBIzUCvy8Tl69M0OATyymkYRn/xz96XifsxOLISLVkmnj0+firGK6W+iPTCMMBrfvJUlMIAh2YX8dTh+XjMnmXi1YrfbcwXwtiGXjNYWZ+LBytmgNM0xjDaDA4FOO0UVcm81wB9bamS+pbuz9SL61gxQ6odgL2aMg+76Kt1Gne0bpHvUWb3iq6i6n2KSrfyiBMJW9eOq/R+KoMjfHCcImPH90XzJZ1rLu6X6a7FHpXJJ89xf5Zeep+CwYmxsj99n2B27k1hcJijrlxs03ZnSZGxpD+7ZXDM48k+VGoc7bGNlUPVFoLnsHspzrS9L8Htg8N+Z5M2DZHmxfQycRZI9EkYCMTXzGmb4pYD39s9DSA9UOXnwJfBiYPl+HezkWD292MwOGxBWSoaHACqRcOuo+0AUqRIAH3v8J5RDQ9GzRecGQJsDI6eE4CsCj/kGhcFoUkfnOQGSgc42YFWHpgi4/igP33mFvzmC0/D7+74Ec3g1JsJAz+tS4r/7/LByarelPfxZJUYnFbi+yCGstc+OP0SGWsNTjvAGTEWddBY2Z++T+Ai44QPDvuvZBIAzty0X28LcMSOU94s3e6cZYBkczIG9M20plrW1Dujln07VXc7PlfgwYOTUmBzMvZZRHiA09tmkRJnHh833aNdo6/I2LfXWBAErBdSK1G5lwYeVD95aB7Pu+E2/OV//iCRojLZy9GaXojBoXWzLO41/phRRdVDDQ7dJ+RgLFMiSZft7GszrwbH5YOjxsicnAn9dDJeM17BGy45A2dum1JjbEXJcyPPiVODo9hw+/Un72NKUUkfHEDfi11rcKQPTp+Cf7qXlcjY0+l7uWK0ZqBlAt6PSrILQRAkbkybo2xahYTRz8Xmg9Nl1C6PZ3My5uNYPV5mvXu0vqVfVVTJ3kEOkTF72J6iytYI8bfyTQV1ijNZV2EgPUAIOwhwAKYBaegqKp/vh19zX3vkAHYdXcC/3PeUSiWMqxSVO7gfNijAIdACyBdCOpfKB4f1I+rFdTyueiHF5006GddVmXg2cxIoltezTFwxOOI44v9hkAwO+pGism3e+D0me0XJc6I1OK4Axz4GeR9Ptjt3x4Jy8/tYbPYmRZXwwelT8K9SVMqAc2Uv8Sv70/cJvB+V7QJzLbSAXmz17sz9eiAORhI+ON0yOOz4MTuTHkCsrpYNYzQgX5+j3OMTny+t2SYhDJJOxj4aIf63fqaoAODM49ca/0/1QDHG5c8sWRduj++HB+a7jiwAAJ44PKfMFrXImF/7oxbgTBr/txr92RicHqaoaG5YaJ83ul8oMMyjDysFyfGnQTE4GSmqcinJ4PSuikrfQ/ZmmKFifKUXjkzbZTbbTPHB4dApKovImNjULj9/QoMzsCqq0boHB40iwOkDTB+c5AUmUyWcFpWVHbZJwCwTDwx2wvWeecAnoXWWNg0EmkBWVxmDYzGR67Uw15vBcYmMIzOwTPXBGWSKSjA4Wc02CXkYHNvC7bt40XvumY41LPVmpCqSlAaHMzgjtntcO1ExOp6XLCJjqcGpMw1OL1o1UIAztxhrTIixocpL0oBEHsGnreIyDTQvpVlX0PGydDqdwgwmk+czCHQ7ASk0lkEfDclVJu5Kvcr7ZZUSGbeSIuMeMTgJDU7fRMbx+yw00k1QVwpW9qfvE7LcXLXYNZmiouuebk7bIle2TMg8rdS9k7E+vqtEnI9xzbjW4DQsrFTPGRzx+XxbNciO2LLs1Aaziqq/t8uGVWM4fq3u/OvbbLOaYxLjC7fsYJ8FmjyJwQGA7++dicdQIZHx6FZRASaLY2NwZBVVoxWp66UXn4cCmfl6E82WTuPS44rBUS7g7mPR9+G7iF16zjace+Ja/PSZW43H5XvEfcX6FOBYNDgSNH86U1Q0R7pExhmMm9yoTPAyceFL1DMNzoB8cOh9iMEZJTfxYWBlf/o+wayiSl7IiRSVRYNjczQmGPoL4dsB9CJFpS+LtQ79Tfw8zeDITtVmn6OuhpOAZKh8nIxjH5z4d8ng+Keo+svgAFpoDPgzOHm0QXzhztslm8azu12FBAA/2NcOcJTImLOXoze9cB0OnQvZ2y3+2Q7YGYPTi0V+glUJceZ2nISu1OndJ0Ul0tlZuOCUDfiXa56P87avMx5Pq6JSz+nRV8mZCxfTrBkcM9iQDA6N0aXBcTE4iRQVO/eybL9XRodJIXd/U1TEPI2N4CZjkBi9GWgZwBQZezA4FraDbk776/XvNEmM9dBgzZvBad+0q6rlRPmraSLX25tMpgp8U1QqNZgoxXW/1yA1OICZpkoLVDspEwf0dcJ9cHx3p3RN7mv3KAN0Ow9dJq6v/V4zd72AGeAkGRzZH6nXzTaJLZivN43mljRnUNCT59rsNiWd0IeEgcXtuDffZZqBKYGXinNIDU5Wispbg9NOUTVbSZFxrQ8+OJVS0tusV1ApKmq2OWJp4kFjZX/6PmFiLL2SRLYMmK3FjpoTzEgujcGx+XbwRb77MnEe4KRocFgVVbJTtX5erwMcX5GxdDKm85arFxU7dL+rqACTwfHRXwA5q6iYGDxPFRV/T+kcC/BWDWyHPoKT63aWorJpcJIpqlZupisNKkW12DTYggnFIsgqqrRr0z1H5IEXg9Oje9hgmh3XBwV7ksFpirRdpsjYW4OjPbzq4mBKg9PlpRxaguh+gJYB5YMzgE3ZKGNlf/o+YbycXkWlmj62b6bp+TjAmZpgAsgUDY7sRQWYN223C4uRokphcJSPBUtRqQk6R6fq/OMzj+fawXJioxTqSaoTt1hgQCkqzuCkfI+lDpklCoh5L6q8KSobqAnp6GtwbAyObcNAjGSkhL890eAwkXGDtQFQrRHy+ODk1OC4kNTghKn+Xd3AqKJynE+Xm3HSDJUed6SonAyOS4PTUq7VhF5pcPjL+3lfaA1OW2Q8gizqIFEEOH0ALxO3Lb4ltfNoBzgLcVfbqXHNlqTtzmwpql4yODxYSmNw6GZazVNUttL3Ht9jUjiXt4qqJRictN0pn9j6LTIGYoZhddu63jtFlauKir4nbvTn99q082RjcEYzRcUYHJUO1n9XDI4KBCMcml0E4Da8zAOuwSF2oFLSAUUzhw9O3ioqF4IgsAjyzYuiHykqpwhY9aPy88FpCdaFnue6f+SGYIJpcGSZeD80OP3Upmmjv6JVA1AEOH1B1bOKSjM4cYDDBb1pHhclQ6jXhxQVe880DY4SGY9XDGYAAOvC2/8ycR8nY+6Dk9Q5pO2S+fv0n8EJwwCvfeFp+PGnH2ekqxLj6kWKKqeBHX9PaZpn0+CM4uR6QgaDQ+wn7XwbrRb2tzVHm1ZXu37/cVZFRSmqCvOd0a0a4uf7Ndvs/jzz9ymXgmQpeY+CVb7hy9bgpPvguFo1ZDI4LAgfK4fqO481OPYUVbcmj1KD0y/IVg2jyKIOEqM3Ay0DjBvpIkuKSVTzKAaHBTi0aNkWL5PB6UOKir3B2hQGZ9u6eLE4ZeMqvSCICbrXLsZAHh8c/XspDNTuJora/b9ylOICgxEZA8Bvvejp+NivP9eoxpMwRcY5jP5sPjg5y8QB4KQNk9gypRd8CuqJwQmC0WRw1k5UMNX2wtFOxubiHv/UDA4FOJunug9wqGJnfrGpNgOxl5VeZAHWdynlkqNh90L8LO8V+d316ja29aKSoPkzWSYe/6TrNStF5dTgsPmiWgrV8+JWDfbu7r0sE++nBkd+5FHcZAwSK/vT9wmGk7HlAqMbjLwKlAaHmZBddNpx+Llzt+Hqnzg18Xqz2WbQPqa+6X0XLBd8q6hu+Pmz8bnXPR8/dtI6vXC2zCqqXguMgeSuxCWkM7qJB+autNlKNji1YRgBjg86LRPnKapWzu+In/ctU+N42oZVegyCwRlFgTGB0lRaUJxMH9Bnnak1cKxdBLBpTfcBDq+iIramzBrmapFx/HyfPmm92KUHxgKcZHD6YfSXl8FxpqjyOhmzjcNYmacH01JU3acBCf1kcOTcXzgZF+g5xjOElpTLPzIX5/ZtDM7qahnv/eVn4SU/ujXx+rQUVS+MnfgOI02Ds6paxlknrEUQaGMwmiDy6js6HR/gLpPmk1LIGBwgZs98moGaVVT9T1H5gk/eecrEK4yZaOVk2fjzNq+p4qTjtJ5F+uCMIntDoPSaTcOiNG3tL373kdjzp1oOsaZaRrfQjsUR5pkQlLMIQL4qqkEwOD3rReVhSJpXZJzwwaHr2qXxYRuValkzOPWmW2TcvQZH/95P8z15LxcMToGewxAZW25iChqOtLU3pMHhIuM0pKWoejHZ8aBs3YSfsLIiqqh8NASdIlEm7uNkHJjeHq3W6PWiygNegZJPg6O/pyxLewn+vM1T43jaBhbgqGab8bU/yrn/C089DgBw+pY1ANJbNexqBzibp6o90ZLxtOOx9samUtb95BpNGeC4j6V0ej1YxFyCfELvUlT+DE5NBjiCcdQanMj6PHeAIxgclh6UDI7ywelhKX4/e7TJca50J+PutyQFEsiqJCEG57BicJJl4mkIbTvOUu8CnApLd/HePWmQPji97MCceC/J4Pg4GYtJu9Fq+TE4PMAZUQYnT+BVYd+TXgj8Xsuvrc1rqmit1sEvjeGUjatwwckbEn21Rgmvfv4p+PlnnYD1q+LxGynf0Awa9vZQYAzE5ykMYjbiWPu+L6cwOD590nrD4PDAw9Zss1cMTvZCr5yMG/YUVZYPDqXJfXpRxQEO0+A4RMbdfvxOiwLyQn5PhZNxH3Ho0CG84hWvwNTUFNatW4dXv/rVmJmZSX3NC1/4wnbZov73G7/xG8Zzdu7ciUsvvRSTk5PYvHkzXv/616PRaPTzo+SCWUliCXDaqagjc50xODbNwJhIVXUDOsbaiYr3zkWLV80y134YdiY0OI6b2Jy0zUmm1fItE9e/jxSDwzU4nfjgNFqZWgUJfp42r6ni5OO4BqetvSmF+H+/cRHe/nM/6j2mYYCCG6Cdvmx/NJXyFZWOm9eMoxcIgkClqSg1XelQg7OhzQQft7r78nXDp8WWouqZBic+v2laQZeTsa8PTpaBpZmiKik2tGkRGfdOg6N/7yeDI6f/gsHpI17xildg9+7duPXWW1Gv13HVVVfhNa95DT7+8Y+nvu7qq6/G9ddfr/4/Oamp8GaziUsvvRRbt27F1772NezevRtXXnklKpUK/viP/7hvnyUPskTGNLkennVrcNKQZvTXi9QATUJpAmMJWUWVRRN3Az5BpFXrpKWouAbHx2sEGN0AJ5cPDit/zpui4u+5ZWoc65g+a5TOTScohyEWmy2WojI/Ty8ExoSJsRJmF5uquKAiKnkAvxTV5c85CetXjeGFp2/uekwyRSVZ0l4zOGmVRNmtGtpjYlWRHNrA0n78agqDMwgfnEKDMzj0LcB54IEHcMstt+DrX/86nv3sZwMA3ve+9+FlL3sZ3vWud2Hbtm3O105OTmLr1qS4FgC+8IUv4Hvf+x7+8z//E1u2bMF5552Hd7zjHXjjG9+It7/97Rgb63430y2ymm2uS2hwqIoqP4NTVimq7InDF1T+e/LGVRnPZGNyOLH2RYPDPuNYKXTS+DJFZYiMW5GXD86gnYx9YYiMczE47RRVk6WoOvDB2TxVxeRYGZvWVLH/WM1gRJYiSmEANHnK1zwnvQxwxgWDE/vOmFWISh+WsrJOjJXw8vNO6MmYDBGsTYPTo3WS5q60jY+72aZ5v7qM/nSZuH3QRhUVCy6tTsZLzAdHzoVFFVWfcMcdd2DdunUquAGAHTt2IAxD3HXXXamv/djHPoaNGzfirLPOwnXXXYe5uTnjuGeffTa2bNmiHrv44osxPT2N7373u9bj1Wo1TE9PG//6iYkKT1HZRMb2Kqq0zt0ctkaLsgtyNzhv+zp8/OoL8c5fOMf7Na5mm92K82zgLFVaBZGxKw3MiZWXiafqHHiKagBOxr7gIuM8gRf3wckbhPLWHNSg8G9e8WP4yyvOwwnrJtJeOvKQrVHkRmFzDwMc8sI5xlJUZcngeHg09RJJBqc/KSofrWBmiirhgwPxvHQGh88ZXGRsczLW/a+6DXC49qiPDI4UGY+wXcMg0DcGZ8+ePdi82aROy+UyNmzYgD179jhf9yu/8it42tOehm3btuHb3/423vjGN+Khhx7Cpz71KXVcHtwAUP93HfeGG27AH/zBH3TzcXKBi4ytZeLtQOaw1OB0JDKWKaruL+ggCPC80zbmeg2fJAA2yfTZ6C+NvZClr/Sz2YrQjCJWReV+Lz4xjY8qg5OnmzjzK9IpqnzvuYkZ3j375A14tusFSwh0fVQcqd6epqiIwVEpqsAIPIH+MqA2SB8cuTHpdasGVx8qgFVROUTGSR8cMyihc+jjZFxlPjg2oz/CUvHBkQHOSmdwcgc4b3rTm/DOd74z9TkPPPBAxwN6zWteo34/++yzcfzxx+PFL34xHnnkEZx22mkdHfO6667Dtddeq/4/PT2N7du3dzzGLIyXS9i2dhzz9aY17cR9cBrNFmYX451KNymqMY+dUT+hq6j8e+l0/F4WHyAbpA8OwPqAtSI/rxGjimp0dkO96SaeTydFp31LjwS3owQ6L6pVg1gYeiUyBpIpKs7g5GkE20uYm4FkFVWv4qztGyaxfrKCs05Y63yOywdH2jq4fHA0g+MIcBwaHFurBkIvfXD6qYuR31OhwcmJ3/u938OrXvWq1Oeceuqp2Lp1K/bt22c83mg0cOjQIae+xoYLL7wQAPDwww/jtNNOw9atW3H33Xcbz9m7dy8AOI9brVZRrfZuB5aFMAzwud/+CTRbkXXxWb9KV1FRiTjgX5IdWnbvtp5Ug8SY2IEOKkVVKfsFJ7YUlRQtWo/Bjf5GSEjbbRVVo9nK7TZNgWUvWhaMGqQrsKT2ey0yBniZeMi0UaTB6R8DaoP0aemX0d/qahlfe9OLU4NyYkplmbhMF9HPZmRPK/n64GiBdyuRoiL01AenEBkPDLkDnE2bNmHTpk2Zz7voootw5MgR3HPPPTj//PMBAP/1X/+FVqulghYf3HfffQCA448/Xh33j/7oj7Bv3z6VArv11lsxNTWFM888M+en6R82pIguSYPTaEXKSGzVWMn7wjedV80U1bAcZOUE3V+RsacGJ+S/m7u+uIoq/j0tv87/NlIi406rqNj31MzY6UrQOeylHmVUoBgcYfQHxLviXpRiE0iDQ6npsXJgYXDovQcV4OjfS5Zmm728j3mVqQ1Ooz/fVg0ZweGYKBPnRn+uFFW3H99gcPpaJi40OCs8RdW38O6Zz3wmLrnkElx99dW4++678dWvfhXXXHMNrrjiClVB9dRTT+GMM85QjMwjjzyCd7zjHbjnnnvw2GOP4V//9V9x5ZVX4id/8idxzjmx4PUlL3kJzjzzTPzqr/4qvvWtb+Hzn/883vzmN+O3fuu3BsrSdIPxSkntunceigXUviXigL05YC9Fxp1AV4EIDU5fGBy/FFWQweD4pKg6NdTrNzpPUelqt9xVVO2nbZlafimqUAU45v0EABsmx3q6E05UUYWh0dwT0Iv0sDQ4sgJpkPumrls1ZDI4pgZHt2qI1PwlT3u330NombP7ARkQ59HnLUf09dN/7GMfwxlnnIEXv/jFeNnLXobnP//5eP/736/+Xq/X8dBDD6kqqbGxMfznf/4nXvKSl+CMM87A7/3e7+EXfuEX8NnPfla9plQq4XOf+xxKpRIuuugivPKVr8SVV15p+OYsBRCL8/jBdoDjqb8B7AxOL8vEO0HCqKyPVSBGFVXK4m402wzpp96t6QnT/V4jW0XVIbNUZt9TXh+cC045DuOVEBeeclyOkS4NyOopzmD0Mj0FJEXG5RJncMwU1cA0OOzSLoVBgsEaFJME6OtZlom7fHAk6ZKVHk/X4MQHmxCu5UvHB8f8/0pncPpq9Ldhw4ZUU7+TTz7ZiL63b9+OL33pS5nHfdrTnoZ///d/78kYh4V1kxXsmV7AzkOzAPwrqAB7BU21h0Z/ncC1A+17FVWqyJj9rqqo4v8bGpyU2cuoohqlVg0danB0IOoX4HG89oWn4dd/4pRlmdeXGhz+GfsV4FCzzTEmMlY+Uu1Fux8aNhvSNDiDYpEIisFp2FNUWoNjPq6el8FMBkGAsXKIxUYrPveqiqqlNIQTlRLmFvX7d83gsJf3k1VJVlEtv3s1D1b2px8iiMF57EB3DA7dnLT4Dksnon08TKOy/jgZ56+iUikqVlrq1018eaWoVDfxVv4qKv765YaN7V5TFMzwjUKvA5xJoUEplwKU2ue1KWwWhuODY1ZRDVrWl+WDQ9drpgYnzUyQbQyNZpvtg8nNTC/LxPtZ6VoY/Zkomm0OCVRJRRocX5M/wJ6i+qkzNuOy87bh8uec1MNR+kN2Q+6VQZYN3j44IZ+0A+OxRsvP6G5QO6+8sJk9+qCbKqrljD//pXPxg30z+NFtcfkyD6J7HeCMiwCnUgqNFhrAMHxw9O+lUFgsDPj6qCoNTgtRFCW6hmuRcfwzqcGJf6YGOJUQx2qyikqnqMZFOnrJpKgKBsdAEeAMCWsnYgZn99G4iiqPyDi0LG7Hra7iPVc8q4cjzIdkFVW+TtV5QM0RW1EWg8N/J40F5e39elHR66pld0uIYcBIUeXQBtFCWm9GfRWCLzWcuH4SJ67XPe/4zreXHjhAUt9h60U1cA1OKoMz6BSVPj+1Rkv9P+mDYwY+BNIxpbeDiI/JNThRpOevBIPTdZm4/r2vRn/iu1rpTsYr+9MPEevb/ahopzbl6YED2I3+ho2Ko4qqX5Mj3bhjvj44gsGJNTjJ50kELMAZJXTqZKy1Uvm7ia8klPuowZEpqkopSDCg/b5/JPgCXA4Dw2l44CmqshngEFw+OIkUlQczScwv7yYOaGGzDEKXSqsGGYilzY8rAaM1a68gyE7ducrEDdvv0fgKy0y8CmiauG8BjnBwtoHf7NLJuGlocNxjpMOPksAY4DqEfJR3hQkq81ZRrSRwr5JNq3ucohLXUjk0zeaAwaeo0npRDfr6qJQCFVRxLxxXiiohMm7/N03rQhuWsXKoNmeAFn5Lr55uTwH/GtPMSbuFHGfB4BQYCqijOCGPyNjsRTUai9MgWzUArHeQZ4pK+uC0Wn5jVCmqESoRB/TnyCsqVz44rFVDEd8kwYPGXjs3J1NUgVHdBgy+VUOaD86gU1RBEDChsWZwJONK90DkYHB8OpZzDU78fs3233srMuavr/Qx6EhocEaMeR40VvanHyLWJRiczlJUo8Lg0E0bRW2PmQ4qdHK9n3BwtkGWvvLHGq2Wp8i4s0Ci36DzmqeCCjB9cGRVSgENfl57XiZuERmXXCnegZWJ698TDM4QLg8V4DTcDI4UHxN82sSQ3uqEdRPGZ1Upqh4zOKbIuJ8MjghwVvi9XYiMh4T1PWNwRiPA4TdtnS2e/RLm+jE4SZqdxtmKtA9O2gi5yHiU0GmAo7uJR0UVVQpWV8v4zReehnIpzHVv+iBZJm56sQDaB2dwrRokg5PUrw0S4+Wkm7EM+niK6s4fHsQn7t6Jt/zMmV4eXDf8wtm4+idPxbknrkUQBAiCeHNWawdU4+K+6vYe4dNUP6uoEgHOiKwPw0IR4AwJSQanUw3OaCxO/EYy+hz1aYKueCzwpq7AfKzZ8jNToz+NXIDTYeClq92KKqosvOGSM/pyXKnBGWNOxsNKURkMTslM2wyjetCWokq2atAi4w98+Ye47cF9OP/kDYzBcR9/aryC87avU/8vhwHqzQg1J4PT3TkIjBRV/86njGdGpQhlWCgCnCGhGwZHua6GwciULnOat9HU7Ei/Fk/aBXk7Gaf0ovIx+ls2KSpKhRQ+OEOD1OCUS9psTqeo4r8NrxfVcFNUdF0/eXgON/zHA3jR6ZuTrRqIwWlFONJuXLr36II6Rh6Bbakd4Cy6WjV0ub8ZlA9OweCYKAKcIUEa+3Vi9DdKFy+fEOutFivp7M/7ye7PNtiM/lQVlbfRX/w3afw1bKgAJ+c1oKuootzdxAv0BjYNDn0vdN8M3gdH/y41OMOwESAG533/9TAePTCLvUcX8PxnbARgKxOPVGf2PdM6wMkz7koYYgGaLar22Ml4UD44RYBjYmV/+iGiXAqxhnnfrM7hgyP75owCgsCsBPGpZOgGqkzcN0UlGRymwUmvoop/jiqDkz9FFT9/sdHquxC8gB2TFfNer5Q0YyKNMkdBgzOcFFV8nT56IO7Vd3B2MWE9wVNU1Jl9D2Nw8rAuJTGX9tMHp59Bh/m9Ffd2weAMEesnx3BsoYE11XKuC/GkDZM4deMqnHXC2j6OLj/KYYh6s9lOUcWP9Ytip6qTtN2QkaIKzQDHdDLOZnBGrUycAuI82i3A7BnWVELWng6tQAbGx8xrqRyGatFrDilFlfTBYWXiQ7j0pU6p1mhhthZ3X7f54BxbiP9mMDg55lTpmTPR41YN/GvsZy+qUSxAGSaKAGeIWD9Zwc5D+Rep8UoJt/3eC0ZGf0MolwKg3k5R9dmJtdIhg8OdjH0qvUZVZPzjp23E77/sDDz/6ZtyvY475rb6LAQvYMdYKVStRgDJ4Jgi40GtUeYCbIqMh5KisjCmB2ZqAJIMzmKjpTp/cw1OngBHPrfXzTYHxuAMSMy8VFAEOEMEVVKtyZGeIoxacAPYF89+G/35OhlrDU78/2YUgdwz0sa4aSruQ7R17UTng+0DxsohXvOTp+V+XUVVUbWKKqohIQgCTFRKmG0vyqYGJ6bV+s2ASsiKw2H2ogJ0iuqcE9fiwLEadh1dUAEODYd+TrfZGwA4VtO/5wnMpCA5GeB4H8qKwfng6N9XuskfUGhwhgpyM87L4IwqyiFbPPtt9KdSVJ5VVCpFpVMBPimqX3r2ifjQVc/Bb7zg1G6HPBJQQWjhgzNUTIzpTY1h9KdanQxYg8Nuo1IYGpqUYVweLz37eJy6cRXe9rNnYsPqeCN4YGaxPT6TwTk6t2g9xmgxOPr3fjI4fFO30ts0AAWDM1RQP6peG4kNC3zx7LeGgIJDmvxssIuM4//HzTazWaZquYQXnb65y9GODnjPsILBGR4mmA6nzH1wEs1qBzOehMjY0qh2kLj4R7fi4h/dCgDYsCp2kp5RGhwzwKEScY4gyBccSlal1z44g2qvw7+rsREqQhkWigBniNiwKl6c85SIjzJ4GwAfu/Ru8JafORMv+dGt+Imnb3Q+x/DyCM3HWlE0cLfYUYDqRcVK+YsAZ/DgVTqVUpBwMh5uikq2ahju9bFBeIZJkTHpbzjy6oaSIuM++uD0s5s411IVIuMiwBkmfu7cbfjWE0fwKxduH/ZQegKdouIC1v681/YNk9i+YTL1OXyOK4ldn2+KarmBdo9RBOydNjUNBQYHM8CxGf0NzwdnFMrEOYjBIdB40jZPeYP2UkKDY/6/+zJx/fugelGNisv9MFEEOEPEyRtX4YOves6wh9Ez6BRVaySCB7kr5T9No7+BD21o4Lu6L31/PwDg3BPXDWk0Kxc8BVIOQ7U5UM1qh+iDUwoDYxEeNhFwnEhDyxSVDXkDHMng9FqDY7RqGJAPTlEmXoiMC/QQZcPoL35sUN2QbUjzwTE1OCsnwpET+dt/9kz8eEqar0B/wBmcsbIZUPh2uu8ljFYNpcBgNIZ9f1AqnyBTVDbkTVHJgEjaQnRfRaV/76sPzoACqaWC4gwU6BmUvqPZGjjFboNVZEwpqsivF9Vyw1gpxFTbluAPLzsLr/rxU4Y8opWJJIOjp2Ley204rRpCYxEefooqP4OTd2NVNpiPIKFfWSo+OIMqR18qKFJUBXoG3udoFNoApKWoWgOo9BpFhGGAj1/9XNQaTZz/tA3DHs6KBU+BVMqhYHDYtTmg+yetVcOw18njRIAjfXAI29aOY1fb6C8vS8LPfzkMEyZ53U4Rw2jVUDA4RYBToIcwGZz4sWHu/owUVcLJmAk5V9g8MGotPlYiJhmDUxFVS43m4DVspg+O7CY+WgyO9MEhnLZ5tQpw8jM4omxfBAfdujkbTtF9LRPXvxci4yJFVaCHMDQ4I9AGwNiFUoNSFeC0Bl6KW6AAgWtwyqUQQRAMVQAfpDA4w74/fFNUp21arX7vRoMjPz/QPZNmMDh93FENSsy8VFCcgQI9A6+iGrSGwIbASFHFP1WZeDT4SpUCBQjSBwfQi2ydpXhHwgdnyKvE1Hgl0SUbSM4tp21apX7vpoqqzFpnELoWGbNz2FcGZ0B+O0sFxRko0DNwH5x+G/35gE/a9HvJlqIq4psCA8b4mOmDA+jmiM3m4AXwZpVPOFIMThgGyvUd0OORG5MTN0yqwCTv2m5oV8IgERx0uwkqDUWDU0xsRYBToGfQzTa1Bme4KSr+uxAZRytTZFxgNDBZSQY4msEZggaHMzglc4EfhfuDC411isp8ztqJCjatjk0B87IXPOgol8KESLm3Pjj9O598mEWKqghwCvQQRhVVNHwGJ7AwOPSTl+IOf/ousNJAZeJBoAMbWpBij6b4eYPzwdG/lwKhwRkBinODEeC0f4pxTY2XsWlN1XiOLwwNTilAGAaiSCHf8STo9aUw6GtKXH6OlY4iwCnQM1DlgZGiGjEn47KFwSk0OAUGDSoT//+3d+/BUZV3H8C/e8vmniXkuoZLuAkqIAbZBl+1NXkh6CuojArNDJdSUAxFBNpI34lU2hIG+uIMvoy0M1za8Vr6io7Y2oZ7rTFiIK+jQgaYCFGy4S1pboQkm+zz/hH25JzdQy5kd8/u2e9nZgdy9uzmefbJOfs7z/k9zyO/ypZ6cDSYR6rvtaiCU4a+yBfVNdykBycx2iIFOEPJwfEkActHUvlrHpxATvIHKHvMo9iDwwCH/MciW2wzFPJb1G5R9Q4TFyFRRopMniRj+Xwr8h6cYAff3jPtGhUBjvYHSLIsB8dzLHt/NokxFqQmRAMYfJnVRlxa/PgZDI+PQrTFiJH9rJ83VN4zUkc6zoNDftO7UnXoTfQnJRkrRlH57kcUDLFRPades2oPTvCDb+8cHJPKsaMltVtUJq+8FqvZKPXgDPbLXb6/50Ktp216Viof6oCkhGgLDq19EPHWwH7lcqI/JQY45DdmRQ9OzzYtb/8YVG5ReY55t3wtKp4HKMhiom6MnFIkt/b2LnpycIJ1geA9D47y4iAoReiTfMFNtXlwEqMtMBgMvbeohjIPjmdUm8m/QV7WsMD23gDBG60VLgL6CTQ0NKCwsBCJiYmw2WxYtmwZWltbb7r/N998A4PBoPrYv3+/tJ/a82+//XYgq0ID0DsPTmhM9KdYX0caJt5bRs6DQ1qx22JgNAC32aKlbZ78DE1mMpYfKz45ONofH/IeHLWlGhJjLACALFsMACBukD0lipmMvUZcAqER5A2E/GKNw8QD3INTWFiIuro6lJWVweVyYenSpVixYgXefPNN1f1HjBiBuro6xbbf/e532LZtG+bMmaPYvnfvXhQUFEg/22w2v5efBqd3Hhw3Olw9y4lbzBoOE5dfhd448OU9OG7PiuchcAKnyJKZFIMPV9+PlBvDmoHeL9kud2+SfvDmwZH34Bh9Ah6tJasOE+8tV8KNBWTvH5+CF+dMxL+NSxnU+5tVbu3Ig55wuQjybsdIF7AA58yZM/joo49w8uRJTJ8+HQDw6quv4uGHH8ZvfvMb2O12n9eYTCZkZGQoth04cABPPfUU4uPjFdttNpvPvqQtszQPjkDDtQ4AvgvlBZPaLSr5TMY37gKEzdUZ6cukzETFz9ItXrc76Plh3kOiPUtHdLtF0IKsvgyP6w0Ee9eV630+MbqnB8dsMuLZB8cO+v1NJt9zhb9vUQWDPBiNMjPACdgnUF5eDpvNJgU3AJCfnw+j0YiKiooBvUdlZSWqqqqwbNkyn+eKioqQkpKCGTNmYM+ePVI+hZqOjg40NzcrHuR/nlEHXW43rl7rBADFFWqwqS226TnoO7vky0mEx8mL9K33FpUI+t+m52LALJun5WaLWmphWJxF+r80D448BydmaNfqyh4ceZKx8neGOu9V4SNdwHpwnE4n0tLSlL/MbEZycjKcTueA3mP37t2YNGkSZs6cqdi+adMmPPTQQ4iNjcXf/vY3PPfcc2htbcXq1atV36e0tBQvv/zyrVWEBkw+D87V1p4AZ7iGAY7a0E/bjeGmDdc6gz4dPlFfzCr5YcEeReW96GQnQuMWlXypBrV5cBKsFu+XDIpJkYPjuUUVfj048s+ESca30IPz4osv3jQR2PM4e/bskAt2/fp1vPnmm6q9NyUlJbjvvvswbdo0FBcX42c/+xm2bdt20/fasGEDmpqapEdtbe2Qy0e+5PPg/F+r9reoFENfb/zfU56eAMd3PyKtmBUzgfdsC/Y8OPIvdc8xEwqHh8VkRNKNRGK1eXD82YNjlnpw5Dl8IfAhDADXolIa9F/FunXrsGTJkj73GTNmDDIyMnDlyhXF9q6uLjQ0NAwod+ZPf/oT2trasGjRon73dTgc+OUvf4mOjg5Yrb49BlarVXU7+ZfnJPGvNhc6u3oyeOXDO4NNfmL2nKCSZQFOUmzPCZMBDoUCk7H3AiHoPTgqI4c8eSmhcnyMTonD/9Y2Ssew9zDxoVDOg+ObZBwm8c2NTgZACPbgALcQ4KSmpiI1NbXf/XJzc9HY2IjKykrk5OQAAI4cOQK32w2Hw9Hv63fv3o25c+cO6HdVVVVh2LBhDGI05rlFVd/cDgCIjTJJE5ppwXuBTaA34Gpo65SGkobLyYv0TT7NglZrUcnzTqTh0iES4Pz3wmn45uo1jEvrGXCiuEUV7b8enHBOMgZ6ytothKItI1XAvn0mTZqEgoICLF++HLt27YLL5cKqVauwYMECaQTVd999h7y8PPzhD3/AjBkzpNeeP38eJ06cwJ///Gef9/3ggw9QX1+P733ve4iOjkZZWRk2b96M9evXB6oqNECeE0JdU0+Ao2WCMeA7ezHQey9fCOBfNxKhw2UIKOlbbw9Obw5OsPJf1HJwepc3CUoR+jUiORYjZEsdKJOM/ZeDIyUZK4aJD+ntg8pkMKAbgreoEOB5cN544w2sWrUKeXl5MBqNmD9/Pnbs2CE973K5UF1djba2NsXr9uzZg6ysLMyaNcvnPS0WC3bu3IkXXngBQgiMGzcO27dvx/LlywNZFRoAzwmh6boLgLa3p4Dek5Jy8quee/lN111o6ejqeZ7nAQoBFmkmY3fQE+DVlj/wHM+hegGgmOhvqLeojL71NodrD86NFSZ4iyrAAU5ycvJNJ/UDgNGjR6sO7968eTM2b96s+pqCggLFBH8UOrzXf5HPXaEFk0oPDtCTaOwJwoDwOnmRfnl6EXrWourZFrx5cG7egxMqt6i8qU30d6tMaknGYTiKCuhtLw4T52ri5EfeVwwpGvfgqCVOAspZUYHwOnmRfsnnkfIsVhv0eXBM8p4M3+HYoUR+XA/1FpXqTMZhOA8O0Ps3Y+FEfwxwyH+8rxi0z8Hp+be/AIfxDYUCKQdHk3lwlGUAei8QwuIW1VADHJVV3eU5LKH6GajxtJslVJKnNMRPgPzGuwdH+xwc9R4c73KxB4dCgXypk+DPg+N7W0Nt0clQ4s9bVIoeHKnevkFPOFAL0CKVdmN4SXd8cnA07sHx3Iv2DmB8blExzKcQIF9N3CP4PTi+X+qh+t0ebzUjxmJCTJQJ8UOcjkKZg9PzGVjkvVkh+hmokSZtZJIxAxzyH+/Va1M0nMUYAKItJgBATJRJsT3ZK/mZPTgUCjwXCB2KACfIOTgqPTihenxEW0z4n5UzEWU2Dnmm4X5nMg7Rz0CNlIPDHhwGOOQ/3geU1j04E9LjsSZ/PO6yJym2eyc/h9PVGemX50vW1dU7sjQURlGF8jIFd9gT+99pABRLHBh9k4zDKL7BvLvtqKhpwIT0BK2LojkGOOQ33l2iWo+iMhgMWJM/wWe79y0qIIzOXqRbnuOns7tb2has26dqa1F5emRDOL7xG8XosTAfJv6fj9yhdRFCBm/Skd+Yve5Z22K1DXBuxneYuEYFIZLxHD/N17ukbSHRgxNGX+63yqxYTdx3JuNI+Az0iAEO+Y18FFVyXFTIjjzwnoCQJy8KBZ4v1I++dAIAxqXFI9YrfyxQetei8u3JiITjw6yWZGxSXrBR+GGAQ34jPzlqPYtxX4bFKefMiIQTOIU+z/HTeSPJeP2sCUEfJm5S6bWIhOPDpJJcbQ7TeXCoFwMc8hv5xFIpCaF5ewoArGYTEqy96Wc8d1EokPciTM1Kwuw7M4L2u9VzcEJ7mLg/yYMZaSbjG+ezUO2Jpv4xwCG/CZceHABIliVAh/IoEYoc8i/S4oKJQe01MKrk24TDKCp/kfdceScZR0D1dYsBDvmNIsDReARVf+SJxjyBUSjITIoBADwwIRUzx6UE9Xd7ZgJOjOnt2YzYHBzpFlVor6ZO/eMwcfIbxS0qjefA6c9wRYDDExhpb/ad6di75F7MyE4O+u8uuDMTTY+6kDcpXdpmitRh4kZlknEk1F+vGOCQ38hPElrPgdMf+S00xjcUCswmI34wMU2T3x0TZcKS+7IV2zwXAcM0npE8GFRnMo6gYfJ6xQCH/EY+TDyscnB4AiPysTpvPKaNtAU12Vkr8hwcz3nMZPL0YPH8EK4Y4JDfyK+CQj0Hh7eoiPqWHBeFeXffpnUxgkItB8ez2CZPD+GLScbkNyajQRp5Eeo5OEwyJiIPk9otKvbghD324JDfGAwGFP1gHP7Z2oGsYTFaF6dP8gCHoySIIltfScacByd8McAhv1r7776LW4YieY4Qz19Ekc2sOg9O5Iwi0yveoqKIxCRjIvKQ99JIScZSDg7PD+GKPTgUkVLio6TlGqLMjPOJIplqkjHnwQl7DHAoIlnNJrzzTC4EhGJ4OxFFHrUeHCYZhz8GOBSx7rAnal0EIgoB8h4ck9cwcQY44YuXrkREFNFMRoN0SyraYpK2AZwHJ5yxB4eIiCKawWBAyX/cgaY2lzSFhGeJioRoi5ZFoyFggENERBFvUe5oxc/j0+LxX09Oxe0ZCdoUiIaMAQ4REZEXg8GA+TlZWheDhoA5OERERKQ7DHCIiIhIdxjgEBERke4wwCEiIiLdYYBDREREuhOwAOfXv/41Zs6cidjYWNhstgG9RgiBl156CZmZmYiJiUF+fj7OnTun2KehoQGFhYVITEyEzWbDsmXL0NraGoAaEBERUbgKWIDT2dmJJ598EitXrhzwa7Zu3YodO3Zg165dqKioQFxcHGbPno329nZpn8LCQnz11VcoKyvDwYMHceLECaxYsSIQVSAiIqIwZRBCiED+gn379mHNmjVobGzscz8hBOx2O9atW4f169cDAJqampCeno59+/ZhwYIFOHPmDO644w6cPHkS06dPBwB89NFHePjhh/Htt9/CbrcPqEzNzc1ISkpCU1MTEhO5HhEREVE4GMz3d8jk4NTU1MDpdCI/P1/alpSUBIfDgfLycgBAeXk5bDabFNwAQH5+PoxGIyoqKm763h0dHWhublY8iIiISL9CJsBxOp0AgPT0dMX29PR06Tmn04m0tDTF82azGcnJydI+akpLS5GUlCQ9RowY4efSExERUSgZVIDz4osvwmAw9Pk4e/ZsoMp6yzZs2ICmpibpUVtbq3WRiIiIKIAGtRbVunXrsGTJkj73GTNmzC0VJCMjAwBQX1+PzMxMaXt9fT3uvvtuaZ8rV64oXtfV1YWGhgbp9WqsViusVustlYuIiIjCz6ACnNTUVKSmpgakINnZ2cjIyMDhw4elgKa5uRkVFRXSSKzc3Fw0NjaisrISOTk5AIAjR47A7XbD4XAEpFxEREQUfgK2mvilS5fQ0NCAS5cuobu7G1VVVQCAcePGIT4+HgAwceJElJaW4vHHH4fBYMCaNWvwq1/9CuPHj0d2djZKSkpgt9vx2GOPAQAmTZqEgoICLF++HLt27YLL5cKqVauwYMGCAY+gAnpGbAFgsjEREVEY8XxvD2gAuAiQxYsXCwA+j6NHj0r7ABB79+6Vfna73aKkpESkp6cLq9Uq8vLyRHV1teJ9r169KhYuXCji4+NFYmKiWLp0qWhpaRlU2Wpra1XLxgcffPDBBx98hP6jtra23+/6gM+DE4rcbjcuX76MhIQEGAwGv753c3MzRowYgdraWt3PsRNJdQVYXz2LpLoCrK+e6b2uQgi0tLTAbrfDaOx7nFTAblGFMqPRiKysrID+jsTERF3+camJpLoCrK+eRVJdAdZXz/Rc16SkpAHtFzLz4BARERH5CwMcIiIi0h0GOH5mtVqxcePGiJh3J5LqCrC+ehZJdQVYXz2LpLr2JyKTjImIiEjf2INDREREusMAh4iIiHSHAQ4RERHpDgMcIiIi0h0GOH60c+dOjB49GtHR0XA4HPjss8+0LpJflJaW4t5770VCQgLS0tLw2GOPobq6WrHP97//fRgMBsXj2Wef1ajEt+4Xv/iFTz0mTpwoPd/e3o6ioiIMHz4c8fHxmD9/Purr6zUs8dCMHj3ap74GgwFFRUUAwr9dT5w4gUcffRR2ux0GgwHvvfee4nkhBF566SVkZmYiJiYG+fn5OHfunGKfhoYGFBYWIjExETabDcuWLUNra2sQazEwfdXV5XKhuLgYkydPRlxcHOx2OxYtWoTLly8r3kPt72HLli1BrsnA9Ne2S5Ys8alLQUGBYp9waVug//qqHccGgwHbtm2T9gmn9vUHBjh+8s4772Dt2rXYuHEjTp06halTp2L27Nm4cuWK1kUbsuPHj6OoqAiffvopysrK4HK5MGvWLFy7dk2x3/Lly1FXVyc9tm7dqlGJh+bOO+9U1OPjjz+WnnvhhRfwwQcfYP/+/Th+/DguX76MJ554QsPSDs3JkycVdS0rKwMAPPnkk9I+4dyu165dw9SpU7Fz507V57du3YodO3Zg165dqKioQFxcHGbPno329nZpn8LCQnz11VcoKyvDwYMHceLECaxYsSJYVRiwvura1taGU6dOoaSkBKdOncK7776L6upqzJ0712ffTZs2Kdr7Jz/5STCKP2j9tS0AFBQUKOry1ltvKZ4Pl7YF+q+vvJ51dXXYs2cPDAYD5s+fr9gvXNrXLwa1SiXd1IwZM0RRUZH0c3d3t7Db7aK0tFTDUgXGlStXBABx/PhxaduDDz4onn/+ee0K5ScbN24UU6dOVX2usbFRWCwWsX//fmnbmTNnBABRXl4epBIG1vPPPy/Gjh0r3G63EEI/7SpEz+K+Bw4ckH52u90iIyNDbNu2TdrW2NgorFareOutt4QQQnz99dcCgDh58qS0z1/+8hdhMBjEd999F7SyD5Z3XdV89tlnAoC4ePGitG3UqFHilVdeCWzhAkCtvosXLxbz5s276WvCtW2FGFj7zps3Tzz00EOKbeHavreKPTh+0NnZicrKSuTn50vbjEYj8vPzUV5ermHJAqOpqQkAkJycrNj+xhtvICUlBXfddRc2bNiAtrY2LYo3ZOfOnYPdbseYMWNQWFiIS5cuAQAqKyvhcrkU7Txx4kSMHDlSF+3c2dmJ119/HT/60Y8Ui9DqpV291dTUwOl0KtozKSkJDodDas/y8nLYbDZMnz5d2ic/Px9GoxEVFRVBL7M/NTU1wWAwwGazKbZv2bIFw4cPx7Rp07Bt2zZ0dXVpU0A/OHbsGNLS0nD77bdj5cqVuHr1qvScntu2vr4eH374IZYtW+bznJ7atz8Rudimv/3zn/9Ed3c30tPTFdvT09Nx9uxZjUoVGG63G2vWrMF9992Hu+66S9r+wx/+EKNGjYLdbscXX3yB4uJiVFdX491339WwtIPncDiwb98+3H777airq8PLL7+M+++/H19++SWcTieioqJ8vhDS09PhdDq1KbAfvffee2hsbMSSJUukbXppVzWeNlM7bj3POZ1OpKWlKZ43m81ITk4O6zZvb29HcXExFi5cqFiQcfXq1bjnnnuQnJyMTz75BBs2bEBdXR22b9+uYWlvTUFBAZ544glkZ2fjwoUL+PnPf445c+agvLwcJpNJt20LAL///e+RkJDgc/tcT+07EAxwaFCKiorw5ZdfKvJSACjuW0+ePBmZmZnIy8vDhQsXMHbs2GAX85bNmTNH+v+UKVPgcDgwatQo/PGPf0RMTIyGJQu83bt3Y86cObDb7dI2vbQr9XK5XHjqqacghMBrr72meG7t2rXS/6dMmYKoqCg888wzKC0tDbup/xcsWCD9f/LkyZgyZQrGjh2LY8eOIS8vT8OSBd6ePXtQWFiI6OhoxXY9te9A8BaVH6SkpMBkMvmMpqmvr0dGRoZGpfK/VatW4eDBgzh69CiysrL63NfhcAAAzp8/H4yiBYzNZsOECRNw/vx5ZGRkoLOzE42NjYp99NDOFy9exKFDh/DjH/+4z/300q4ApDbr67jNyMjwGSjQ1dWFhoaGsGxzT3Bz8eJFlJWVKXpv1DgcDnR1deGbb74JTgEDaMyYMUhJSZH+dvXWth5///vfUV1d3e+xDOirfdUwwPGDqKgo5OTk4PDhw9I2t9uNw4cPIzc3V8OS+YcQAqtWrcKBAwdw5MgRZGdn9/uaqqoqAEBmZmaASxdYra2tuHDhAjIzM5GTkwOLxaJo5+rqaly6dCns23nv3r1IS0vDI4880ud+emlXAMjOzkZGRoaiPZubm1FRUSG1Z25uLhobG1FZWSntc+TIEbjdbinYCxee4ObcuXM4dOgQhg8f3u9rqqqqYDQafW7lhKNvv/0WV69elf529dS2crt370ZOTg6mTp3a7756al9VWmc568Xbb78trFar2Ldvn/j666/FihUrhM1mE06nU+uiDdnKlStFUlKSOHbsmKirq5MebW1tQgghzp8/LzZt2iQ+//xzUVNTI95//30xZswY8cADD2hc8sFbt26dOHbsmKipqRH/+Mc/RH5+vkhJSRFXrlwRQgjx7LPPipEjR4ojR46Izz//XOTm5orc3FyNSz003d3dYuTIkaK4uFixXQ/t2tLSIk6fPi1Onz4tAIjt27eL06dPSyOHtmzZImw2m3j//ffFF198IebNmyeys7PF9evXpfcoKCgQ06ZNExUVFeLjjz8W48ePFwsXLtSqSjfVV107OzvF3LlzRVZWlqiqqlIcxx0dHUIIIT755BPxyiuviKqqKnHhwgXx+uuvi9TUVLFo0SKNa6aur/q2tLSI9evXi/LyclFTUyMOHTok7rnnHjF+/HjR3t4uvUe4tK0Q/f8tCyFEU1OTiI2NFa+99prP68Otff2BAY4fvfrqq2LkyJEiKipKzJgxQ3z66adaF8kvAKg+9u7dK4QQ4tKlS+KBBx4QycnJwmq1inHjxomf/vSnoqmpSduC34Knn35aZGZmiqioKHHbbbeJp59+Wpw/f156/vr16+K5554Tw4YNE7GxseLxxx8XdXV1GpZ46P76178KAKK6ulqxXQ/tevToUdW/3cWLFwsheoaKl5SUiPT0dGG1WkVeXp7P53D16lWxcOFCER8fLxITE8XSpUtFS0uLBrXpW191rampuelxfPToUSGEEJWVlcLhcIikpCQRHR0tJk2aJDZv3qwICEJJX/Vta2sTs2bNEqmpqcJisYhRo0aJ5cuX+1xwhkvbCtH/37IQQvz2t78VMTExorGx0ef14da+/mAQQoiAdhERERERBRlzcIoc+iwAAABZSURBVIiIiEh3GOAQERGR7jDAISIiIt1hgENERES6wwCHiIiIdIcBDhEREekOAxwiIiLSHQY4REREpDsMcIiIiEh3GOAQERGR7jDAISIiIt1hgENERES68/+8pnSB5k4hHAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(sims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "79e7e873-7950-4123-ab13-299360ae19ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from transformers import BertConfig, BertModel, AutoTokenizer\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "def initialize_model_and_tokenizer():\n",
+ " \"\"\"Initialize BERT model from config and ChemBERTa tokenizer\"\"\"\n",
+ " \n",
+ " \n",
+ " tokenizer = AutoTokenizer.from_pretrained(\"DeepChem/ChemBERTa-77M-MTR\")\n",
+ " config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512,\n",
+ " )\n",
+ " model = SimSonEncoder(config=config, max_len=512).cuda()\n",
+ " return model, tokenizer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "8a3adaff-da65-46b4-b9ee-95851d786a67",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "\n",
+ "class MolecularContrastiveDataset(Dataset):\n",
+ " def __init__(self, data_list, tokenizer, positive_threshold=0.9, cache_path=None, split_type='train'):\n",
+ " \"\"\"\n",
+ " Dataset that only contains positive pairs for NT-Xent contrastive learning\n",
+ " \"\"\"\n",
+ " self.data_list = data_list\n",
+ " self.tokenizer = tokenizer\n",
+ " self.positive_threshold = positive_threshold\n",
+ " self.cache_path = cache_path\n",
+ " self.split_type = split_type\n",
+ "\n",
+ " # Load or compute pairs\n",
+ " if cache_path and os.path.exists(cache_path) and os.path.getsize(cache_path) > 0:\n",
+ " print(f\"Loading cached pairs from {cache_path}\")\n",
+ " self._load_pairs()\n",
+ " else:\n",
+ " print(\"Computing positive pairs only...\")\n",
+ " self._compute_positive_pairs()\n",
+ " if cache_path:\n",
+ " self._save_pairs()\n",
+ " \n",
+ " def _compute_positive_pairs(self):\n",
+ " \"\"\"\n",
+ " Compute ONLY positive pairs based on descriptor similarity\n",
+ " \"\"\"\n",
+ " # --- 1. Cosine-similarity matrix ---------------------------------------\n",
+ " prop_vectors = torch.stack(\n",
+ " [item['property_tensor'] for item in self.data_list]\n",
+ " ).numpy()\n",
+ " sim_matrix = cosine_similarity(prop_vectors)\n",
+ "\n",
+ " n = len(self.data_list)\n",
+ " positive_pairs = []\n",
+ " pairs_per_molecule = 1 # STRICTLY ONE FOR CREATING PROPER NEGATIVE PAIRS\n",
+ " current_pairs_per_molecule = 0\n",
+ " # --- 2. Collect only positive pairs ------------------------------------\n",
+ " print(f'Collecting positive pairs with similarity threshold {self.positive_threshold}')\n",
+ " for i in tqdm(range(n)):\n",
+ " for j in range(i + 1, n):\n",
+ " sim = sim_matrix[i, j]\n",
+ " if sim > self.positive_threshold:\n",
+ " positive_pairs.append((i, j, sim))\n",
+ " current_pairs_per_molecule += 1\n",
+ " if current_pairs_per_molecule > pairs_per_molecule:\n",
+ " current_pairs_per_molecule = 0\n",
+ " break\n",
+ "\n",
+ " # --- 3. Store only positive pairs --------------------------------------\n",
+ " if len(positive_pairs) == 0:\n",
+ " raise ValueError(\"No positive pairs found – lower the positive_threshold.\")\n",
+ "\n",
+ " # No shuffling - we want consistent positive pairs\n",
+ " self.pairs = [(i, j) for i, j, _ in positive_pairs]\n",
+ " self.descriptor_similarities = [sim for _, _, sim in positive_pairs]\n",
+ "\n",
+ " print(f\"Generated {len(self.pairs)} positive pairs\")\n",
+ "\n",
+ " def _save_pairs(self):\n",
+ " \"\"\"Save computed pairs to cache file\"\"\"\n",
+ " cache_data = {\n",
+ " 'pairs': self.pairs,\n",
+ " 'descriptor_similarities': self.descriptor_similarities\n",
+ " }\n",
+ " with open(self.cache_path, 'wb') as f:\n",
+ " pickle.dump(cache_data, f)\n",
+ " print(f\"Cached pairs saved to {self.cache_path}\")\n",
+ " \n",
+ " def _load_pairs(self):\n",
+ " \"\"\"Load pairs from cache file\"\"\"\n",
+ " with open(self.cache_path, 'rb') as f:\n",
+ " cache_data = pickle.load(f)\n",
+ " \n",
+ " self.pairs = cache_data['pairs']\n",
+ " self.descriptor_similarities = cache_data['descriptor_similarities']\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.pairs)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " i, j = self.pairs[idx]\n",
+ " desc_sim = self.descriptor_similarities[idx]\n",
+ " \n",
+ " # Get SMILES for both molecules\n",
+ " smiles_i = self.data_list[i]['Smiles']\n",
+ " smiles_j = self.data_list[j]['Smiles']\n",
+ " if self.split_type == 'val':\n",
+ " print(f'POSITIVE PAIR SMILES: \\n{smiles_i} \\n {smiles_j}')\n",
+ " # Tokenize SMILES\n",
+ " tokens_i = self.tokenizer(\n",
+ " smiles_i, \n",
+ " return_tensors='pt', \n",
+ " padding='max_length', \n",
+ " truncation=True, \n",
+ " max_length=256\n",
+ " )\n",
+ " tokens_j = self.tokenizer(\n",
+ " smiles_j, \n",
+ " return_tensors='pt', \n",
+ " padding='max_length', \n",
+ " truncation=True, \n",
+ " max_length=256\n",
+ " )\n",
+ " \n",
+ " # Remove batch dimension\n",
+ " tokens_i = {key: val.squeeze(0) for key, val in tokens_i.items()}\n",
+ " tokens_j = {key: val.squeeze(0) for key, val in tokens_j.items()}\n",
+ " \n",
+ " # Get property vectors\n",
+ " prop_vec_i = self.data_list[i]['property_tensor']\n",
+ " prop_vec_j = self.data_list[j]['property_tensor']\n",
+ " \n",
+ " return {\n",
+ " 'tokens_i': tokens_i,\n",
+ " 'tokens_j': tokens_j,\n",
+ " 'descriptor_similarity': torch.tensor(desc_sim, dtype=torch.float32),\n",
+ " 'property_tensor_i': prop_vec_i,\n",
+ " 'property_tensor_j': prop_vec_j\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def contrastive_collate_fn(batch):\n",
+ " \"\"\"\n",
+ " Collate function that creates proper NT-Xent batches:\n",
+ " - Element 0 and 1 are positive pairs\n",
+ " - Element 2 and 3 are positive pairs \n",
+ " - etc.\n",
+ " \"\"\"\n",
+ " batch_size = len(batch)\n",
+ " \n",
+ " # Ensure even batch size for proper pairing\n",
+ " if batch_size % 2 != 0:\n",
+ " batch = batch[:-1] # Drop last element if odd\n",
+ " batch_size = len(batch)\n",
+ " \n",
+ " # Interleave: [sample1_i, sample1_j, sample2_i, sample2_j, ...]\n",
+ " tokens_list = []\n",
+ " desc_similarities = []\n",
+ " \n",
+ " for i in range(0, batch_size, 1):\n",
+ " # Add first molecule of pair i\n",
+ " tokens_list.append(batch[i]['tokens_i'])\n",
+ " desc_similarities.append(batch[i]['descriptor_similarity'])\n",
+ " \n",
+ " # Add second molecule of pair i (positive pair)\n",
+ " tokens_list.append(batch[i]['tokens_j'])\n",
+ " desc_similarities.append(batch[i]['descriptor_similarity']) # Same similarity for both elements in pair\n",
+ " \n",
+ " # Stack all tokens\n",
+ " tokens = {}\n",
+ " for key in tokens_list[0].keys():\n",
+ " tokens[key] = torch.stack([item[key] for item in tokens_list])\n",
+ " \n",
+ " desc_similarities_tensor = torch.stack(desc_similarities)\n",
+ " \n",
+ " return {\n",
+ " 'tokens': tokens,\n",
+ " 'descriptor_similarities': desc_similarities_tensor,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def create_dataloaders(train_list, val_list, tokenizer, batch_size=32, \n",
+ " positive_threshold=0.85, cache_dir=\"cache\"):\n",
+ " \"\"\"Create train and validation dataloaders for NT-Xent\"\"\"\n",
+ " os.makedirs(cache_dir, exist_ok=True)\n",
+ " \n",
+ " # Ensure even batch size for proper pairing\n",
+ " if batch_size % 2 != 0:\n",
+ " batch_size += 1\n",
+ " print(f\"Adjusted batch_size to {batch_size} (must be even for NT-Xent)\")\n",
+ " \n",
+ " train_cache = os.path.join(cache_dir, 'train_positive_pairs.pkl')\n",
+ " val_cache = os.path.join(cache_dir, 'val_positive_pairs.pkl')\n",
+ " \n",
+ " train_dataset = MolecularContrastiveDataset(\n",
+ " train_list, tokenizer, positive_threshold=positive_threshold, cache_path=train_cache\n",
+ " )\n",
+ " val_dataset = MolecularContrastiveDataset(\n",
+ " val_list, tokenizer, positive_threshold=positive_threshold, cache_path=val_cache, split_type='val',\n",
+ " )\n",
+ " \n",
+ " train_loader = DataLoader(\n",
+ " train_dataset, batch_size=batch_size, shuffle=True, collate_fn=contrastive_collate_fn, drop_last=True, pin_memory=True\n",
+ " )\n",
+ " val_loader = DataLoader(\n",
+ " val_dataset, batch_size=batch_size, shuffle=False, collate_fn=contrastive_collate_fn, drop_last=True, pin_memory=True\n",
+ " )\n",
+ " \n",
+ " return train_loader, val_loader\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "f956a50b-85a5-49df-b7c6-6e40dce160e1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized with 23,299,840 trainable parameters\n"
+ ]
+ }
+ ],
+ "source": [
+ "def nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, descriptor_similarity, base_temp=0.02):\n",
+ " batch_size = embeddings1.shape[0]\n",
+ " device = embeddings1.device\n",
+ " #individual_temperatures = sigmoid_temp_scaling(descriptor_similarity, base_temp)\n",
+ " #temperature = individual_temperatures.mean() # Single temperature for the whole batch\n",
+ " temperature = base_temp\n",
+ " # Normalize projections\n",
+ " z_i = F.normalize(embeddings1, p=2, dim=1)\n",
+ " z_j = F.normalize(embeddings2, p=2, dim=1)\n",
+ " \n",
+ " # Concatenate for similarity matrix calculation\n",
+ " representations = torch.cat([z_i, z_j], dim=0)\n",
+ " # Calculate cosine similarity between all pairs\n",
+ " similarity_matrix = F.cosine_similarity(representations.unsqueeze(1), representations.unsqueeze(0), dim=2)\n",
+ " #similarity_matrix = torch.clamp(similarity_matrix, min=-0.999, max=0.999)\n",
+ " sim_ij = torch.diag(similarity_matrix, batch_size)\n",
+ " sim_ji = torch.diag(similarity_matrix, -batch_size)\n",
+ " positives = torch.cat([sim_ij, sim_ji], dim=0)\n",
+ " \n",
+ " # Create a mask to exclude self-comparisons\n",
+ " nominator = torch.exp(positives / temperature)\n",
+ " mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool, device=device)).float()\n",
+ " denominator = mask * torch.exp(similarity_matrix / temperature)\n",
+ " \n",
+ " # Calculate the final loss\n",
+ " loss = -torch.log(nominator / torch.sum(denominator, dim=1))\n",
+ " if torch.isnan(loss).any():\n",
+ " print(similarity_matrix)\n",
+ " print(f\"Temperature: {temperature}\")\n",
+ " print(f\"Nominator range: {nominator.min().item():.6f} to {nominator.max().item():.6f}\")\n",
+ " \n",
+ " return torch.sum(loss) / (2 * batch_size)\n",
+ "\n",
+ "\n",
+ "def sigmoid_temp_scaling(descriptor_similarity, base_temp=0.05, steepness=10.0, midpoint=0.5):\n",
+ " \"\"\"Smooth sigmoid-based temperature scaling\"\"\"\n",
+ " sigmoid_factor = torch.sigmoid(steepness * (descriptor_similarity - midpoint))\n",
+ " temperature = base_temp * (2.0 - sigmoid_factor)\n",
+ " return temperature\n",
+ "\n",
+ "\n",
+ "def train_step(batch, model, optimizer, device, scheduler, base_temp=0.1):\n",
+ " \"\"\"Single training step for NT-Xent\"\"\"\n",
+ " model.train()\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Move batch to device\n",
+ " tokens = {k: v.to(device) for k, v in batch['tokens'].items()}\n",
+ " desc_similarities = batch['descriptor_similarities'].to(device)\n",
+ " \n",
+ " # Forward pass - get embeddings for all samples\n",
+ " outputs = model(**tokens) # i1, j1, i2, j2 ...\n",
+ " embeddings = outputs\n",
+ " \n",
+ " # Split embeddings: even indices are embeddings1, odd indices are embeddings2\n",
+ " embeddings1 = embeddings[::2] # [0, 2, 4, ...]\n",
+ " embeddings2 = embeddings[1::2] # [1, 3, 5, ...]\n",
+ " \n",
+ " # Get descriptor similarities for each pair (take every other one since they're duplicated)\n",
+ " pair_desc_similarities = desc_similarities[::2]\n",
+ " #print(f'FIRST TRAIN EMBED: {embeddings1}')\n",
+ " #print(f'SECOND TRAIN EMBED: {embeddings2}')\n",
+ " #print(f'COSINE SIM BETWEEN THEM TRAIN: {F.cosine_similarity(embeddings1, embeddings2, dim=1)}')\n",
+ " # Calculate NT-Xent loss\n",
+ " loss = nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, pair_desc_similarities, base_temp=base_temp)\n",
+ " \n",
+ " # Backward pass\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " scheduler.step()\n",
+ " return loss.item()\n",
+ "\n",
+ "def val_step(batch, model, device, base_temp=0.1):\n",
+ " \"\"\"Single validation step for NT-Xent\"\"\"\n",
+ " model.eval()\n",
+ " with torch.no_grad():\n",
+ " # Move batch to device\n",
+ " tokens = {k: v.to(device) for k, v in batch['tokens'].items()}\n",
+ " desc_similarities = batch['descriptor_similarities'].to(device)\n",
+ " \n",
+ " # Forward pass\n",
+ " outputs = model(**tokens)\n",
+ " embeddings = outputs\n",
+ " \n",
+ " # Split embeddings\n",
+ " embeddings1 = embeddings[::2]\n",
+ " embeddings2 = embeddings[1::2]\n",
+ " \n",
+ " # Get descriptor similarities for pairs\n",
+ " pair_desc_similarities = desc_similarities[::2]\n",
+ " \n",
+ " print(f'FIRST VAL EMBED: {embeddings1}')\n",
+ " print(f'SECOND VAL EMBED: {embeddings2}')\n",
+ " print(f'COSINE SIM BETWEEN THEM: {F.cosine_similarity(embeddings1, embeddings2, dim=1)}')\n",
+ " #print(f'SECOND VAL EMBED: {embeddings2}')\n",
+ " loss = nt_xent_loss_with_temp_scaling(embeddings1, embeddings2, pair_desc_similarities, base_temp=base_temp)\n",
+ " print(f'VAL LOSS: {loss}')\n",
+ " \n",
+ " return loss.item()\n",
+ "\n",
+ "def train_epoch(train_loader, model, optimizer, scheduler, base_temp=0.01):\n",
+ " \"\"\"Train for one epoch\"\"\"\n",
+ " total_loss = 0\n",
+ " num_batches = 0\n",
+ " \n",
+ " progress_bar = tqdm(train_loader, desc=\"Training\")\n",
+ " \n",
+ " for batch in progress_bar:\n",
+ " loss = train_step(batch, model, optimizer, 'cuda', scheduler, base_temp=base_temp)\n",
+ " total_loss += loss\n",
+ " num_batches += 1\n",
+ " \n",
+ " # Calculate running average loss\n",
+ " avg_loss = total_loss / num_batches\n",
+ " \n",
+ " # Update progress bar with current loss info\n",
+ " progress_bar.set_postfix({\n",
+ " 'Loss': f'{loss:.4f}',\n",
+ " 'Avg Loss': f'{avg_loss:.4f}'\n",
+ " })\n",
+ " \n",
+ " return total_loss / num_batches if num_batches > 0 else 0\n",
+ "\n",
+ "\n",
+ "def validate_epoch(val_loader, model, base_temp=0.01):\n",
+ " \"\"\"Validate for one epoch\"\"\"\n",
+ " total_loss = 0\n",
+ " num_batches = 0\n",
+ " print('nah twin')\n",
+ " return 0\n",
+ " for batch in val_loader:\n",
+ " loss = val_step(batch, model, 'cuda', base_temp=base_temp)\n",
+ " total_loss += loss\n",
+ " num_batches += 1\n",
+ " \n",
+ " return total_loss / num_batches if num_batches > 0 else 0\n",
+ "\n",
+ "def training_loop(train_loader, val_loader, model, tokenizer, epochs=50, patience=5, lr=1e-4, base_temp=0.02,\n",
+ " device_name='cuda', save_path='best_model.pt'):\n",
+ " \"\"\"Main training loop with early stopping\"\"\"\n",
+ " device = torch.device(device_name if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " \n",
+ " # Initialize model and optimizer\n",
+ " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " total_steps = epochs * len(train_loader)\n",
+ " scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_mult=1, T_0=total_steps)\n",
+ " # Early stopping variables\n",
+ " best_val_loss = float('inf')\n",
+ " no_improve_epochs = 0\n",
+ " \n",
+ " print(\"Starting training...\")\n",
+ " \n",
+ " for epoch in range(epochs):\n",
+ " # Training\n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " train_loss = train_epoch(train_loader, model, optimizer, scheduler, base_temp=base_temp)\n",
+ " print('END TRAIN')\n",
+ " # Validation\n",
+ " val_loss = validate_epoch(val_loader, model)\n",
+ " \n",
+ " print(f\"Epoch {epoch + 1}/{epochs}: Train Loss = {train_loss:.4f}, Val Loss = {val_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check\n",
+ " if val_loss < best_val_loss:\n",
+ " best_val_loss = val_loss\n",
+ " no_improve_epochs = 0\n",
+ " # Save best model\n",
+ " torch.save(model.state_dict(), save_path)\n",
+ " print(f\"New best model saved with val loss: {val_loss:.4f}\")\n",
+ " else:\n",
+ " no_improve_epochs += 1\n",
+ " print(f\"No improvement for {no_improve_epochs} epochs\")\n",
+ " \n",
+ " if no_improve_epochs >= patience:\n",
+ " print(f\"Early stopping triggered after {epoch + 1} epochs\")\n",
+ " break\n",
+ " \n",
+ " # Load best model\n",
+ " print(f\"Loading best model from {save_path}\")\n",
+ " model.load_state_dict(torch.load(save_path))\n",
+ " model.eval()\n",
+ " \n",
+ " print(f\"Training completed. Best validation loss: {best_val_loss:.4f}\")\n",
+ "\n",
+ "\n",
+ "model, tokenizer = initialize_model_and_tokenizer()\n",
+ "#model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl', weights_only=False))\n",
+ "print(f\"Model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad):,} trainable parameters\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "c73e2bba-59c1-4b41-b2ff-235526dd2912",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!rm -rf cache"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0072c8f5-c5e9-4590-9544-c73cf1fac1e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing positive pairs only...\n",
+ "Collecting positive pairs with similarity threshold 0.8\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|████████████████████████████████████| 6378/6378 [00:00<00:00, 55896.48it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated 12538 positive pairs\n",
+ "Cached pairs saved to cache/train_positive_pairs.pkl\n",
+ "Computing positive pairs only...\n",
+ "Collecting positive pairs with similarity threshold 0.8\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████| 100/100 [00:00<00:00, 206209.64it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated 129 positive pairs\n",
+ "Cached pairs saved to cache/val_positive_pairs.pkl\n",
+ "Using device: cuda\n",
+ "Starting training...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1567/1567 [00:31<00:00, 49.37it/s, Loss=0.9300, Avg Loss=0.989\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 1/10: Train Loss = 0.9891, Val Loss = 0.0000\n",
+ "New best model saved with val loss: 0.0000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 100%|█| 1567/1567 [00:31<00:00, 50.05it/s, Loss=2.7072, Avg Loss=2.712\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "END TRAIN\n",
+ "nah twin\n",
+ "Epoch 2/10: Train Loss = 2.7125, Val Loss = 0.0000\n",
+ "No improvement for 1 epochs\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 77%|▊| 1204/1567 [00:24<00:07, 49.73it/s, Loss=2.7080, Avg Loss=2.708"
+ ]
+ }
+ ],
+ "source": [
+ "train_loader, val_loader = create_dataloaders(\n",
+ " train_list, val_list[:100], tokenizer, \n",
+ " batch_size=128, positive_threshold=0.8\n",
+ ")\n",
+ "\n",
+ "training_loop(\n",
+ " train_loader, val_loader, model, tokenizer,\n",
+ " epochs=10, patience=5, lr=1e-3, \n",
+ " device_name='cuda', base_temp=0.1\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "58343b16-1bdb-4476-ac61-e797fbc661d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(train_list[:5], '\\n\\n', val_list[:5])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47561022-5f57-4b7b-b903-ef1f8773f903",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5fcef978-3630-4201-9301-6963a8560517",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/kaggle_comp/.ipynb_checkpoints/simson-fine-tune-checkpoint.ipynb b/simson_modeling/kaggle_comp/.ipynb_checkpoints/simson-fine-tune-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bcb82957f8a266898569d44fc72c3675599e964e
--- /dev/null
+++ b/simson_modeling/kaggle_comp/.ipynb_checkpoints/simson-fine-tune-checkpoint.ipynb
@@ -0,0 +1,1608 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import PreTrainedModel, AutoConfig, BertModel, BertTokenizerFast, BertConfig, AutoModel, AutoTokenizer\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "import os\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from tqdm import tqdm\n",
+ "import joblib\n",
+ "\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/kaggle_comp/train.csv')\n",
+ "\n",
+ "targets = ['Tg', 'FFV', 'Tc', 'Density', 'Rg']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 16958 | \n",
+ " NaN | \n",
+ " */C=C/c1ccc2c(c1)Sc1cc(*)ccc1N2c1ccc(OCCCCCCCC... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 16959 | \n",
+ " NaN | \n",
+ " *Cc1ccc(CSSS*)cc1 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 16960 | \n",
+ " 1.595107e+09 | \n",
+ " *Oc1ccc(C2(c3ccc(Oc4nc(*)nc(OC)n4)cc3)CCCCC2)cc1 | \n",
+ " NaN | \n",
+ " 0.363540 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 16961 | \n",
+ " 8.406988e+08 | \n",
+ " *CC(*)C(=O)OCC(C)CC | \n",
+ " NaN | \n",
+ " 0.372858 | \n",
+ " 0.221 | \n",
+ " 0.919641 | \n",
+ " 13.549867 | \n",
+ "
\n",
+ " \n",
+ " | 16962 | \n",
+ " 1.563977e+08 | \n",
+ " *c1cc(*)cc(-c2nc3ccccc3o2)c1 | \n",
+ " NaN | \n",
+ " 0.390044 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
16963 rows × 7 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id SMILES Tg \\\n",
+ "0 NaN */C=C/C1CC(*)C(C#N)(CCC)C1 NaN \n",
+ "1 NaN *CCCCCCCCCCCCNC(=O)c1ccc(C(=O)N*)cc1 NaN \n",
+ "2 NaN *c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8... NaN \n",
+ "3 1.522414e+09 *CC(*)c1cc(C(=O)OCCCC)ccc1-c1ccc(OCCCCCCCC)cc1 NaN \n",
+ "4 NaN *Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)c... NaN \n",
+ "... ... ... .. \n",
+ "16958 NaN */C=C/c1ccc2c(c1)Sc1cc(*)ccc1N2c1ccc(OCCCCCCCC... NaN \n",
+ "16959 NaN *Cc1ccc(CSSS*)cc1 NaN \n",
+ "16960 1.595107e+09 *Oc1ccc(C2(c3ccc(Oc4nc(*)nc(OC)n4)cc3)CCCCC2)cc1 NaN \n",
+ "16961 8.406988e+08 *CC(*)C(=O)OCC(C)CC NaN \n",
+ "16962 1.563977e+08 *c1cc(*)cc(-c2nc3ccccc3o2)c1 NaN \n",
+ "\n",
+ " FFV Tc Density Rg \n",
+ "0 NaN NaN NaN NaN \n",
+ "1 NaN NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN \n",
+ "3 0.385500 NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN \n",
+ "... ... ... ... ... \n",
+ "16958 NaN NaN NaN NaN \n",
+ "16959 NaN NaN NaN NaN \n",
+ "16960 0.363540 NaN NaN NaN \n",
+ "16961 0.372858 0.221 0.919641 13.549867 \n",
+ "16962 0.390044 NaN NaN NaN \n",
+ "\n",
+ "[16963 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "for i in range(1, 5):\n",
+ " supplement_path = f'/home/jovyan/simson_training_bolgov/kaggle_comp/train_supplement/dataset{i}.csv'\n",
+ " supplement_ds = pd.read_csv(supplement_path)\n",
+ "\n",
+ " if 'TC_mean' in supplement_ds.columns:\n",
+ " supplement_ds = supplement_ds.rename(columns = {'TC_mean': 'Tc'})\n",
+ "\n",
+ " df = pd.concat([df, supplement_ds], axis=0)\n",
+ "\n",
+ "df = df.sample(frac=1).reset_index(drop=True)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from rdkit import Chem\n",
+ "import random\n",
+ "from typing import Optional, List, Union\n",
+ "\n",
+ "def augment_smiles_dataset(df: pd.DataFrame,\n",
+ " smiles_column: str = 'SMILES',\n",
+ " augmentation_strategies: List[str] = ['enumeration', 'kekulize', 'stereo_enum'],\n",
+ " n_augmentations: int = 10,\n",
+ " preserve_original: bool = True,\n",
+ " random_seed: Optional[int] = None) -> pd.DataFrame:\n",
+ " \"\"\"\n",
+ " Advanced SMILES augmentation with multiple strategies.\n",
+ " \n",
+ " Parameters:\n",
+ " -----------\n",
+ " augmentation_strategies : List[str]\n",
+ " List of augmentation strategies: 'enumeration', 'kekulize', 'stereo_enum'\n",
+ " \"\"\"\n",
+ " \n",
+ " if random_seed is not None:\n",
+ " random.seed(random_seed)\n",
+ " np.random.seed(random_seed)\n",
+ " \n",
+ " def apply_augmentation_strategy(smiles: str, strategy: str) -> List[str]:\n",
+ " \"\"\"Apply specific augmentation strategy\"\"\"\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return [smiles]\n",
+ " \n",
+ " augmented = []\n",
+ " \n",
+ " if strategy == 'enumeration':\n",
+ " # Standard SMILES enumeration\n",
+ " for _ in range(n_augmentations):\n",
+ " enum_smiles = Chem.MolToSmiles(mol, \n",
+ " canonical=False, \n",
+ " doRandom=True,\n",
+ " isomericSmiles=True)\n",
+ " augmented.append(enum_smiles)\n",
+ " \n",
+ " elif strategy == 'kekulize':\n",
+ " # Kekulization variants\n",
+ " try:\n",
+ " Chem.Kekulize(mol)\n",
+ " kek_smiles = Chem.MolToSmiles(mol, kekuleSmiles=True)\n",
+ " augmented.append(kek_smiles)\n",
+ " except:\n",
+ " pass\n",
+ " \n",
+ " elif strategy == 'stereo_enum':\n",
+ " # Stereochemistry enumeration\n",
+ " for _ in range(n_augmentations // 2):\n",
+ " # Remove stereochemistry\n",
+ " Chem.RemoveStereochemistry(mol)\n",
+ " no_stereo = Chem.MolToSmiles(mol)\n",
+ " augmented.append(no_stereo)\n",
+ " \n",
+ " return list(set(augmented)) # Remove duplicates\n",
+ " \n",
+ " except Exception as e:\n",
+ " print(f\"Error in {strategy} for {smiles}: {e}\")\n",
+ " return [smiles]\n",
+ " \n",
+ " augmented_rows = []\n",
+ " \n",
+ " for idx, row in tqdm(df.iterrows(), total=len(df)):\n",
+ " original_smiles = row[smiles_column]\n",
+ " \n",
+ " # Add original if requested\n",
+ " if preserve_original:\n",
+ " original_row = row.to_dict()\n",
+ " original_row['augmentation_strategy'] = 'original'\n",
+ " original_row['is_augmented'] = False\n",
+ " augmented_rows.append(original_row)\n",
+ " \n",
+ " # Apply each augmentation strategy\n",
+ " for strategy in augmentation_strategies:\n",
+ " strategy_smiles = apply_augmentation_strategy(original_smiles, strategy)\n",
+ " \n",
+ " for aug_smiles in strategy_smiles:\n",
+ " if aug_smiles != original_smiles: # Avoid duplicating original\n",
+ " new_row = row.to_dict().copy()\n",
+ " new_row[smiles_column] = aug_smiles\n",
+ " new_row['augmentation_strategy'] = strategy\n",
+ " new_row['is_augmented'] = True\n",
+ " augmented_rows.append(new_row)\n",
+ " \n",
+ " augmented_df = pd.DataFrame(augmented_rows)\n",
+ " augmented_df = augmented_df.reset_index(drop=True)\n",
+ " \n",
+ " print(f\"Advanced augmentation completed:\")\n",
+ " print(f\"Original size: {len(df)}, Augmented size: {len(augmented_df)}\")\n",
+ " print(f\"Augmentation factor: {len(augmented_df) / len(df):.2f}x\")\n",
+ " \n",
+ " return augmented_df.reset_index(drop=True)\n",
+ "\n",
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.85 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)\n",
+ "#train = augment_smiles_dataset(train)\n",
+ "#test = augment_smiles_dataset(test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['SMILES']\n",
+ "smiles_test = test['SMILES']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['target_scalers.pkl']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joblib.dump(scalers, 'target_scalers.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_273264/2507782815.py:68: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " simson_params = torch.load('/home/jovyan/simson_training_bolgov/kaggle_comp/simson_polymer_1m_uncompiled.pth')\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from transformers import AutoTokenizer, BertModel\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ "\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ "\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ "\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x\n",
+ "\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/kaggle_comp/simson_polymer_1m_uncompiled.pth')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 5) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability for 5 labels\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_2_count': label_counts[2],\n",
+ " 'label_3_count': label_counts[3],\n",
+ " 'label_4_count': label_counts[4],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'label_2_ratio': label_counts[2] / total_samples,\n",
+ " 'label_3_ratio': label_counts[3] / total_samples,\n",
+ " 'label_4_ratio': label_counts[4] / total_samples,\n",
+ " 'all_labels_count': (self.label_masks.sum(axis=1) == 5).sum(),\n",
+ " 'partial_labels_count': ((self.label_masks.sum(axis=1) > 0) & (self.label_masks.sum(axis=1) < 5)).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, label_mask, label_weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for five labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 5)\n",
+ " labels: Ground truth labels (batch_size, 5)\n",
+ " label_mask: Mask for valid labels (batch_size, 5)\n",
+ " label_weights: Weights for each label (5,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 5)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Apply label-specific weights\n",
+ " weighted_losses = masked_losses * label_weights.unsqueeze(0) # Broadcast weights\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = weighted_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "def compute_label_weights(dataset):\n",
+ " \"\"\"\n",
+ " Compute inverse frequency weights based on label availability\n",
+ " \n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " \n",
+ " Returns:\n",
+ " torch.Tensor: Normalized weights for each label\n",
+ " \"\"\"\n",
+ " # Get label counts from dataset\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = len(dataset)\n",
+ " \n",
+ " # Inverse frequency weighting\n",
+ " weights = total_samples / (5 * label_counts) # 5 is the number of labels\n",
+ " \n",
+ " # Normalize weights so they sum to number of labels (5)\n",
+ " weights = weights / weights.sum() * 5\n",
+ " \n",
+ " return torch.tensor(weights, dtype=torch.float32)\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 5).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 5).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 5).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \n",
+ " Returns:\n",
+ " float: Average MAE across all valid samples\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(5):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "def calculate_individual_label_losses(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for each individual label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 5).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 5).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 5).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \n",
+ " Returns:\n",
+ " dict: Dictionary with MAE for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " individual_losses = {}\n",
+ " \n",
+ " for label_idx in range(5):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " individual_losses[f'label_{label_idx}'] = mae\n",
+ " else:\n",
+ " individual_losses[f'label_{label_idx}'] = None # No valid samples for this label\n",
+ " \n",
+ " return individual_losses\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, label_weights, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with weighted loss for five labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 5 labels)\n",
+ " train_dataloader: Training data loader\n",
+ " val_dataloader: Validation data loader \n",
+ " label_weights: Tensor with weights for each label\n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " label_weights = label_weights.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Label weights: {label_weights.cpu().numpy()}\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate weighted loss\n",
+ " loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ " \n",
+ " # Track individual label losses across all validation batches\n",
+ " accumulated_individual_losses = {f'label_{i}': [] for i in range(5)}\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Calculate individual label losses for this batch\n",
+ " individual_losses = calculate_individual_label_losses(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate individual losses\n",
+ " for label_key, loss_value in individual_losses.items():\n",
+ " if loss_value is not None:\n",
+ " accumulated_individual_losses[label_key].append(loss_value)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " # Calculate average individual label losses\n",
+ " avg_individual_losses = {}\n",
+ " for label_key, losses in accumulated_individual_losses.items():\n",
+ " if losses:\n",
+ " avg_individual_losses[label_key] = np.mean(losses)\n",
+ " else:\n",
+ " avg_individual_losses[label_key] = None\n",
+ " \n",
+ " # Print validation results with individual label losses\n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " print(\"Individual label losses (unscaled):\")\n",
+ " for i in range(5):\n",
+ " label_key = f'label_{i}'\n",
+ " if avg_individual_losses[label_key] is not None:\n",
+ " print(f\" Label {i}: {avg_individual_losses[label_key]:.4f}\")\n",
+ " else:\n",
+ " print(f\" Label {i}: No valid samples\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for five labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 5) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 5 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 5 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for five-label training (labels assumed pre-scaled)\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Compute label weights based on training data\n",
+ " label_weights = compute_label_weights(train_dataset)\n",
+ " print(f\"Computed label weights: {label_weights.numpy()}\")\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " label_weights=label_weights,\n",
+ " scalers=scalers, # Still pass scalers for true loss calculation\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/kaggle_comp/checkpoints/simson_clf_kaggle.bin')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for five-label training (labels assumed pre-scaled)\n",
+ "Training dataset statistics:\n",
+ " total_samples: 14419\n",
+ " label_0_count: 472\n",
+ " label_1_count: 6700\n",
+ " label_2_count: 1368\n",
+ " label_3_count: 517\n",
+ " label_4_count: 519\n",
+ " label_0_ratio: 0.03273458630973022\n",
+ " label_1_ratio: 0.46466467854913657\n",
+ " label_2_ratio: 0.09487481794854012\n",
+ " label_3_ratio: 0.03585546847909009\n",
+ " label_4_ratio: 0.035994174353283864\n",
+ " all_labels_count: 0\n",
+ " partial_labels_count: 8286\n",
+ " no_labels_count: 6133\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 2545\n",
+ " label_0_count: 86\n",
+ " label_1_count: 1192\n",
+ " label_2_count: 243\n",
+ " label_3_count: 96\n",
+ " label_4_count: 95\n",
+ " label_0_ratio: 0.03379174852652259\n",
+ " label_1_ratio: 0.46836935166994104\n",
+ " label_2_ratio: 0.09548133595284872\n",
+ " label_3_ratio: 0.037721021611001965\n",
+ " label_4_ratio: 0.03732809430255403\n",
+ " all_labels_count: 0\n",
+ " partial_labels_count: 1470\n",
+ " no_labels_count: 1075\n",
+ "Computed label weights: [1.5442214 0.10878694 0.53280157 1.4098115 1.4043787 ]\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 113\n",
+ "Total training steps: 2260\n",
+ "Label weights: [1.5442214 0.10878694 0.53280157 1.4098115 1.4043787 ]\n",
+ "Validation will be performed every 113 steps\n",
+ "\n",
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 113 | Train Loss: 0.2634 | Val Loss: 0.2308 | True train loss: 3.6083 | True val loss: 3.8929\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 74.8777\n",
+ " Label 1: 0.0177\n",
+ " Label 2: 0.0347\n",
+ " Label 3: 0.0917\n",
+ " Label 4: 2.2249\n",
+ "New best validation loss: 0.2308\n",
+ "\n",
+ "Epoch 2/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 226 | Train Loss: 0.2031 | Val Loss: 0.1903 | True train loss: 2.9943 | True val loss: 3.2239\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 60.3146\n",
+ " Label 1: 0.0170\n",
+ " Label 2: 0.0394\n",
+ " Label 3: 0.0517\n",
+ " Label 4: 2.7417\n",
+ "New best validation loss: 0.1903\n",
+ "\n",
+ "Epoch 3/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 339 | Train Loss: 0.1796 | Val Loss: 0.1766 | True train loss: 2.8803 | True val loss: 3.3839\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.8170\n",
+ " Label 1: 0.0136\n",
+ " Label 2: 0.0362\n",
+ " Label 3: 0.0785\n",
+ " Label 4: 1.9163\n",
+ "New best validation loss: 0.1766\n",
+ "\n",
+ "Epoch 4/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 452 | Train Loss: 0.1538 | Val Loss: 0.1525 | True train loss: 2.6186 | True val loss: 3.2207\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.0257\n",
+ " Label 1: 0.0142\n",
+ " Label 2: 0.0347\n",
+ " Label 3: 0.0523\n",
+ " Label 4: 2.1894\n",
+ "New best validation loss: 0.1525\n",
+ "\n",
+ "Epoch 5/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 565 | Train Loss: 0.1461 | Val Loss: 0.1432 | True train loss: 2.6254 | True val loss: 3.1313\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 57.6456\n",
+ " Label 1: 0.0131\n",
+ " Label 2: 0.0318\n",
+ " Label 3: 0.0394\n",
+ " Label 4: 1.9632\n",
+ "New best validation loss: 0.1432\n",
+ "\n",
+ "Epoch 6/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 678 | Train Loss: 0.1344 | Val Loss: 0.1506 | True train loss: 2.4023 | True val loss: 3.0162\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 55.6189\n",
+ " Label 1: 0.0154\n",
+ " Label 2: 0.0315\n",
+ " Label 3: 0.0464\n",
+ " Label 4: 1.7522\n",
+ "\n",
+ "Epoch 7/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 791 | Train Loss: 0.1240 | Val Loss: 0.1399 | True train loss: 2.2227 | True val loss: 3.3619\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.3619\n",
+ " Label 1: 0.0119\n",
+ " Label 2: 0.0301\n",
+ " Label 3: 0.0426\n",
+ " Label 4: 1.8000\n",
+ "New best validation loss: 0.1399\n",
+ "\n",
+ "Epoch 8/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 904 | Train Loss: 0.1149 | Val Loss: 0.1359 | True train loss: 2.2502 | True val loss: 3.2314\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.6004\n",
+ " Label 1: 0.0121\n",
+ " Label 2: 0.0311\n",
+ " Label 3: 0.0403\n",
+ " Label 4: 1.7468\n",
+ "New best validation loss: 0.1359\n",
+ "\n",
+ "Epoch 9/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1017 | Train Loss: 0.1094 | Val Loss: 0.1506 | True train loss: 2.2638 | True val loss: 3.4009\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.4786\n",
+ " Label 1: 0.0127\n",
+ " Label 2: 0.0280\n",
+ " Label 3: 0.0495\n",
+ " Label 4: 2.0883\n",
+ "\n",
+ "Epoch 10/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1130 | Train Loss: 0.0963 | Val Loss: 0.1467 | True train loss: 1.9351 | True val loss: 3.3818\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 62.9852\n",
+ " Label 1: 0.0127\n",
+ " Label 2: 0.0298\n",
+ " Label 3: 0.0511\n",
+ " Label 4: 1.9974\n",
+ "\n",
+ "Epoch 11/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1243 | Train Loss: 0.0957 | Val Loss: 0.1321 | True train loss: 1.9361 | True val loss: 2.9691\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 55.0661\n",
+ " Label 1: 0.0117\n",
+ " Label 2: 0.0284\n",
+ " Label 3: 0.0351\n",
+ " Label 4: 2.0390\n",
+ "New best validation loss: 0.1321\n",
+ "\n",
+ "Epoch 12/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1356 | Train Loss: 0.0823 | Val Loss: 0.1424 | True train loss: 1.8204 | True val loss: 3.5836\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 67.2052\n",
+ " Label 1: 0.0123\n",
+ " Label 2: 0.0262\n",
+ " Label 3: 0.0391\n",
+ " Label 4: 1.9690\n",
+ "\n",
+ "Epoch 13/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1469 | Train Loss: 0.0797 | Val Loss: 0.1382 | True train loss: 1.7223 | True val loss: 3.2234\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 59.5507\n",
+ " Label 1: 0.0118\n",
+ " Label 2: 0.0282\n",
+ " Label 3: 0.0381\n",
+ " Label 4: 1.9066\n",
+ "\n",
+ "Epoch 14/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1582 | Train Loss: 0.0728 | Val Loss: 0.1321 | True train loss: 1.5747 | True val loss: 3.3817\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.2403\n",
+ " Label 1: 0.0115\n",
+ " Label 2: 0.0262\n",
+ " Label 3: 0.0339\n",
+ " Label 4: 1.7621\n",
+ "New best validation loss: 0.1321\n",
+ "\n",
+ "Epoch 15/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1695 | Train Loss: 0.0676 | Val Loss: 0.1437 | True train loss: 1.5251 | True val loss: 3.4306\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.7060\n",
+ " Label 1: 0.0121\n",
+ " Label 2: 0.0274\n",
+ " Label 3: 0.0442\n",
+ " Label 4: 1.9844\n",
+ "\n",
+ "Epoch 16/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1808 | Train Loss: 0.0617 | Val Loss: 0.1494 | True train loss: 1.3651 | True val loss: 3.3514\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.4547\n",
+ " Label 1: 0.0118\n",
+ " Label 2: 0.0260\n",
+ " Label 3: 0.0504\n",
+ " Label 4: 2.0026\n",
+ "\n",
+ "Epoch 17/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1921 | Train Loss: 0.0580 | Val Loss: 0.1424 | True train loss: 1.3237 | True val loss: 3.3568\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.8486\n",
+ " Label 1: 0.0116\n",
+ " Label 2: 0.0252\n",
+ " Label 3: 0.0430\n",
+ " Label 4: 1.9207\n",
+ "\n",
+ "Epoch 18/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 2034 | Train Loss: 0.0534 | Val Loss: 0.1376 | True train loss: 1.2378 | True val loss: 3.3407\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.5502\n",
+ " Label 1: 0.0115\n",
+ " Label 2: 0.0247\n",
+ " Label 3: 0.0433\n",
+ " Label 4: 1.7560\n",
+ "\n",
+ "Epoch 19/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 2147 | Train Loss: 0.0497 | Val Loss: 0.1416 | True train loss: 1.1018 | True val loss: 3.2781\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 59.6542\n",
+ " Label 1: 0.0115\n",
+ " Label 2: 0.0251\n",
+ " Label 3: 0.0438\n",
+ " Label 4: 1.8405\n",
+ "\n",
+ "Epoch 20/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 2260 | Train Loss: 0.0484 | Val Loss: 0.1329 | True train loss: 1.1016 | True val loss: 3.3233\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.2343\n",
+ " Label 1: 0.0114\n",
+ " Label 2: 0.0243\n",
+ " Label 3: 0.0402\n",
+ " Label 4: 1.7624\n",
+ "Loaded best model with validation loss: 0.1321\n",
+ "Training completed.\n",
+ "Number of validation checkpoints: 20\n",
+ "Final training losses: [0.061716757780682724, 0.05798421218266002, 0.05344583738628214, 0.04969686268111773, 0.04844354389779336]\n",
+ "Best validation loss: 0.1321\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r"
+ ]
+ },
+ {
+ "ename": "RuntimeError",
+ "evalue": "File /home/jovyan/simson_training_bolgov/kaggle_comp/checkpoints cannot be opened.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataLoader\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtqdm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m train_losses, val_losses, best_loss = \u001b[43mrun_training\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[43msmiles_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msmiles_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokenizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m128\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m113\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m)\u001b[49m\n",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 480\u001b[39m, in \u001b[36mrun_training\u001b[39m\u001b[34m(smiles_train, smiles_test, labels_train, labels_test, model, tokenizer, scalers, num_epochs, learning_rate, batch_size, validation_steps)\u001b[39m\n\u001b[32m 477\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mBest validation loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbest_val_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n\u001b[32m 479\u001b[39m \u001b[38;5;66;03m# Save model\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m480\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/home/jovyan/simson_training_bolgov/kaggle_comp/checkpoints\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 481\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mModel saved successfully!\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 483\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m train_losses, val_losses, best_val_loss\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/serialization.py:849\u001b[39m, in \u001b[36msave\u001b[39m\u001b[34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[39m\n\u001b[32m 846\u001b[39m _check_save_filelike(f)\n\u001b[32m 848\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m _use_new_zipfile_serialization:\n\u001b[32m--> \u001b[39m\u001b[32m849\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_zipfile_writer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[32m 850\u001b[39m _save(\n\u001b[32m 851\u001b[39m obj,\n\u001b[32m 852\u001b[39m opened_zipfile,\n\u001b[32m (...)\u001b[39m\u001b[32m 855\u001b[39m _disable_byteorder_record,\n\u001b[32m 856\u001b[39m )\n\u001b[32m 857\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/serialization.py:716\u001b[39m, in \u001b[36m_open_zipfile_writer\u001b[39m\u001b[34m(name_or_buffer)\u001b[39m\n\u001b[32m 714\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 715\u001b[39m container = _open_zipfile_writer_buffer\n\u001b[32m--> \u001b[39m\u001b[32m716\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcontainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[36mFile \u001b[39m\u001b[32m~/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/serialization.py:687\u001b[39m, in \u001b[36m_open_zipfile_writer_file.__init__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 685\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(torch._C.PyTorchFileWriter(\u001b[38;5;28mself\u001b[39m.file_stream))\n\u001b[32m 686\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m687\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_C\u001b[49m\u001b[43m.\u001b[49m\u001b[43mPyTorchFileWriter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m)\n",
+ "\u001b[31mRuntimeError\u001b[39m: File /home/jovyan/simson_training_bolgov/kaggle_comp/checkpoints cannot be opened."
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=20, learning_rate=1e-4, batch_size=128, validation_steps=113,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kaggle": {
+ "accelerator": "gpu",
+ "dataSources": [
+ {
+ "databundleVersionId": 12966160,
+ "sourceId": 74608,
+ "sourceType": "competition"
+ },
+ {
+ "datasetId": 7678100,
+ "sourceId": 12189904,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7690162,
+ "sourceId": 12207625,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7716502,
+ "sourceId": 12322957,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7801155,
+ "sourceId": 12372847,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7809006,
+ "sourceId": 12525286,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7912957,
+ "sourceId": 12668147,
+ "sourceType": "datasetVersion"
+ }
+ ],
+ "dockerImageVersionId": 31041,
+ "isGpuEnabled": true,
+ "isInternetEnabled": true,
+ "language": "python",
+ "sourceType": "notebook"
+ },
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/simson_modeling/kaggle_comp/checkpoints/clf_kaggle.bin b/simson_modeling/kaggle_comp/checkpoints/clf_kaggle.bin
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index 0000000000000000000000000000000000000000..1b086d3c3c94d830b1863d0de0d4768e2a808523
--- /dev/null
+++ b/simson_modeling/kaggle_comp/checkpoints/clf_kaggle.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 93240610
diff --git a/simson_modeling/kaggle_comp/sample_submission.csv b/simson_modeling/kaggle_comp/sample_submission.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6f917ee6e3047f946f548203f0ed940125ae3fcd
--- /dev/null
+++ b/simson_modeling/kaggle_comp/sample_submission.csv
@@ -0,0 +1,4 @@
+id,Tg,FFV,Tc,Density,Rg
+1109053969,0,0,0,0,0
+1422188626,0,0,0,0,0
+2032016830,0,0,0,0,0
diff --git a/simson_modeling/kaggle_comp/simson-fine-tune.ipynb b/simson_modeling/kaggle_comp/simson-fine-tune.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..99e79b0f2cb187298639c5919bd8e5615e7f7092
--- /dev/null
+++ b/simson_modeling/kaggle_comp/simson-fine-tune.ipynb
@@ -0,0 +1,1742 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import PreTrainedModel, AutoConfig, BertModel, BertTokenizerFast, BertConfig, AutoModel, AutoTokenizer\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "import os\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from tqdm import tqdm\n",
+ "import joblib\n",
+ "\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/kaggle_comp/train.csv')\n",
+ "\n",
+ "targets = ['Tg', 'FFV', 'Tc', 'Density', 'Rg']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " \n",
+ " | 2 | \n",
+ " NaN | \n",
+ " *CC/C=C(/*)C | \n",
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+ " NaN | \n",
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+ " | 16958 | \n",
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+ " *OC(=O)Oc1ccc(S(=O)(=O)c2ccc(OC(=O)OC3CC4CC(*)... | \n",
+ " NaN | \n",
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+ "
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+ " | 16961 | \n",
+ " 1.973417e+09 | \n",
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+ " | 16962 | \n",
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+ " NaN | \n",
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+ " \n",
+ "
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+ "
16963 rows × 7 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id SMILES Tg \\\n",
+ "0 4.215886e+08 *C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5... NaN \n",
+ "1 7.984549e+08 *c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C... NaN \n",
+ "2 NaN *CC/C=C(/*)C NaN \n",
+ "3 NaN *CC(*)(C)C(=O)OCCN(CC)c1ccc(/N=N/c2ccc(OC)cc2)... NaN \n",
+ "4 NaN *Oc1cc(OC(=O)c2ccc(OCC)cc2)c(OC(=O)CCCC(*)=O)c... NaN \n",
+ "... ... ... .. \n",
+ "16958 2.389975e+08 *OC(=O)Oc1ccc(S(=O)(=O)c2ccc(OC(=O)OC3CC4CC(*)... NaN \n",
+ "16959 NaN *c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6c... NaN \n",
+ "16960 NaN *OC(F)(F)COC(=O)c1cc(OCCCCC)cc(C(=O)OCC(*)(F)F)c1 NaN \n",
+ "16961 1.973417e+09 *C=CC1CC(*)C2C(=O)N(c3ccc(F)cc3)C(=O)C12 NaN \n",
+ "16962 NaN */C=C/[Ge](/C=C/[Si](*)(c1ccccc1)c1ccccc1)(c1c... NaN \n",
+ "\n",
+ " FFV Tc Density Rg \n",
+ "0 0.376767 NaN NaN NaN \n",
+ "1 0.346993 NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN \n",
+ "... ... .. ... .. \n",
+ "16958 0.339596 NaN NaN NaN \n",
+ "16959 NaN NaN NaN NaN \n",
+ "16960 NaN NaN NaN NaN \n",
+ "16961 0.374710 NaN NaN NaN \n",
+ "16962 NaN NaN NaN NaN \n",
+ "\n",
+ "[16963 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "for i in range(1, 5):\n",
+ " supplement_path = f'/home/jovyan/simson_training_bolgov/kaggle_comp/train_supplement/dataset{i}.csv'\n",
+ " supplement_ds = pd.read_csv(supplement_path)\n",
+ "\n",
+ " if 'TC_mean' in supplement_ds.columns:\n",
+ " supplement_ds = supplement_ds.rename(columns = {'TC_mean': 'Tc'})\n",
+ "\n",
+ " df = pd.concat([df, supplement_ds], axis=0)\n",
+ "\n",
+ "df = df.sample(frac=1).reset_index(drop=True)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|████████████████████████████████████| 14419/14419 [00:43<00:00, 328.78it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Advanced augmentation completed:\n",
+ "Original size: 14419, Augmented size: 168551\n",
+ "Augmentation factor: 11.69x\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████████████████████████████████| 2545/2545 [00:07<00:00, 333.57it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Advanced augmentation completed:\n",
+ "Original size: 2545, Augmented size: 29716\n",
+ "Augmentation factor: 11.68x\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from rdkit import Chem\n",
+ "import random\n",
+ "from typing import Optional, List, Union\n",
+ "\n",
+ "def augment_smiles_dataset(df: pd.DataFrame,\n",
+ " smiles_column: str = 'SMILES',\n",
+ " augmentation_strategies: List[str] = ['enumeration', 'kekulize', 'stereo_enum'],\n",
+ " n_augmentations: int = 10,\n",
+ " preserve_original: bool = True,\n",
+ " random_seed: Optional[int] = None) -> pd.DataFrame:\n",
+ " \"\"\"\n",
+ " Advanced SMILES augmentation with multiple strategies.\n",
+ " \n",
+ " Parameters:\n",
+ " -----------\n",
+ " augmentation_strategies : List[str]\n",
+ " List of augmentation strategies: 'enumeration', 'kekulize', 'stereo_enum'\n",
+ " \"\"\"\n",
+ " \n",
+ " if random_seed is not None:\n",
+ " random.seed(random_seed)\n",
+ " np.random.seed(random_seed)\n",
+ " \n",
+ " def apply_augmentation_strategy(smiles: str, strategy: str) -> List[str]:\n",
+ " \"\"\"Apply specific augmentation strategy\"\"\"\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return [smiles]\n",
+ " \n",
+ " augmented = []\n",
+ " \n",
+ " if strategy == 'enumeration':\n",
+ " # Standard SMILES enumeration\n",
+ " for _ in range(n_augmentations):\n",
+ " enum_smiles = Chem.MolToSmiles(mol, \n",
+ " canonical=False, \n",
+ " doRandom=True,\n",
+ " isomericSmiles=True)\n",
+ " augmented.append(enum_smiles)\n",
+ " \n",
+ " elif strategy == 'kekulize':\n",
+ " # Kekulization variants\n",
+ " try:\n",
+ " Chem.Kekulize(mol)\n",
+ " kek_smiles = Chem.MolToSmiles(mol, kekuleSmiles=True)\n",
+ " augmented.append(kek_smiles)\n",
+ " except:\n",
+ " pass\n",
+ " \n",
+ " elif strategy == 'stereo_enum':\n",
+ " # Stereochemistry enumeration\n",
+ " for _ in range(n_augmentations // 2):\n",
+ " # Remove stereochemistry\n",
+ " Chem.RemoveStereochemistry(mol)\n",
+ " no_stereo = Chem.MolToSmiles(mol)\n",
+ " augmented.append(no_stereo)\n",
+ " \n",
+ " return list(set(augmented)) # Remove duplicates\n",
+ " \n",
+ " except Exception as e:\n",
+ " print(f\"Error in {strategy} for {smiles}: {e}\")\n",
+ " return [smiles]\n",
+ " \n",
+ " augmented_rows = []\n",
+ " \n",
+ " for idx, row in tqdm(df.iterrows(), total=len(df)):\n",
+ " original_smiles = row[smiles_column]\n",
+ " \n",
+ " # Add original if requested\n",
+ " if preserve_original:\n",
+ " original_row = row.to_dict()\n",
+ " original_row['augmentation_strategy'] = 'original'\n",
+ " original_row['is_augmented'] = False\n",
+ " augmented_rows.append(original_row)\n",
+ " \n",
+ " # Apply each augmentation strategy\n",
+ " for strategy in augmentation_strategies:\n",
+ " strategy_smiles = apply_augmentation_strategy(original_smiles, strategy)\n",
+ " \n",
+ " for aug_smiles in strategy_smiles:\n",
+ " if aug_smiles != original_smiles: # Avoid duplicating original\n",
+ " new_row = row.to_dict().copy()\n",
+ " new_row[smiles_column] = aug_smiles\n",
+ " new_row['augmentation_strategy'] = strategy\n",
+ " new_row['is_augmented'] = True\n",
+ " augmented_rows.append(new_row)\n",
+ " \n",
+ " augmented_df = pd.DataFrame(augmented_rows)\n",
+ " augmented_df = augmented_df.reset_index(drop=True)\n",
+ " \n",
+ " print(f\"Advanced augmentation completed:\")\n",
+ " print(f\"Original size: {len(df)}, Augmented size: {len(augmented_df)}\")\n",
+ " print(f\"Augmentation factor: {len(augmented_df) / len(df):.2f}x\")\n",
+ " \n",
+ " return augmented_df.reset_index(drop=True)\n",
+ "\n",
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.85 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)\n",
+ "\n",
+ "train = augment_smiles_dataset(train)\n",
+ "test = augment_smiles_dataset(test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['SMILES']\n",
+ "smiles_test = test['SMILES']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['target_scalers.pkl']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joblib.dump(scalers, 'target_scalers.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_279009/2507782815.py:68: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " simson_params = torch.load('/home/jovyan/simson_training_bolgov/kaggle_comp/simson_polymer_1m_uncompiled.pth')\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from transformers import AutoTokenizer, BertModel\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ "\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ "\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ "\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x\n",
+ "\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/kaggle_comp/simson_polymer_1m_uncompiled.pth')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, Sampler, DataLoader\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 5) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability for 5 labels\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_2_count': label_counts[2],\n",
+ " 'label_3_count': label_counts[3],\n",
+ " 'label_4_count': label_counts[4],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'label_2_ratio': label_counts[2] / total_samples,\n",
+ " 'label_3_ratio': label_counts[3] / total_samples,\n",
+ " 'label_4_ratio': label_counts[4] / total_samples,\n",
+ " 'all_labels_count': (self.label_masks.sum(axis=1) == 5).sum(),\n",
+ " 'partial_labels_count': ((self.label_masks.sum(axis=1) > 0) & (self.label_masks.sum(axis=1) < 5)).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "\n",
+ "class UnderrepresentedLabelSampler(Sampler):\n",
+ " \"\"\"\n",
+ " Custom sampler that gives higher sampling probability to samples containing under-represented labels.\n",
+ " This ensures each batch contains a good mix of samples with different label availability patterns.\n",
+ " \"\"\"\n",
+ " def __init__(self, dataset, num_labels=5, underrep_boost=2.0):\n",
+ " \"\"\"\n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " num_labels: Number of labels (5)\n",
+ " underrep_boost: Multiplier to boost probability of under-represented labels\n",
+ " \"\"\"\n",
+ " self.dataset = dataset\n",
+ " self.num_samples = len(dataset)\n",
+ " self.num_labels = num_labels\n",
+ " self.underrep_boost = underrep_boost\n",
+ " \n",
+ " # Calculate label frequencies\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = self.num_samples\n",
+ " \n",
+ " # Label frequencies (proportion of samples with each label)\n",
+ " label_freq = label_counts / total_samples\n",
+ " \n",
+ " # Inverse frequency weights (higher for under-represented labels)\n",
+ " # Add small epsilon to avoid division by zero\n",
+ " self.label_weights = 1.0 / (label_freq + 1e-6)\n",
+ " \n",
+ " # Apply boost to under-represented labels\n",
+ " # Labels with frequency < median get boosted\n",
+ " median_freq = np.median(label_freq)\n",
+ " underrep_mask = label_freq < median_freq\n",
+ " self.label_weights[underrep_mask] *= self.underrep_boost\n",
+ " \n",
+ " # Calculate sample weights based on which labels are present\n",
+ " sample_weights = []\n",
+ " for i in range(self.num_samples):\n",
+ " mask = dataset.label_masks[i] # Boolean mask for present labels\n",
+ " if mask.sum() > 0:\n",
+ " # Weight is average of present labels' weights\n",
+ " weights = self.label_weights[mask]\n",
+ " sample_weight = weights.mean()\n",
+ " else:\n",
+ " # If no labels present, give minimal weight\n",
+ " sample_weight = 0.1\n",
+ " sample_weights.append(sample_weight)\n",
+ " \n",
+ " self.sample_weights = torch.tensor(sample_weights, dtype=torch.double)\n",
+ " \n",
+ " # Print sampling statistics\n",
+ " print(f\"Label frequencies: {label_freq}\")\n",
+ " print(f\"Label weights: {self.label_weights}\")\n",
+ " print(f\"Under-represented labels (< median freq {median_freq:.3f}): {np.where(underrep_mask)[0]}\")\n",
+ " print(f\"Sample weight range: [{self.sample_weights.min():.3f}, {self.sample_weights.max():.3f}]\")\n",
+ " \n",
+ " def __iter__(self):\n",
+ " # Sample with replacement according to calculated weights\n",
+ " indices = torch.multinomial(self.sample_weights, self.num_samples, replacement=True)\n",
+ " return iter(indices.tolist())\n",
+ " \n",
+ " def __len__(self):\n",
+ " return self.num_samples\n",
+ "\n",
+ "\n",
+ "def calculate_unweighted_loss(predictions, labels, label_mask):\n",
+ " \"\"\"\n",
+ " Calculate simple unweighted MSE loss with masking (no label weights)\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 5)\n",
+ " labels: Ground truth labels (batch_size, 5)\n",
+ " label_mask: Mask for valid labels (batch_size, 5)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 5)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = masked_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 5).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 5).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 5).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \n",
+ " Returns:\n",
+ " float: Average MAE across all valid samples\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(5):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "\n",
+ "def calculate_individual_label_losses(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for each individual label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 5).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 5).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 5).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \n",
+ " Returns:\n",
+ " dict: Dictionary with MAE for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " individual_losses = {}\n",
+ " \n",
+ " for label_idx in range(5):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " individual_losses[f'label_{label_idx}'] = mae\n",
+ " else:\n",
+ " individual_losses[f'label_{label_idx}'] = None # No valid samples for this label\n",
+ " \n",
+ " return individual_losses\n",
+ "\n",
+ "\n",
+ "def analyze_batch_composition(dataloader, num_batches=5):\n",
+ " \"\"\"\n",
+ " Analyze the composition of batches to see label distribution\n",
+ " \"\"\"\n",
+ " print(\"Analyzing batch composition:\")\n",
+ " \n",
+ " for batch_idx, batch in enumerate(dataloader):\n",
+ " if batch_idx >= num_batches:\n",
+ " break\n",
+ " \n",
+ " label_mask = batch['label_mask'].numpy()\n",
+ " \n",
+ " # Count samples with each label in this batch\n",
+ " label_counts = label_mask.sum(axis=0)\n",
+ " batch_size = label_mask.shape[0]\n",
+ " \n",
+ " print(f\"Batch {batch_idx + 1}: Size={batch_size}\")\n",
+ " for i in range(5):\n",
+ " print(f\" Label {i}: {label_counts[i]}/{batch_size} ({label_counts[i]/batch_size:.2%})\")\n",
+ " print()\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with unweighted loss and custom sampler for five labels\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 5 labels)\n",
+ " train_dataloader: Training data loader with custom sampler\n",
+ " val_dataloader: Validation data loader \n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Training with custom sampler (no label weights)\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate unweighted loss (sampler handles the balancing)\n",
+ " loss = calculate_unweighted_loss(outputs, labels, label_mask)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ " \n",
+ " # Track individual label losses across all validation batches\n",
+ " accumulated_individual_losses = {f'label_{i}': [] for i in range(5)}\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_unweighted_loss(outputs, labels, label_mask)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Calculate individual label losses for this batch\n",
+ " individual_losses = calculate_individual_label_losses(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate individual losses\n",
+ " for label_key, loss_value in individual_losses.items():\n",
+ " if loss_value is not None:\n",
+ " accumulated_individual_losses[label_key].append(loss_value)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " # Calculate average individual label losses\n",
+ " avg_individual_losses = {}\n",
+ " for label_key, losses in accumulated_individual_losses.items():\n",
+ " if losses:\n",
+ " avg_individual_losses[label_key] = np.mean(losses)\n",
+ " else:\n",
+ " avg_individual_losses[label_key] = None\n",
+ " \n",
+ " # Print validation results with individual label losses\n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " print(\"Individual label losses (unscaled):\")\n",
+ " for i in range(5):\n",
+ " label_key = f'label_{i}'\n",
+ " if avg_individual_losses[label_key] is not None:\n",
+ " print(f\" Label {i}: {avg_individual_losses[label_key]:.4f}\")\n",
+ " else:\n",
+ " print(f\" Label {i}: No valid samples\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500, underrep_boost=2.0):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for five labels with custom sampler\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 5) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 5 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 5 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " underrep_boost: Boost factor for under-represented labels in sampler\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for five-label training with custom sampler\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Create custom sampler for balanced training\n",
+ " train_sampler = UnderrepresentedLabelSampler(\n",
+ " train_dataset, \n",
+ " num_labels=5, \n",
+ " underrep_boost=underrep_boost\n",
+ " )\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " sampler=None, # Use custom sampler instead of shuffle=True\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Analyze batch composition to verify sampler effectiveness\n",
+ " print(\"\\n\" + \"=\"*50)\n",
+ " #analyze_batch_composition(train_dataloader, num_batches=3)\n",
+ " print(\"=\"*50)\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " scalers=scalers,\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/kaggle_comp/checkpoints/clf_kaggle.bin')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for five-label training with custom sampler\n",
+ "Training dataset statistics:\n",
+ " total_samples: 168551\n",
+ " label_0_count: 5446\n",
+ " label_1_count: 78850\n",
+ " label_2_count: 14846\n",
+ " label_3_count: 5779\n",
+ " label_4_count: 5782\n",
+ " label_0_ratio: 0.032310695279173664\n",
+ " label_1_ratio: 0.46781092962960763\n",
+ " label_2_ratio: 0.08808016564719284\n",
+ " label_3_ratio: 0.03428635843157264\n",
+ " label_4_ratio: 0.03430415719871137\n",
+ " all_labels_count: 0\n",
+ " partial_labels_count: 96406\n",
+ " no_labels_count: 72145\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 29716\n",
+ " label_0_count: 947\n",
+ " label_1_count: 13878\n",
+ " label_2_count: 2764\n",
+ " label_3_count: 957\n",
+ " label_4_count: 955\n",
+ " label_0_ratio: 0.03186835374882218\n",
+ " label_1_ratio: 0.4670211333961502\n",
+ " label_2_ratio: 0.0930138645847355\n",
+ " label_3_ratio: 0.03220487279580024\n",
+ " label_4_ratio: 0.03213756898640463\n",
+ " all_labels_count: 0\n",
+ " partial_labels_count: 17016\n",
+ " no_labels_count: 12700\n",
+ "Label frequencies: [0.0323107 0.46781093 0.08808017 0.03428636 0.03430416]\n",
+ "Label weights: [61.89709276 2.13761116 11.35316492 58.33053614 29.15013606]\n",
+ "Under-represented labels (< median freq 0.034): [0 3]\n",
+ "Sample weight range: [0.100, 61.897]\n",
+ "\n",
+ "==================================================\n",
+ "==================================================\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 1317\n",
+ "Total training steps: 26340\n",
+ "Training with custom sampler (no label weights)\n",
+ "Validation will be performed every 1316 steps\n",
+ "\n",
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 1316 | Train Loss: 0.6250 | Val Loss: 0.4127 | True train loss: 3.9762 | True val loss: 3.8368\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 76.7992\n",
+ " Label 1: 0.0127\n",
+ " Label 2: 0.0372\n",
+ " Label 3: 0.0987\n",
+ " Label 4: 3.3515\n",
+ "New best validation loss: 0.4127\n",
+ "\n",
+ "Epoch 2/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 2632 | Train Loss: 0.5464 | Val Loss: 0.4244 | True train loss: 3.5447 | True val loss: 3.4895\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 68.7228\n",
+ " Label 1: 0.0130\n",
+ " Label 2: 0.0379\n",
+ " Label 3: 0.0952\n",
+ " Label 4: 3.8732\n",
+ "\n",
+ "Epoch 3/20\n"
+ ]
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+ "text": [
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+ " Label 1: 0.0130\n",
+ " Label 2: 0.0362\n",
+ " Label 3: 0.1013\n",
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+ " Label 2: 0.0382\n",
+ " Label 3: 0.0951\n",
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+ " Label 0: 67.9448\n",
+ " Label 1: 0.0116\n",
+ " Label 2: 0.0392\n",
+ " Label 3: 0.0810\n",
+ " Label 4: 3.3704\n",
+ "New best validation loss: 0.3498\n"
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+ " \r"
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+ "Epoch 6/20\n"
+ ]
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+ " Label 0: 63.2215\n",
+ " Label 1: 0.0117\n",
+ " Label 2: 0.0362\n",
+ " Label 3: 0.0827\n",
+ " Label 4: 3.2292\n",
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+ "Training: 100%|▉| 1313/1317 [01:22<00:02, 1.86it/s, step=9215, loss=0.2901, tru"
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+ "Step 9212 | Train Loss: 0.4557 | Val Loss: 0.3389 | True train loss: 3.0609 | True val loss: 3.2751\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 63.4267\n",
+ " Label 1: 0.0114\n",
+ " Label 2: 0.0381\n",
+ " Label 3: 0.0815\n",
+ " Label 4: 2.8806\n",
+ "New best validation loss: 0.3389\n"
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+ "text": [
+ " \r"
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+ "\n",
+ "Epoch 8/20\n"
+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Training: 100%|▉| 1311/1317 [01:22<00:03, 1.87it/s, step=10531, loss=0.4604, tr"
+ ]
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+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 10528 | Train Loss: 0.4474 | Val Loss: 0.3379 | True train loss: 3.0718 | True val loss: 3.2051\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.2247\n",
+ " Label 1: 0.0113\n",
+ " Label 2: 0.0372\n",
+ " Label 3: 0.0828\n",
+ " Label 4: 2.9602\n",
+ "New best validation loss: 0.3379\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 9/20\n"
+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
+ "Training: 100%|▉| 1311/1317 [01:21<00:03, 1.87it/s, step=11847, loss=0.2547, tr"
+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 11844 | Train Loss: 0.4285 | Val Loss: 0.3416 | True train loss: 3.0075 | True val loss: 3.1697\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.3822\n",
+ " Label 1: 0.0112\n",
+ " Label 2: 0.0421\n",
+ " Label 3: 0.0847\n",
+ " Label 4: 3.3251\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 10/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 99%|▉| 1309/1317 [01:21<00:04, 1.87it/s, step=13163, loss=0.2791, tr"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 13160 | Train Loss: 0.4116 | Val Loss: 0.3174 | True train loss: 2.9027 | True val loss: 3.1666\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 59.6537\n",
+ " Label 1: 0.0110\n",
+ " Label 2: 0.0365\n",
+ " Label 3: 0.0877\n",
+ " Label 4: 3.1535\n",
+ "New best validation loss: 0.3174\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 11/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 99%|▉| 1309/1317 [01:21<00:04, 1.87it/s, step=14479, loss=0.3915, tr"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 14476 | Train Loss: 0.3983 | Val Loss: 0.3039 | True train loss: 2.8602 | True val loss: 3.1240\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 60.6528\n",
+ " Label 1: 0.0107\n",
+ " Label 2: 0.0371\n",
+ " Label 3: 0.0827\n",
+ " Label 4: 3.2043\n",
+ "New best validation loss: 0.3039\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 12/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 99%|▉| 1307/1317 [01:21<00:05, 1.87it/s, step=15795, loss=0.1155, tr"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 15792 | Train Loss: 0.3863 | Val Loss: 0.3050 | True train loss: 2.7796 | True val loss: 3.0697\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 59.8002\n",
+ " Label 1: 0.0108\n",
+ " Label 2: 0.0371\n",
+ " Label 3: 0.0815\n",
+ " Label 4: 3.1037\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 13/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 99%|▉| 1307/1317 [01:21<00:05, 1.87it/s, step=17111, loss=0.2704, tr"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 17108 | Train Loss: 0.3779 | Val Loss: 0.2881 | True train loss: 2.7442 | True val loss: 3.1636\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.2941\n",
+ " Label 1: 0.0102\n",
+ " Label 2: 0.0361\n",
+ " Label 3: 0.0836\n",
+ " Label 4: 3.1077\n",
+ "New best validation loss: 0.2881\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 14/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 99%|▉| 1305/1317 [01:21<00:06, 1.87it/s, step=18427, loss=0.4965, tr"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 18424 | Train Loss: 0.3645 | Val Loss: 0.2822 | True train loss: 2.6844 | True val loss: 3.1494\n",
+ "Individual label losses (unscaled):\n",
+ " Label 0: 61.1663\n",
+ " Label 1: 0.0100\n",
+ " Label 2: 0.0365\n",
+ " Label 3: 0.0743\n",
+ " Label 4: 3.2309\n",
+ "New best validation loss: 0.2822\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 15/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtqdm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[32m 8\u001b[39m BATCH_SIZE = \u001b[32m128\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m train_losses, val_losses, best_loss = \u001b[43mrun_training\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[43msmiles_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msmiles_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 12\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokenizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mBATCH_SIZE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msmiles_train\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m/\u001b[49m\u001b[43m/\u001b[49m\u001b[43m \u001b[49m\u001b[43mBATCH_SIZE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 13\u001b[39m \u001b[43m)\u001b[49m\n",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 532\u001b[39m, in \u001b[36mrun_training\u001b[39m\u001b[34m(smiles_train, smiles_test, labels_train, labels_test, model, tokenizer, scalers, num_epochs, learning_rate, batch_size, validation_steps, underrep_boost)\u001b[39m\n\u001b[32m 529\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal training steps: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;250m \u001b[39m*\u001b[38;5;250m \u001b[39mnum_epochs\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 531\u001b[39m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m532\u001b[39m train_losses, val_losses, best_val_loss = \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 534\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m=\u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 536\u001b[39m \u001b[43m \u001b[49m\u001b[43mscalers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscalers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 537\u001b[39m \u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 538\u001b[39m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 539\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 540\u001b[39m \u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 541\u001b[39m \u001b[43m \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 542\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 544\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mTraining completed.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 545\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mNumber of validation checkpoints: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(val_losses)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 351\u001b[39m, in \u001b[36mtrain_model\u001b[39m\u001b[34m(model, train_dataloader, val_dataloader, scalers, num_epochs, learning_rate, device, patience, validation_steps)\u001b[39m\n\u001b[32m 345\u001b[39m scheduler.step()\n\u001b[32m 347\u001b[39m global_step += \u001b[32m1\u001b[39m\n\u001b[32m 349\u001b[39m train_progress.set_postfix({\n\u001b[32m 350\u001b[39m \u001b[33m'\u001b[39m\u001b[33mstep\u001b[39m\u001b[33m'\u001b[39m: global_step,\n\u001b[32m--> \u001b[39m\u001b[32m351\u001b[39m \u001b[33m'\u001b[39m\u001b[33mloss\u001b[39m\u001b[33m'\u001b[39m: \u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mloss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m,\n\u001b[32m 352\u001b[39m \u001b[33m'\u001b[39m\u001b[33mtrue_loss\u001b[39m\u001b[33m'\u001b[39m: \u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrue_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m,\n\u001b[32m 353\u001b[39m \u001b[33m'\u001b[39m\u001b[33mlr\u001b[39m\u001b[33m'\u001b[39m: \u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscheduler.get_last_lr()[\u001b[32m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2e\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\n\u001b[32m 354\u001b[39m })\n\u001b[32m 356\u001b[39m \u001b[38;5;66;03m# Perform validation every validation_steps\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m global_step % validation_steps == \u001b[32m0\u001b[39m:\n\u001b[32m 358\u001b[39m \u001b[38;5;66;03m# Calculate average training losses since last validation\u001b[39;00m\n",
+ "\u001b[31mKeyboardInterrupt\u001b[39m: "
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "BATCH_SIZE = 128\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=20, learning_rate=1e-4, batch_size=BATCH_SIZE, validation_steps=len(smiles_train) // BATCH_SIZE,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kaggle": {
+ "accelerator": "gpu",
+ "dataSources": [
+ {
+ "databundleVersionId": 12966160,
+ "sourceId": 74608,
+ "sourceType": "competition"
+ },
+ {
+ "datasetId": 7678100,
+ "sourceId": 12189904,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7690162,
+ "sourceId": 12207625,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7716502,
+ "sourceId": 12322957,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7801155,
+ "sourceId": 12372847,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7809006,
+ "sourceId": 12525286,
+ "sourceType": "datasetVersion"
+ },
+ {
+ "datasetId": 7912957,
+ "sourceId": 12668147,
+ "sourceType": "datasetVersion"
+ }
+ ],
+ "dockerImageVersionId": 31041,
+ "isGpuEnabled": true,
+ "isInternetEnabled": true,
+ "language": "python",
+ "sourceType": "notebook"
+ },
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/simson_modeling/kaggle_comp/simson_polymer_1m_uncompiled.pth b/simson_modeling/kaggle_comp/simson_polymer_1m_uncompiled.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a757f6d28af9ed4b3b23afe8961737c953bb51fd
--- /dev/null
+++ b/simson_modeling/kaggle_comp/simson_polymer_1m_uncompiled.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:447522e6635568b0a9f5ca015910bcb1fc98a56e60cb6b90a10080b15611ef3f
+size 93224872
diff --git a/simson_modeling/kaggle_comp/test.csv b/simson_modeling/kaggle_comp/test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c264e19837fc6c71aaf63a9a33f9a0531480567e
--- /dev/null
+++ b/simson_modeling/kaggle_comp/test.csv
@@ -0,0 +1,4 @@
+id,SMILES
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+2032016830,*c1cccc(OCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1
diff --git a/simson_modeling/kaggle_comp/train.csv b/simson_modeling/kaggle_comp/train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..82c950309b6fa96cc98cade15bf93748f0b2f82c
--- /dev/null
+++ b/simson_modeling/kaggle_comp/train.csv
@@ -0,0 +1,7974 @@
+id,SMILES,Tg,FFV,Tc,Density,Rg
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+519416,*Nc1ccc(-c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(N*)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)cc1,,0.3873239,,,
+539187,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.35547017,,,
+950661,*OC(=O)CCCCCCCCC(=O)OC1COC2C(*)COC12,,0.33909279,,,
+1012066,*Nc1ccc([C@H]2[C@@H]3C[C@H]4C[C@@H](C3)C[C@@H]2C4)cc1N*,,0.3476159,,,
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+1722264,*CC(*)(C)C(=O)OCCCCCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.35917558,,,
+2265305,*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)cc2)cc1,,0.341878125,,,
+2986007,*c1ccc(-c2ccc3c(c2)C(CCCCCCC#N)(CCCCCCC#N)c2cc(*)ccc2-3)cc1,,0.40239665,0.487,0.901122805,28.6824407
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+4658296,*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.36326495,,,
+4834929,*Nc1ccc2c(c1)[C@H]1CC[C@@H]2c2cc(N*)ccc21,,0.36594162,,,
+5118841,*C(=O)c1ccc(N(*)CCCCCCCC)cc1,,0.40101187,,,
+5350166,*CC(C)(COC(=O)NCCCCCCNC(=O)O*)C(=O)O,,0.31898697,,,
+5650517,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCC)cc1,,0.33573247,,,
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+5709902,*Nc1ccc(NC(=O)CCCCCCC(*)=O)cc1,,0.33579028,,,
+5724071,*NC(=O)Nc1ccc(Cc2ccc(NC(=O)Nc3cccc(*)n3)cc2)cc1,,0.33930299,,,
+5857263,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36614256,,,
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+9284896,*O[Si](C)(C)CCCNC(=O)C(=O)NCCC[Si](*)(C)C,,0.38708542,,,
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+10557152,*C(C)=C(*)[Ge](C)(C)C,,0.39996108,,,
+11301237,*c1ccc(Oc2ccc(-c3nc4ccc(Oc5ccc(N6C(=O)c7ccc(C(c8ccc9c(c8)C(=O)N(c8ccc(Oc%10ccc%11nc(*)c(-c%12ccccc%12)nc%11c%10)cc8)C9=O)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4nc3-c3ccccc3)cc2)cc1,,0.40256926,,,
+11664621,*OC(COC(*)=O)COC(C)C,,0.35338284,,,
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+11796436,*Nc1cc2c(c(-c3ccccc3)c1N*)[C@@H](c1ccccc1)c1ccccc1-2,,0.37078536,,,
+11894206,*CC(*)C(=O)Oc1ccccc1C,,0.35783052,0.1735,1.061864136,12.7374634
+11955349,*C(=Nc1ccccc1)c1ccc(-c2ccc(*)cc2)cc1,,0.39901234,,,
+12008966,*CC(*)c1ccc(C(C)(C)C)cc1,,,0.184,,
+12265790,*CC(*)C(=O)OC(C)C,,0.36594213,,,
+12381586,*CC(*)C(=O)OCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.35839221,,,
+12395811,*C(=Cc1cc(OC)c(C=C(c2ccc(*)cc2)c2ccc(OC)cc2)cc1OC)c1ccc(OC)cc1,,0.37715059,,,
+12627006,*c1ccc(OCCN(CC)c2ccc(C(C#N)=C(C#N)C#N)cc2)c(-c2cc(-c3ccccc3)c3cc(Oc4ccc5nc(*)cc(-c6ccccc6)c5c4)ccc3n2)c1,,0.39541698,,,
+12724819,*C=Cc1ccc2c(c1)Sc1cc(-c3ccc4c(c3)Sc3cc(*)ccc3N4c3ccc(OCCCCCCCCCCCC)cc3)ccc1N2c1ccc(OCCCCCCCCCCCC)cc1,,0.400601,,,
+13021733,*O[Si](*)(CCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOC,,0.36551909,,,
+13364814,*CCCP(C)(=O)CCCNC(=O)CCCCC(=O)N*,,0.33815952,,,
+13630833,*c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)cc1C(C)C)C2=O,,0.42131241,,,
+13838538,*CCCCCCSSCCCCSS*,-41.26672381,,0.192,,
+13850404,*OC(=O)OCC(O)C(O)COC(=O)OC1COC2C(*)COC12,,0.29768641,,,
+13874722,*CCCCCOCO*,,0.36424093,,,
+15891916,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7cccc(-c8nc(*)c(-c9ccccc9)[nH]8)c7)[nH]c6-c6ccccc6)cc5)cc4)c3)cc2)cc1,,0.39177495,,,
+15979280,*Oc1ccc(NC(=O)c2cc(NC(=O)CCCCCN3C(=O)c4ccccc4C3=O)cc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.33547772,,,
+16364869,*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc(C(F)(F)F)c1)C2=O,,0.39841654,,,
+16498242,*C=CCCCCCCCC*,-17.2820223,,,,
+16855553,*CC(*)C(=O)Oc1ccccc1,,0.3539411,0.196,1.109388621,13.43533854
+17114758,*Oc1ccc(Cc2ccc(OC(=O)c3ccc(C(C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36534872,,,
+17899555,*C=C(*)c1ccc(-n2c(=O)c3cc4c(=O)n(-c5ccc(C#C)cc5)c(=O)c4cc3c2=O)cc1,,0.37446752,,,
+17943713,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(OCCCCCCOc3ccc(/C=C/c4ccc(F)cc4)cc3)c2)cc1OCCCCCCOc1ccc(/C=C/c2ccc(F)cc2)cc1,,0.36214283,,,
+18227797,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.35827772,,,
+18308835,*O[Si](C)(C)c1ccc2ccc([Si](*)(C)C)cc2c1,,0.41603966,,,
+18339242,*CC(*)C(=O)OC([2H])([2H])[2H],,0.32713966,,,
+18520951,*CC(C)(C)C1C(=O)N(c2c(C)cccc2C)C(=O)C1*,,0.36567807,,,
+18696710,*CC(O)COc1c(C)cc(S(=O)(=O)c2cc(C)c(O*)c(C)c2)cc1C,,0.36816578,,,
+19277692,*c1ccc(Oc2ccc(-c3cnc4ccc(Oc5ccc6nc(*)cnc6c5)cc4n3)cc2)cc1,,0.39336683,,,
+19303052,*CC(*)(C)C(=O)OCc1cc(Cl)ccc1Cl,,0.36119278,,,
+19522327,*CC(*)OC(=O)c1cccc(Br)c1,,0.37722551,,,
+20039468,*Oc1ccc(-c2ccc(-c3ccc(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)cc3)cc2)cc1,,0.36836692,,,
+20273521,*CC(CN1C(=O)C2C3C=CC(C3)C2C1=O)O*,,0.34392035,,,
+20627281,*Nc1cc2ccccc2c(-c2cccc3ccccc23)c1N*,,0.35509262,,,
+20883601,*Cc1cc(C)c(CNC(=O)c2ccc(C(=O)N*)cc2)cc1C,,0.33875515,,,
+21279513,*Oc1ccc(C2(c3ccc(OC4(F)C(*)(F)C(F)(F)C4(F)F)cc3)c3ccccc3-c3ccccc32)cc1,,0.38927007,,,
+21520341,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CCC(C)CC2)cc1,,0.37589597,,,
+22184710,*CC(*)c1ccc(C(=O)OCCC)cc1,,0.37674615,,,
+22597605,*c1ccc(Oc2ccc(-c3nc4cc(-c5ccc6nc(-c7ccccc7)c(*)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.41714937,,,
+22905078,*CC(*)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32506583,,,
+22928574,*O[Sn](CCCC)(CCCC)OC(=O)/C=C/C(*)=O,,0.35027613,,,
+22953969,*Oc1ccc(-c2ccc(OC(=O)CCCCCCCCCCCCC(*)=O)cc2)cc1,,0.35595348,,,
+23036398,*c1ccc(S(=O)(=O)c2ccc(N3C(=O)C(C)C(SCCOCCSC4C(=O)N(c5ccc(S(=O)(=O)c6ccc(N7C(=O)CC(C)(SCCOCCSC8(C)CC(=O)N(*)C8=O)C7=O)cc6)cc5)C(=O)C4C)C3=O)cc2)cc1,,0.34421859,,,
+23192453,*CC(C)S(*)(=O)=O,,,,1.293314518,20.99132259
+23694396,*N=P(*)(Cl)Cl,,0.34644364,,,
+23713841,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36615689,,,
+24351648,*Nc1ccc(C2(c3ccc(N*)cc3)C=CCCC2)cc1,,0.34884686,,,
+24969052,*CCCCCOC(=O)c1cccc(C(=O)O*)c1,,0.34539931,,,
+24982830,*c1ccc(NC(=O)CN2C(=O)c3ccc(C(c4ccc5c(c4)C(=O)N(CC(=O)Nc4ccc(-c6nnc(*)o6)cc4)C5=O)(C(F)(F)F)C(F)(F)F)cc3C2=O)cc1,,0.35544781,,,
+25581268,*c1ccc(S(=O)(=O)c2ccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.37886829,,,
+25596707,*c1ccc(Oc2ccc(N3C(=O)c4cc(-c5ccc([Si](C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc([Si](C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.42813779,,,
+25599707,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5nc(-c6cccc(-c7nc(*)c(-c8ccccc8)[nH]7)c6)[nH]c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.40187067,,,
+25748429,*c1ccc(Cc2ccc(N3C(=O)C4CCC5C(=O)N(*)C(=O)C5C4C3=O)c(C)c2)cc1C,,0.38793304,,,
+26299008,*CC(*)c1cc(C(C)C)ccc1C(C)C,,,0.1616666666666666,0.830463672,11.48207818
+26367456,*Nc1ccc(C[C@](CC)(CCC)c2ccc(N)c(C[C@](C=C)(CC)c3ccc(N*)cc3)c2)cc1,,0.35808156,,,
+26995134,*Nc1ccc(/C=C/c2ccc(N*)cc2)cc1,,0.34415534,,,
+28121925,*c1ccc(C(c2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.39361972,,,
+28200504,*CCOC(=O)NCCCCCCNC(=O)O*,,0.32916214,,,
+28479659,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(-c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2)cc1,,0.36156161,,,
+29482987,*C(=C(*)c1ccc(Oc2ccccc2)cc1)c1ccccc1,,0.40046864,,,
+29766304,*CC(*)(C)C(=O)Oc1ccc(C(=O)c2ccccc2)cc1,,0.35600022,,,
+29881762,*CCCCCCN1C(=O)C(=O)N(*)C1=O,,0.31655437,,,
+29928066,*CC(*)(C)C(=O)OCCn1c2ccccc2c2ccccc21,,0.35181885,,,
+30582999,*CCCCCCCCCCOC(=O)c1ccc(C(=O)NCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,4.250402609,,,,
+30682090,*c1ccc(C(C)(C)c2ccc(N3C(=O)c4ccc(Oc5ccc6cc(Oc7ccc8c(c7)C(=O)N(*)C8=O)ccc6c5)cc4C3=O)cc2)cc1,,0.38208553,,,
+31430153,*OC(COC(=O)Nc1ccc(Cc2ccc(NC(*)=O)cc2)cc1)c1ccco1,,0.34289328,,,
+31529134,*C(=O)Nc1cccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)c1,,0.33798245,,,
+31841016,*CC(*)(C)C(=O)OCCOC(=O)c1cc([N+](=O)[O-])cc([N+](=O)[O-])c1,,0.41138901,,,
+32316139,*CCN(*)C(=O)c1cc(F)cc(F)c1F,,0.33952456,,,
+32586958,*NC(N*)=C(N)/C(N)=N/C,,0.29714034,,,
+34026248,*Nc1ccc(Cc2ccc(NC(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36719294,,,
+34042817,*CCCCCCOC(=O)c1cccc(C(=O)O*)c1,,0.34938247,,,
+34234994,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)c1)C2=O,,0.36162895,,,
+34546505,*Oc1cccc(Oc2ccc(C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1,,0.37794539,,,
+35288312,*c1ccc(C=Nc2ccc(C(c3ccccc3)c3ccc(N=Cc4ccc(-c5ccc(*)s5)cc4)cc3)cc2)cc1,,0.40126347,,,
+35380876,*c1ccc(Oc2ccc(-n3c(=O)c4cc5c(=O)n(-c6ccc(Oc7ccc(-c8nc9cc%10sc(*)nc%10cc9s8)cc7)cc6)c(=O)c5cc4c3=O)cc2)cc1,,0.422749,,,
+35399593,*N=P(*)(Oc1ccc(C)cc1)Oc1ccc(C)cc1,,0.39264145,,,
+35822036,*CC1(C)CC(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC(C)(C)C1,,0.36993749,,,
+36217683,*c1nc2cc3sc(-c4cc(OCCCCCC)c(*)cc4OCCCCCC)nc3cc2s1,168.5263131,,,,
+36780446,*Oc1cc(CCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.36080704,,,
+37511814,*Nc1ccc2c(c1)c1ccccc1c1c(-c3ccc(CCCCC)cc3)c(N*)c(-c3ccc(CCCCC)cc3)cc21,,0.36461838,,,
+37571918,*Nc1cc2ccc3ccc4c(N*)cc5ccc6ccc1c1c6c5c4c3c21,,0.33562005,,,
+37623475,*Nc1ccc(-c2ccc(N*)c(-c3cccc(C)c3)c2-c2cccc(C)c2)cc1,,0.37320004,,,
+37727451,*C(=O)Nc1ccc(C)cc1N1C(=O)CC(N2C(=O)c3ccc(*)cc3C2=O)C1=O,,0.34915334,,,
+38112797,*Nc1cc(C2CCCCC2)c2ccc3c(N*)c(-c4ccccc4)c(C4CCCCC4)c4c(-c5ccccc5)cc1c2c34,,0.39389056,,,
+38228922,*C(=O)Nc1cccc(NC(=O)c2ccc3[nH]c(-c4cc(-c5nc6cc(*)ccc6[nH]5)cc(N5C(=O)c6ccccc6C5=O)c4)nc3c2)n1,,0.36599842,,,
+38242048,*/C(F)=C(\F)C(F)(C(*)(F)F)C(F)(F)F,,,0.102,,
+39109326,*CCN(CCOC(=O)NCC1(C)CC(NC(=O)O*)CC(C)(C)C1)c1ccc(N=Nc2ccc(P(=O)(c3ccccc3)c3ccccc3)cc2)cc1,,0.36633956,,,
+40088405,*c1ccc(C(c2ccccc2)c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.39645555,,,
+40146140,*C(=O)Nc1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nc5ccccc5o4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(-c2nc3ccccc3[nH]2)c1,,0.37477933,,,
+40417262,*Nc1ccc(Oc2cccc(N*)c2)cc1,,0.33227609,,,
+40655089,*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(S(=O)(=O)c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36114325,,,
+40953121,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(C)c(N8C(=O)c9ccc(*)cc9C8=O)c6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.37070669,,,
+41386383,*CCCCCC(*)CCCCCC,,0.40628619,,0.797183169,16.82724961
+41421570,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)n4)cc3)cc1)C2=O,,0.36563224,,,
+41644742,*Oc1ccc(OC(=O)c2cc(OCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.34909461,,,
+41684723,*Oc1ccc(-c2cc(OCCCCCC)c(-c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)cc2OCCCCCC)cc1,81.55306128,,,,
+41977066,*CC(*)(C)C(=O)NC(=O)Oc1c(Br)cc(S(=O)(=O)c2cc(Br)c(O)c(Br)c2)cc1Br,,0.37549639,0.109,,
+42779237,*c1cccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCCOc2cccc(-c3nnc(*)s3)c2)c1,35.51374296,,,,
+43489811,*CCCNC(=O)c1ccc(C(=O)NCCCO*)cc1,,0.32981777,,,
+43517583,*CC(*)c1ccc(C)c(C)c1,,,0.1905,,
+44130721,*Oc1ccc(OC(=O)c2cccc(Sc3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(Sc6cccc(C(*)=O)c6)cc5)cc4)cc3)c2)cc1C,,0.35931108,,,
+44332410,*CC(*)CCCCCCC,,0.40501273,0.2669999999999999,0.7966448,10.79657738
+45108843,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.36246435,,,
+45704443,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(C(c5ccc(C(=O)c6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2)cc1,,0.3798743,,,
+45818224,*OC(F)(F)C(F)(F)C(*)(F)F,,0.2987695,,,
+45908417,*C(=O)c1cccc(C(=O)N(*)c2ccccc2)c1,,0.38456711,,,
+46067784,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCC,,0.3630391,,,
+46369100,*Nc1ccccc1C(C)(c1ccccc1N)c1ccccc1N*,,0.35719832,,,
+46473006,*C=CC1CC(*)C(C#N)(CCC)C1,,0.39981874,,,
+46656995,*CC(*)CCCN(CC(C)C)CC(C)C,,,0.201,0.817146131,9.7283551
+47046176,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)c7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36207286,,,
+48005195,*C=Nc1cccc(N=Cc2ccc(Sc3ccc(*)o3)o2)c1,122.5813993,,,,
+48202315,*c1ccc(Oc2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(Oc5ccc(Oc6ccc(-c7nc8cc9sc(*)nc9cc8s7)cc6)cc5)cc4)cc3)cc2)cc1,,0.40182864,,,
+48308812,*CCCCCCNC(=O)C(=O)N*,,0.3220757,,,
+48554956,*c1ccc(NC=Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.39082082,,,
+48717962,*Nc1ccc([C@](C)(c2ccccc2)c2cccc(N*)c2)cc1,,0.35081859,,,
+49062920,*CCOCCOCCOC(=O)c1cc(N=Nc2ccc(OCC)cc2)ccc1-c1ccc(N=Nc2ccc(OCC)cc2)cc1C(=O)O*,,0.36389929,,,
+49325830,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc3c(c1)Cc1cc(N4C(=O)c5ccc(*)cc5C4=O)ccc1-3)C2=O,,0.38603498,,,
+49367014,*Oc1cccc(C(=O)c2ccc(*)cc2)c1,,0.36502137,,,
+49737348,*c1ccc2c(c1)c1cc(*)ccc1n2CC,206.6525359,,,,
+49886836,*Oc1ccc(C2(c3ccc(Oc4c(F)c(F)c(C(=O)c5c(F)c(F)c(*)c(F)c5F)c(F)c4F)cc3)c3ccccc3-c3ccccc32)cc1,,0.39033829,,,
+50053709,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C(F)(F)F)cc3)c1)C2=O,,0.37058659,,,
+50438012,*CCCCCCCCCOC(=O)c1ccc(C(=O)NCCCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,,,0.315,,
+51223278,*C(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3cccc(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.3563254,,,
+51367162,*C(=O)c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(*)cc5C4=O)C(C)C3)CC1C)C2=O,,0.38365388,,,
+51939851,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccccc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38673273,,,
+51976096,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CC(C)CCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35062977,,,
+52181246,*=C=C=C(COS(=O)(=O)c1ccc(OC)cc1)C(=*)COS(=O)(=O)c1ccc(OC)cc1,55.30555285,,,,
+52357313,*c1ccc(-c2ccc(C(*)(c3ccccc3)C(F)(F)F)cc2)cc1,,,0.233,1.06572076,19.28801766
+52649125,*Oc1ccc(N=CCCCC=Nc2ccc(OC(=O)NC3CC(C)(C)CC(C)(CNC(*)=O)C3)cc2)cc1,,0.35087282,,,
+52868719,*CC(*)C(=O)OCCSCCCC#N,,0.38452978,0.18,1.085387972,12.35091595
+53248904,*CCCCCCCCCCn1c(=O)c2cc3c(=O)n(*)c(=O)c3c(Br)c2c1=O,137.1087261,,,,
+53311142,*CCOC(=O)CC(C)O*,,0.33475958,,,
+53548350,*Nc1ccc(-c2ccc(N*)c3c2CCCC3)cc1,,0.35475566,,,
+53844809,*CC(*)(C)C(=O)OCCCCCCCC[n+]1ccc(N(C)C)cc1,,0.36783355,,,
+53951865,*Nc1c(O)c(N)c(N)c(O)c1N*,,0.31749993,,,
+54084083,*CC(*)CCCCC,,,0.2583999999999999,0.771934125,12.37609961
+54206705,*C=C(*)C(CCCCCCC)[Si](C)(C)C,,0.41060554,,,
+54625460,*c1cccc(NC(=O)c2ccc(OCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)c1,,0.35047536,,,
+55326263,*C(=O)Nc1cccc(S(=O)(=O)c2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)c2)c1,,0.38854979,,,
+55400154,*c1ccc(Cc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C(C)(C)C)c2)cc1C(C)(C)C,,0.40159385,,,
+55701750,*CC(*)SCC,,0.43094205,,,
+55715472,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)c4ccncc4)cc(C(*)=O)c3)cc2)cc1,,0.34502809,,,
+55938751,*N=P(*)(Oc1ccc(Cl)cc1)Oc1ccc(Cl)cc1,,0.39730375,,,
+56235690,*Oc1ccc(C2(c3ccc(Oc4ccc5c(=O)n6c7cc(-c8ccc9c(c8)nc8c%10ccc(*)c%11cccc(c(=O)n98)c%11%10)ccc7nc6c6cccc4c56)cc3)c3ccccc3C(=O)c3ccccc32)cc1,,0.39530481,,,
+56319655,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4cc(C(=O)Nc5ccc(*)cc5)cc(C(C)(C)C)c4)cc3)cc12,,0.36260258,,,
+56734204,*CC(*)(C)C(=O)OC1CCCCC1,,,0.2239999999999999,0.91386168,10.8595327
+56881469,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.38067611,,,
+57070808,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)cc2)cc1,,0.38843318,,,
+57222914,*Nc1ccc(C2(c3ccc(N*)cc3)C3=CCC=CC3=Cc3ccccc32)cc1,,0.36488932,,,
+57727191,*CCCCCCNC(=O)CC/C=C/CCC(=O)N*,,,0.278,,
+57989832,*OC(COC(*)=O)COc1ccccc1,,0.33796381,,,
+58312031,*C(=O)c1ccc(C(=O)N(*)c2ccccc2)c(Sc2ccccc2)c1,,0.3849002,,,
+58492814,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O,,0.37263667,,,
+59233337,*c1cc(N2C(=O)c3ccc(Oc4cc5ccccc5cc4Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc(C(F)(F)F)c1,,0.37041296,,,
+59411967,*c1ccc(Oc2cc(C(C)(C)C)ccc2Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37867118,,,
+59671897,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37897847,,,
+59967073,*Nc1cccc(NC(=O)CCCCC(*)=O)c1,,0.32263599,,,
+60015302,*CCCCOC(=O)OCC1CCC(COC(=O)O*)CC1,,0.33756711,,,
+60029399,*Oc1ccc(Oc2ccc(C(=O)c3ccccc3)c(-c3cc(*)ccc3C(=O)c3ccccc3)c2)cc1,,0.37726196,,,
+60738401,*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1,,0.38354504,,,
+60763059,*c1ccc(-c2cnc3ccc(S(=O)(=O)c4ccc5ncc(*)nc5c4)cc3n2)cc1,,0.4054389,,,
+60840755,*CC(*)c1ccc(OC(C)=O)c(OC)c1,,0.349142,,,
+61414691,*OC(CCC)COC(*)=O,,0.35170263,,,
+61470142,*Oc1ccc(Sc2ccc(Oc3ccc(-c4ccc(-c5ccc(-c6ccc(*)c(C(F)(F)F)c6)cc5)cc4)cc3C(F)(F)F)cc2)cc1,,0.37984577,,,
+61636599,*Nc1ccc(-c2ccc(N*)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)cc1,,0.37033233,,,
+61782925,*CC(*)C(=O)OC(OCC)C(F)(F)F,,0.35306137,,,
+61915107,*c1ccc2c(c1)C(=O)N(c1cc(C)c(-c3c(C)cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C)c(C)c1)C2=O,,0.43074666,,,
+62163979,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(N=Nc3ccc(C(C#N)=C(C#N)C#N)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccc(N=Nc2ccc(C(C#N)=C(C#N)C#N)cc2)cc1,,0.37162023,,,
+62269402,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(Cc4cc(C)c(*)c(C)c4)cc3C)cc2)cc1,,0.3923948,,1.073138282,20.42938322
+62619308,*CC(*)(C)C(=O)OC(C)(C)C,,,0.1525,0.826660163,12.18535631
+62619449,*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.347323,,,
+63090033,*CC(*)OC(=O)c1ccc(Cl)cc1,,0.36499516,,,
+63333676,*CCCN(Cc1ccccc1)C(=O)S*,,0.36838082,,,
+63397318,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1S(=O)(=O)O,,0.36684049,,,
+63969847,*CCCCCc1nc2cc3sc(*)nc3cc2s1,87.36313445,,,,
+64606581,*CC1CCC(*)C1,42.35931484,,0.3353333333333333,0.892956655,21.77794424
+64725995,*Nc1c([C@H](C)CC)cc(C2(c3cc([C@H](C)CC)c(N*)c([C@H](C)CC)c3)c3ccccc3-c3ccccc32)cc1[C@H](C)CC,,0.41165064,,,
+65098291,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccccc6Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.36614027,,,
+66059501,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccccc5Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc12,,0.35799579,,,
+66287866,*Oc1ccc(C(c2ccccc2)c2ccc(OC(*)=S)cc2)cc1,,0.36796394,,,
+66714118,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.36875438,,,
+66764672,*C=CC1CC(*)C(C#N)(C[Si](C)(C)O[Si](C)(C)C)C1,,0.43283262,,,
+66797597,*Nc1ccc(-c2ccc(C(=Cc3ccccc3)c3ccc(-c4ccc(N*)cc4)cc3)cc2)cc1,,0.36634183,,,
+67251636,*OC(=O)C(C)NC(=O)CCCCCCCCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.34210323,,,
+67510171,*C=C(*)c1c(F)c(F)c(CCCC)c(F)c1F,,0.41251953,,,
+67637672,*C=CC1CC(*)C2C(=O)N(c3ccccc3Cl)C(=O)C12,,0.39149217,,,
+67701587,*Oc1ccc(C(CC)(c2ccccc2)c2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)cc1,,0.36176424,,,
+67919894,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.37844588,,1.144330703,21.53470493
+68067670,*Nc1ccc(C2(c3ccc(NC(=O)c4ccc(-c5ccc(C(*)=O)c(C)c5)cc4C)cc3)c3ccccc3-c3ccccc32)cc1,,0.38008517,,,
+68287641,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3-c3cccc4ccccc34)c2-c2ccccc2-c2cccc3ccccc23)cc1,,0.36814299,,,
+68342514,*CC(*)(C)C(=O)OCCN(C)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.35608042,,,
+68581387,*CCCCc1nc(-c2ccc(-c3csc(*)n3)cc2)cs1,110.5188915,,,,
+68792546,*CCCCCCCCCCC(=O)NCCCCCCNC(=O)CCCCO*,,,0.3565,0.939635943,24.65708372
+68905066,*Nc1ccc(CC(C)(C)NC(=O)c2ccc(C(*)=O)cc2)cc1,,0.34949792,,,
+69087168,*Nc1ccc(-c2cccc(C[C@@H](C)c3ccc(N*)cc3)c2)cc1,,0.35001333,,,
+69121777,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(C(=O)Nc4ccc(O*)cc4)cc3)cc2)cc1,,0.34123375,,,
+69694252,*CCOC(=O)CCCCCC(=O)O*,,0.3344037,,,
+70273581,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1-c1ccccc1,65.88587862,,,,
+70329705,*CC(COC(F)(C(F)(F)F)C(F)(F)F)O*,,0.33503076,,,
+70414190,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.34508223,,,
+71102106,*/C=C/c1ccc(*)cc1,43.3930774,,,1.006135387,25.25187791
+71135797,*CCCCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34204989,,,
+71203158,*Oc1ccc(C2(c3ccc(OC(=O)CCCCCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.3625688,,,
+71861682,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)cc1)C2=O,,0.36767673,,,
+72083210,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.341417,,,
+72192148,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cl)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.38538463,,,
+72343609,*c1ccc(C(=O)NCCCCCCNC(=O)c2ccc(-c3nc4ccccc4nc3*)cc2)cc1,,0.36004959,,,
+72459734,*OC1C(COCO)OC(*)C(O)C1O,,0.29530816,,,
+72780876,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C5(c6ccc(*)cc6)CC6CCC5C6)cc4)cc3)cc2)cc1,,0.36666266,,,
+73164224,*Nc1cc(N*)c(O)cc1O,,0.30277596,,,
+73437424,*c1ccc(Sc2ccc(S(*)(=O)=O)cc2)cc1,,0.38393848,,,
+73489735,*CCN(*)C(=O)c1c(F)c(F)c(F)c(F)c1F,,0.33673412,,,
+74066581,*Oc1ccc2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2c1,,0.34864845,,,
+74313131,*OC(COC(=O)c1cccc(-c2ccc(C(*)=O)cc2)c1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35765237,,,
+74688893,*CC(*)(CC(=O)OCCCCC)C(=O)OCCCCC,,0.38050025,0.215,0.955743343,10.24635518
+74865291,*OC(=O)OCCOCCOC(=O)OC1COC2C(*)COC12,,0.31603172,,,
+75135470,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccc(C#N)cc3)c3ccccc23)cc1,,0.35826873,,,
+75252869,*O[Si](*)(C)CCCCCCn1c2ccccc2c2ccccc21,,0.37569363,,,
+75381552,*c1ccc(Oc2ccc(N3C(=O)c4cc(-c5ccc(C(C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc(C(C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.40978294,,,
+75443529,*CCCCCCCOc1ccc(C=C(C)c2ccc(O*)cc2)cc1,36.76976951,,,,
+75466481,*Nc1c(N*)c2cccc3ccc4cccc1c4c32,,0.34202194,,,
+75536580,*Oc1ccc(NC(=O)c2ccc(S(=O)(=O)c3ccc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)C7CC8CC(C7)CC6C8)cc5)cc4)cc3)cc2)cc1,,0.36857397,,,
+75538162,*Nc1ccc([C@H]2SC=C(c3ccccc3)[C@@]2(c2ccccc2)c2ccc(N*)cc2)cc1,,0.36360706,,,
+75851204,*C[Si](*)(CCCCCC)CCCCCC,,0.40662633,,,
+76090504,*CC(*)OC(=O)C(Cl)(Cl)Cl,,0.35515201,,,
+76415472,*Oc1ccc2cc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)ccc2c1,,0.37630261,,,
+76724169,*CCCC=C(*)c1ccc(OC)cc1,,0.37755684,,,
+76990549,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.35064415,,,
+77456502,*c1ccc(-c2nc3cc(S(=O)(=O)c4ccc5nc(-c6ccccc6)c(-c6ccc(N7C(=O)C8OC9C(=O)N(*)C(=O)C9C8C7=O)cc6)nc5c4)ccc3nc2-c2ccccc2)cc1,373.476202,,,,
+79415769,*CCOC(=O)N(C)c1ccc(C)c(N(C)C(=O)O*)c1,42.46691504,,,,
+79940149,*NCCCCCCNc1nc(*)nc(-c2ccccc2)n1,,0.39025113,,,
+80116442,*CCOC(=O)CCCCCCCCC(=O)OCCOC(=O)c1ccc(C(=O)O*)cc1,,0.34043284,,,
+80376161,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1-c1ccc(S(=O)(=O)O)cc1,,0.37247097,,,
+80487296,*C=Cc1c(OC(=O)c2ccc(C(=O)Oc3cccc4ccc(*)nc34)cc2)ccc2ccccc12,,0.36132487,,,
+80883741,*NC(C#N)=C(C#N)NC(=O)c1ccc(C(*)=O)cc1,,0.34231811,,,
+80922127,*Nc1ccc(C2(c3ccc(N*)cc3)[C@H]3C[C@@H](C)C[C@@H]2C[C@@H](C)C3)cc1,,0.37552155,,,
+80929895,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)CN3CCN(*)CC3)cc2)cc1,,0.35130308,,,
+81065395,*Nc1ccc(C2(c3ccc(N*)cc3)CCCCCCC2)cc1,,0.35406496,,,
+81137216,*CCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*,,0.37913267,0.425,0.889143617,23.85283439
+81531670,*CCCC1CCN(SC(=O)OCCCCOC(=O)SN2CCC(*)CC2)CC1,-6.032415023,,,,
+81725470,*/C=C/c1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(N(c2ccc(OC)cc2)c2ccc(OC)cc2)cc1,,,0.242,0.969832402,13.49779574
+82529518,*NC1=CC[C@@H](C(c2ccc(N)cc2)(c2ccc(N*)cc2)c2ccc(N)cc2C)C=C1,,0.37144246,,,
+82633177,*C1Cc2ccccc2C1*,,0.42698874,0.13,0.889251685,12.77290213
+82784880,*CC(*)C(=O)Oc1ccc(C(=O)OC)cc1,,0.3308451,,,
+82911538,*Nc1ccc(-c2ccc3c(c2-c2ccc(N*)cc2)Cc2ccccc2-3)cc1,,0.35793265,,,
+82950702,*CCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.34443591,,,
+82977315,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCCCCC(=O)O*,,,0.507,0.89522718,20.57750836
+83711501,*NCCCCCCN*,,0.328363025,,,
+83752896,*CC(*)c1cccc(Br)c1,,0.42093042,,,
+83890516,*CC(*)c1ccc(COCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.35853406,,,
+84000426,*c1ccc(C(c2ccc(-c3nc4ccc(-c5ccc6nc(*)[nH]c6c5)cc4[nH]3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.42292902,,,
+84183521,*Nc1ccc([C@H](CCC)c2ccc(C(CCCC)c3ccc([C@@H](CCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36924197,,,
+84553861,*CC(*)[Si](C)(C)C,,0.41330473,,,
+84753546,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(Sc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2)cc1,,0.36216048,,,
+85350789,*O[Si](*)(CCCOCCOC)CCCOCCOC,,0.37513853,,,
+85512320,*c1ccc(Oc2cccc(N3C(=O)c4ccc(Oc5cc6ccccc6cc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.365223,,,
+86043004,*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCC,,0.48499786,,,
+86169021,*Nc1ccc(Cc2ccc(C3(c4ccc(Cc5ccc(N*)cc5)cc4)CCC(CC[C@H]4CC[C@H](CCCC)CC4)CC3)cc2)cc1,,0.36481591,,,
+86465615,*C1C(=O)OC(=O)C1C1C2CC(C(=O)OCCOCC)C(C2)C1*,,0.32509916,,,
+86721128,*c1ccc(C(C#N)(C(=O)OCCCC)C(*)(C#N)C(=O)OCCCC)cc1,,0.39356228,,,
+86909617,*Nc1ccc2c(c1)C1(c3ccccc3-2)c2ccccc2-c2ccc(N*)cc21,,0.37643744,,,
+86993682,*Oc1cccc2c(OC3(F)C(*)(F)C(F)(F)C3(F)F)cccc12,,0.3436634,,,
+87338341,*CCCCCCCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1,,0.35145128,,,
+87721068,*CC#CCOC(=O)NCCCCCCNC(=O)O*,,0.34308994,,,
+88136935,*C=CC1CC(*)C2C(=O)N(c3ccccc3C)C(=O)C12,,0.36253604,,,
+88832421,*CCCC(Cl)C(*)Cl,,0.38583905,,,
+89077090,*Cc1ccc(CN(CC)C(=O)CCCCCCCCCCCCCCCCC(=O)N(*)CC)cc1,4.279694171,,,,
+89099554,*CC(*)(CC(=O)OCCc1ccccc1)C(=O)OCCc1ccccc1,,0.35265054,0.199,1.092735648,10.74609762
+89164117,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(Sc7ccc(Oc8cccc9c8C(=O)N(*)C9=O)cc7)cc6)c5C4=O)cc3)c2)cc1,,0.36405508,,,
+89377326,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2cccc3ccccc23)cc1,,0.3649896,,,
+89692209,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)c7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36204045,,,
+90074942,*Oc1ccc(OC(=O)c2ccc(OCCCCCCOc3ccc(C(*)=O)cc3)cc2)cc1S(=O)(=O)c1ccccc1,,0.35188537,,,
+90183382,*CC(*)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.38043932,,,
+90208491,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)cc1,,0.33785141,,,
+90217162,*CC(OC(=O)C(Cc1ccccc1)NC(=O)OC(C)(C)C)C(COC(=O)O*)OC(=O)C(Cc1ccccc1)NC(=O)OC(C)(C)C,,0.35315661,,,
+90460674,*c1cccc(OCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34628726,,,
+90954727,*ON(C(F)(F)F)C(F)(Cl)C(*)(F)F,,0.31968065,,,
+91029862,*c1ccc(Oc2ccc(-c3cc(-c4cccc(-c5cc(*)c6ccccc6n5)c4)nc4ccccc34)cc2)cc1,,0.40199506,,,
+91669068,*Oc1ccc(Nc2ccc(S(=O)(=O)c3ccc(Nc4ccc(Oc5ccc(C(C)(C)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38534918,,,
+91718962,*OC(CCC)CCC(C(*)=O)C1OCCCO1,,0.36177328,,,
+91927958,*CC(*)(C)C(=O)OCCCN(C)c1ccc(N(C)C)cc1,,0.36463818,,,
+92209780,*CC(CCCCCCCCCCCCCCCC)C1C(=O)N(CCCCNC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)C(=O)C1*,,0.35619083,,,
+92330874,*Oc1ccc(Cc2ccc(OC(=O)c3cc(OCCCCC)cc(C(*)=O)c3)cc2)cc1,,0.3613898,,,
+92595615,*CC(*)C(=O)OCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.35001678,,,
+92619090,*CC(*)C(=O)Oc1ccc(C)cc1,,0.3609429,0.193,1.051609028,13.5752804
+92814383,*CCCCCCCCCCCCCCCCCCCCCC(=O)N*,,,0.4325,0.866351429,21.86411406
+92922401,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c(C)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1C)C2=O,,0.36870251,,,
+92946122,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cc2)cc1,,0.36600725,,,
+93073928,*Nc1ccc(-c2c(-c3ccc(N*)c(-c4ccc(N)cc4)c3-c3ccc(N)cc3)ccc(N)c2-c2ccc(N)cc2)cc1,,0.35447107,,,
+93319116,*COCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.32347655,,,
+93467042,*Nc1ccc(/C=C/c2ccccc2)c(/C=C/c2ccccc2)c1N*,,0.35176694,,,
+94248723,*CC(*)C(=O)OCCCOC,,0.34969049,,,
+94344742,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5nnc(*)c6c(-c7ccccc7)c(-c7ccc8ccccc8c7)c(-c7ccc8ccccc8c7)c(-c7ccccc7)c56)cc4)cc3)cc2)cc1,,0.39742985,,,
+94543909,*CC(CCCCCCOc1ccc(N=Nc2ccc(Br)cc2)cc1)COC(=O)CCCCC(=O)O*,,0.37066043,,,
+95310452,*CC(*)c1ccccc1C(=O)OCC,,0.36304161,0.1816666666666666,,
+95396471,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(*)ccc1-2,,0.42664647,,,
+95579704,*Nc1cc(C(c2ccccc2)([C@@H]2C[C@@H](C)CC[C@H]2C(C)C)[C@@H]2C[C@@H](C)CC[C@H]2C(C)C)c(CC)c(CC)c1N*,,0.3914683,,,
+95950675,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7c(C)cc(C(C)(C)c8cc(C)c(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)c(C)c8)cc7C)cc6C5=O)cc4)cc3C)c(C)c2)cc1,,0.3950498,,,
+96953852,*Nc1cc2ccccc2c(N*)c1-c1ccc2ccccc2c1,,0.3505442,,,
+97597340,*CCCCCCCCCCCCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.37330351,0.348,,
+97633900,*C=Cc1ccc(N=Nc2ccc(N(C)CCOC(=O)Nc3ccc(C)c(NC(=O)OCCN(C)c4ccc(N=Nc5ccc(C=CC6=CC(=C(C#N)C#N)C=C(*)O6)cc5)cc4)c3)cc2)cc1,,0.37425307,,,
+98585154,*c1ccc(-c2cnc3cc(S(=O)(=O)c4ccc5nc(*)cnc5c4)ccc3n2)cc1,,0.43056013,,,
+98641557,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1-c1ccc(S(=O)(=O)O)c(C)c1,,0.36929236,,,
+98940224,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4ccc(-c5ccc(C(=O)Nc6ccc(*)cc6)c(C)c5)cc4C)cc3)cc2)cc1,,0.36387145,,,
+99321215,*Oc1ccc(C(C)(C)c2ccc(OC(=O)OCCN(CCOC(*)=O)c3ccc4[nH]c5ccccc5c4c3)cc2)cc1,,0.3473227,,,
+99440116,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc(F)cc2)cc1,,0.37077177,,,
+99885429,*Oc1ccc(C(=O)Nc2ccc(Br)cc2-c2ccc(NC(=O)c3ccc(*)cc3)cc2)cc1,,0.36393423,,,
+99908460,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccc(Oc3ccccc3)cc2)cc1,,0.34257003,,,
+100028701,*Nc1nnc(-c2nnc(N*)n2N)n1N,,0.3228214,,,
+100174621,*CC(*)(C)C(=O)OC1CCC(C(C)(C)C)CC1,,0.38851796,0.153,0.856410503,10.86090269
+100767985,*CCCCCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.35104794,,,
+101151007,*C=Cc1cccc(OC(=O)CCCCCCCCC(=O)Oc2cccc(-c3nnc(*)s3)c2)c1,,0.35908585,,,
+101268497,*CC(c1ccccc1)C1C(=O)N(C(C)(C)C)C(=O)C1*,,0.37467147,,,
+101351388,*CCc1ccc(CCNC(=O)CCCCCCCCCC(=O)N*)cc1,,0.35500792,,,
+101355121,*Nc1ccc2c(c1)[C@@H]1c3ccc(N)cc3[C@H]2c2cc(N*)ccc21,,0.38382631,,,
+101541565,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc(*)c4nsnc34)ccc1-2,,0.4103927,,,
+101762204,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36946761,,,
+102315862,*OP(=O)(Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1)OC1CCCCC1,,0.36199402,,,
+102324503,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(C)(CC)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.37130166,,,
+102434627,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(C(C)(C)c5ccc(C(C)(C)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36683276,,,
+102464376,*Nc1ccc(/C=C/c2c3ccccc3c(-c3c4ccccc4c(CCc4ccc(N*)cc4)c4ccccc34)c3ccccc23)cc1,,0.36999333,,,
+102545459,*Cc1ccc(CNC(=O)CCCCCCCCCC(=O)N*)cc1,,0.35148366,,,
+102606896,*CCCCCCCCCCC(=O)N*,,0.36981309,,,
+102779162,*c1cc(CCCCCCCCCCCCCCCCCCCCCC)c(*)s1,,,0.39725,0.883511374,15.35786351
+103257739,*c1ccc2c(c1)C(=O)N(c1cccc(C(c3ccccc3)(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)C(F)(F)F)c1)C2=O,,0.38996211,,,
+103265366,*CCN(*)C(=O)c1ccc(F)c(F)c1F,,0.34115264,,,
+103451865,*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C,,0.49824253,,,
+103929649,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc4c(c3)C(=O)N(N3C(=O)c5ccc(*)cc5C3=O)C4=O)cc2)cc1,,0.37987007,,,
+104251154,*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.34734825,,,
+104578958,*c1cccc(N2C(=O)c3cccc(Oc4c(C)cc(Cc5cc(C)c(Oc6cccc7c6C(=O)N(*)C7=O)c(C)c5)cc4C)c3C2=O)c1,,0.38564585,,,
+104743895,*C1CCCCC1S(*)(=O)=O,,0.35327947,,1.174206042,15.15828898
+104905382,*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6C)c(C)c5C)cc4)ccc3c2)cc1,,0.39776859,,,
+104973461,*CCN(CCOC(=O)NCCCCCCNC(=O)O*)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)c(C)c1,,,,1.1275851,
+105391120,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccccc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.36153949,,,
+105613036,*c1ccc2c(c1)C(=O)N(c1ccc(OCCN(CCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c3ccc(C(C#N)=C(C#N)C#N)cc3)cc1)C2=O,,0.35967534,,,
+105789643,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6cc7ccccc7cc6Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c2)cc1,,0.36484641,,,
+106050783,*c1ccc(Oc2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2C(C)(C)C)cc1,,0.40376048,,,
+106083096,*Nc1c(/C=N/O)cc(/C=N/O)c(N*)c1N,,0.3106329,,,
+106612314,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(Cl)cc5)C6=O)cc4)cc3)cc2)cc1,,0.38149457,,,
+106732867,*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C8(c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)CC9CC8C8CCCC98)cc7)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.37100735,,,
+106795224,*CCCCCCCCCCCC(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)CCCCC2)cc1,,0.35744625,,,
+106825904,*CC(*)C(=O)OCCCCCCOc1ccc(C=Nc2ccc(OCCCCC)cc2)c(O)c1,,0.36508444,,,
+106963136,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.35278394,,,
+107158685,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)cc1,,0.40390264,,,
+107530968,*CCCCCCCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37988049,,,
+107562247,*Oc1ccc(C(=O)Nc2cc(C(c3ccc(C)c(NC(=O)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)c3)(C(F)(F)F)C(F)(F)F)ccc2C)cc1,,0.34711182,,,
+108528555,*Nc1c(CCCCC)c2ccc3cccc4ccc(c1N*)c2c34,,0.34744506,,,
+108998578,*c1ccc(Sc2cccc(Sc3ccc(S(*)=O)cc3)c2)cc1,,0.37723883,,,
+109027922,*c1cc(C=Nc2ccc3c(c2)c2cc(N=Cc4cc(*)c(O)c(*)c4)ccc2n3CCCCCC)ccc1O,,0.37196889,,,
+109081671,*CCN(*)C(=O)C(C)C,,0.3630483,,,
+109313016,*c1cc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c(O)cc1O,,0.36149499,,,
+109395625,*CC(*)(C)C(=O)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl,,0.36603626,0.0829999999999999,1.354866985,14.15089535
+109705207,*Nc1c(N)c(N)c2c(c1N*)C(C=C)(C=C)OC2=O,,0.32341384,,,
+110596247,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCC,,0.35205318,,,
+110986650,*CCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.3581334,,,
+111009795,*Oc1ccc(NC(=O)CCCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.34325805,,,
+111108341,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23,,0.42229227,,,
+111650158,*c1cccc(Oc2ccc(Oc3cccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)cc2)c1,,0.35930853,,,
+111668732,*Oc1cccc(OC(=O)Oc2ccc(C(C)(C)c3ccc(OC(*)=O)cc3)cc2)c1,,0.34341642,,,
+111707044,*Nc1ccc(C2(c3ccc(N*)cc3)CCC[C@@H](CC=C)C2)cc1,,0.3496808,,,
+111708400,*CCC(C)(C)CC(C)COC(=O)CCCCC(=O)O*,,0.34655026,,,
+112017015,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)c(C(=O)OCC)cc2C(=O)OCC)cc1,,0.34702207,,,
+112351140,*CCC(F)(F)C(*)(F)Cl,-41.84388225,,,,
+112553722,*Nc1ccc(CC(*)=O)cc1,63.68828699,,,,
+112989596,*CCOCCOC(=O)CC(=O)O*,,0.31955507,,,
+113181684,*C/C=C\C[Si](*)(C)C,,0.41205158,,,
+113263690,*Nc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(N*)cc2)cc1,,0.36641724,,,
+113817075,*CC(*)c1ccc(C(Cc2ccccc2)=NO)cc1,,0.35451524,,,
+113985401,*Nc1ccc(Sc2ccc(N*)cc2)cc1,,0.34614089,,,
+114257024,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=Nc4ccc(Oc5ccc(N=Cc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37338058,,,
+114287113,*C=Cc1cc(OCC2CC3CC2C2CCCC32)c(*)cc1OC,,0.36979921,,,
+114988183,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5c(C)cc(Cc6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)c(C)c2)cc1C,,0.40345337,,,
+115242060,*CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)OCCOc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)cc2)cc1,,0.3438323,,,
+115627985,*Nc1ccc(C2(c3ccc(N*)cc3)CCC[C@@H](CCCCCCCCCCCCCCC)C2)cc1,,0.36007197,,,
+116018739,*C(C)=C(*)CCCCC,,0.40782502,,,
+116082085,*Nc1ccc(-c2ccc(N*)c(CC=C)c2CC=C)cc1,,0.3502113,,,
+116488904,*CCCCCCCCCCCCOC(=O)CCCCCCCCCCCCC(=O)O*,,,0.294,0.910678639,20.53765653
+116842973,*Nc1ccc(-c2cccc(N*)c2-c2ccccc2)cc1,,0.35759716,,,
+116907306,*CC(*)(C)C(=O)OCCOCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.356451,,,
+117726673,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(C(C)(C)c3ccccc3)cc2)cc1,,0.35725227,,,
+118056137,*CC(*)OC(=O)C(F)(F)C(F)(F)C(F)(F)F,,0.33120706,,,
+118556246,*Oc1c(C)cc(-c2cc(CBr)c(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)c(CBr)c2)cc1CBr,,0.4178534,,,
+118829848,*CC1CC2CC1C1CCC(CN3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C21,,0.3636249,,,
+118919299,*C(=C(*)c1ccc(C(C)(C)C)cc1)c1ccccc1,,0.47395904,,,
+118991284,*CCOCCOC(=O)C(C)C(=O)O*,,0.33298601,,,
+119301714,*CCNC(=O)CCCCCCCC(=O)NCCNC(=O)C(=O)N*,112.5256843,,,,
+119392727,*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35699838,,,
+120060047,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)c3cc(N(C)C)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.38701526,,,
+120425171,*CCCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.37294076,,,
+120794322,*c1ccc(-c2nc3ccc(-c4ccc5nc(*)c(-c6ccccc6)c(-c6ccccc6)c5c4)cc3c(-c3ccccc3)c2-c2ccccc2)cc1,,0.43936443,,,
+121214961,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2C(O)(C(F)(F)F)C(F)(F)F)c(C(O)(C(F)(F)F)C(F)(F)F)c1,,0.35851232,,,
+122170493,*c1ccc([Si](C)(C)[Si](*)(C)C)s1,,0.46355784,,,
+122752033,*CCCCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.34313577,,,
+122815777,*Oc1ccc(S(=O)(=O)c2ccc(-c3ccc(-c4ccc(S(=O)(=O)c5ccc(Oc6ccc(-c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.3713436,,,
+123479038,*CC(*)(CF)C(=O)OC,,,0.1365,1.048000594,18.18607796
+123524661,*Oc1c(F)c(F)c(COC(c2cccc(C(OCc3c(F)c(F)c(Oc4ccc(Cc5ccc(*)cc5)cc4)c(F)c3F)(C(F)(F)F)C(F)(F)F)c2)(C(F)(F)F)C(F)(F)F)c(F)c1F,,0.34625699,,,
+123705047,*C=CC1CC(*)C2C(=O)N(CCCCCCCCCC)C(=O)C12,,0.37406215,,,
+124053674,*Nc1c(Cl)cc(*)cc1Cl,-0.214281278,,,,
+124111614,*c1ccc(Oc2ccc(-c3ccc4nc(Oc5cc(-c6ccccc6)c6cc(*)ccc6n5)cc(-c5ccccc5)c4c3)cc2)cc1,,0.38805343,,,
+124665758,*CC(*)CCCC,,,0.2306666666666666,0.776895965,12.02548488
+124836094,*CC(=O)Nc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(NC(=O)CN5C(=O)c6ccc(C(c7ccc8c(c7)C(=O)N(*)C8=O)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc2)cc1,,0.34942241,,,
+125425163,*c1ccc(-c2c(-c3ccccc3)nc3ccc(Oc4ccc5nc(-c6ccccc6)c(*)c(-c6ccccc6)c5c4)cc3c2-c2ccccc2)cc1,,0.40668067,,,
+125472122,*CCCNC(=O)O*,,0.31360037,,,
+125514208,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5c(C)cc(C(C)(C)c6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.40081529,,,
+125846951,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCCCCCCCC)cc1,,0.38047103,,,
+125969488,*CC(OC(C)=O)C(COC(=O)O*)OC(C)=O,,0.3184913,,,
+126334280,*c1ccc(OC(=O)c2cccc(C(=O)Oc3ccc(-c4nc5ccccc5nc4*)cc3)c2)cc1,,0.3678366,,,
+126444620,*OP(=O)(Oc1ccccc1)Oc1cccc2c(*)cccc12,,0.34871423,,,
+126841859,*CC(=O)NCCCCCCNC(=O)COc1ccc(O*)cc1,,0.3285221,,,
+127626732,*CC(*)(Cl)C(=O)OC,,0.32998468,,,
+127834200,*CC(*)c1c(F)c(F)c(P(=O)(O)O)c(F)c1F,,0.32683261,,,
+127883611,*Nc1cc2c(cc1N*)C(CC)(CC)CC2(C)C,,0.36408964,,,
+127891371,*Oc1ccc(C(=O)c2cccc(*)c2)cc1,,0.36659274,,,
+128067074,*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C(C)C)c4)c4ccccc4-c4ccccc43)cc1C(C)C)C2=O,,0.43454903,,,
+128211105,*Oc1ccc(C2(c3ccc(OC(=O)C45CC6CC(CC(C(*)=O)(C6)C4)C5)cc3)c3ccccc3-c3ccccc32)cc1,,0.38474727,,,
+129058504,*CCOC(=O)Nc1cc(NC(=O)O*)ccc1C,,0.32591099,,,
+129677973,*Oc1c(Cl)cc(C(=C(Cl)Cl)c2cc(Cl)c(OC(*)=O)c(Cl)c2)cc1Cl,,0.40362445,,,
+130236950,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5cccc6c(Oc7ccc8c(c7)C(=O)N(*)C8=O)cccc56)cc4C3=O)cc2)cc1,,0.37077884,,,
+130348754,*CCC(C)CCC(=O)S*,,0.38621425,,,
+130603875,*Nc1ccc(-c2ccc(N*)c3cc4ccccc4cc23)c2cc3ccccc3cc12,,0.3627942,,,
+131030961,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc([Si](c4ccccc4)(c4ccccc4)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.38062557,,,
+131086449,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)cc2)cc1,,0.39388382,,,
+131094319,*C=C[Ge](*)(c1ccccc1)c1ccccc1,,0.39302909,,,
+131123838,*CCCCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.36034333,,,
+131275030,*CC(*)C(=O)OCC(CC)CC,,,0.2095,0.91413913,12.27591848
+131448132,*NC1=C(N*)C(=Nc2c(C)cccc2C)C(N)=C(N)C1=O,,0.33122927,,,
+131486630,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.34979846,,,
+131992742,*c1cc(-c2ccccc2)c(*)[nH]1,120.4503456,,,,
+132133665,*OC(COC(=O)C1CCC(C(*)=O)CC1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.36227874,,,
+132181866,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3ncnc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)n3)c1)C2=O,,0.36454415,,,
+132706466,*c1ccc(-c2ccc(-c3ccc(-c4ccc(-c5cc(-c6ccccc6)c6cc(Oc7ccc8nc(*)cc(-c9ccccc9)c8c7)ccc6n5)cc4)cc3)cc2)cc1,,0.40016775,,,
+132725176,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)cc(C(C)(C)C)c1)C2=O,,0.41625991,,,
+133123853,*CCNC(=O)c1ccc(O*)cc1,,0.3294033,,,
+133124835,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c(C(C)(C)C)c1,,0.36528492,,,
+133986624,*C#Cc1ccc(*)c(SCCCCCCCCCCCC)c1,53.41644401,,,,
+134240821,*c1ccc(-c2ccc3c(c2)C(CCCCCCCC)(CCCCCCCC)c2cc(-c4ccc(N(*)c5ccc(N(c6ccc(C(C)(C)c7ccccc7)cc6)c6ccc(C(C)(C)c7ccccc7)cc6)cc5)cc4)ccc2-3)cc1,,0.42294334,,,
+134957671,*OC(=O)C(CC(C)C)NC(=O)CCCCC(=O)NC(CC(C)C)C(=O)OC1COC2C(*)COC12,,0.34245153,,,
+135202428,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccccc8)[nH]c7*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38523355,,,
+135272498,*NC(CCC(=O)OCCCCCCCC)C(*)=O,,0.36388977,,,
+135399849,*Oc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(Oc3ccc(C(=O)Nc4ccc(NC(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.37438756,,,
+135571738,*Nc1ccc(C2(c3ccc(N*)cc3)[C@@H]3C[C@H]4C[C@@H](C3)C[C@]2([C@]23C[C@H]5C[C@H](C[C@H](C5)C2)C3)C4)cc1,,0.36595891,,,
+136037972,*Nc1ccc(-c2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1,,0.39144941,,,
+136482029,*c1ccc(Cc2ccc(N3C(=O)c4ccc(OCCN(CCOc5ccc6c(c5)C(=O)N(*)C6=O)c5ccc(C=Cc6ccc([N+](=O)[O-])cc6)cc5)cc4C3=O)cc2)cc1,,0.35258622,,,
+136535007,*Nc1ccc2c(c1N*)[C@H]1c3ccccc3[C@@H]2c2ccccc21,,0.37638503,,,
+136564910,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(C=C2NC(=O)NC2=O)cc1,,0.32170639,,,
+136668087,*CCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.35168899,,,
+136676580,*Nc1cc(Oc2cccc(OCC)c2)c(N*)c2c1C(=O)c1c(N)c(Oc3cccc(OCC)c3)cc(N)c1C2=O,,0.33487592,,,
+137060513,*CC1CCC(COP(=O)(N=Nc2ccc(-c3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)CC1,,0.3611032,,,
+137117611,*Nc1cc2c(cc1N*)C(C)(C)C(C)(C)C2(C)C,,0.35975635,,,
+137415290,*CC(=O)NCCCCCCNC(=O)COC(=O)CCC(=O)O*,,0.31440807,,,
+138808143,*CCOCCOC(=O)CCCC(=O)O*,,0.32465489,,,
+138809640,*CC(*)OC(=O)c1ccccc1Cl,,0.35127788,,,
+139085360,*CCCC(*)(C)C(=O)O,,0.31720928,0.1985,0.996128545,14.52607342
+139188730,*Oc1ccc(NC(=O)c2ccc([Si](c3ccccc3)(c3ccccc3)c3ccc(C(*)=O)cc3)cc2)cc1,,0.3724538,,,
+139200098,*OC(C)COC(*)=O,,0.32644555,,,
+139585639,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCC(=Cc5ccc(*)cc5)C4=O)cc3)cc2)cc1,,0.3884516,,,
+139914608,*Nc1ccc(-c2nc(-c3ccc(N)cc3)nc(-c3ccc(N*)cc3)n2)cc1,,0.388028525,,,
+139977598,*Oc1c(C)cc(C2(c3cc(C)c(OC(=O)c4ccc(C(*)=O)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.40878882,,,
+140168832,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34533311,,,
+140415340,*C=CCCC*,,0.41369555,,,
+141066783,*c1ccc(NC(=O)Nc2ccc(NC(=O)Nc3ccc(-c4nc(C#N)c(C#N)nc4*)cc3)cc2)cc1,,0.3670318,,1.154810331,19.52705482
+141135744,*c1cc2cccccc-2c1*,,,0.163,0.814226421,15.84965404
+141962685,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C7(c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)CCCCCCCCCCC7)cc6)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.38366054,,,
+142147775,*CCCCCCOC(=O)OCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.3498749,,,
+142186672,*CC(*)(C)C,,,0.2125,,
+142224028,*c1ccc(C(c2ccc(N3C(=O)c4cc(-c5ccc(C(C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc(C(C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.41914206,,,
+142367496,*C=C(*)C(CCC)[Si](C)(C)c1ccccc1,,0.39420659,,,
+142490647,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.3726534,,,
+142802262,*N(CCC)[Si](C)(C)C[Si](*)(C)C,,0.36931239,,,
+143774509,*Nc1cc(CCc2ccccc2)cc(-c2c(Cc3ccccc3)cccc2N*)c1,,0.34620954,,,
+143809812,*Nc1cccc(C2=C(c3ccccc3)C(c3ccccc3)=C[C@@H](c3ccccc3)[C@@]2(c2ccccc2)c2cccc(N*)c2)c1,,0.38536846,,,
+143899690,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C)cc1,,0.36754794,,,
+143924095,*CCCCCCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.36294756,,,
+144238786,*CC(*)C(=O)OCCCCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.35420513,,,
+144472572,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3cccc(C)c3)c2-c2cccc(C)c2)cc1,,0.3833667,,,
+144839830,*Oc1ccc(OC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2-c2ccccc2)cc1-c1ccccc1,,0.36302483,,,
+145920765,*Nc1ccc(-c2ccccc2)cc1-c1cc(-c2ccccc2)ccc1N*,,0.34380582,,,
+146156380,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(Cc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37347921,,,
+146165767,*CC(*)C(=O)Oc1ccccc1C(=O)OCC,,0.34193667,,,
+146221251,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O[Si](C)(C)C)cc5C4=O)cc3)cc1)C2=O,,0.41117013,,,
+146716935,*Nc1ccc([C@@H](Cc2ccccc2)c2cccc(N*)c2)cc1,,0.348692,,,
+147030895,*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(C(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)(C(F)(F)F)C(F)(F)F)cc2)C3=O)c1,,0.37036169,,,
+147633092,*OC(C)COC(=O)c1ccccc1C(*)=O,,0.33635489,,,
+147677118,*CC(*)(C)C(=O)OCCOCC,,,0.212,1.013471369,12.39029756
+148111753,*CCOCCC(=O)O*,,0.3272277,,,
+148248890,*CC(*)(C)C(=O)OCCN(CC)CC,,0.3615965,0.2375,0.960101927,11.45757736
+148252767,*CC(*)OC(=O)c1ccc([N+](=O)[O-])cc1,,0.38294962,,,
+148616974,*CCCOC(=O)C(C)O*,,0.34983248,,,
+149053950,*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(Sc5ccc(Sc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)cc4)cc3)cc1)C2=O,,0.38371723,,,
+149101883,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6nc7ccccc7n6-c6ccc(-n7c(*)nc8ccccc87)cc6)cc5)cc4)c3)cc2)cc1,,0.37557123,,,
+149152941,*C(=O)c1ccc(Oc2ccc(Oc3ccc(C(=O)c4ccc5c(c4)C(=O)N(c4ccc(Cc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc4)C5=O)cc3)cc2)cc1,,0.37562904,,,
+150274872,*Oc1ccc(C(C)(C)c2ccc(Oc3cccc(*)n3)cc2)cc1,,0.3589821,,,
+150383443,*CCOCCOCCOC(=O)CCC(=O)O*,14.21396213,,,,
+150398974,*CC(c1ccccc1)C1C(=O)N(CCCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3Cl)c(C)c2)C(=O)C1*,,0.37423891,,,
+150541495,*C=C(*)c1cccc(N2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(c4cccc(Oc6cccc(Oc7cccc(N8C(=O)c9ccc(C(=O)c%10ccc%11c(c%10)C(=O)N(c%10cccc(C(*)=C)c%10)C%11=O)cc9C8=O)c7)c6)c4)C5=O)cc3C2=O)c1,,0.36553114,,,
+151009154,*Oc1cc(Oc2cccc(NC(=O)c3cc(Oc4ccc(C(=O)O)c(C(=O)Nc5cccc(*)c5)c4)ccc3C(=O)O)c2)ccc1C#N,,0.33806241,,,
+151282337,*c1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(N4C(=O)c5ccc(Oc6ccc7cc(Oc8ccc9c(c8)C(=O)N(*)C9=O)ccc7c6)cc5C4=O)cc3)cc2)cc1,,0.38326204,,,
+151355449,*CCOCCOC(=O)CCSCCC(=O)O*,,0.34050222,,,
+151560379,*CC(CCCCCCOc1ccc(N=Nc2ccc(OC)cc2)cc1)COC(=O)CCCCC(=O)O*,,0.35673043,,,
+151874358,*CCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)c(C)c1,,0.34027795,,,
+151905187,*CCOCCOCCOCCOCCOCCOC(=O)NCCCCCCNC(=O)O*,,0.33926085,,,
+152255453,*Oc1ccc(Oc2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.34811185,,,
+152289390,*c1ccc(Oc2cccc(N3C(=O)c4ccc(Oc5ccc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6c5)cc4C3=O)c2)cc1,,0.36602103,,,
+152393943,*Nc1ccc(C(CCC)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36407339,,,
+152467749,*c1cccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4cccc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)c4)cc3)cc2)c1,199.6619548,,,,
+152612020,*CC(*)(C)C(=O)OCCCCCCCCCCCOc1ccc(C=Nc2ccc(C#N)cc2)cc1,,0.36948879,,,
+152623829,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36060711,,,
+153052486,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(-c5nc(*)nc(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.40426914,,,
+153647431,*Cc1cc2cc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.32655452,,,
+153735651,*Nc1cccc2c(NC(=O)c3cc(NC(=O)C(CCSC)N4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cccc12,,0.34651154,,,
+153736542,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.34429229,,,
+153783159,*CC(*)c1ccc(C(=O)OCCN(C)C)cc1,,0.37142885,0.2314999999999999,1.0222365,11.6806072
+153852064,*Nc1ccccc1-c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccccc3N*)ccc1-2,,0.36887719,,,
+154178646,*Nc1ccc(-c2ccc([C@@H](C)c3ccc(N*)cc3)cc2)cc1,,0.34963968,,,
+154318869,*CC(*)(C)C(=O)NC(=O)OCCOc1c(Br)cc(S(=O)(=O)c2cc(Br)c(OCCO)c(Br)c2)cc1Br,,0.36467521,,,
+154607078,*OS(=O)(=O)c1cccc(C(=O)Oc2ccc(C(C)(C)c3ccc(*)cc3)cc2)c1,,0.36064897,,,
+154881704,*c1cc(C(=O)O)cc(N2C(=O)CC(C3CC4C(=O)N(*)C(=O)C4c4ccccc43)C2=O)c1,,0.3520042,,,
+154956060,*Nc1ccc2c(c1)-c1ccc(N*)c3cccc-2c13,,0.33585092,,,
+155701787,*C=CC(C(=O)OCC)C(*)C(=O)OCC,,0.36320884,,,
+155951033,*CCOC(=O)c1ccc(CCc2ccc(C(=O)O*)cc2)cc1,53.2795594,,,,
+156397667,*c1cc(*)cc(-c2nc3ccccc3o2)c1,,0.39004417,,,
+156526173,*CC(*)(C)C(=O)OCCCCCCCCCCn1c2ccccc2c2cc(C=CC3=C(C#N)C(=C(C#N)C#N)OC3(C)C)ccc21,,0.37456116,,,
+157448813,*C=C(C#N)C(=O)OCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCCCCCCn1c2ccccc2c2ccccc21,,0.36772669,,,
+157842166,*CC(*)c1cccc(C)n1,,0.39815003,,,
+157943579,*C=CC1CC(*)C(C#N)([Si](C)(C)C)C1,,0.40700493,,,
+158153643,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5C(F)(F)F)C6=O)cc4)cc3)cc2)cc1,,0.39560548,,,
+159062110,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7cccc(-c8nc(*)c(-c9ccccc9)[nH]8)c7)[nH]c6-c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.40285522,,,
+159202130,*NCc1c2ccccc2c(CN*)c2c(N)cccc12,,0.33082098,,,
+159242278,*CCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC,,0.35402154,,,
+159632571,*Nc1ccc(C2=Cc3ccccc3C3(c4ccccc42)c2cc(C(C)(C)C)ccc2-c2ccc(C(C)(C)C)cc23)cc1,,0.38649399,,,
+159895291,*CC(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)CN5C(=O)c6ccc(C(c7ccc8c(c7)C(=O)N(*)C8=O)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc2)cc1,,0.35255561,,,
+160086436,*Oc1ccc(C(CC)(c2ccccc2)c2ccc(OC(*)=S)cc2)cc1,,0.36657618,,,
+160203629,*CC(*)C(=O)OCc1ccc(C#N)cc1,,0.35524043,0.182,1.094513754,11.86407254
+160829220,*c1cc(/C=N/c2cccc(C)c2)cc(*)c1O,,,0.192,0.99179579,13.46142519
+161010769,*CCC(*)(C)c1ccccc1,,0.38823069,,,
+161201125,*CC(*)C(=O)OC1CC(C)CC(C)(C)C1,,0.38012306,0.2205,0.909612952,11.78379961
+161607336,*Oc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc(-c5ccc(C(=O)Nc6ccc(*)cc6)c(C)c5)cc4C)cc3)cc2)cc1,,0.36165997,,,
+161723503,*c1cccc(Oc2cccc(Oc3cccc(Oc4cccc(Oc5cccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c5)c4C#N)c3)c2C#N)c1,,0.36435311,,,
+162025525,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(*)nn3)cc2)cc1,,0.36876206,,,
+162133188,*Nc1ccc(CC(CC)(CC)c2ccc(N)c(CC(C)(C)c3ccc(N*)cc3)c2)cc1,,0.35644292,,,
+163611533,*Nc1ccc([C@@H](C2=CCC(N)(N*)C=C2)c2ccccc2)cc1,,0.3443427,,,
+163724879,*c1cccc(N2C(=O)c3cccc(Oc4ccc(C(=O)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.36803803,,,
+163827064,*Oc1ccc(Oc2ccc(P(=O)(c3ccccc3)c3ccc(Oc4ccc(Oc5cccc(*)n5)cc4)cc3)cc2)cc1,,0.36767472,,,
+164182648,*CC(*)OC(=O)c1cccnc1,,0.34320724,,,
+164916720,*CCCCCCOC(=O)C(CCCCCCCCCCBr)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.37107899,,,
+164960718,*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.3879693,,,
+165064963,*Nc1c(CC)cc(Cc2cc(CC)c(N*)c3ccccc23)c2ccccc12,,0.36700436,,,
+165170378,*CC(*)Cc1ccc(C)cc1,,0.3852781,,,
+165675920,*CC(*)(CC(=O)OC1CCCCCC1)C(=O)OC1CCCCCC1,,0.36822035,,,
+165704886,*Nc1cc(Cl)c(-c2cc(Cl)c(N*)cc2Cl)cc1Cl,,0.36444978,,,
+165718490,*Oc1ccc(OC(=O)c2cccc(C(*)=O)c2)cc1,,0.34002777,,,
+166073456,*CC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)O*,,0.35691907,0.186,1.089194614,17.59804845
+166405771,*CCCCCCNC(=O)C(CCCCCCCCCCCCCC)C(=O)N*,,,0.319,,
+166484126,*Nc1ccc(-c2ccccc2)c(-c2ccccc2CC)c1N*,,0.35913139,,,
+166538508,*CC(O)CN(*)c1cc(C)c(N=Nc2ccc([N+](=O)[O-])cc2C)c(C)c1,,0.37035052,,,
+166991206,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCC(=O)O*,,,0.3545,0.899322314,23.96801429
+167315243,*CCc1ccc(*)c2ccccc12,,0.37967697,,1.050887217,20.17560548
+167636925,*Sc1ccc2c(c1)Sc1ccc(*)cc1S2,,0.40790715,,,
+168537867,*c1ccc(C=Nc2cccc(N=Cc3ccc(-c4ccc(*)s4)cc3)c2)cc1,,0.39327219,,,
+169123787,*Nc1c(C)cc(C2(c3cc(C(C)C)c(N*)c(C(C)C)c3)c3ccccc3-c3ccccc32)cc1C,,0.40404304,,,
+169172468,*c1ccc(Cc2ccc(N3C(=O)c4ccc(-c5ccc(-c6ccc(-c7ccc(-c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6)cc5)cc4C3=O)cc2)cc1,,0.37649698,,,
+169292990,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc(Cl)cc2)cc1,,0.37292398,,,
+169412293,*CCCCCCCCCCOCCCCCCCCCCOCCCCCCO*,,,0.269,,
+169491723,*c1ccc(Oc2cccc3c(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cccc23)cc1,,0.37148684,,,
+169582663,*CC(*)(C)C(=O)OCCN(C)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36754584,,,
+169699943,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(C)cn2)C3=O)cc(C(=O)NNC(=O)CCCC(*)=O)c1,,0.33325517,,,
+169832897,*CC(*)C(=O)c1ccc(Br)c(OC)c1,,0.37684712,,,
+170092813,*Oc1ccc2cc(Oc3ccc(S(=O)(=O)c4ccc(*)cc4)cc3)ccc2c1,,0.36801316,,,
+170253465,*Oc1cccc(Oc2cccc(*)n2)c1,,0.35077616,,,
+170762410,*Nc1ccc([C@H](CC)c2ccc(C3(c4ccc([C@@H](CC)c5ccc(N*)cc5)cc4)CCC(CCCCC)CC3)cc2)cc1,,0.3695966,,,
+171128702,*Oc1ccc(C(=O)OCCCCCCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34460962,,,
+171195170,*CC(*)CCCC1CCCCC1,,,0.2463333333333333,,
+172004497,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5ccc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6c5)cc4C3=O)c(C)c2)cc1C,,0.39303801,,,
+172704550,*O[Si](C)(C)Oc1ccc(*)cc1,,0.41626444,,,
+173018796,*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc2C(=O)c2ccccc2)c1,,0.37955991,,,
+173214636,*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(c4ccc(C(*)=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.36717844,,,
+173616711,*c1ccc2c(c1)C(=O)N(c1cccc3c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cccc13)C2=O,,0.40266196,,,
+174572139,*CC(*)OC(=O)C(C)(C)CC(C)(C)C,,0.37520737,,,
+174718000,*Nc1c(C)cc(-c2cc(C)c(N*)c3ccccc23)c2ccccc12,,0.3715986,,,
+175197734,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CC(C)CCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34861668,,,
+175226564,*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nc5cc(-c6ccc7oc(*)nc7c6)ccc5o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.36313657,,,
+175456331,*C(=O)CCN1CCN(CCC(=O)N2CCN(*)CC2)CC1,40.58060093,,,,
+175513548,*CCCCCCCCCCOC(=O)CCC(=O)O*,1.783806133,,0.285,,
+175985223,*CC(*)C(=O)OCCCCCCSCC#N,,0.39181191,0.2175,1.039476536,13.18158821
+176911109,*C1C(=O)OC(=O)C1*,,,0.1425,1.365901109,17.04190977
+177156003,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.34368741,,,
+177272960,*c1ccc(*)[nH]1,,0.56389708,,,
+177304158,*CC(*)(C)C(=O)OC(C(F)(F)F)(C(F)(F)F)C(F)(F)F,,,0.0465,1.240147043,13.23674474
+177634653,*Nc1cc(Oc2cc(C)cc(C)c2)c(N)c2c1C(=O)c1c(ccc(Oc3cc(C)cc(C)c3)c1N*)C2=O,,0.35668305,,,
+177677392,*CC(CC)(CO*)COC(=O)C(=C)C,,0.35959663,,,
+177977769,*Oc1ccc2nc3c(nc2c1)Sc1cc(-c2ccc4c(c2)Sc2nc5cc(*)ccc5nc2N4)ccc1N3,383.4,,,,
+177999649,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(c1ccccc1)c1cccc([Si](*)(C)c2ccccc2)c1,,0.41493555,,,
+178586120,*C1C(=O)N(CCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.34432797,,,
+179710096,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OC)cc1,,0.33270357,,,
+179732838,*Oc1ccc(NC(=O)C=CC(=O)NNC(=O)C=CC(=O)Nc2ccc(OC(=O)c3ccccc3C(=O)NNC(=O)c3ccccc3C(*)=O)cc2)cc1,,0.32132816,,,
+179923409,*CCCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.34462277,,,
+179976364,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.35575698,,,
+180279074,*C(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c6C#N)cc4)C5=O)cc3)c2C#N)cc1,,0.35942779,,,
+180321996,*CCCCCCCCCC[N+](*)(C)C,,0.76931541,,,
+180324539,*C1OC(C(F)(F)F)(C(F)(F)F)OC1(*)F,,0.36688477,,,
+180355416,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(*)cc6)ccc5c4)cc3)cc2)cc1,,0.3816141,,,
+180651708,*CCCCCCSC(=O)CCCCCCCCC(=O)S*,-39.13613776,,,,
+180714059,*CCCCCCNC(=O)CCP(=O)(CCC(=O)N*)c1ccccc1,,0.34647823,,,
+180949734,*N=P(*)(OCCc1ccccc1)OCCc1ccccc1,,0.36125209,,,
+181297795,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)c4)cc3)cc2)cc1,,0.36487066,,,
+181346795,*CCN(CCOC(=O)NCCCCCCNC(=O)O*)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)c(C)c1,,0.35275364,,,
+181691220,*CC(F)(F)C(F)(F)COC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.30107699,,,
+182042962,*C=CC(*)(C)c1ccccc1,,0.37130723,,,
+182803961,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2cccc3ccccc23)c(-c2cccc3ccccc23)c1-c1ccccc1,,0.37664419,,,
+182838821,*C[Si](*)(CCCCC)CCCCC,,0.41744785,,,
+182963487,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4cccc5cccc(C(=O)c6ccc(*)cc6)c45)cc3)cc2)cc1,,0.37806189,,,
+183287787,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)o5)cc4)cc3)cc2)cc1,,0.38996204,,,
+183910750,*CC(OC(=O)C(=O)OCC)C(COC(=O)O*)OC(=O)C(=O)OCC,,0.31291135,,,
+184043122,*[Si](*)(CCCCCC)CCCCCC,-11.37918107,,,,
+184129336,*Nc1cc(-c2ccccc2)c(N*)c(-c2ccccc2)c1-c1ccccc1,,0.36779908,,,
+184214887,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O,,0.37908759,,,
+184273461,*Oc1ccc(OC(=O)CCCCC(*)=O)cc1,,0.3300236,,,
+184399060,*Cc1ccc(CSSS*)cc1,,0.41886249,,,
+184854748,*C1C(=O)N(c2ccccc2C(=O)OC)C(=O)C1*,,0.36145459,0.143,1.070408699,12.24675377
+184918589,*CC#CCOC(=O)CCCCCCCCC(=O)O*,,0.35810319,,,
+185502634,*Nc1ccc(CCCC(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.34680666,,,
+185789282,*CC(*)c1cc(Br)ccc1OCCCCC,,0.41131014,0.1545,,
+185974329,*CC(*)c1cc(C)ccc1F,,,0.171,0.955409279,13.40669452
+186514002,*Oc1ccc(C(=O)OCCCCOC(=O)c2ccc(*)cc2)cc1,,0.34336471,,,
+187577688,*CCCC(CC)CCNC(=O)c1ccc(C(=O)N*)cc1,,0.34121483,,,
+187968183,*CCCN(*)C(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.3285942,,,
+188178084,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)cc1)C2=O,,0.36659408,,,
+188574710,*Oc1cccc(NC(=O)CCCCCCCC(=O)Nc2ccc(*)cc2)c1,,0.3432777,,,
+188691845,*Nc1c(C)cc(-c2cc(C)c(N*)c(C)c2)cc1C,,0.36477351,,,
+189225250,*C1CCC(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC1,,0.36716787,,,
+189385552,*CC(*)(F)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32470853,,,
+189585434,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Cc4ccc(NC(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.35716326,,,
+189637201,*CC(*)c1ccc(COC(C)CC)cc1,,0.38400866,,,
+189875275,*C#CC(COS(=O)(=O)c1ccc(C)cc1)=C(*)COS(=O)(=O)c1ccc(C)cc1,,0.37237347,,,
+189877162,*Nc1ccc(C2(c3ccc(N*)cc3)c3cc([C@]45C[C@H]6C[C@H](C[C@H](C6)C4)C5)ccc3-c3ccc([C@]45C[C@H]6C[C@H](C[C@H](C6)C4)C5)cc32)cc1,,0.39779257,,,
+190124790,*CCN(*)C(=O)c1ccc2ccccc2c1,,0.35477943,,,
+190194752,*CCCCCCCCc1nnc(*)n1N,-41.92176029,,,,
+190344544,*Nc1ccc(NC(=O)c2cc(C(*)=O)c(C(=O)O)cc2C(=O)O)cc1,,0.29592785,,,
+190407869,*CC(*)O,,,0.424,1.081206735,22.51988264
+190689808,*CC(*)c1ccccc1COC(C)C,,0.38497632,0.171,0.933317386,12.10539551
+190870051,*CC(*)c1ccc(Cn2c3ccccc3c3ccccc32)cc1,,0.36892883,,,
+191380298,*CC(*)(C)C(=O)OCCCCCc1ccc(N=Nc2ccc(OC(F)(F)F)cc2)cc1,,0.35838591,,,
+191414295,*Nc1ccc(-c2cc(N*)c3ccccc3c2/N=N/c2cccc3ccccc23)cc1,,0.35297912,,,
+191656364,*CCc1ccc(-c2ccc(*)cc2)cc1,,,,1.042562737,30.03662597
+191836476,*CCCC(=O)NNC(=O)c1ccc(C(=O)NNC(=O)CCCOc2ccc(O*)c(C)c2)cc1,,0.33330839,,,
+192948428,*CCOCCOCCOC(=O)C1CCC(C(=O)O*)CC1,,0.33745828,,,
+192960125,*Nc1ccc2c(N*)c3ccccc3cc2c1,,0.34619216,,,
+193203313,*CCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.34023648,,,
+193475796,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(-c6cccc7c6C(=O)N(*)C7=O)cc5C4=O)cc3)c2)cc1,,0.36194506,,,
+193587045,*CC(O)CO*,,0.31201093,,,
+194134308,*CCOCCOC(=O)CCCCCCC(=O)O*,,0.34329076,,,
+195032699,*Nc1ccc(-c2ccc(N*)c(Cc3ccccc3)c2)cc1Cc1ccccc1,,0.3464418,,,
+195040498,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)CCC3CCCCC3C2)cc1,,0.387972,,,
+195313590,*CC(*)(C)C(=O)OCCOc1ccc(N=Nc2ccc(OC)cc2)cc1,,0.36003088,,,
+195349220,*COc1ccc(C(=C(Cl)Cl)c2ccc(O*)cc2)cc1,,0.3737643,,,
+195585477,*C(=O)c1ccc(N(*)CCC)cc1,,0.39207045,,,
+196134048,*CC(*)c1cc(Br)ccc1OCCC(C)C,,0.40859696,0.1674999999999999,,
+196264517,*CCCCCCNC(=O)CCP(CCC(=O)N*)c1ccccc1,9.575432391,,,,
+197075441,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C45CC6CC(CC(c7ccc(*)cc7)(C6)C4)C5)cc3)cc2)cc1,,0.37269141,,,
+197916751,*Oc1ccc(OC(=O)c2cc(OCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.35296749,,,
+197924325,*CC(*)(C)C(=O)OC1CCCC1,,0.36428861,,,
+198466108,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(-c3ccccc3)cc2)cc1,,0.35036936,,,
+199188552,*Nc1ccc(C2(N*)C=CC(c3ccc(N)cc3)(c3ccc(N)cc3)C=C2)cc1,,0.36932897,,,
+199261804,*c1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(Oc7ccc(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)ccc3ccccc23)cc1,,0.36179389,,,
+199401973,*Oc1ccc(OC(=O)c2ccc(OCCCCCCCCCCOc3ccc(C(*)=O)cc3)cc2)cc1S(=O)(=O)c1ccccc1,,0.35673875,,,
+199573915,*CC(*)c1c(F)c(F)c(OCC(F)(F)C(F)(F)F)c(F)c1F,,0.35026517,,,
+199989730,*c1ccc(Oc2ccc(Oc3ccc(-c4ccc(Oc5ccc(Oc6ccc(C7(*)OC(=O)c8ccccc87)cc6)cc5)c5ccccc45)c4ccccc34)cc2)cc1,,0.38367366,,,
+200483739,*c1cccc(OCCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.35053912,,,
+200513526,*CC#CC#CCOC(=O)c1ccc(C(=O)O*)cc1,,0.37145321,,,
+200916875,*c1cccc(-c2nc3ccc(-c4ccc5nc(*)[nH]c5c4)cc3[nH]2)c1,,0.42946115,,,
+200989241,*N=P(*)(Oc1ccccc1)Oc1ccccc1,,0.40515813,,,
+201251785,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C2(CCCCC2)C1=O,277.0792131,,,,
+201564281,*Oc1cccc(Oc2ccc(Oc3cccc(OC(=O)c4ccc(C(*)=O)cc4)c3)cc2)c1,,0.35180531,,,
+201596108,*CCCCCCNC(=O)C(CCCCCCCCCCCCC)C(=O)N*,,,0.2925,0.899716416,13.5860206
+201709435,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(Sc9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)cc8)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.36404303,,,
+201838282,*CC(CCCCCCCCCC)O*,,0.40374823,,,
+202074426,*Oc1ccc(Oc2ccc(S(=O)c3ccc(Oc4ccc(Oc5cccc(*)n5)cc4)cc3)cc2)cc1,,0.35458448,,,
+202457389,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(C)c(N5C(=O)c6ccc(*)cc6C5=O)c3C)C4=O)cc2C)c(C)c1,,0.3862015,,,
+203166993,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.34147075,,,
+203328995,*CCCCCCCCCCOCCCCCCCCCCOCCCCCO*,,,0.38525,,
+203569820,*c1cc(*)c(O)c(N=Cc2ccc(F)cc2)c1,,0.37230953,,,
+204188551,*CC(*)c1ccc(Cl)c(Cl)c1,,,0.114,,
+204433294,*C(=O)OC1CC(*)N(C(=O)OCc2ccccc2)C1,,0.33476928,,,
+204583818,*CC(OC)C1(C)C(=O)OC(=O)C1(*)C,,0.31336206,,,
+204710529,*c1ccc(Oc2ccc(P(=O)(c3ccccc3)c3ccc(Oc4ccc(-c5cc(*)[nH]n5)cc4)cc3)cc2)cc1,,0.39032537,,,
+204753687,*CCCCCCOC(=O)OCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35004228,,,
+204849258,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(OCCCC)cc2)cc1,,0.36120255,,,
+205078323,*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc(OCC)cc3)cc2)cc1,,0.36210675,,,
+205238044,*CC(*)(C)C(=O)OCC1(CC)COC1,,0.36219625,,,
+205810542,*Nc1ccc(Cc2ccc(N*)c3ccccc23)c2ccccc12,,0.34615054,,,
+206145001,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.37060642,,,
+206778782,*c1c(C)cc(C(c2cccc(C(F)(F)F)c2)c2cc(C)c(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.42612429,,,
+207198573,*C(*)C(=O)OC(C)(C)CC,136.5678336,,,,
+207258565,*c1ccc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(*)ccc2-3)cc1,,0.41767157,0.376,0.786503256,27.96449264
+207627808,*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nc5cc(C(c6ccc7oc(*)nc7c6)(C(F)(F)F)C(F)(F)F)ccc5o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.36859748,,,
+207945160,*c1cccc(P(=O)(c2ccccc2)c2cccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.37508167,,,
+208376671,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)c6c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c56)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.40028133,,,
+208448147,*CC(*)(C)C(=O)OCc1ccco1,,0.3375562,0.172,1.081813241,12.32614241
+208451383,*OC(COc1ccccc1)CC(*)=O,,0.3399788,,,
+209437861,*CC(CCCCCCCCCCCC)COC(=O)NC(=O)c1ccc(C(=O)NC(=O)O*)cc1,,0.34646463,,,
+210091509,*CCCCCCCCOc1c(OC)cc(/C=C/c2ccc(/C=C/c3ccc(/C=C/c4cc(OC)c(O*)c(OC)c4)cc3)cc2)cc1OC,,0.36466644,,,
+210562725,*Oc1ccc(-c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34119605,,,
+210731746,*Nc1nc(N)c(N)c(N*)n1,,0.31899407,,,
+210803302,*NC(C(*)=O)C(C)C,153.0278775,,,,
+210909359,*CCOCCOP(=O)(N=Nc1ccc(C(=O)c2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.35159394,,,
+210914084,*CCCCCCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.35319666,,,
+210989195,*c1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(N4C(=O)c5ccc(Oc6ccc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7c6)cc5C4=O)cc3)cc2)cc1,,0.37859256,,,
+211760119,*C#Cc1cc(C(C)(C)C)cc2c3cc(C(C)(C)C)cc(*)c3n(CCCCCCCCCCCCCCCC)c12,,0.40884949,,,
+211825763,*OC(*)C,,0.3634429,,,
+212654540,*CC(*)c1ccccc1C(=O)OCCCC,,0.37028116,0.1953333333333333,,
+212760661,*CCCCCC(=O)Nc1ccc(Oc2cccc(NC(=O)CCCCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.33649448,,,
+213054047,*O[Si](O[Si](O[Si](O[Si](CC[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)CC[Si](*)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1,,0.4006666,,,
+213771383,*Oc1ccc(Oc2ccc(C(=O)c3cccc(NC(=O)CCCCCCCCC(=O)Nc4cccc(C(=O)c5ccc(*)cc5)c4)c3)cc2)cc1,,0.35474585,,,
+213984999,*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.37483877,,,
+214162824,*CC/C=C(/*)CCC,,,0.2233333333333333,,
+214279994,*CCCCCCCCCCC(=O)NCCc1ccc(CCNC(=O)CCCCCCCCCCS*)cc1,,0.38077316,0.296,0.962511767,22.76143272
+214573613,*CCC(=O)NCc1c(C)c(C)c(CNC(=O)CCO*)c(C)c1C,,0.336425,,,
+214883571,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(C(c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1)C2=O,,0.37434007,,,
+215105340,*c1c(C)cc(C)c(N2C(=O)c3ccc(S(=O)(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1C,,0.40691826,,,
+215128069,*CCCCCCCCCCNC(=O)CC/C=C/CCC(=O)N*,,,0.3125,0.952363037,18.83632464
+215274198,*C#CC#Cc1ccc(*)s1,49.13696662,,,,
+215595067,*OC(COC(=O)c1ccc(C(*)=O)c2ccccc12)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.353962,,,
+215880524,*Nc1ccc2[nH]c3ccc(N*)cc3c2c1,,0.33036591,,,
+215968936,*c1ccc(-c2sc(-c3ccc(-c4cc(SCCCCCCCC)c(*)s4)cc3)cc2SCCCCCCCC)cc1,,0.46732994,,,
+216165703,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(N)c4)cc3N)cc2)c(N)c1,,0.37345371,,,
+216288111,*C(=O)Nc1cccc(Oc2cccc(Oc3cccc(NC(=O)c4ccc5c(c4)C(=O)N(c4cccc(Oc6cccc(Oc7cccc(N8C(=O)c9ccc(*)cc9C8=O)c7)c6C#N)c4)C5=O)c3)c2C#N)c1,,0.35823538,,,
+216414863,*Nc1cccc([C@](N)(c2ccccc2)c2cccc3ccccc23)c1N*,,0.35839515,,,
+216608930,*CCOC(=O)CCCCCCC(=O)OCCOC(=O)c1ccc(C(=O)O*)cc1,,0.33135087,,,
+216626801,*/C=C/c1ccc(*)c2ccc(CCCCCC)cc12,,,0.339,0.946624284,20.81567785
+216772383,*CC(*)c1ccccc1COCCCC,,0.38677194,0.2096666666666666,0.940569983,11.37399833
+218059466,*/C(=C(/*)c1ccccc1)c1ccccc1,206.5698859,,,,
+218216912,*CCCCOC(=O)OCCCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.32834937,,,
+218350404,*CCCCCCCCCOC(=O)CCCCCCCC(=O)O*,3.667443154,,0.294,0.941870155,20.56975581
+219039564,*c1ccc2c(c1)SC1=Nc3cc(-c4ccc5c(c4)N=C4Sc6cc(*)ccc6N=C4N5)ccc3NC1=N2,418.69,,0.506,1.086875784,27.5268485
+219386483,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O)c1ccccc1,,0.37130199,,,
+219718402,*Nc1ccc(CCc2cc(CCc3ccc(N)cc3)cc(CCc3ccc(N*)cc3)c2)cc1,,0.35342866,,,
+219873035,*O[Si](C)(C)O[Si](*)(C)CCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.38755299,,,
+220000766,*CC(*)C(=O)OCC(CC)CCCC,,0.3869788,0.2309999999999999,0.893389668,10.99379023
+220223317,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37640128,,,
+220286370,*CCCCOC(=O)c1ccc([Si](C)(C)c2ccc(C(=O)O*)cc2)cc1,,0.36459187,,,
+221632888,*O[Si](*)(C)CCc1ccc(O)cc1,,0.35484533,,,
+222144803,*Nc1cc2ccc(N*)c3c4cccc5cccc(c(c1)c23)c54,,0.33646603,,,
+222164121,*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)OCCOC(=O)C=Cc2ccc(N(C)C)cc2)c1,,0.34211001,,,
+222724508,*CC(*)c1ccccc1C(=O)OCCC,,,0.1929999999999999,,
+223538531,*Nc1cc(NC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2)cc(C(=O)OCCOc2ccc(C=CC(=O)c3ccccc3)cc2)c1,,0.34446761,,,
+223648222,*Nc1cc2cc3cc4cc5cc6ccccc6cc5cc4cc3cc2cc1N*,,0.34605853,,,
+223739255,*CC(*)(C)C(=O)Nc1cccc(-c2nnc(-c3ccccc3)o2)c1,,0.38307246,,,
+223791997,*C(C)C=CC(C)S(*)(=O)=O,33.82674704,,,,
+224878400,*CC(*)(C)C(=O)OC1CCCCCCCCCCC1,,0.37197293,,,
+225113015,*N=P(N=P(N=S(*)(=O)NC)(NC)NC)(NC)NC,,0.34459575,,,
+225199973,*NC1=CC=C(c2ccc(N*)cc2)C(C)(C)[C@@H]1c1ccccc1,,0.35401521,,,
+225304239,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc7c(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cccc67)cc4)C5=O)cc3)cc2)cc1,,0.36000298,,,
+225689380,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccccc3N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37500133,,,
+225811773,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2)cc1,,0.34463445,,,
+226076507,*C1C(=O)N(CCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.35053641,,,
+226216876,*Nc1cccc(C(=O)Oc2ccc(OC(=O)c3cccc(N*)c3)c3c2[C@H]2c4ccccc4[C@@H]3c3ccccc32)c1,,0.35943133,,,
+226520604,*CC(*)(C)C(=O)Oc1ccc([N+](=O)[O-])c(C)c1,,0.37079204,,,
+226546629,*NC(CCC(=O)OCCCCC)C(*)=O,,0.35413594,,,
+226561298,*Oc1ccc2ccc(Oc3ccc(C(=Nc4ccc(Oc5ccc(N=C(c6ccccc6)c6ccc(*)cc6)cc5)cc4)c4ccccc4)cc3)cc2c1,,0.38273527,,,
+226567495,*Nc1ccc2c(c1N*)-c1ccccc1C2(CC)CC,,0.35949967,,,
+226643768,*CC(*)CC(C)C(F)(F)F,,,0.148,1.093859396,13.31500903
+226759816,*Nc1c(-c2ccccc2)cc(/C=C/c2ccccc2)c(/C=C/c2ccccc2)c1N*,,0.36364076,,,
+227298587,*CCOCCOC(=O)CCCCCCCC(=O)O*,,0.34727992,,,
+227838325,*Oc1c(Cl)cc(C(c2cc(Cl)c(OC(*)=O)c(Cl)c2)C2CC3CCC2C3)cc1Cl,,0.4079258,,,
+228251537,*Nc1ccc(Cc2ccc(C(CC)c3ccc(Cc4ccc(N*)cc4)cc3)cc2)cc1,,0.35878998,,,
+228252916,*C=Cc1sc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(-c4sc(C=CC5=CC(=C(C#N)C#N)C=C(*)O5)c(CCCCCC)c4CCCCCC)ccc2-3)c(CCCCCC)c1CCCCCC,,0.42087423,,,
+228347866,*CCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.32982991,,,
+228476998,*c1ccc(C(C#N)(C(=O)O)C(*)(C#N)C(=O)O)cc1,,0.35362996,,,
+228510296,*CCN(CCOC(=O)NCC1(C)CC(NC(=O)O*)CC(C)(C)C1)c1ccc(N=Nc2ccc(B(c3c(C)cc(C)cc3C)c3c(C)cc(C)cc3C)cc2)cc1,,0.34069727,,,
+228992973,*c1ccc(N(c2ccc(OC)cc2)c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.40284317,,,
+229505065,*OS(=O)(=O)c1ccc(Oc2ccc(S(=O)(=O)Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.36351582,,,
+229703472,*C(=O)c1ccc(Oc2ccc(-c3ccc(Oc4ccc(C(=O)c5ccc6nc(*)ccc6c5)cc4)cc3)cc2)cc1,,0.38017203,,,
+229766352,*c1ccc2c(c1)C(=O)N(c1ccc(N(C)CCN(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.37991556,,,
+230018905,*Oc1ccc(C(C)(C)c2ccc(Oc3nc(*)nc(Cl)n3)cc2)cc1,,0.36279792,,,
+230210475,*Oc1ccc(C=C2CCC(=Cc3ccc(OC(=O)c4ccc(N=Cc5ccc(C=Nc6ccc(C(*)=O)cc6)cc5)cc4)c(OC)c3)C2=O)cc1OC,,0.36413773,,,
+230305493,*CCCCCCCCC(=O)NCCCCCOCCCCCNC(=O)CCCO*,,,0.289,0.958044519,16.89480393
+230397925,*CCCCCCCC(*)C,,0.40146108,0.371,0.806729205,19.12833616
+230432971,*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nnc(-c5ccc(-c6nnc(*)o6)c(Oc6ccccc6)c5)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.36767095,,,
+231066951,*Nc1cc(C)c(C(c2ccccc2)c2cc(C)c(N*)cc2C)cc1C,,0.37754837,,,
+231144775,*Nc1ccc2c(c1)C1(c3ccc(C)cc3-2)c2cc(N)ccc2-c2ccc(N*)cc21,,0.38585396,,,
+231347203,*NCCCN(C)CCCN*,,0.32591931,,,
+231567628,*CC(*)(C)C(=O)OCc1cccs1,,0.35173384,,,
+231654446,*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5ccc(-c6ccc(*)cc6)cc5)cc4)ccc3c2)cc1,,0.37364004,,,
+232089797,*Nc1ccc([C@H](C)Cc2cccc(C[C@@H](C)c3ccc(N*)cc3)c2)cc1,,0.35754875,,,
+232335054,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O,,0.38115589,,,
+232528549,*Nc1cc(NC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2)cc(-c2nnc(-c3ccccn3)o2)c1,,0.37471715,,,
+232607063,*CCCCCCCCCCCOC(=O)Nc1ccc(NC(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(O*)cc3)cc2)c(C)c1,,0.36758677,,,
+232782325,*Oc1ccc(N[Se]Nc2ccc(*)cc2)cc1,,0.38958964,,,
+232863145,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc([N+](=O)[O-])cc1C,,0.33326912,,,
+233782359,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(C#N)cc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.41938162,,,
+233996434,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)(CC)C1=O,242.6534046,,,,
+234298821,*Nc1ccc(-c2ccc(-c3cc(-c4ccc(N*)cc4)cc(-c4ccc(-c5ccc(C)cc5)cc4)c3)cc2)cc1,,0.35439862,,,
+234298840,*CC(O)CN(*)c1cc(C)c(N=Nc2ccc(C#N)cc2)c(C)c1,,0.38210588,,,
+234377391,*CCCCCCCCCCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,0.37489895,0.368,0.906986266,23.47308455
+234483624,*C1CC(*)OC(O)O1,136.0523749,,,,
+234538951,*=Nc1cccc(Oc2cccc(Oc3cccc(N=C4OC(=O)c5cc6c(cc54)C(=*)OC6=O)c3)c2)c1,132.5253262,,,,
+234724783,*Nc1ccc2c(c1)-c1cc(N*)ccc1C21c2ccc(N)cc2-c2cc(N)ccc21,,0.38703609,,,
+234741357,*CCCCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.34015211,,,
+234747929,*Nc1ccc2c(c1)C(C1c3ccccc3-c3ccccc31)c1cc(N*)ccc1-2,,0.36346408,,,
+235082589,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(*)c(S(=O)(=O)O)c4)cc3S(=O)(=O)O)cc2)cc1,,0.36352019,,,
+235587729,*c1ccc(CC(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)Cc4ccc(-c5sc(*)c(-c6ccccc6)c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.37882713,,,
+235628108,*c1ccc(-c2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.40933062,,,
+236163730,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)=C(C(=O)OCC)C1c1ccc(Cl)cc1,170.3261787,,,,
+236477815,*Nc1cccc(C(=C(c2ccccc2)c2ccccc2)c2cccc(N*)c2)c1,,0.37210124,,,
+236843078,*C(=O)Nc1cc(C(c2cc(C)c(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc5c(N6C(=O)c7ccc(*)cc7C6=O)cccc35)C4=O)c2)(C(F)(F)F)C(F)(F)F)cc(C)c1C,,0.3730141,,,
+238179937,*Nc1ccc(C)c2c(N*)ccc(C)c12,,0.33933851,,,
+238223157,*C(=O)N(*)c1ccccc1,150.7130657,,,,
+238571886,*Oc1ccc2cc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)ccc2c1,,0.34998488,,,
+238619204,*C(=O)OCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.33594994,,,
+238841218,*Nc1ccc(C2(c3ccc(N*)cc3[C@]34C[C@@H]5C[C@H](C[C@@H](C5)C3)C4)c3ccccc3-c3ccccc32)c([C@]23C[C@H]4C[C@H](C[C@H](C4)C2)C3)c1,,0.3538557,,,
+238997456,*OC(=O)Oc1ccc(S(=O)(=O)c2ccc(OC(=O)OC3CC4CC(*)CC(C3)O4)cc2)cc1,,0.3395961,,,
+239786978,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5cccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)c5)cc4C3=O)cc2)cc1,,0.35739857,,,
+240082161,*NCCCCCCCCCCCN*,,0.34603548,,,
+240111092,*CC(*)c1ccc(C(C)(O)CCC)cc1,,0.37492289,,,
+240232361,*N=Cc1ccc(-c2ccc(C=Nc3ccc4c(c3)c3cc(*)ccc3n4CCCCCC)s2)s1,95.07860424,,,,
+240796718,*CC(*)(CF)C(=O)OCC,,0.33962658,0.1684999999999999,1.040351207,13.63736539
+241359312,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O,,0.38315859,,,
+241371117,*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCCCCCC,,0.47689077,,,
+241512397,*CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)OCCOc3ccc(C4(c5ccc(O*)cc5)c5ccccc5-c5ccccc54)cc3)cc2)cc1,,0.35416456,,,
+241814157,*CCCCCCCCCCOC(=O)CCCC(=O)O*,,,0.263,,
+241915001,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34542529,,,
+242653142,*Cc1cc2cc(C(=O)Nc3ccc(NC(=O)c4cc5cc(*)c(OC(=O)COc6ccc7ccc(=O)oc7c6)cc5oc4=O)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.31935888,,,
+242836460,*CC(*)C(=O)OCCOCC(F)(F)F,,0.33534513,,,
+242840603,*c1ccc2c3ccc(-c4ccc(-c5ccc(-c6ccc(*)s6)c6nccnc56)s4)cc3n(C(CCCCCCCC)CCCCCCCC)c2c1,,0.45544781,,,
+242927288,*CC(*)C(=O)OCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.34363418,,,
+243127357,*Cc1ccccc1CN(CC)c1ccc(/C=C/c2ccc(/C=C(\C#N)C(=O)NC3CCCCC3NC(=O)/C(C#N)=C/c3ccc(/C=C/c4ccc(N(*)CC)cc4)cc3)cc2)cc1,,0.37544351,,,
+243290882,*Nc1ccc2c(c1)-c1ccccc1C21c2ccccc2-c2cc(N*)ccc21,,0.38182575,,,
+243418714,*CC(*)(C)C(=O)OCCOc1ccc(C(=O)Oc2ccc(OCCCC)cc2)cc1,,0.34294351,,,
+243622593,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OC)cc1,,0.33521285,,,
+243711331,*Oc1cc(CCCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.36723846,,,
+243853291,*c1ccc(C(=O)OCCCCCCOC(=O)c2ccc(N3C(=O)CC(SCCOCCSC4CC(=O)N(*)C4=O)C3=O)cc2)cc1,19.42674542,,,,
+243952176,*CCCCCCOC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.3240054,,,
+244278527,*CCCCCCOC(=O)OCCCCCCCCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.36332291,,,
+244304146,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.3968899,,,
+244332189,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccc(Br)cc3)c3ccccc23)cc1,,0.36678226,,,
+244803582,*Oc1cc2ccccc2cc1OC(=O)c1ccc(Oc2ccc(C(*)=O)cc2)cc1,,0.35353153,,,
+244913940,*Oc1ccc(C(=O)c2ccc(NC(=O)c3ccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.35794829,,,
+245591935,*Oc1cccc(Oc2cccc(NC(=O)c3cc(Oc4ccc(C(=O)O)c(C(=O)Nc5cccc(*)c5)c4)ccc3C(=O)O)c2)c1,,0.32944697,,,
+245779778,*C(=O)C(*)(C)C,105.8292511,,,0.919975664,25.84971423
+246955408,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(C(c9ccc(*)cc9)(C(F)(F)F)C(F)(F)F)cc8)cc7)cc6)cc5)c4)cc3)cc2)cc1,,0.3636083,,,
+247237663,*c1ccc(Oc2ccc(N3C(=O)c4cccc(Oc5cccc6c5C(=O)N(*)C6=O)c4C3=O)cc2)cc1,,0.3708276,,,
+247440971,*NCc1cccc(-c2cccc(C/C=C(/c3ccccc3)c3cc4c(cc3N*)C(C)(C)c3ccccc3-4)c2)c1,,0.36399382,,,
+247441113,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(Oc4ccc(C5(c6ccc(*)cc6)CC6CCC5C6)cc4)cc3)cc(C(C)(C)C)c2)cc1,,0.37881264,,,
+247473503,*CC(*)C(=O)OCCSCCC#N,,0.38180613,0.1875,1.111901096,13.24819081
+247558689,*CC(*)c1ccc(OP2(OCCOCCOCCOC)=NP(OCCOCCOCCOC)(OCCOCCOCCOC)=NP(OCCOCCOCCOC)(OCCOCCOCCOC)=N2)cc1,,0.35241412,,,
+247866107,*C(=O)Nc1ccc(Oc2ccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)cc2)cc1,,0.35047717,,,
+247996319,*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)c1ccccc1,,0.35589429,,,
+248016214,*CC(*)(C)C(=O)OCCc1ccccc1,,0.35596729,0.168,1.048543977,11.43837267
+248038345,*CC(*)(C)C(=O)Oc1c(OC)cc(C=Cc2ccccc2)cc1OC,,0.36033906,,,
+248424742,*Nc1ccc(-c2ccc(N*)c(-c3ccc4ccccc4c3)c2-c2ccccc2)c(-c2ccccc2)c1-c1ccc2ccccc2c1,,0.37176001,,,
+248434754,*CCCC(C)CN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34161456,,,
+248544464,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1,,0.36643788,,,
+249614010,*Nc1ccc(C(c2ccc(NC(=O)c3ccc(-c4ccc(C(*)=O)cc4)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.36288949,,,
+250010482,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8cccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.35957001,,,
+250039671,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(S(=O)(=O)c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36225862,,,
+250587037,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C(c7ccccc7)(c7ccccc7)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.39689903,,,
+251202338,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34943466,,,
+251297590,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(c3ccccc3)(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)C(F)(F)F)c1)C2=O,,0.38575647,,,
+252276774,*OC(=O)OCCCCCCCCCCOC(=O)OC1COC2C(*)COC12,,0.34183349,,,
+252299281,*CC(*)C(=O)NC(C)CC,,,0.2259999999999999,0.92947228,12.6370902
+252836035,*c1ccc(Oc2cccc3c(NC(=O)c4ccc(C(c5ccc(C(=O)Nc6cccc7c(Oc8ccc(-c9nnc(*)o9)cc8)cccc67)cc5)(C(F)(F)F)C(F)(F)F)cc4)cccc23)cc1,,0.37104971,,,
+253276168,*CC(*)(C)C(=O)OCC(O)CO,,0.28803561,,,
+253355524,*Nc1cnc(*)s1,,0.41199988,,,
+253518655,*CCC=C(*)CCCCCCC,,0.41315553,,,
+253610686,*c1ccc(-c2ccc3nc(*)cc(-c4ccccc4)c3c2)cc1,,0.41535856,,,
+253726027,*c1ccc(-c2ccc(NC(=O)c3cccc(C(=O)Nc4ccc(-c5ccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.35611323,,,
+254267891,*CCCCSSCCCCO*,,0.41742983,0.199,,
+254408923,*c1cccc(OCCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.35408719,,,
+254419051,*Nc1ccc(N*)c(-c2ccc3ccccc3c2)c1-c1ccc2ccccc2c1,,0.35818164,,,
+254630145,*c1ccc2c(c1)C(=O)N(c1ccc(OC(CCCCCC)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.36437379,,,
+254694603,*OC(=O)c1cccc(C(=O)OC2CC3CC(*)CC(C2)O3)c1,,0.34862273,,,
+254845509,*CC(*)(C)C(=O)OCCN(CC)S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,,0.1095,1.49945764,12.31638522
+255108764,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O,,0.36084666,,,
+255514575,*C(=O)Nc1cccc(C(=O)c2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)c2)c1,,0.39087447,,,
+255574677,*c1ccc(Cc2ccc(NC(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2)cc1,,0.36146511,,,
+255628383,*CC(*)(C#N)C(=O)OCCCC,,0.37262971,0.179,0.98541293,13.85902924
+255934897,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCC)cc1,,0.33648171,,,
+256392827,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc(NC(=O)c6cc(C(=O)Nc7ccc(Oc8ccc9nc(-c%10ccc([N+](=O)[O-])cc%10)c(*)nc9c8)cc7)cc(N7C(=O)c8ccccc8C7=O)c6)cc5)ccc4nc3-c3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.37884475,,,
+256452381,*Nc1ccc(-c2ccc3ccccc3c2)c(-c2ccc3ccccc3c2)c1N*,,0.35367739,,,
+256964530,*C(=O)NC1CCCC(*)O1,,0.32360402,,,
+257055666,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5C(F)(F)F)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(C(F)(F)F)c1)C2=O,,0.37771161,,,
+257349913,*CC(*)c1cc(C(=O)OC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.37587647,,,
+257379568,*c1ccc(N2C(=O)c3ccc(-c4cccc5c4C(=O)N(*)C5=O)cc3C2=O)cc1,,0.37822963,0.233,1.15692241,22.05257254
+257538013,*CC[Si](C)(C)O[Si]1(*)O[Si](C)(C)O[Si](C)(C)O1,,0.44963517,,,
+257696451,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cccc(C(=O)O*)c1,,0.32102943,,,
+257850952,*Oc1ccc(C(C)=Cc2ccc(OC(=O)OCCCCCOC(*)=O)cc2)cc1,39.38257392,,,,
+258103927,*c1ccc(-c2ccc(N(*)c3ccc(C)cc3)cc2)cc1,,0.44281771,,,
+258160847,*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.36051806,,,
+258763444,*c1nc2cc3sc(-c4cc(OCCCCCCCCCCCC)c(*)cc4OCCCCCCCCCCCC)nc3cc2s1,,,0.3647499999999999,0.992488687,22.57173018
+258775514,*CC(*)(C)C(=O)OCCCCCCCCCCCCCCCCCC,,0.39721388,0.317,0.861197166,12.32577198
+258956403,*Nc1ccc(-c2ccc(N*)c(-c3ccc(-c4ccccc4)cc3)c2)cc1,,0.34800933,,,
+258994540,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(N=Nc2ccc(C=O)cc2)cc1,,0.35665131,,,
+258997794,*CCCCCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.40056446,,,
+259074348,*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)OCCOc2ccc(C=CC(=O)c3ccccc3)cc2)c1,,0.34041504,,,
+259180745,*CC(*)C(=O)c1ccc(Br)cc1,,0.39504944,,,
+259297341,*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)C(=O)N(N3C(=O)c5cccc(*)c5C3=O)C4=O)cc2)cc1,,0.38796769,,,
+259345713,*Nc1ccc(C(c2ccc(N*)cc2)(C2CCCCC2)C2CCCCC2)cc1,,0.3823599,,,
+259345728,*Oc1ccc(NC(=O)c2cc(C(c3ccc(C(=O)O)c(C(=O)Nc4ccc(Oc5ccc(C(c6ccc(*)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4)c3)(C(F)(F)F)C(F)(F)F)ccc2C(=O)O)cc1,,0.34130286,,,
+259439423,*Nc1ccccc1-c1c(N*)cccc1-c1ccccc1-c1ccccc1-c1ccccc1,,0.3650485,,,
+259788945,*CCC1C(=O)N(CCCC)C(=O)C1*,,0.3477642,,,
+259984370,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)C3(CCC(C5=NC(Cc6ccccc6)CO5)(C5=NC(Cc6ccccc6)CO5)CC3)c3cc(*)ccc3-4)ccc1-2,,0.3966168,,,
+260073812,*Oc1cc(CCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.35907903,0.234,1.125159714,19.71902254
+260077968,*Cc1ccc(CO*)cc1,,0.35484206,,,
+260201518,*OC(F)(F)COC(=O)c1cc(OCCCCC)cc(C(=O)OCC(*)(F)F)c1,,0.33975909,,,
+260366998,*CCCCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.35659382,,,
+260773607,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(-c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37808348,,,
+260812845,*CC(*)c1ccccc1COCCCCCCCC,,0.38758383,,,
+261071879,*CC(*)c1ccc(C)cc1C,,,0.1856666666666666,0.894079517,12.1318108
+261334701,*Nc1ccc(-c2ccc(-c3c(-c4ccc5ccccc5c4)c(-c4ccc5ccccc5c4)c(N*)c(-c4ccc5ccccc5c4)c3-c3ccc4ccccc4c3)cc2)cc1,,0.37406348,,,
+262074826,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(S(=O)(=O)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36927526,,,
+262353559,*Nc1ccc(C2(c3ccc(N*)c([C@]45C[C@H]6C[C@H](C[C@H](C6)C4)C5)c3)c3ccccc3-c3ccccc32)cc1[C@]12C[C@H]3C[C@H](C[C@H](C3)C1)C2,,0.39273082,,,
+262479772,*C(=O)OCC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)N1CC(C)N(*)CC1C,,0.35492706,,,
+262850499,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(C)(C)c4cc(C)c(*)c(C)c4Br)c(Br)c3C)cc2)cc1,,0.41845298,,,
+263068772,*C=CCCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCc1ccc2ccc3cccc4ccc1c2c34,,0.37379395,,,
+263087206,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(OC)c(Br)c2)cc1,,0.35326079,,,
+263236795,*NCCOCCOCCOCCOCCN*,,0.3235597,,,
+263279017,*c1ccc(OC(=O)c2cccc(C(=O)Oc3ccc(C4(*)C(=O)Nc5ccccc54)cc3)c2)cc1,,0.35442058,,,
+263293393,*Oc1ccc2ccc(Oc3ccc(C(=Nc4ccc(N=C(c5ccccc5)c5ccc(*)cc5)cc4)c4ccccc4)cc3)cc2c1,,0.38174047,,,
+264131865,*C(=O)c1ccc(C(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(*)s5)cc4)cc3)s2)cc1,,0.37349224,,,
+264402592,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)c(C(=O)O)cc2C(=O)O)cc1,,0.31854458,,,
+264552260,*Nc1cc(N*)c2cc1N(C)c1cc(C(=O)OC)cc(c1)N(C)c1cc(c(N)cc1N)N(C)c1cc(C(=O)OC)cc(c1)N2C,,0.37551441,,,
+264939963,*CC(*)(CC(=O)Oc1ccc(OCC)cc1)C(=O)Oc1ccc(OCC)cc1,,0.35102684,,,
+265038627,*CCCCCCOCCCCCCOCCCCCO*,-72.01996519,,,,
+265862660,*Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.36689919,0.268,,
+265889946,*Oc1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(Oc5cccc(*)n5)cc4)cc3)cc2)cc1,,0.35865674,,,
+265939765,*CCCN*,,,0.354,0.92848585,19.40336561
+266241866,*CC(*)(C)C(=O)OCCCCCCCCCCCC[n+]1ccc(N(C)C)cc1,,0.37084263,,,
+266415058,*CC(*)OC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32424675,,,
+266479070,*CCC[Sn](C)(C)CCCOC(=O)C(CCCCCCOc1ccc(-c2ccc(OCc3ccc([N+](=O)[O-])cc3)cc2)cc1)C(=O)O*,,0.35896412,,,
+266620737,*Oc1ccc(-c2ccc(OC(=O)Nc3cccc(P(=O)(c4ccccc4)c4cccc(NC(*)=O)c4)c3)cc2)cc1,,0.35820494,,,
+266988735,*CCCCCCOC(=O)OCCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.35359242,,,
+267049794,*CC(F)(F)C(F)(F)C(F)(F)C(F)(F)COC(=O)CCCC(=O)O*,-64.11657159,,,,
+267912832,*CCCCCCCCCCCCn1c(=O)c2cc3c(=O)n(*)c(=O)c3cc2c1=O,73.37639604,,,,
+268323392,*Nc1cc(C)c(Cc2ccc(CCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35627685,,,
+268434909,*Nc1cc(Br)c(N*)c(Br)c1,,0.37180837,,,
+268436916,*CC(CC)S(*)(=O)=O,,0.3604463,,,
+268533943,*O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F,,0.35719446,,,
+268805413,*c1nc(C)nc(N(CCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.3854823,0.21,1.056565871,14.20899208
+269136469,*Nc1cccc2cc3cccc(N*)c3c(N)c12,,0.33466725,,,
+269301575,*Nc1cccc(CCc2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)c1,,0.3513212,,,
+269388015,*Oc1ccc(C(c2ccc(OP3(Oc4ccc(C(=O)OCCC)cc4)=NP(Oc4ccc(C(=O)OCCC)cc4)(Oc4ccc(C(=O)OCCC)cc4)=NP(*)(Oc4ccc(C(=O)OCCC)cc4)=N3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35657352,,,
+269527017,*CCN(CCOC(=O)c1cccc(C(=O)O*)c1)c1ccc(N=Nc2ccc(-c3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.36042586,,,
+270193915,*C=CC(C*)COCC(=O)OCCCCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.35801119,,,
+270468068,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(S(=O)(=O)c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36832614,,,
+270662526,*c1ccc(Sc2ccc(-c3nc(-c4cccc(-c5nnc(-c6ccccc6)c(*)n5)n4)nnc3-c3ccccc3)cc2)cc1,419.5781202,,,,
+270794936,*Nc1ccc(-c2ccc(N*)c3ccccc23)c2ccccc12,,0.35345588,,,
+271080458,*CC(O)CN(C)S(=O)(=O)c1cccc(S(=O)(=O)N(C)CC(O)COc2ccc(Sc3ccc(O*)cc3)cc2)c1,,0.3444038,,,
+271082869,*CC(*)C(=O)OCCOC,,0.33856563,,,
+271540328,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(C)(C)CCCCCCCCC(*)(C)C)cc5C4=O)cc3)cc1)C2=O,,0.36399911,,,
+271698010,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3[N+](=O)[O-])cc2)cc1,,0.37040317,,,
+271737793,*c1ccc(Oc2ccc(N3C(=O)CC4C(CC5C(=O)N(*)C(=O)C54)C3=O)cc2)cc1,,0.38077271,,,
+272150874,*CC(*)c1ccc(CCCCCCCCCCCCCCCC)cc1,,0.40149462,0.3409999999999999,0.848617516,12.88977254
+272832139,*CC(*)(C)C(=O)OCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.34243851,,,
+273139332,*c1sc(*)c(OCCCCCCCCCCCCCCCCCCCC)c1C,,,0.3747499999999999,0.90435478,14.34826035
+273204422,*CC(*)c1ccccc1CC,,,0.1843333333333333,0.918485757,13.27096728
+273571217,*N/N=C(\N*)c1c(N)nc2[nH]ncc2c1N,,0.32001736,,,
+273593115,*c1ccc(-c2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2C)c(C)c1,,0.39155142,,,
+273667291,*CC(O)COC(=O)C1CCC(C(=O)OCC(O)COc2ccc(C3(c4ccc(O*)cc4)c4ccccc4-c4ccccc43)cc2)CC1,,0.34780473,,,
+274064100,*Nc1cccc2cc3ccccc3c(N*)c12,,0.34139872,,,
+274098381,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1-c1cc(C(F)(F)F)cc(C(F)(F)F)c1,,0.37731962,,,
+274614510,*OC(CC(*)=O)C(C)C,-24.82439314,,,,
+275666084,*CC(*)(C)C(=O)OCCCCCCCCOc1ccc(N=Nc2ccc(C3OCC4(CO3)CC3C=CC4C3)cc2)cc1,,0.36026363,,,
+276378650,*CC(*)C(=O)OCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(C)CCCCCC)cc3)cc2)cc1,,0.37426308,,,
+277002638,*CCCCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.34342057,,,
+277986602,*Oc1ccc(Oc2ccc(C=NCCCCCCCCCCN=Cc3ccc(*)cc3)cc2)cc1,,0.37760079,,,
+278061089,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc6cccc(C(=O)c7ccc(*)cc7)c56)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.38951494,,,
+278123349,*CC(*)C(=O)C1CCCCC1,,,0.204,0.931405406,11.28939719
+278169291,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.34618661,,,
+278692232,*Oc1ccc(NC(=O)c2ccc3cc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)C7CC8CC(C7)CC6C8)cc5)cc4)ccc3c2)cc1,,0.36862142,,,
+278844630,*CCOCCOC(=O)c1cccc(C(=O)O*)c1,,0.33032067,,,
+278926278,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)CCC3CCCCC3C2)cc1,,0.37490919,,,
+279552323,*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36505048,,,
+279624532,*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc2)cc1,,0.38445052,,,
+279990409,*CCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,,0.412,,
+280111011,*Nc1ccc([C@H](C)c2ccc(C(C)c3ccc([C@@H](C)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36122133,,,
+280819340,*Nc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(C(c3ccccc3)(c3ccccc3)c3ccc(N*)cc3)cc2)cc1,,0.39075711,,,
+280901586,*c1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccccc8)[nH]c7*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38363841,,,
+281000054,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2[N+](=O)[O-])cc1,,0.37040323,,,
+281136178,*CNC(=O)OCc1ccc(COC(=O)NCc2ccc(C(C)(C)c3ccc(*)o3)o2)o1,,0.33949603,,,
+281150361,*O[Si](C)(C)CC[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C,,0.40021921,,,
+281517661,*=C=C=C(Cn1c2ccccc2c2ccccc21)C(=*)Cn1c2ccccc2c2ccccc21,,,,1.114657106,12.21503123
+281814625,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)c1)C2=O,,0.35895487,,,
+281920156,*CC(*)C(=O)OCCCCCC(=O)Oc1ccc(-c2ccc(C#N)cc2)cc1,,0.34688893,0.2299999999999999,1.092668338,12.97526971
+281967969,*Nc1ccc(C[C@@H](C)c2ccc([C@H](C)Cc3ccc(N*)cc3)cc2)cc1,,0.36054021,,,
+282298315,*O[Si](C)(C=C)O[Si](C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.43139276,,,
+282747611,*C=CC1CC(*)C2C(=O)N(c3cccc(C)c3)C(=O)C12,,0.39070984,,,
+283523819,*C(C#N)=Cc1cc(OCCCCCCCC)c(C=C(C#N)c2cc(OCCCCCCCC)c(*)cc2OC)cc1OC,,0.39126569,,,
+283582804,*N=P(*)(OCCCC)OCCCC,,0.40534797,,,
+284421147,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(-c5cc(C)c(N6C(=O)c7ccc(*)cc7C6=O)c6ccccc56)c5ccccc35)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37733686,,,
+284595179,*CCOC(=O)NCCSCCCCSCCNC(=O)O*,26.25521298,,,,
+285133703,*Nc1ccc2c(c1)[C@H]1c3cc(N*)ccc3[C@@H]2c2c(N)cccc21,,0.38618396,,,
+285241910,*CC(*)(C)C(=O)OCCN(C)c1ccc(N=Nc2ccc(N=Nc3ccc(C#N)cc3)cc2C)cc1,,0.38137962,,,
+285299858,*N[C@@H](O)N[C@@]1(N*)[C@@H](O)N=CNC1(N)N,,0.30799614,,,
+285419416,*CCCCOCC(O)CN(CC(O)CO*)c1ccc(N(C)C)cc1,,0.35989821,,,
+285781116,*Nc1ccccc1C1(c2cc(CC)ccc2N*)CCCCC1,,0.36032585,,,
+286143083,*Nc1cc(-c2c(C)cccc2C)cc(-c2c(C)cccc2C)c1N*,,0.36856174,,,
+286285970,*CC(*)(F)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32305957,,,
+286520814,*CC1CC1CO*,-28.98581149,,,,
+286659449,*CC(*)(C)C(=O)OC(C)Oc1ccccc1,31.196908,,,,
+286702486,*Nc1ccc(CC(C)(Cc2ccc(N)cc2)Cc2ccc(N*)cc2)cc1,,0.34821769,,,
+286760389,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(C)cc5)cc4)cc3)cc2)cc1,,0.39161329,,,
+286863590,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5nc(-c6ccccn6)nnc5*)cc4)cc3)cc2)cc1,,0.38856425,,,
+286904793,*Nc1ccc(-c2ccc(N*)cc2/C=C/c2ccccc2)cc1,,0.35172909,,,
+286907970,*c1ccc([Si](*)(OCC)OCC)cc1,7.02246836,,,,
+286918876,*CCCCCCCNC(=O)C(CCCCCCCCCCCC)C(=O)N*,,,0.3455,0.900783398,13.84301628
+287377564,*c1ccc(Oc2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2Br)c(Br)c1,,0.40143862,,,
+287416791,*c1ccc(S(=O)(=O)NCCNS(*)(=O)=O)cc1,7.54081281,,,,
+287463310,*CC(*)C1=NC(C)(C)C(=O)O1,,0.35364772,,,
+287576128,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(N=Nc3cc(OC)c(N=Nc4ccc([N+](=O)[O-])cc4)cc3OC)c(C)c2)cc1,,0.36102011,,,
+287781923,*/C=C/c1sc(*)c(OCCCCCCCCCCCC)c1OCCCCCCCCCCCC,,,0.365,0.900179408,11.69721179
+288191338,*c1cccc(P(C)(=O)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(C(C)(C)c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.34522255,,,
+288320875,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(-c6cccc(*)n6)cc5C(F)(F)F)cc4)c4ccccc4-c4ccccc43)cc2)c(C(F)(F)F)c1,,0.41115337,,,
+288350972,*OS(=O)(=O)c1cccc(C(=O)c2cccc(S(=O)(=O)Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)c2)c1,,0.36729276,,1.237872849,16.18613012
+288433754,*CC(*)C(=O)Oc1cccc2ccccc12,,0.35105406,0.1644999999999999,1.120527982,12.47317176
+288686571,*Nc1ccc(C(c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.3682145,,,
+288758624,*CCN(*)Cc1ccccc1,,0.36209961,,,
+288986880,*CC(COC(=O)CCCCCCCCCCN1C(=O)C2C3C=CC(C3)C2C1=O)O*,,0.34865046,,,
+289030100,*Nc1ccc2c(c1)-c1ccccc1[C@]21c2ccccc2-c2ccc(N*)cc21,,0.38578532,,,
+289287510,*Nc1cc2cc3cc4ccccc4cc3cc2cc1N*,,0.3410204,,,
+289478370,*Oc1cccc(C(=O)Nc2ccc(Oc3ccc(NC(=O)c4cccc(Oc5nc(*)nc(Sc6ccccc6)n5)c4)cc3)cc2)c1,184.9515774,,,,
+289637508,*CC(*)(CC(=O)OCC1CCCCC1)C(=O)OCC1CCCCC1,,0.3794888,0.19,,
+289857593,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCS(=O)(=O)CCCCC(=O)O*,,,0.322,,
+289881295,*CCCCCCCCCCCCCCS*,,,0.34125,0.881232316,22.79770371
+289972048,*=Cc1ccc(C=C2SC(=S)N(c3ccc(N4C(=O)C(=*)SC4=S)cc3)C2=O)cc1,187.1036635,,,,
+290244345,*Nc1cc(S(=O)(=O)c2ccc(O)c(N*)c2)ccc1O,,0.32138495,,,
+290253227,*Oc1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1-c1cccc(C)c1,,0.38116077,,,
+290300802,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.29982078,,,
+290418374,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.35769875,,,
+291057839,*CC(*)(C)C(=O)OCCCCCCN1CCN(c2ccc(N=Nc3ccc(C=C(C#N)C#N)cc3)cc2)CC1,,0.37590326,,,
+291499422,*CC(CCCC)COC(=O)NC(=O)c1cccc(C(=O)NC(=O)O*)c1,,0.32018631,,,
+291719291,*c1ccc(C=Cc2cc(OCC(CC)CCCC)c(C=Cc3ccc(N(*)c4ccccc4)cc3)cc2OC)cc1,,0.39827635,,,
+291812607,*CCN(CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)c(C)c1,,0.35280461,,,
+292223018,*CCN(*)C(=O)c1c(F)cc(F)cc1F,,0.34607139,,,
+292271271,*Oc1ccc(S(=O)(=O)c2ccc(Oc3cccc(NC(=O)c4ccc(-c5ccc(C(=O)Nc6cccc(*)c6)c(C)c5)cc4C)c3)cc2)cc1,,0.35676805,,,
+292300264,*Nc1ccc(Nc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.3913528,,,
+292368159,*OC(=O)C=CC(=O)OC1COC2C(*)COC12,,0.31212102,,,
+292543407,*C1C(=O)N(c2c(CC)cccc2CC)C(=O)C1*,,0.41528164,,,
+293023790,*C=Cc1cc(OCCCCCCOc2ccc(C3CCC(CCCCC)CC3)cc2)c(*)cc1OCCCCCCOc1ccc(C2CCC(CCCCC)CC2)cc1,36.67582945,,,,
+293350949,*c1ccc2c(c1)C(=O)N(c1cc(Br)c(Oc3c(Br)cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3Br)c(Br)c1)C2=O,,0.43086202,,,
+293363115,*/C=C/C(C*)C(C)CC,,,0.203,0.82398912,14.35476002
+293508970,*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2-c2cccc3ccccc23)cc1,,0.35954171,,,
+293562405,*CC(*)(C)C(=O)Oc1cccc(C)c1,,,0.151,1.017456329,12.06665155
+293886642,*c1ccc(*)s1,,0.49681739,,,
+294224638,*CC(*)C(=O)C(C)C,,,0.203,0.886596201,12.63604574
+294498348,*CCOCCOc1ccc(C=C(C)c2ccc(O*)cc2)cc1,,0.36557344,,,
+294894075,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(C6(c7ccc8nc(*)cc(-c9ccccc9)c8c7)c7ccccc7C(=O)c7ccccc76)ccc5n4)cc3)c3ccccc3-c3ccccc32)cc1,,0.47578212,,,
+295260492,*C[Si](*)(C)CCCOCCOCCOC,,0.36062899,,,
+295275980,*CC(*)(C)C(=O)OCCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(-c3cccc(OC(=O)c4ccc(OC(=O)c5ccc(OCCCCCCCCCCCC)cc5)cc4)c3)cc2)cc1,,0.36392032,,,
+295305734,*CC(*)(CC(=O)Oc1ccc(C)cc1)C(=O)Oc1ccc(C)cc1,,,0.182,1.062096371,11.04114403
+295343824,*C(=O)c1ccc(C(=O)N(CCC)c2ccc(Cc3ccc(N(*)CCC)c(C)c3)cc2C)cc1,179.9023847,,,,
+295492872,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4cccc(Oc5ccc(C(C)(C)c6ccc(Oc7cccc8c7C(=O)N(*)C8=O)cc6)cc5)c4C3=O)c(C)c2)cc1C,,0.38770631,,,
+295884244,*Oc1ccc(C(=Nc2ccc(N=C(c3ccccc3)c3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)c2ccccc2)cc1,,0.39566509,,,
+296205481,*CCCCCCCCCCOC(=O)CCCCCCC(=O)O*,,0.36776724,0.3445,0.938959785,22.70955044
+296253043,*Nc1ccc(CCCCCc2ccc(N*)cc2)cc1,,0.34584996,,,
+296446712,*CC(*)C(C)=O,,0.34334548,0.179,0.955845655,17.46864859
+296474292,*C(F)(F)C(*)(F)OC(F)(F)C(F)(F)F,,0.3417127,,,
+296559312,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4)cc3)cc2c1,,0.3598661,,,
+296620653,*Nc1ccc(Cc2ccc(-c3ccc(N*)cc3)cc2)cc1,,0.34759302,,,
+296709911,*Nc1ccc(C(c2ccc(N)cc2)(c2ccc(N)cc2)[C@H]2CC[C@H](N*)CC2)cc1,,0.37489694,,,
+296780712,*CCCOCCOCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,-26.75831261,,,,
+296827731,*CCN(CCOC(=O)NC(C)(C)c1cccc(C(C)(C)NC(=O)O*)c1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.34971424,,,
+296831504,*Oc1c(C)cc(-c2cc(C)c(OC(=O)CCCCCCCCC(*)=O)c(C)c2)cc1C,,0.36469857,,,
+297020286,*/C=C/c1ccc(*)c(-c2cc(-c3ccccc3)c(OCC(CC)CCCC)cc2OCC(CC)CCCC)c1,,,0.256,0.919376466,12.57676107
+297297701,*C1CCC(N2C(=O)c3ccc(S(=O)(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC1,,0.37391211,,,
+297474668,*C(=O)Nc1cc(C(F)(F)F)cc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(C(F)(F)F)cc(N4C(=O)c5ccc(*)cc5C4=O)c2Oc2ccc(C(F)(F)F)cc2)C3=O)c1Oc1ccc(C(F)(F)F)cc1,,0.3682254,,,
+297502549,*NCC1CCCC(CN*)C1,,0.33163789,,,
+297731512,*c1ccc(C(=O)Nc2ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)cc6)cc5)cc4)cc3)cc2)cc1,,0.35562062,,,
+298003696,*Nc1c(C)c(C)c(C(=O)c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)c(C)c1C,,0.37168327,,,
+298173145,*NC(=O)NCCCCCCNC(=O)Nc1cccc(*)n1,,0.32647685,,,
+298248052,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5c(c(-c6ccccc6)c(-c6ccc(Oc7ccc(Oc8ccc(-c9c(-c%10ccccc%10)c%10c(c(-c%11ccccc%11)c9-c9ccccc9)C(=O)N(*)C%10=O)cc8)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)C4=O)c3)c2)c1,,0.38817266,,,
+298750870,*c1c(-c2ccccc2)c(-c2ccccc2)c(*)c2cc(C(c3ccc(C#Cc4ccccc4)c(C#Cc4ccccc4)c3)(C(F)(F)F)C(F)(F)F)ccc12,,,0.103,1.001212493,12.31035991
+299097300,*C(*)C(=O)OC,,0.33876364,,,
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+299349220,*NC(C)C(=O)NCC(*)=O,19.96697332,,,,
+299618473,*C(=O)Nc1ccc(C(=O)Nc2ccc(NC(=O)c3ccc4[nH]c(-c5cccc(-c6nc7cc(*)ccc7[nH]6)c5)nc4c3)cc2)cc1,,0.3655908,,,
+299922696,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7ccccc7)[nH]c6*)cc5)cc4)cc3)cc2)cc1,,0.38697246,,,
+300005769,*CC1CC2CC1C1CCC(CN3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C21,,0.36282504,,,
+300892142,*CCSCCOC(=O)CCC(=O)O*,,0.34112021,,,
+300941372,*CC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2)cc1,,0.34047754,,,
+301268217,*CCCCCCCCCCO*,,,0.353,,
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+301935252,*C(=O)OCCCCCCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.35218285,,,
+302206099,*Nc1ccc([C@H](CC)C[C@H](C[C@@H](C[C@@H](C)c2ccc(N*)cc2)c2ccc(N)cc2)c2ccc(N)cc2)cc1,,0.36066913,,,
+302309984,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(Oc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.36551009,,,
+302979803,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34376894,,,
+303176560,*Nc1ccc(C2(c3ccccc3N*)CCCCC2)cc1,,0.35257654,,,
+303191070,*N=P(*)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1,,0.36538573,,,
+303219921,*Nc1ccc(-c2ccc(N*)cc2CCCCc2cc(N)ccc2-c2ccc(N)cc2)cc1,,0.35208407,,,
+303267980,*CCCCCCN(C)C(=O)CCC(=O)N(*)C,,0.34567408,,,
+303343344,*C#Cc1cccc(C#C[SiH2]*)c1,,0.47028838,,,
+303682893,*Oc1ccc(C2(c3ccc(OC(=O)c4cccc(C(*)=O)c4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.38690449,,,
+303795473,*CCCC(C)(C)CCCNC(=O)c1ccc(C(=O)N*)cc1,,0.34132981,,,
+304139587,*c1ccc(C(C#N)(C(=O)OCC)C(*)(C#N)C(=O)OCC)cc1,,0.38803882,,,
+305079648,*Oc1ccc(SC(=O)Oc2ccc(C(C)(C)c3ccc(OC(=O)Sc4ccc(*)cc4)cc3)cc2)cc1,,0.34795585,,,
+305318799,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(CCCCCC)c(*)cc3CCCCCC)ccc1-2,,0.42097491,0.46,0.85144236,20.23991085
+305819221,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cc(Cl)cc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)c1)C2=O,,0.39611043,,,
+307030577,*Nc1ccc2c(c1)[C@@H](c1ccccc1)c1c-2ccc(N*)c1-c1ccccc1,,0.36614228,,,
+307396008,*CC(*)OC(=O)CC1CCCC1,,0.3604384,,,
+307817942,*c1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5cccc6c5C(=O)N(*)C6=O)cc4)cc3)cc2)cc1,,0.35768777,,,
+308504449,*CCCCCCCCCCSCCCCCCS*,,,0.3305,0.931588685,28.27457475
+308535213,*CC=CN(C=CCC1CC(=O)N(c2ccc(Cc3ccc(N4C(=O)CC(*)C4=O)cc3)cc2)C1=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36429958,,,
+308745808,*CC1CCC(COC(=O)NCCSCCCCCSCCNC(=O)O*)CC1,77.84678315,,,,
+308992648,*CC(*)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)F,,0.32276362,,,
+309072874,*Oc1ccc(C2(c3ccc(OC(*)=O)c(C)c3)CC3CC2C2CCCC32)cc1C,,0.40611726,,,
+309419979,*C(=O)c1ccc2nc(-c3ccc(-c4cnc5cc(*)ccc5n4)cc3)cnc2c1,,0.42198142,,,
+309733373,*OC(=O)c1ccc(CCCCCCc2ccc(C(*)=O)cc2)cc1,,0.36077317,,,
+310196546,*Oc1cccc(NC(=O)CCCCCCCCCC(=O)Nc2ccc(*)cc2)c1,,0.34944602,,,
+310395295,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5nc(-c6ccc(-c7nc(*)c(-c8ccccc8)[nH]7)cc6)[nH]c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.40672617,,,
+310494439,*CC(*)n1ccnc1,,0.35410749,,,
+310611274,*CC(C)COC(=O)c1ccc(C(=O)O*)cc1,,0.3435142,,,
+310843241,*CC(*)C(=O)OCC1(COCC2(COC(=O)C(*)(C)C*)COC(CP(=O)(OCC)OCC)OC2)COC(CP(=O)(OCC)OCC)OC1,,0.34782459,,,
+310898724,*Nc1ccc2ccc3c(N*)c(-c4ccc(C5(c6ccccc6)c6ccccc6-c6ccccc65)cc4)c(-c4ccc(C5(c6ccccc6)c6ccccc6-c6ccccc65)cc4)c4ccc1c2c34,,0.38241769,,,
+311037297,*CC(*)(C)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32611657,,,
+311737289,*Oc1ccc(-c2ccc(Oc3ccc(Oc4cc(Oc5ccc(*)cc5)ccc4CCCCCC)cc3)c3ccccc23)c2ccccc12,,0.37973562,,,
+311750318,*CC(=O)Nc1ccc(Cc2ccc(NC(=O)COc3ccc4ccccc4c3Sc3c(O*)ccc4ccccc34)cc2)cc1,,0.34521945,,,
+312401249,*OC(=O)Oc1ccc2c(c1)Oc1cc(*)ccc1C21c2ccccc2-c2ccccc21,,0.39255677,,,
+312938401,*Nc1ccc(N[Se]*)cc1,,0.39082082,,,
+313210022,*/C=C/CCCCC*,,,0.3143333333333333,0.823772683,18.69881957
+313256095,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3ccc4ccccc4c3)c2-c2ccc3ccccc3c2)cc1,,0.3722511,,,
+313737003,*Oc1ccc(N=Nc2ccc(Cc3ccc(N=Nc4ccc(OC(=O)c5ccc(N=Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1,,0.36942122,,,
+313765532,*C=CC1CC(*)C(F)(F)C1(F)OC(F)(F)C(F)(F)C(F)(F)F,,0.35218928,,,
+313831314,*CC(*)C1CCCCC1,,,0.1906666666666666,,
+313842947,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(-c6ccc7nc(*)cc(-c8ccccc8)c7c6)ccc5n4)cc3)c3ccccc3-c3ccccc32)cc1,,0.42784564,,,
+313907766,*CCCCCCCCc1nc2cc(-c3ccc4oc(*)nc4c3)ccc2o1,61.93714913,,,,
+313933456,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(S(=O)(=O)c7ccc(Oc8cccc9c8C(=O)N(*)C9=O)cc7)cc6)c5C4=O)cc3)c2)cc1,,0.36100204,,,
+314234289,*Nc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(NC(=S)NC(=O)c4cccc(C(=O)NC(*)=S)c4)c3)cc2)c1,,0.34075834,,,
+314354319,*CCN(CCOC(=O)NCCCCCCNC(=O)O*)c1ccc(N=Nc2ccc(P(=O)(c3ccccc3)c3ccccc3)cc2)cc1,,0.36051644,,,
+314797178,*CC(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)CN5C(=O)c6ccc(C(c7ccc8c(c7)C(=O)N(*)C8=O)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc2)cc1,,0.34625008,,,
+314863833,*C=C(*)C(C)(C)C,,0.39086188,,,
+314916411,*Nc1ccc(NC(=O)c2cc(C(=O)O)c(C(*)=O)cc2C(=O)O)cc1,240.3936293,,,,
+315380987,*c1ccc(C(c2ccc(-c3nc4ccc(-c5ccc6nc(*)oc6c5)cc4o3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.41473896,,,
+315519001,*CCN(*)C(=O)c1ccc(SC(F)(F)F)cc1,,0.36079153,,,
+315538569,*c1ccc(Oc2ccc(-c3c(-c4ccccc4)c4c(c(-c5ccccc5)c3-c3ccccc3)C(=O)N(c3cccc(N5C(=O)c6c(c(-c7ccccc7)c(-c7ccccc7)c(*)c6-c6ccccc6)C5=O)c3)C4=O)cc2)cc1,,0.40245453,,,
+315696598,*CCOCCOC(=O)C(CCC)C(=O)O*,,0.3478015,,,
+316809152,*c1ccc(-c2ccc(-c3cc(-c4ccccc4)c4cc5c(-c6ccccc6)cc(*)nc5cc4n3)cc2)cc1,,0.42319171,,,
+317007708,*CCCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.35069342,,,
+317020980,*CC(*)(C)C(=O)OCC[N+](C)(C)CCCCCCC,,0.48837644,,,
+317112247,*/C=C/c1ccc(*)c(-c2cc(OCC(CC)CCCC)c(OCC(CC)CCCC)cc2-c2ccccc2)c1,,,0.196,0.892538398,13.24845393
+317286954,*CC(CC)(CC)CS*,,0.39177135,,,
+317794773,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.34144884,,,
+317893081,*Nc1c(N*)c2c3ccccc3ccc2c2ccccc12,,0.33765399,,,
+318193611,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc([Si](C)(C)c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.37988388,,,
+318514421,*CC(COCC=C)O*,,0.37390145,,,
+318909763,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3cccc(C(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3)cc1)C2=O,,0.35870758,,,
+318923469,*CC(O)COC(=O)CCCCC(=O)OCC(O)COc1ccc(O*)cc1,,0.3223982,,,
+318933021,*c1ccc(C(=O)OCCCCCCCCCCOc2ccc(C=C3CCCCC(=Cc4ccc(OCCCCCCCCCCOC(=O)c5ccc(-c6nnc(*)o6)cc5)cc4)C3=O)cc2)cc1,,0.36697686,,,
+319236312,*C=CC1CC(*)C2C(=O)N(c3cccc(Cl)c3)C(=O)C12,,0.38072604,,,
+319319228,*CCCCCCCCNC(=O)Cc1ccc(CC(=O)N*)cc1,,0.34606066,,,
+320070845,*Nc1ccc(-c2ccc(N*)c(-c3ccc(C(C)(C)C)cc3)c2)cc1,,0.35848188,,,
+321187243,*c1cccc(OCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.35149035,,,
+321215899,*Oc1ccc(N=CCCCC=Nc2ccc(OC(=O)Nc3cc(NC(*)=O)ccc3C)cc2)cc1,,0.34931445,,,
+321268777,*Nc1ccc(CC(C)(C)NC(=O)CCCCCCCCC(*)=O)cc1,,0.35281212,,,
+321308254,*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCCCCCCCCCC)C1=O,,0.40747116,,,
+321519190,*CCCCCNC(=O)CCCCCCCCOCCCCCCCCC(=O)NCCCCCO*,,,0.267,0.932721325,20.89267331
+321552204,*/C=C/CCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCCCCCCCCCCCCCC,,,0.332,,
+321955140,*NC(CC(*)=O)C(=O)OCCCCCC,0.713924343,,,,
+322065979,*c1ccc(Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(Oc4ccc(N5C(=O)CC(Nc6ccc(NC7CC(=O)N(*)C7=O)cc6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.37995335,,,
+322351570,*OCCOC(=O)CCCCC(=O)OCCOc1nc(-c2ccc(OC)cc2)nc(*)c1-c1ccc(OC)cc1,,0.34860734,,,
+322605911,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34897667,,,
+323040540,*Nc1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(N*)ccc1-2,,0.36646307,,,
+323411656,*Cc1cc(CNC(=O)CCCCC(=O)N*)cc(C(C)(C)C)c1,,0.34360475,,,
+323702728,*Nc1ccc(Cc2ccccc2C(C)(CCCCCCCCC)c2ccccc2Cc2ccc(N*)cc2)cc1,,0.36176304,,,
+324341393,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(*)cc6)ccc5c4)cc3)cc2)cc1,,0.38062816,,,
+324348126,*CC(*)c1ccc(C([Si](C)(C)C)[Si](C)(C)C)cc1,,0.43765265,,,
+324392817,*CCN(*)C(=O)CCCCCCCCCCC,,0.38506928,,,
+324827592,*Oc1cccc(Oc2ccc(N=Cc3ccc(N(c4ccccc4)c4ccc(C=Nc5ccc(*)cc5)cc4)cc3)cc2)c1,,0.38883651,,,
+324904007,*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2CC3CCC2C3)cc1,,0.37329335,,,
+324919224,*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)O*,,0.3533756,,,
+325038822,*Nc1ccc(C(=O)c2ccc(C(=O)c3ccc(Nc4ccc5c(c4)c4cc(*)ccc4n5CC(CC)CCCC)cc3)cc2)cc1,,0.38731405,,,
+325850735,*Nc1ccc(-c2ccc(N*)cc2Cc2ccccc2)c(Cc2ccccc2)c1,,0.34802156,,,
+325853121,*Oc1c(Br)cc(*)cc1Br,,0.44645964,,,
+326740697,*CC(*)(C)C(=O)OCc1cc(F)ccc1F,,0.34376939,,,
+327326654,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)C(C)C)cc3)cc2)cc1,,0.38318487,,,
+327432808,*Oc1ccc(S(=O)(=O)c2ccc(*)cc2)c2ccccc12,,0.36846516,,,
+327552819,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N2C(=O)C(=Cc3c(C)c4ccccc4n3C)C(=C(C)C)C2=O)cc1,-17.96880959,,,,
+327962548,*N=P(*)(OCC(F)(F)F)OCC(F)(F)F,,0.35273936,,,
+328019914,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(OC)c(Br)c2)cc1,,0.35295173,,,
+328180933,*Oc1ccc2c(c1)C(C)(c1ccc(OC(=O)c3cccc(C(*)=O)c3)cc1)CC2(C)C,,0.37445789,,,
+328387168,*Nc1ccccc1C(CC)(c1ccccc1N)c1ccccc1N*,,0.3617022,,,
+328610666,*Nc1ccc(-c2ccc(N*)cc2S(=O)(=O)O)c(S(=O)(=O)O)c1,,0.32787588,,,
+328753275,*CCCCNC(=O)CC/C=C/CCC(=O)N*,,,0.258,,
+328792977,*Oc1ccc(C(C)(C)C)cc1Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.38170267,,,
+329116469,*c1nnc(-c2ccc(Oc3ccc(C=Cc4ccc(Oc5ccc(*)c6ccccc56)cc4)cc3)c3ccccc23)o1,,0.38851942,,,
+329479579,*Nc1ccc(C(=C(c2ccccc2)c2ccccc2)c2ccc(N*)cc2)cc1,,0.37080803,,,
+329844440,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.38825862,,,
+330131370,*c1nnc(-c2cc(CCCCCCCCCC)c(-c3sc(-c4nnc(-c5cc(OCCCCCCCC)c(*)cc5OCCCCCCCC)o4)cc3CCCCCCCCCC)s2)o1,,0.40215236,,,
+330131913,*CCCCOC(=O)SSC(=O)O*,-58.94578339,,,,
+330440675,*Nc1ccc(-c2ccc(N*)c(-c3cc(-c4ccc(N)c(-c5ccccc5)c4)ccc3N)c2)cc1-c1ccccc1,,0.35788637,,,
+330874137,*Nc1ccc(C(=O)c2c(C)c(C)c(C(=O)c3ccc(NC(=O)c4ccc(C(*)=O)cc4)cc3)c(C)c2C)cc1,,0.37667535,,,
+330921769,*CCCCCCCCCCCCCCCCCCCCC(*)COCCOCCOCCOCCOCCCCCCCCCCCCCC,,,0.38425,0.882777598,18.64436035
+331019668,*CCCOC(=O)CCCCC(=O)O*,,0.33351251,,,
+331170381,*CC(*)(F)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32650351,,,
+331790904,*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1OCCCCCCOc1ccc(N=Nc3ccc(OCC)cc3)cc1)C2=O,,0.36774153,,,
+333349586,*CCCCNC(=O)CCCCCCCCCC(=O)N*,,,0.3229999999999999,0.965368624,22.50813311
+333365529,*Nc1ccc(-c2c3ccccc3c(-c3ccc(N*)c4ccccc34)c3ccccc23)c2ccccc12,,0.37137106,,,
+333446735,*c1ccc(Oc2ccc(N3C(=O)c4cccc(Oc5cccc(Oc6cccc7c6C(=O)N(*)C7=O)c5)c4C3=O)cc2)cc1,,0.35735202,,,
+333695147,*Nc1ccc(-c2ccc(N*)c(CC)c2)cc1CC,,0.3583446,,,
+334420972,*c1ccc(Oc2ccccc2N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.37092475,,,
+334571792,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.36816596,,,
+334621145,*CCCCOC(=O)CCSCCCCCCSCCC(=O)O*,,0.37393849,,,
+334995050,*Nc1cccc(-c2cc(N*)ccc2-c2ccccc2-c2ccccc2-c2ccccc2)c1,,0.35857012,,,
+335146594,*c1ccc(Cc2ccc(N3C(=O)C(C)C(SCCOCCSC4C(=O)N(c5ccc(Cc6ccc(N7C(=O)CC(C)(SCCOCCSC8(C)CC(=O)N(*)C8=O)C7=O)cc6)cc5)C(=O)C4C)C3=O)cc2)cc1,,0.34652308,,,
+335695120,*CC(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.33962097,,,
+335867964,*c1ccc(-c2sc(-c3cc(SCCCCCCCC)c(*)s3)cc2SCCCCCCCC)cc1,,0.46090001,,,
+336148935,*Oc1ccc(CC(C#N)(C#N)Cc2ccc(*)cc2)cc1,,0.38321738,,,
+336321161,*c1ccc(CC(=O)Nc2ccc(Cc3ccc(NC(=O)Cc4ccc(-c5sc(*)c(-c6ccccc6)c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.38110654,,,
+336593955,*CC(F)(F)C(F)(F)C(F)(F)C(F)(F)COC(=O)NCCCCCCNC(=O)O*,,0.31989921,,,
+336780982,*C=Cc1ccc(-c2nnc(-c3ccc(C=Cc4ccc5c(c4)c4cc(*)ccc4n5CCCCCCCC)cc3)n2-c2ccccc2)cc1,,0.39201873,,,
+337489231,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O,,0.40122658,,,
+337825369,*C1CCOC1*,,0.35254503,,,
+338101273,*C(=O)Nc1cccc(S(=O)(=O)c2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cc(-c5nnc(-c6ccccn6)o5)cc(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c2)c1,,0.37898693,,,
+338143659,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)cc4)cc3)cc1)C2=O,,0.35983591,,,
+338510207,*CC(*)(CC(=O)OC(C)C)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2,,0.39295654,,,
+339460948,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5cccc(Oc6cccc7c6C(=O)N(*)C7=O)c5C4=O)cc3)cc2)cc1,,0.37064472,,,
+339625856,*CC(*)C(=O)c1ccc(C(C)(C)C)cc1,,0.39538031,0.1915,0.936469283,10.58961551
+340997850,*Nc1cc(NC(=O)c2ccccc2C(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1,,0.37065155,,,
+341123684,*CC(*)N,,0.38223216,,,
+341439882,*Oc1cccc(NC(=O)CCCCCC(=O)Nc2ccc(*)cc2)c1,,0.33827626,,,
+341816925,*CC(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)c2)cc1,,0.34068841,,,
+341876947,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.39839006,,,
+342399286,*CCCCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1,,0.34643018,,,
+342537511,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)C(CC)C(CC)C(*)=O,,0.33445863,,,
+343006911,*N=P(*)(OCCC)OCCCCC,,0.4014852,,,
+343110199,*CCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.3359349,,,
+343123949,*CCC(C)CCC(=O)O*,,,0.253,0.980916925,20.5666232
+343155685,*c1ccc(C=Cc2cc(OCCCCCCCC)c(C=Cc3ccc(N(*)c4ccccc4)cc3)cc2OCCCCCCCC)cc1,,0.3949659,,,
+343326237,*C(=O)c1ccc(Oc2c(C)cc(-c3cc(C)c(Oc4ccc(C(=O)c5ccc6nc(*)ccc6c5)cc4)c(C)c3)cc2C)cc1,,0.39852455,,,
+343355330,*CCCCCCCCCCCCCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.40045418,,,
+343928203,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(*)o3)cc2)cc1,,0.44572292,,,
+344065840,*c1ccc(C(c2ccc(N3C(=O)c4ccc(Oc5cc6ccccc6cc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37439693,,,
+344567495,*/C=C/c1cc(CCCCCCCCCCCC)c(*)s1,,,0.368,,
+344756877,*CC(*)c1cc[n+](CCCCCCCC)cc1,,0.39455232,,,
+344924762,*Nc1cc2cc3ccc4cccc5ccc(c2cc1N*)c3c45,,0.33084477,,,
+345530183,*CC(C)(C)C1C(=O)N(CCCC)C(=O)C1*,,0.35157211,,,
+346660576,*c1ccc(-c2ccc(C3(*)CCCCC3)cc2)cc1,,0.39773705,0.252,0.96749891,16.14335044
+346744541,*CC(=O)Nc1ccc(Cc2ccc(NC(=O)CN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33703091,,,
+346973737,*CC(*)(F)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.34227403,0.121,1.608016391,12.86015216
+347210288,*CC(*)(CCC)C(=O)OC,,0.37329926,,,
+347336274,*Oc1cc(Cl)cc(Cl)c1*,,0.39826398,,,
+347361170,*c1ccc(Oc2ccc(C(=O)c3cccc(-c4cccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8cccc(-c9nc(*)c(-c%10ccccc%10)[nH]9)c8)[nH]c7-c7ccccc7)cc6)cc5)c4)c3)cc2)cc1,,0.39209457,,,
+347389709,*CCOC(=O)C=CC(=O)NCCCCCCNC(=O)C=CC(=O)O*,,0.31787399,,,
+348198056,*c1cccc(N2C(=O)c3ccc(Oc4ccc(-c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.36818227,,,
+348809308,*CCCCCCCCCCCCCCCCCCNC(=O)NCCCCCCCCCCNC(=O)N*,,,0.39,,
+348992789,*Oc1ccc(Cc2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.35373472,,,
+349443030,*c1ccc(Oc2ccc(C=Cc3ccc(Oc4ccc(-c5nnc(*)o5)cc4)cc3)cc2)cc1,,0.38304714,,,
+349759369,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)Nc3ccc(C(C)(C)c4ccc(NC(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1C(C)(C)C,,0.3776416,,,
+350457801,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2)cc1-c1ccccc1,,0.35335103,,,
+350689430,*COC(=O)NCCCCCCNC(=O)OCc1ccc(*)o1,,0.33438968,,,
+350799877,*C1C(=O)N(c2ccccc2C)C(=O)C1*,,0.37508855,,,
+351260510,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(NC(=O)CCCCC(=O)Nc4ccc(O*)cc4)cc3)cc2)cc1,,0.3386598,,,
+351343100,*Oc1ccc(C2(c3ccc(OC(=O)CCCCC(*)=O)cc3)c3cc(N(C)C)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.37034086,,,
+351687879,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C=Nc2ccc(CCCC)cc2)cc1,12.41121342,,,,
+351780192,*CCOCCOC(=O)CCCCCC(=O)O*,,0.33523458,,,
+351892249,*OP(=O)(Oc1ccccc1)Oc1ccc(*)cc1,,0.34040236,,,
+351941026,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.34489828,,,
+351987442,*c1ccc(C(=O)Nc2cccc(S(=O)(=O)c3cccc(NC(=O)c4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)c3)c2)cc1,,0.35305375,,,
+352084468,*c1ccc(Cc2ccc(-c3nc(-c4cccc(-c5nnc(-c6ccccc6)c(*)n5)n4)nnc3-c3ccccc3)cc2)cc1,,0.42632673,,,
+352515684,*Nc1ccc(C2(c3ccc(NC(=O)c4cc(C(*)=O)cc(C(C)(C)C)c4)cc3)c3ccccc3-c3ccccc32)cc1,,0.38327124,,,
+353134698,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4CCCC(C(C)c5ccc(*)cc5)C4)cc3)cc2)cc1,,0.37685467,,,
+353348926,*CC(*)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32579382,,,
+354177724,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(C2CCC(N4C(=O)c5ccc(*)cc5C4=O)CC2)C3=O)cc1,,0.34499607,,,
+354467864,*C1C(=O)N(c2cc(Br)c(O[Si](C)(C)C)c(Br)c2)C(=O)C1*,,0.45389033,,,
+354485735,*CCCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.34817572,,,
+354498686,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(Cl)cc2)cc1,,0.35330585,,,
+354785060,*Nc1ccc(C(C)(C)c2ccc(C(C)(c3ccc(N)cc3)c3ccc(N*)cc3)cc2)cc1,,0.36875033,,,
+354906741,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,,,0.328,1.206756714,23.97728876
+355069751,*CC(C)(CCC)COC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35957248,,,
+355158617,*CC(*)(CC(=O)Oc1ccc(OCCCCCCC)cc1)C(=O)Oc1ccc(OCCCCCCC)cc1,,0.37071795,,,
+355564083,*Oc1ccc(C(=Nc2ccc(N=C(c3ccccc3)c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)cc2)c2ccccc2)cc1,,0.38832422,,,
+355968898,*C=CC1CC(*)C(C(=O)OC(C)(C)C)C1C(=O)OC(C)(C)C,,0.38329687,,,
+356782883,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.35742127,,,
+356916100,*CC(*)(C)c1ccccc1,,,0.164,0.833452729,14.83591616
+356993441,*Oc1ccc(C(c2ccc(OC(=O)c3ccc(C(c4ccc(C(*)=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35824759,,,
+357011061,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2ccc(-c3ccccc3)cc2)c1,,0.34236864,,,
+357282616,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.36712892,,,
+357513605,*CCCCCNC(=O)CCCCCCCC(=O)N*,,,0.301,,
+357543937,*c1ccc(N2C(=O)c3ccc(Oc4ccc(Oc5ccc(P(=O)(c6ccccc6)c6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5)cc4)cc3C2=O)cc1,,0.37803621,,,
+358009894,*Nc1cc(NC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1,,0.33952674,,,
+358172536,*C=C(*)CCCOc1ccc(OC(=O)c2ccc(OCCCC)cc2)cc1,,0.35841923,,,
+358272082,*Nc1c(N)c(O)c2c(c1N*)CN(O)N=C2,,0.30616083,,,
+359188290,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccc(-c9nc(*)c(-c%10ccccc%10)[nH]9)cc8)[nH]c7-c7ccccc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.40281752,,,
+359434805,*Nc1cc2c(cc1N*)[C@@](C)(c1ccccc1)CC2(C)C,,0.36320235,,,
+359677253,*Oc1cccc(NC(=O)c2ccc(C(=O)Nc3ccc(*)cc3)cc2)c1,,0.34535991,,,
+359720037,*Nc1ccc(-c2ccc(NC(=O)Cc3cc(C)c(CC(*)=O)cc3C)cc2)cc1,,0.34598025,,,
+359727146,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(c6ccccc6)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc12,,0.36713277,,,
+359769379,*CCCCCCCCCCOC(=O)c1ccccc1C(=O)O*,,0.35843646,,,
+360768817,*CC(*)(C)C(=O)OCCc1ccc(N=Nc2ccccc2)cc1,,0.3677443,,,
+360823125,*CC(*)(C)C(=O)OCc1ccc(Cl)cc1Cl,,0.3570137,,,
+360959263,*CCNC(=O)c1ccc(-c2ccc(O*)cc2)cc1,,0.33621674,,,
+361088502,*CC(*)(C)C(=O)OCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.34900517,,,
+361254383,*Nc1cc(-c2ccccc2)c(-c2ccccc2)c(-c2cccc3ccccc23)c1N*,,0.36562173,,,
+361271652,*N[C@@H]1C=c2c([nH]c(=O)n2N*)=C1N,,0.31105409,,,
+361586466,*CCSSSSCCO*,,0.45802948,,,
+362892396,*Nc1ccc(CCCCc2ccc(N*)cc2)cc1,,0.34199263,,,
+363282083,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.363459,,,
+363316865,*Oc1c(-c2ccccc2)cc(*)cc1-c1cccc(C)c1,,0.40463316,,,
+363884489,*CC(*)OC(=O)c1ccc(C)cc1,,0.36495332,,,
+363982437,*c1cc(CCCCCCCCCCCCCCCCCCC)c(*)s1,,,0.34,,
+364021169,*=C=C=C(COS(=O)(=O)c1ccc(C)cc1)C(=*)COS(=O)(=O)c1ccc(C)cc1,76.80290526,,,,
+364113470,*c1ccc(-c2ccc(-c3nc4ccccc4c(-c4ccc(Oc5ccc(-c6c(-c7ccccc7)c(*)nc7ccccc67)cc5)cc4)c3-c3ccccc3)cc2)cc1,,0.40290946,,,
+364555377,*CC(*)OC(=O)c1ccc(OC(C)=O)cc1,,0.3315989,,,
+364982310,*CC(*)c1ccc(C(=O)OC)cc1,,,0.2033333333333333,1.032355465,13.1037956
+365169812,*Oc1ccc(NC(=C(C#N)C#N)c2cccc(C(Nc3ccc(Oc4ccc(NC(=O)c5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)=C(C#N)C#N)c2)cc1,,0.37385099,,,
+366601299,*Oc1cc(CC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.35709462,,,
+366883952,*CCCCOCCCCOCCCCOC(=O)c1ccc(N=Cc2ccc(C=Nc3ccc(C(=O)O*)cc3)cc2)cc1,,0.36331077,,,
+366967975,*Nc1ccc(Cc2ccccc2-c2ccccc2Cc2ccc(N*)cc2)cc1,,0.35066059,,,
+367099874,*CC(*)(C)C(=O)OCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35958926,,,
+367205774,*c1ccc(CCc2ccccc2N2C(=O)C(=O)N(c3ccc(Cc4ccc(N5C(=O)C(=O)N(*)C5=O)cc4)cc3)C2=O)cc1,,0.36251555,,,
+367282123,*OC(C)COC(C)COC(=O)c1ccc(C(*)=O)cc1,,0.3488532,,,
+367646881,*c1ccc(Oc2ccc(-c3cnc4cc(Oc5ccc6nc(*)cnc6c5)ccc4n3)cc2)cc1,,0.40222325,,,
+367702294,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(O)C(O)c4ccc(*)cc4)cc3)cc2)cc1,,0.36610689,,,
+367735151,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc2)C3=O)cc1,,0.35220439,,,
+367931242,*CCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.33070211,,,
+367944221,*Cc1cc2cc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.32641041,,,
+368322688,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35498117,,,
+368356823,*CC(*)(C)C(=O)OCc1cccc([N+](=O)[O-])c1,,,0.158,1.162628481,11.3618897
+368542881,*C/C=C\C[Si](*)(CCCOc1ccccc1)c1ccccc1,,0.36365457,,,
+368592179,*Nc1ccc(C(*)=O)cc1,92.05287264,,,,
+368595823,*C(=O)NCCCCCCCCCCNC(=O)c1cncc(*)c1,85.60365033,,,,
+368968267,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OCCC)c1)c1ccc(N=Nc2ccc(-c3nc4cc([N+](=O)[O-])ccc4[nH]3)cc2)cc1,,0.35758422,,,
+369213312,*C(=O)OCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.33915819,,,
+369610662,*SCC(=O)NN=Cc1ccc(OCCCCOc2ccc(C=NNC(=O)CSc3nnc(*)s3)cc2)cc1,39.79677892,,,,
+369639389,*CCCCCCN(CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)C(=O)C(F)(F)C(F)(F)C(F)(F)C(=O)N(*)CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32766144,,,
+369676731,*Oc1ccc(C(=Cc2ccc(-c3ccc(C=C(c4ccccc4)c4ccc(OC(*)=O)cc4)cc3)cc2)c2ccccc2)cc1,,0.38025773,,,
+369827633,*c1cccc(OCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34661118,,,
+370323439,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(c6ccccc6)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1,,0.36558414,,,
+370727281,*Nc1ccc([C@@H](CCCCCCCCCCC)c2ccc(C3(c4ccc([C@@H](CCCCCCCCCCC)c5ccc(N*)cc5)cc4)CCCCC3)cc2)cc1,,0.36981871,,,
+370833911,*CCSSSS*,,0.52820271,,,
+370860168,*CCCCOC(=O)NCCCCCCNC(=O)O*,,0.33574734,,,
+370933065,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3ccccc3)(c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)C(F)(F)F)cc1C(C)C)C2=O,,0.43647003,,,
+371139580,*Nc1ccc(-c2c(-c3ccccc3)cc(N*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.36585327,,,
+371520322,*Oc1ccc(C(*)=O)cc1CCCCC,,0.38126522,,,
+371572748,*CCCC(C)(C)CNC(=O)c1cccc(C(=O)N*)c1,,0.34209913,,,
+371620331,*Oc1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(*)cc4)c3)cc2)cc1,,0.36833963,,,
+371754917,*Oc1ccc(NC(=O)c2cc(NC(=O)CCN3C(=O)c4ccccc4C3=O)cc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.33234463,,,
+371907376,*c1nc(C(F)(F)C(F)(F)C(F)(F)F)nc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)n1,,0.34763212,,,
+372375740,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cc(-c5nc6ccccc6s5)cc(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.37167468,,,
+372416932,*Nc1ccccc1C(c1ccccc1N)(c1ccccc1N*)C1CCCCC1,,0.37800543,,,
+372715194,*CC(*)OC(=O)c1ccc(OC(=O)CCC)cc1,,0.34765241,0.223,1.086283422,12.25368206
+372897734,*NC(C)CCC(C)(C)CCCNC(=O)c1ccc(C(*)=O)cc1,,0.34899433,,,
+373230047,*Nc1ccc(Cc2ccc(Cc3ccc(Cc4ccc(N*)cc4)c(C)c3)cc2C)cc1,,0.35373723,,,
+373313460,*CC(*)OC(=O)CC,,,0.212,0.995365589,14.03059072
+373751734,*c1ccc(OCCOc2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.34666922,,,
+373797398,*CC(O*)C1CC(F)(F)C1(F)F,,0.35714922,,,
+373831673,*CCCC(=O)N*,,0.31089518,,,
+373885914,*OC(C)CCOC(=O)c1ccc(-c2ccc(C(*)=O)cc2)cc1,,0.34779893,,,
+374709833,*CCCCCCCCCCCCCCOC(=O)CCCCCCC(=O)O*,,,0.3165,0.919731294,21.54233832
+374925730,*CCC(Cl)C(*)Cl,,0.37989615,0.1795,1.273131418,21.78634698
+375189153,*c1ccc(Oc2cccc(NC(=O)c3cc(NC(=O)c4ccc(OC(C)=O)cc4)cc(C(=O)Nc4cccc(Oc5ccc(-c6nnc(*)o6)cc5)c4)c3)c2)cc1,,0.35615516,,,
+375346790,*C(=O)N1C(=S)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)=C(C(=O)OCC)C1c1ccc(OC)cc1OC,,0.37126454,,,
+375374780,*CCCCCCCCCCNC(=O)NCCCCCCCCNC(=O)N*,,,0.366,0.969724413,20.6076312
+375389813,*Oc1ccc(S(=O)c2ccc(Oc3ccc(C=NN=Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37027564,,,
+375630257,*CC=CC(C)S(*)(=O)=O,,0.38544075,,,
+376977834,*CCCCCCCNC(=O)CCCCCCCCCC(=O)N*,,,0.4065,0.940664668,22.70774878
+377086807,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4-c4ccccc4)cc3)cc2)cc1,,0.36142293,,,
+377154393,*CC(*)c1ccccc1COCc1ccccc1,,0.35933332,0.1913333333333333,,
+377318088,*C(=O)c1ccc2c(c1OCc1ccccc1[N+](=O)[O-])C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)c(OCc6ccccc6[N+](=O)[O-])c5C4=O)cc3)cc1)C2=O,,0.36920726,,,
+377845835,*c1ccc(-c2nc3cc(Oc4ccc(NC(=O)CN5C(=O)c6ccc(C(c7ccc8c(c7)C(=O)N(CC(=O)Nc7ccc(Oc9ccc%10nc(-c%11ccccc%11)c(*)nc%10c9)cc7)C8=O)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)ccc3nc2-c2ccccc2)cc1,,0.37297342,,,
+377905218,*CC(=O)NCCCCCCNC(=O)Cc1ccc(O*)cc1,,0.33550386,,,
+378178373,*c1cc(Br)c(OC(=O)c2ccc(C(=O)Oc3c(Br)cc(C4(*)OC(=O)c5ccccc54)cc3Br)cc2)c(Br)c1,,0.41948189,,,
+378689716,*c1cc(C(=O)OCCCC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.36220805,,,
+378754763,*CC1(*)CC(=O)N(c2ccccc2)C1=O,,0.34449463,,,
+378853299,*CCCC(C)(C)CNC(=O)CCCCC(=O)N*,,0.33764161,,,
+378871007,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(C4(*)NC(=O)c5ccccc54)cc3)cc2)cc1,,0.36254131,,,
+378915192,*CC(*)c1ccc(Cl)c(C)c1,,,0.154,,
+379560437,*CCCCP(C)(=O)CCCNC(=O)CCCCC(=O)N*,,0.3443087,,,
+379965710,*Cc1c(C)c(C)c(CSC(=O)c2ccc(C(=O)S*)cc2)c(C)c1C,127.8963011,,,,
+380076712,*Nc1c(N*)c(-c2cccc(C)c2)c(-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.37986013,,,
+380209354,*OC(F)(C(*)(F)F)C(F)(F)F,,0.30186398,,,
+380408256,*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(Cc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36900902,,,
+380596855,*CCCCCCCCCCOC(=O)CCCCSCCCCC(=O)O*,-22.30064955,,0.27,,
+380971589,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2ccc(OC)cc2)c(C)c1,,0.36983634,,,
+381746717,*CCOCCOCCOCCOCCOC(=O)CCSCCC(=O)O*,,0.34968925,,,
+381822281,*CC(*)P(=O)(Oc1ccccc1)N(C)C,,0.35079311,,,
+382336764,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5cccc(*)n5)cc4C(F)(F)F)cc3)(C(F)(F)F)C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.38991023,,,
+382786072,*CCCCCCCCCCCCNC(=O)CCCCCCCCCCCCC(=O)N*,,0.37944839,0.3994999999999999,0.903805754,22.53219601
+382859238,*C=CCCCCCC*,,0.40896445,,,
+382866074,*CCCCCCCCCCCC(=O)Oc1ccc2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2c1,,0.35050831,,,
+382983445,*CCCCCCCCCCOc1ccc(OC(=O)c2ccc(OCCCCCCOc3ccc(C(=O)Oc4ccc(O*)cc4)cc3)cc2)cc1,,,0.344,1.061577448,26.55570698
+383068073,*CCCCCNC(=O)CCCCCCCCCCCCC(=O)N*,,,0.3415,0.933437007,18.89913865
+383183030,*c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.35203295,,,
+383211120,*c1cc(CCCCCCCC)c(*)s1,,0.41466296,,,
+383339614,*OC(=O)c1ccccc1NC(=O)c1ccc(C(=O)Nc2ccccc2C(=O)OC(=O)c2cccc(C(*)=O)c2)cc1,185.0999075,,,,
+383541557,*Oc1ccc(NC(=O)c2cc(NC(=O)c3ccc(OC(C)=O)cc3)cc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)c2)cc1,,0.35350082,,,
+384321860,*CCCCCCCCCCCCCCCCOC(=O)NCCCCCCCCCCNC(=O)O*,,0.3759777,0.275,,
+384392762,*CC(*)c1ccc(Cl)cc1,,,,1.087290755,17.89940277
+384493304,*CCCCCCCCCCCCOC(=O)NCCCCCCCCCCNC(=O)O*,,0.37009027,,,
+384566125,*CC(*)(C)C(=O)OCC(=O)c1cc2cc(Br)ccc2o1,,0.37116728,,,
+384803806,*C(=O)SSCCCCSSC(=O)N1CCN(*)CC1,-3.571974592,,,,
+384892683,*c1cccc(-c2nc3cc(-c4ccc5nc(*)c(-c6ccc7ccccc7c6)nc5c4)ccc3nc2-c2ccc3ccccc3c2)c1,,0.42231854,,,
+386257654,*Nc1ccc2c(c1)-c1cc(N*)ccc1C21C=CCCC1,,0.36307465,,,
+386866912,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(C)CC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.34264843,,,
+387403474,*c1ccccc1CCc1ccc(N2C(=O)C(=O)N(c3ccc(C)c(N4C(=O)C(=O)N(*)C4=O)c3)C2=O)cc1,,0.35428927,,,
+387542061,*CCCCCCNC(=O)C(CCCCCCCCCCCCCCC)C(=O)N*,,,0.3369999999999999,0.897093489,15.49662691
+387982325,*Oc1ccc(C2(c3ccc(OC(*)=S)cc3)CCCCC2)cc1,,0.36130705,,,
+388271536,*CC(*)C(=O)OC(C1COC(CP(=O)(OCC)OCC)O1)C(OC(=O)C(*)(C)C*)C1COC(CP(=O)(OCC)OCC)O1,,0.34820034,,,
+388880540,*CC(*)c1cc(C(=O)OCCCCCCCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.38484165,,,
+389198623,*C(=O)c1ccc(C(=O)N(*)c2ccccc2)cc1,,0.38571493,,,
+389422500,*Nc1ccc(-c2ccc(Cc3ccc(-c4ccc(N*)c(C)c4)cc3)cc2)cc1C,,0.35725789,,,
+389523248,*Cc1cccc(CNC(=O)C=C(C)C(=O)N*)c1,,0.32039136,,,
+390128853,*Nc1ccc(Cc2ccc(N*)c3c2CCCC3)c2c1CCCC2,,0.3574561,,,
+390205045,*Nc1cc2c(cc1N*)[C@]1(C)CC[C@@]2(C)C1,,0.36723278,,,
+390452260,*Oc1cccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)c4)cc3)cc2)c1,,0.3649328,,,
+390786079,*C1C(*)C2CC1C(C(=O)OCCC13CC4CC(CC(C4)C1)C3)C2C(=O)OCCC12CC3CC(CC(C3)C1)C2,,0.37541018,,,
+390859311,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)CC(=O)N(*)C4=S)cc3)c2)cc1,,0.36549422,,,
+391030269,*c1cccc(P(=O)(c2ccccc2)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(C(C)(C)c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.35076041,,,
+391108347,*Oc1ccc(OC(=O)c2ccc(OC(=O)CCCCCCC(*)=O)cc2)cc1,72.32081554,,,,
+391439612,*CCCCCCC(Cl)C(*)Cl,,0.39133632,,,
+391755316,*C=CC1CC(*)C2C3CCC(C3)C12,,0.38257932,,,
+391808404,*CCCCSSCCCCOCO*,,0.40349527,0.196,,
+391913299,*Oc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(OC(*)=O)cc2)cc1,,0.38338026,,,
+392174026,*CCOCCOC(=O)c1ccc(-n2on2-c2ccc(C(=O)O*)cc2)cc1,15.34426557,,,,
+392422616,*Cc1ccc(OCCOc2ccc(*)c3cccnc23)c2ncccc12,74.04018308,,,,
+392652504,*C=CC1CC(*)C(C#N)(CCCC)C1,,0.40049659,,,
+392657358,*Oc1ccc(NC(=O)c2ccc(OC3COC4C(Oc5ccc(C(=O)Nc6ccc(OC7COC8C(*)COC78)cc6)cc5)COC34)cc2)cc1,,0.34067358,,,
+392908557,*CCCCCCOc1ccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4ccc(O*)cc4)cc3)cc2)cc1,,0.39376633,,,
+393047512,*Oc1ccc(-c2ccc(OC(=O)c3cc(C(=O)OCCCCCC)c(C(*)=O)cc3C(=O)OCCCCCC)cc2)cc1,77.13970172,,,,
+393084491,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccc(-c4ccccc4)c(-c4ccccc4)c32)cc1,,0.3805448449999999,,,
+393235817,*/C=C/C(C*)CCC,,,0.218,0.803760264,13.41293322
+393266857,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C=Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37112637,,,
+393468444,*CCOCCOC(=O)C(CCCCCCC)C(=O)O*,,0.36413222,,,
+393692859,*C=C(*)CNS(=O)(=O)CC,44.748248,,,,
+393870517,*CC(*)C(=O)c1ccc(CCC)cc1,,0.38559273,,,
+394096295,*CC(*)OC(C)C,,,0.2115,0.839799693,13.44308483
+394475074,*Nc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(N*)cc21,,0.37896533,,,
+394508821,*CC(*)c1ccccc1C(=O)N(C)C,,0.36482534,0.165,,
+394549340,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6c(C)cc(C(C)(C)c7cc(C)c(Oc8ccc9c(c8)C(=O)N(*)C9=O)c(C)c7)cc6C)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.39392877,,,
+395181216,*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(NC(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37043305,,,
+395216039,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(-c4ccc(-c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1,,0.37643427,,,
+395903864,*Nc1cc2c(cc1C)c1cc(N*)c(N)cc1c1cc(N)c(N)cc12,,0.33922457,,,
+396309311,*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc([N+](=O)[O-])cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C,,0.37558403,,,
+396421326,*Oc1ccc(N=Nc2ccc(Cc3ccc(N=Nc4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1,,0.36334689,,,
+396650322,*Nc1ccc(-c2cccc(-c3ccc(N)cc3)c2-c2ccc(N*)cc2)cc1,,0.35433877,,,
+396757724,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(C6(c7ccc8nc(*)cc(-c9ccccc9)c8c7)c7ccccc7-c7ccccc76)ccc5n4)cc3)c3ccccc3C(=O)c3ccccc32)cc1,,0.46689842,,,
+397033633,*CC(*)c1ccc(O)cc1,,0.34302705,0.2073333333333333,1.026280901,13.91356146
+397580304,*C(*)C(=O)OC1CCCCC1,,0.40121578,,,
+398123321,*Oc1cccc(Oc2ccc3c(=O)n4c5cc(Oc6ccc7c(c6)nc6c8ccc(*)c9cccc(c(=O)n76)c98)ccc5nc4c4cccc2c34)c1,,0.36887196,,,
+398381683,*C(=Cc1cc(OC)c(C=C(c2ccc(F)cc2)c2ccc(-c3ccc(*)cc3)cc2)cc1OC)c1ccc(F)cc1,,0.38687983,,,
+398390180,*CC(*)(CC(=O)OCCCCCCCCC)C(=O)OCCCCCCCCC,,0.38527679,,,
+398441562,*Sc1ccc(Sc2ccc(Sc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.39970198,,,
+398923440,*C(=O)Nc1ccc(-c2cc(-c3ccccc3)cc(-c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc7c(N8C(=O)c9ccc(*)cc9C8=O)cccc67)cc4)C5=O)cc3)n2)cc1,,0.37160155,,,
+400083990,*CCN(*)C(=O)CC,,0.34536777,,,
+400248641,*CC(*)c1ccc(C(=O)OCCN(CC)CC)cc1,,0.36818478,,,
+400445600,*c1ccc(Oc2ccc(-c3ccc(-c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccccc6)C7=O)cc5)c(C(F)(F)F)c4)cc3)cc2C(F)(F)F)cc1,,0.38333751,,,
+400718320,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(C#Cc2cc(OC)c(C=Cc3ccncc3)cc2OC)cc1,,0.37047455,,,
+400901906,*Nc1ccc(-c2ccc(NC(=O)c3ccc(NC(=O)c4ccc([Si](C)(C)c5ccc(C(=O)Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3)cc2)cc1,,0.36036817,,,
+400957282,*c1ccc2oc(-c3ccc(Oc4ccc(C(c5ccc(Oc6ccc(-c7nc8cc(C(*)(C(F)(F)F)C(F)(F)F)ccc8o7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)nc2c1,,0.39360428,,,
+401075968,*CCCCCCCCC(=O)NCCCCCCNC(=O)CCCCO*,,,0.3185,0.954015346,20.96547901
+401205052,*CC(*)c1ccccc1COCCC,,0.37983884,0.217,0.94589921,12.52488555
+401382872,*CC(C)(C)COC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.35328142,,,
+401549184,*N=P(*)(OCCOCCOCCOP1(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=N1)OCCOCCOCCOP1(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=N1,,0.3481788,,,
+401691844,*OP(=O)(Oc1ccccc1)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl,,0.35206352,,,
+401918606,*CCCCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,0.37324005,0.3495,0.90486422,20.51772176
+402031428,*Nc1cc2cccc3c4cccc5cccc(c(c1N*)c23)c54,,0.33482328,,,
+402151358,*Nc1ccc(C2(c3ccc(NC(=O)c4cc(NC(=O)C56CC7CC(CC(C7)C5)C6)cc(C(*)=O)c4)cc3)c3ccccc3-c3ccccc32)cc1,,0.37622142,,,
+402476752,*CCCCCOC(=O)CCCCC(=O)O*,,0.34417644,,,
+402492102,*CCCCCOc1ccc(CCCNC(=O)CCCCC(=O)NCCCc2ccc(O*)cc2)cc1,28.2211431,,,,
+402626556,*Nc1ccc2c(c1)C1(c3cc(C)ccc3-2)c2cc(C)ccc2-c2ccc(N*)cc21,,0.38936549,,,
+403135402,*CC(*)(C)C(=O)OCCCCCCCCOC(=O)OC1CCC2(C)C(=CCC3C2CCC2(C)C(C(C)CCCC(C)C)CCC32)C1,47.48888608,,,,
+403160938,*CC(*)(CC(=O)Oc1cccc(C)c1)C(=O)Oc1cccc(C)c1,,,0.165,1.042202692,10.97057697
+404125467,*CCCCCOc1ccc(C=CC(=O)OCCN(CCOC(=O)C=Cc2ccc(O*)cc2)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.35112235,,,
+404307501,*CCCCCCCCC(=O)N*,,0.36229346,,,
+404342250,*OC(*)CCC,,0.41332049,,,
+404367957,*CC(*)c1ccc(C(=O)CCC)cc1,,0.3914716,,,
+404981014,*C/C=C/COC(=O)CCCCCCCCC(=O)O*,,0.35391939,,,
+404993408,*c1ccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(-c8cccc9c8C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)cc3)cc2)cc1,178.6163373,,,,
+405636435,*CC(O)COc1ccc(O*)cc1,,0.32994295,,,
+406335515,*Sc1ccc(C(=O)CNCCCCCCCCNCC(=O)c2ccc(*)cc2)cc1,,0.37618331,,,
+406786412,*Oc1ccc(N=CC=Nc2ccc(OC(=O)NC3CC(C)(C)CC(C)(CNC(*)=O)C3)cc2)cc1,,0.35659879,,,
+406938063,*Oc1cccc(Oc2ccc(NC(=O)c3cc(NC(=O)c4ccc(OC(C)=O)cc4)cc(C(=O)Nc4ccc(*)cc4)c3)cc2)c1,,0.3367398,,,
+407026307,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2cc(OC)c(N=Nc3ccc([N+](=O)[O-])cc3)cc2OC)cc1,,0.35078628,,,
+407061099,*Nc1cccc(C#Cc2cccc(NC(=O)c3ccc(C(*)=O)cc3C(=O)O)c2)c1,187.6187871,,,,
+407729292,*CCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.34323595,,,
+407820973,*=C=C=C(CO)C(=*)CO,42.01413885,,,,
+407930180,*CCNC(=O)c1ccc(C(=O)NCCOc2cc3ccccc3cc2O*)cc1,,0.32766581,,,
+408163304,*Oc1ccc(-c2ccc(OC(=O)c3cc(CCCCC)cc(C(*)=O)c3)cc2)cc1,,0.3626875,,,
+408357369,*c1ccc(Oc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.3737891,,,
+408389877,*CNC(=O)OCc1ccc(COCc2ccc(COC(=O)NCc3ccc(C(C)(C)c4ccc(*)o4)o3)o2)o1,,0.34220533,,,
+408594368,*C(=O)OCCCCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.3463228,,,
+409036126,*Oc1ccc(C2(c3ccc(OC(=O)CCCCCCCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36438576,,,
+409348345,*CC(*)C(=O)OCC(F)(F)F,,0.32327677,,,
+410114761,*CCCC(*)(C)CC,,0.39226416,0.2339999999999999,,
+410273784,*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C(C)(C)c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36250129,,,
+410419753,*N=P(*)(OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32778117,,,
+410618641,*C(=Cc1cc(OC)c(C=C(c2ccccc2)c2ccc3cc(*)ccc3c2)cc1OC)c1ccccc1,,0.39179983,,,
+410876568,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5cc(*)[nH]n5)cc4)cc3)cc2)cc1,,0.3751119,,,
+410915024,*Nc1ccc(C2(c3ccc(N*)cc3)C3=CC[C@@H](C)C=C3c3ccccc32)cc1,,0.37733962,,,
+410978773,*Nc1cc(CCc2ccc(C)c(N*)c2)cc(CCc2cc(N)ccc2C)c1,,0.35220879,,,
+411320818,*c1ccc(C(=O)OCCCCCCCCCOC(=O)c2ccc(-c3nnc(*)s3)cc2)cc1,,0.36418344,,,
+411535293,*Oc1cccc2c(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cccc12,,0.35298988,,,
+411815101,*c1ccc(Oc2ccc(-c3cnc4ccc(-c5ccc6ncc(*)nc6c5)cc4n3)cc2)cc1,,0.40181263,,,
+411845496,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc(OC)cc2)cc1,,0.36656596,,,
+412332648,*c1nnc(-c2cc(CCCCCCCC)c(-c3ccc(-c4sc(-c5nnc(-c6cc(OCCCCCCCC)c(*)cc6OCCCCCCCC)o5)cc4CCCCCCCC)s3)s2)o1,,0.41155198,,,
+413205726,*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(N*)cc3)c2)cc1,,0.35755493,,,
+413548295,*Nc1ccc(CCc2ccc(N*)cc2C)c(C)c1,,0.348463,,,
+414071257,*c1ccc(Oc2ccc(Sc3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(-c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.3661288,,,
+414579766,*c1ccc(Cc2ccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.38469453,,,
+414690406,*Nc1ccc2ccc3c(N*)ccc4ccc1c2c43,,0.3362207299999999,,,
+414711304,*Nc1ccc(-c2ccccc2)c(-c2c(C(C)C)cccc2C(C)C)c1N*,,0.37421435,,,
+414802450,*CCCCCCOC(=O)C(CCCCCBr)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.37141754,,,
+414910324,*CCCCCC(*)CCCCCCCCCC,,0.40749823,0.406,0.801722522,15.17699114
+415110926,*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.36565404,,,
+415720254,*Nc1ccccc1-c1cccc2c1[C@@H](c1ccccc1N*)c1ccccc1-2,,0.3648408,,,
+416197163,*Nc1ccc(C2(c3ccc(N*)cc3)CCC([C@H]3CC[C@H](CCCCCCC)CC3)CC2)cc1,,0.36335204,,,
+416481975,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cccc8c(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cccc78)cc6C5=O)cc4)cc3)cc2)cc1,,0.36755908,,,
+416508716,*CCCS(*)(=O)=O,,,,1.344229366,19.8097907
+416714038,*CC(*)C(=O)Oc1cccc(C(=O)OCC)c1,,0.33862141,,,
+416985872,*O[Si](c1ccccc1)(c1ccccc1)c1ccc([Si](*)(c2ccccc2)c2ccccc2)cc1,,0.41259945,,,
+417097500,*CC(*)OC(=O)c1ccc(C(C)(C)C)cc1,,0.37755899,,,
+417759199,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6C(F)(F)F)cc5)n4)cc3)c(C(F)(F)F)c1)C2=O,,0.36582624,,,
+417837947,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc(-c7nc8ccccc8n7-c7ccc(-n8c(*)nc9ccccc98)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.38408582,,,
+418211347,*C(F)(F)C(*)(F)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.34587055,,,
+418914987,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)s4)s3)s2)cc1,,0.37866254,,,
+418998358,*C(=O)c1ccc2c(c1)C(=Nc1ccc(Oc3cccc(N=C4OC(=O)c5ccc(*)cc54)c3)cc1)OC2=O,,0.35475052,,,
+419242334,*C(=O)OCCOC(=O)c1ccc(C(=O)OCCOC(=O)N2CCN(*)CC2)cc1,,0.32328072,,,
+419308262,*CC(OC(C)=O)C(CCC(C)(O)C=C)C(*)(C)C,,0.34814459,,,
+419514472,*OP(=O)(Oc1ccc(OC)cc1)Oc1c(C)cc(S(=O)(=O)c2cc(C)c(*)c(C)c2)cc1C,,0.35811043,,,
+419730462,*CCCCCNC(=S)N*,,0.36013805,,,
+420554258,*Oc1cc(OC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OCCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)c1,,0.35790237,,,
+420766140,*CC(*)(C)C(=O)Nc1ccc(C(=O)C=Cc2ccc(OC)cc2)cc1,,0.34238812,,,
+421125375,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCC)cc1,,0.33180261,,,
+421588558,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C(F)(F)F)cc3)cc1)C2=O,,0.37676658,,,
+421601596,*Nc1ccc2cc1C(C)(C)c1cc(ccc1N*)-c1ccc-2cc1,,0.34941834,,,
+422376704,*Oc1ccc(P(=O)(c2ccccc2)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.38335836,,,
+423361710,*Cc1ccccc1C[Si](*)(C)C,,0.38237917,,,
+423638166,*Nc1ccc(C2(c3ccc(NC(=O)c4cccc(C(*)=O)c4)cc3)c3ccccc3-c3ccccc32)cc1,,0.37583155,,,
+424246919,*Nc1ccc(-c2c(-c3cccc4ccccc34)c(-c3cccc4ccccc34)c(N*)c(-c3cccc4ccccc34)c2-c2cccc3ccccc23)cc1,,0.37854395,,,
+424737952,*c1ccc(Oc2ccc(N3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(c5ncc(*)s5)C6=O)cc4C3=O)cc2)cc1,,0.39210701,,,
+425753328,*CC(*)CNc1ccc([N+](=O)[O-])cc1C,,0.38329483,,,
+425876006,*c1ccc(Oc2ccc(P(=O)(c3ccccc3)c3ccc(Oc4ccc(-c5nc(-c6ccccc6)[nH]c5*)cc4)cc3)cc2)cc1,,0.39143285,,,
+426452864,*c1ccc2c(c1)c1cc(-c3ccc(-c4ccc(-c5ccc(*)s5)c5nsnc45)s3)ccc1n2C(CCCCCCCC)CCCCCCCC,,0.42372696,,,
+426568984,*CCCCCCCCCNC(=S)N*,,0.38298006,,,
+426591461,*c1ccc(Oc2cccc(-c3cc(-c4ccccc4)cc(-c4cccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)c4)n3)c2)cc1,,0.37263298,,,
+427022128,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3c(F)c(F)c(*)c(F)c3F)ccc1-2,,0.40575377,,,
+427272579,*CC(*)C(=O)OC1CCCCCCCCCCC1,,0.37215946,,,
+427458054,*C=C(*)C,4.023221393,,,,
+427930827,*Oc1cccc(NC(=O)c2ccc([Si](c3ccccc3)(c3ccccc3)c3ccc(C(*)=O)cc3)cc2)c1,,0.37364181,,,
+428473296,*Oc1ccc2cc(OC(=O)c3ccc(-c4ccc(C(*)=O)cc4)cc3-c3ccccc3)ccc2c1,,0.35771091,,,
+428642513,*CC(*)(C)C(=O)Oc1ccc(C(=O)C=Cc2ccc(OC)c(OC)c2)cc1,,0.34006811,,,
+428683484,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)c(C)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36727934,,,
+428785387,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O,,0.36571693,,,
+428829541,*c1ccc(S(=O)(=O)c2ccc(-n3nc(*)c4ccccc4c3=O)cc2)cc1,,0.39580433,,,
+428976415,*CC(C)(C)COC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.3537359,,,
+429074783,*CC(*)C(=O)OCCOC(F)(C(F)(F)F)C(F)(F)F,-51.63721715,,,,
+429504710,*CC(*)C(=O)OCC(C)O,,0.32197696,,,
+429609044,*Oc1ccc(C2(c3ccc(Oc4ccc5c(=O)n6c7cc(-c8ccc9c(c8)nc8c%10ccc(*)c%11cccc(c(=O)n98)c%11%10)ccc7nc6c6cccc4c56)cc3)c3ccccc3-c3ccccc32)cc1,,0.39512322,,,
+429737042,*c1ccc2oc(-c3ccc(C(c4ccccc4)(c4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)C(F)(F)F)cc3)nc2c1,,0.43912224,,,
+429776664,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(OCCCC)cc2)cc1,,0.34886491,,,
+429914397,*CCN(*)C(=O)C12CC3CC(CC(C3)C1)C2,,0.37217616,,,
+430188200,*Nc1ccc(Cc2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34965429,,,
+430475615,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cc2)cc1,,0.36622548,,,
+430498669,*c1cc(CCCCCCCCCCCCC)c(*)s1,,,0.40475,0.919023879,16.27456982
+431067883,*CC1CCC(CNC(=O)CCCCCCCCCCC(=O)N*)CC1,,0.35779902,,,
+431258453,*c1cccc(NC(=O)CCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1,,0.33687844,,,
+431336110,*/C=C/CCCCCC*,,,0.33,0.820824647,19.38675152
+431834567,*CC(*)(C)C(=O)OCCCCCCc1ccc(N=Nc2ccc(OC(F)(F)F)cc2)cc1,,0.36108556,,,
+431972606,*Nc1nc(-c2csc(N*)n2)cs1,,0.3526676199999999,,,
+432187357,*CCOC(=O)NCCCCCCCCCCNC(=O)O*,,0.34759464,,,
+432686237,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O,,0.36335093,,,
+432789142,*CC(CO)O*,,0.30841537,,,
+432947505,*C(=O)Nc1ccc(Sc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cccc56)cc3)C4=O)cc2)cc1,,0.36055336,,,
+433053026,*C1C(=O)N(CCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.34752504,,,
+433063842,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c(C(F)(F)F)c1)C2=O,,0.37864052,,,
+433310037,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.39496147,,,
+433650150,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nc(-c6ccccn6)nnc5*)cc4)cc3)cc2)cc1,,0.39013945,,,
+433804845,*O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1,,0.48218278,,,
+433899559,*c1ccc(*)cc1,,0.37523864,,,
+434366510,*CCCCOC(=O)C1CCC(C(=O)O*)CC1,,0.34010105,,,
+434463983,*CNC(=O)CCCCCCCCC(=O)N*,66.76854223,,,,
+434558166,*COC(=O)OCC1OC(OCC)(OCC)OC1*,,0.34101825,,,
+434569704,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)CC3CC2C2CCCC32)cc1,,0.37807285,,,
+434774652,*CC(*)C(=O)Oc1ccc(C#N)cc1,,0.360284,0.1845,1.134726614,13.89490111
+435176754,*C(=O)Nc1ccc(Oc2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.3555696,0.297,,
+435414001,*Nc1ccc(C(CCCC)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36433979,,,
+436053156,*C=Cc1cccc(OC(=O)CCCCCCCCC(=O)Oc2ccc(-c3nnc(*)s3)cc2)c1,,0.35968354,,,
+436095263,*CC(*)(C)C(=O)OCCN(CCCC)c1ccc(N=Nc2ccc(S(=O)(=O)Nc3nccs3)cc2)cc1,,0.36999432,,,
+436303110,*Oc1ccc(C2(c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)CCC3CCCCC3C2)cc1,,0.37550202,,,
+436672073,*Nc1cc(-c2cc(N*)c(N)c3ccccc23)c2ccccc2c1N,,0.35081452,,,
+438056577,*CCOC(=O)C1CCC(C(=O)OCCOc2ccc(C(C)(C)c3ccc(O*)cc3)cc2)CC1,,0.34686046,,,
+438250982,*Oc1ccc(C2(c3ccc(Oc4ccc(C=CC(=O)c5ccc(*)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.38901721,,,
+439011698,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)cc(-c2ccccc2)c1,,0.36655911,,,
+439136445,*O[Si](C)(C)O[Si](C)(C)O[Si](*)(c1c(F)c(F)c(F)c(F)c1F)c1c(F)c(F)c(F)c(F)c1F,,0.38120028,,,
+439339917,*NCC1C2CCC(C2)C1CN*,,0.32950873,,,
+439415309,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8c(C)cc(C(C)(C)c9cc(C)c(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)c(C)c9)cc8C)cc7C6=O)cc5)cc4)CC4CCC3C4)cc2)cc1,,0.38992646,,,
+439660621,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C8(c9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)CCC(C(C)(C)C)CC8)cc7)cc6C5=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.37744754,,,
+440187772,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCC(C(C)(C)C)CC9)cc8)cc7C6=O)cc5)cc4)C4CC5CC(C4)CC3C5)cc2)cc1,,0.38310066,,,
+440221059,*c1cc(COCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)c(*)s1,59.88076613,,,,
+440284269,*CCCCCC(=O)NCCc1ccc(CCNC(=O)CCCCCS*)cc1,-18.01175462,,,,
+440459365,*CC(*)(C)C(=O)OCCn1c2ccccc2c2cc(N=Nc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])ccc21,,0.37693372,,,
+440694575,*OC1CCC(OC(*)=O)CC1,,0.34419974,,,
+440852775,*Nc1ccc(-c2c(-c3ccccc3)cc(N*)c(-c3ccccc3N)c2N)cc1,,0.35040297,,,
+440867054,*c1ccc(Oc2ccc(-c3csc(N=Cc4ccc(OCCCCCCOc5ccc(C=Nc6nc(*)cs6)cc5)cc4)n3)cc2)cc1,,0.37846605,,,
+440998252,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.34012388,,,
+441045416,*NNC(=O)CCCCCCCCC(=O)NNC(=S)c1cccc(C(*)=S)c1,32.4414411,,,,
+441295392,*Oc1ccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.42813403,,,
+441800426,*CCCCCC(=O)Oc1ccc2cc(OC(=O)CCCCCn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)ccc2c1,-10.30414379,,,,
+441855685,*CCCNC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NCCCOCCOCCO*,,0.35141201,,,
+441924430,*CCCCCCCCCC#CC#CCCCCCCCCCOC(=O)CCCCCCCCC(=O)O*,,,0.307,,
+442093631,*CCCCCCCCCCCC(=O)Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2)cc1,,0.36467261,,,
+442452634,*CC(*)(C)C(=O)OCCBr,,0.37763347,0.128,1.475127343,15.26731429
+442573599,*c1ccc(-c2sc(-c3cc(SCCCCCCCCCCCC)c(*)s3)cc2SCCCCCCCCCCCC)cc1,,0.43658652,0.349,,
+442588281,*c1ccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.35570555,,,
+442933041,*/C=C/c1ccc(*)c(-c2cc(OCCC(C)C)c(OCCC(C)C)cc2-c2ccc(C(F)(F)F)cc2)c1,,,0.184,0.956021414,17.81147708
+443073633,*c1ccc(-c2ccc(-c3cc(-c4ccccc4)c4cc5nc(*)cc(-c6ccccc6)c5cc4n3)cc2)cc1,,0.44606667,,,
+443095393,*c1cc(CCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)c(*)s1,,0.38587992,,,
+443473345,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(c4cc(C)c(*)c(C)c4)(C(F)(F)F)C(F)(F)F)cc3C)cc2C(O)(C(F)(F)F)C(F)(F)F)c(C(O)(C(F)(F)F)C(F)(F)F)c1,,0.37988372,,,
+444454370,*NC(CCC(=O)OCc1ccc(F)cc1)C(*)=O,,0.32718673,,,
+444535836,*CCOC(=O)C1CCC(C(=O)OCCOc2ccc(C3(c4ccc(O*)cc4)c4ccccc4-c4ccccc43)cc2)CC1,,0.35566835,,,
+445398297,*CC(*)c1ccccc1C(=O)Oc1ccccc1,,0.3593053,0.1693333333333333,,
+445451403,*Nc1ccc(-c2ccccc2-c2ccc(N*)cc2)cc1,,0.35240294,,,
+445559767,*c1cc(OCCCCCCCCCCCC)c(*)cc1O,,,0.309,0.933849439,17.49332984
+445698643,*Oc1ccc(N=Nc2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.3780172,,,
+445846164,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)CCc3ccccc32)cc1,,0.36800149,,,
+445938720,*c1ccc(Oc2cccc(N3C(=O)c4ccc5c6c(ccc(c46)C3=O)C(=O)N(c3cccc(Oc4ccc(-c6nnc(*)o6)cc4)c3)C5=O)c2)cc1,,0.37441485,,,
+446207313,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.37411616,,,
+446600296,*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.35092558,,,
+447303285,*CC(*)OC(=O)CCCC(=O)c1ccc(C)cc1,,0.34755673,,,
+447418869,*Oc1cc(C(C)(C)c2ccccc2)c(OC(=O)c2ccc(C(*)=O)cc2)cc1C(C)(C)c1ccccc1,,0.38707826,,,
+447438744,*C1CCC(CC2CCC(N3C(=O)C4C5C=CC(C6C(=O)N(*)C(=O)C56)C4C3=O)C(C)C2)CC1C,,0.38852364,,,
+447444897,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccc(Cl)cc2)c1,,0.35121745,,,
+447477268,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37436742,,,
+447614903,*C=Cc1cc(OCCCCCCCC)c(*)cc1OC,,0.38882132,,,
+447641645,*c1ccc(-c2nnc(-c3cccc(-c4nnc(*)n4-c4ccccc4)c3)n2-c2ccccc2)cc1,,,0.389,1.089021523,24.31804369
+447643667,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.3511763,,,
+447646076,*Oc1c(Br)cc(C(C)(CCC(=O)O)c2cc(Br)c(OC(=O)c3cccc(C(*)=O)c3)c(Br)c2)cc1Br,175.2273279,,,,
+447692460,*C=Cc1cc(C=Cc2ccc(OCCC(C)CCCC(C)C)cc2)c(*)cc1C=Cc1ccc(OCCC(C)CCCC(C)C)cc1,2.868925186,,,,
+447769500,*CCCCCCCCCCCCNC(=O)NCCCCCCNC(=O)N*,,0.35917869,0.3545,0.973061223,17.68935554
+448340777,*CCCCCCCCCCCCC(=O)N*,,0.37800644,0.33,,
+449447335,*Nc1ccc(C(c2ccc(C)cc2)c2ccc(N*)cc2)cc1,,0.35885814,,,
+450028530,*C1C2CC(=CC)C(C2)C1*,,0.42130953,,,
+450668833,*CCCCCCOc1c(OC)cc(/C=C/c2ccc(/C=C/c3cc(OC)c(O*)c(OC)c3)cc2)cc1OC,,0.36288885,,,
+450754739,*CCCCCCCCCCCOC(=O)c1cc(OCCCCCCCCOc2ccc(N=Nc3ccc(OC)cc3)cc2)cc(C(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(O*)cc3)cc2)c1,,0.36775362,,,
+450878191,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Oc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)cc1)C2=O,,0.4007298999999999,,,
+453918545,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(-c4ccc(OC)cc4)cc(-c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)n3)cc1)C2=O,,0.38734029,,,
+454048621,*Nc1ccc(/C=C/c2ccc3cc(/C=C/c4ccc(N*)cc4)ccc3c2)cc1,,0.35285493,,,
+454723677,*Oc1ccc(C(=O)Oc2ccc(Cc3ccc(OC(=O)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3)cc2)cc1,88.16409459,,,,
+454925520,*Oc1ccc(-c2ccc(OC(=O)c3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(C(*)=O)cc5)cc4)cc3)cc2)cc1,213.4133554,,,,
+454931454,*Nc1cccc(-c2cc(-c3cccc(C)c3)c(-c3cccc(C)c3)c(-c3cccc(C)c3)c2-c2cccc(C)c2)c1N*,,0.37902645,,,
+455272434,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(C=Cc3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.35619794,,,
+455575558,*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4ccccc4)c3-c3ccccc3)cc2)cc1,,0.3636002,,,
+455581331,*c1cc(OCCCCCCCCCC)c(*)cc1OCCCCCCCCCC,5.423112055,,0.256,,
+455634892,*C(*)C(=O)OCC,,0.36012778,,,
+455687362,*CC(*)C(=O)OCc1ccccc1,,0.34659314,0.1985,1.072100095,13.15993394
+455760856,*c1ccc(OCCCCCCCCCOc2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,47.98864595,,,,
+455774790,*c1cccc(Oc2ccc(C#N)c(Oc3cccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.36294968,,,
+455931063,*CC(*)Cc1cccc(C)c1,,0.38592652,,,
+456072913,*c1ccc(Oc2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.37265926,,,
+456108334,*CC(*)OC(=O)CCCCCCCCCCCCCCCCCCCCC,,,0.371,0.858583325,13.69025537
+456414718,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)C3(CCC(C5=NC(c6ccccc6)CO5)(C5=NC(c6ccccc6)CO5)CC3)c3cc(*)ccc3-4)ccc1-2,,0.40565042,,,
+457126750,*Oc1ccc(S(=O)(=O)N(C)CCCCN(C)S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.35574227,,,
+457390290,*CC(OCCCC)C1C(=O)OC(=O)C1*,,0.32628739,,,
+457518738,*CCN(CCCC(=O)Nc1ccc(N=Nc2ccccc2)cc1)CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1,-32.25789051,,,,
+457660714,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Cc4ccc(NC(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1-c1ccccc1,,0.36405434,,,
+458141446,*CC(*)C(=O)OCCCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.34948224,,,
+458250694,*CCCC(=O)Nc1ccc(Oc2cccc(NC(=O)CCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.33380775,,,
+458811601,*CCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,0.36402987,,,
+459396662,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1,,0.35031608,,,
+459959706,*c1cccc(C(=O)c2c(C)cc(C)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2C)c1,,0.39121654,,,
+459974276,*CC(*)C(=O)OC(C)CCC(CC)CCCC,,,0.212,0.861701744,10.36326711
+460632304,*Nc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(NC(=S)NC(=O)CCCCC(=O)NC(*)=S)c3)cc2)c1,,0.33577195,,,
+461284204,*c1cc(CCCCBr)c(*)s1,,0.44837181,,,
+461293601,*CCC1C(=O)N(c2ccc(N(C)C)cc2)C(=O)C1*,,0.36050178,,,
+461562711,*CC(*)c1ccccc1COCCc1ccccc1,,0.36290546,0.1883333333333333,,
+461922685,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(c4cc(C)c(*)c(C)c4)(C(F)(F)F)C(F)(F)F)cc3C)cc2C(OC)(C(F)(F)F)C(F)(F)F)c(C(OC)(C(F)(F)F)C(F)(F)F)c1,,0.37962717,,,
+462593659,*CCN(*)CC,,0.38450677,,,
+462771322,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)C(CC(C)C)N4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,36.87047274,,,,
+462814038,*CCOCC(=O)O*,,0.31811694,,,
+462826166,*c1ccc(C(=O)OCCCCCCCCCCCCOC(=O)c2ccc(-c3nnc(*)s3)cc2)cc1,,0.36737797,,,
+462869039,*CC(*)(C)C(=O)OCCOC,,,0.1815,1.033359022,13.61871523
+463127063,*C(=O)OCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.3374244,,,
+463296831,*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(OC)cc3)cc2)cc1,,0.3433683,,,
+463338969,*c1sc(*)c2c1OCC(CCCCCCCCCCCCCCCC)O2,,,0.44475,0.968871976,15.08786185
+463490376,*OC(c1ccco1)C(OC(=O)Nc1ccc(Cc2ccc(NC(*)=O)cc2)cc1)c1ccco1,,0.35498307,,,
+463727468,*CC(*)C(=O)OCC(F)(F)C(F)(F)OC(F)(F)F,,0.31340132,,,
+463770035,*C(C)=C(*)[Si](C)(C)CCCCCC,,0.38363595,,,
+464374325,*CCCCCCC(=O)O*,,0.35774831,,,
+465735962,*CCC1CCCC1*,,,0.2863333333333333,,
+466297996,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)cc(C(F)(F)F)c1)C2=O,,0.39475422,,,
+466352320,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.34219261,,,
+466488035,*c1ccc(Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(Oc4ccc(N5C(=O)CC(Nc6cccc(NC7CC(=O)N(*)C7=O)c6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.3838921,,,
+466577746,*O[Si](C)(C)c1cc(OCCOCc2ccccc2)c([Si](*)(C)C)cc1OCCOCc1ccccc1,,0.37480731,,,
+467415058,*NNc1ccc2ccccc2c1-c1c(NN*)ccc2ccccc12,,0.36169901,,,
+467619338,*CC(C)C=C(*)C,,0.40607353,,,
+467841477,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCCCCCOc4ccc(/C=C/c5ccc(F)cc5)cc4)c3)cc1OCCCCCCOc1ccc(/C=C/c3ccc(F)cc3)cc1)C2=O,,0.36418165,,,
+467957331,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cccc56)cc3)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.36021287,,,
+468123146,*COc1ccc(C(c2ccc(OCC3(*)COC3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.34907081,,,
+468202587,*c1cccc(NC(=O)c2ccc(OCCOCCOCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)c1,,0.34535507,,,
+468435322,*C=Cc1sc(-c2ccc(-c3ccc(-c4ccc(-c5sc(C=CC6=CC(=C(C#N)C#N)C=C(*)O6)c(CCCCCC)c5CCCCCC)s4)s3)s2)c(CCCCCC)c1CCCCCC,,0.42429336,,,
+468647784,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCC,,0.35650224,,,
+469117883,*CCCCCCOC(=O)OCCCCCCOC(=O)OCCCCCCOc1ccc(-c2ccc(O*)cc2)cc1,,0.35478647,0.329,,
+469170848,*COC(=O)OCC1OC(OCC)OC1*,,0.32726581,,,
+469302506,*c1cccc(C(=O)Nc2ccc(Oc3cccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8cccc9c8C(=O)N(*)C9=O)cc7C6=O)c5)cc4)c3)cc2)c1,,0.34972728,,,
+469358545,*Nc1ccc(N*)c2c3ccc(cc3)c12,,0.33588692,,,
+469373885,*C=CC1CC(*)C(C(=O)OCCNC(C)(C)C)C1,,0.37778417,,,
+469522235,*Nc1cccc(-c2cc(-c3cccc(N)c3)cc(-c3cccc(N*)c3)c2)c1,,0.34659743,,,
+469762118,*CC(*)CCCCCCCCCC,,0.40334594,0.33575,0.799124975,11.10583978
+470248513,*CC(*)C(=O)OCCSCC,,0.38932692,0.2175,1.065268296,14.51100233
+470644305,*CC(*)C,,,0.2445,0.77629912,15.63619856
+470882237,*CC(*)(C)C(=O)OCC,,,0.1699999999999999,0.90836326,13.61883804
+471167389,*C(=O)Oc1c(C)cc(C(c2cc(C)c(OC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(-c5cc(C)c(N6C(=O)c7ccc(*)cc7C6=O)c(C)c5)cc3C)C4=O)c(C)c2)(C(F)(F)F)C(F)(F)F)cc1C,,0.39965257,,,
+471229336,*CCCCCCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35885044,,,
+471232533,*c1ccc(C(C#N)(C(=O)OCC(C)C)C(*)(C#N)C(=O)OCC(C)C)cc1,,0.40104325,,,
+471408495,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(C(O)(C(F)(F)F)C(F)(F)F)c4)cc3C(O)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.3757357,,,
+471856670,*C=C(*)c1cc(C(F)(F)F)sc1C(F)(F)F,54.57900551,,,,
+471964961,*c1ccc(Cc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1,,,,1.232934348,29.70086573
+472043812,*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8c7)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.36278753,,,
+472240930,*CC1CC2CC1C1CCC(CN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C21,,0.36457594,,,
+472323951,*CC(CC)(CC)COC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.3477811,,,
+472677846,*CC(C)(C)CS(*)(=O)=O,,0.36380251,,1.140781369,16.55092442
+472726378,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(CCCCCCCCCC)c(*)cc3CCCCCCCCCC)ccc1-2,,,0.216,0.807377101,18.38442171
+472812489,*OC1C(COC(C)=O)OC(*)C(OC(C)=O)C1OC(C)=O,,0.33034582,,,
+473402909,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCC(C(C)(C)C)CC9)cc8)cc7C6=O)cc5)cc4)CC4CC3C3CCCC43)cc2)cc1,,0.37929885,,,
+473547925,*Oc1ccc(NC(=O)C=CC(=O)NNC(=O)C=CC(=O)Nc2ccc(OC(=O)C=CC(=O)NNC(=O)C=CC(*)=O)cc2)cc1,,0.3061006,,,
+473567594,*Oc1ccc(C(C)(C)c2ccc(OP3(Oc4ccc(C(=O)OC)cc4)=NP(Oc4ccc(C(=O)OC)cc4)(Oc4ccc(C(=O)OC)cc4)=NP(*)(Oc4ccc(C(=O)OC)cc4)=N3)cc2)cc1,,0.34690701,,,
+473657934,*Nc1ccc([C@@H]2C=C[C@@](N*)(c3ccccc3-c3ccc4ccccc4c3)C=C2c2ccc3ccccc3c2)c(-c2ccc3ccccc3c2)c1-c1ccc2ccccc2c1,,0.37203518,,,
+474558626,*NCCCc1c2ccccc2c(N*)c2ccccc12,,0.33990323,,,
+475124319,*CC(*)(C)C(=O)Oc1ccc(C#N)cc1,,0.36105716,0.1345,1.058985756,12.9825722
+475611696,*Nc1ccc(C(CCC(CCC(c2ccc(N)cc2)c2ccc(N)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)c2ccc(N)cc2)cc1,,0.35622218,,,
+475921569,*CC(*)c1c([2H])c([2H])c([2H])c([2H])c1[2H],,0.39369072,,0.923679332,14.83081456
+476061574,*CC(*)(CC(=O)OCCCC)C(=O)OCCCC,,0.37326705,0.2249999999999999,0.96210565,10.50145976
+476584063,*c1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Cc5ccc(N6C(=O)c7ccc(C(=O)Nc8ccc(Oc9ccc(C%10(*)NC(=O)c%11ccccc%11%10)cc9)cc8)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.36522559,,,
+476711294,*CC(*)(C)C(=O)Oc1ccc2c(c1)CCC2,,0.36609405,,,
+476733840,*CCCCCCOC(=O)C1CC(=O)C(C(=O)O*)CC1=O,-36.66445232,,,,
+477018729,*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccccc3)cc2)cc1,,0.34320859,,,
+477055088,*c1cc(SCCCCCCCCCCCC)c(*)s1,,0.41856492,,,
+477209068,*CCc1ccc(*)c(C#N)c1,,0.39093091,,,
+477797468,*CC(*)(C)C(=O)OCC[N+](=O)[O-],,,0.1895,1.159740176,14.1297443
+477893372,*CC(*)C1CCCCC1C,,0.40280956,,,
+478096853,*CC(=O)OCCCCCCOC(=O)Cn1c(=O)c2cc3c(=O)n(*)c(=O)c3cc2c1=O,55.49864746,,,,
+478367903,*c1ccc(NC2CC(=O)N(c3ccc(-c4sc(-c5ccc(N6C(=O)CC(Nc7ccc(-c8nc(-c9ccc(-c%10nc(-c%11ccc([N+](=O)[O-])cc%11)c(-c%11ccc([N+](=O)[O-])cc%11)[nH]%10)cc9)[nH]c8*)cc7)C6=O)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)C2=O)cc1,,0.41430705,,,
+478448217,*c1cccc(OCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34783375,,,
+478818056,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(S(C)(=O)=O)cc5)cc4)cc3)cc2)cc1,,0.38732371,,,
+478981386,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1C,,0.36199389,,,
+479722574,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(-c3nc4ccccc4oc3=O)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37512848,,,
+479745021,*C(=O)c1ccc(Oc2ccc(-c3ccc(Oc4ccc(C(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36908001,,,
+479794111,*c1ccc(C(=O)NCCCCCCCCCCNC(=O)c2ccc(-c3nc4ccccc4nc3*)cc2)cc1,,0.36538567,,,
+479814581,*O[Si](O[Si](O[Si](O[Si](CC[Si](C)(C)O[Si](C)(C)CC[Si](*)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1,,0.39773141,,,
+479850250,*Nc1ccc([C@@H](C)c2ccccc2CCc2ccccc2[C@H](C)c2ccc(N*)cc2)cc1,,0.35612182,,,
+479918416,*CC(*)c1ccc(C(O)CCN2CCCCC2)cc1,,0.37433713,,,
+480111697,*CC(*)(C)C(=O)NC(=O)OCCOc1ccc(S(=O)(=O)c2ccc(OCCO)cc2)cc1,,0.32112378,,,
+480125029,*CCCCCCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.34768618,,1.043832714,
+480361178,*CCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.33491871,,,
+480471385,*c1ccc(-c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.39039629,,,
+480495862,*CC(*)CCC(C)(C)C,,,0.1929999999999999,,
+481255154,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=O)c4cc(C(=O)c5ccc(*)cc5)cc(C(C)(C)C)c4)cc3)cc2)cc1,,0.38222676,,,
+481310813,*C=CC1CC(*)C(C(=O)OCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1C(=O)OCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.3627633,,,
+481600989,*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5cccc(NC(=O)c6ccc(*)cc6)c5)cc4)cc3)cc2)cc1,,0.36677611,,,
+482356088,*OP(=O)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)Oc1c(Cl)cc(Cl)cc1Cl,,0.3676303,,,
+482559838,*CC(COc1ccc(-c2ccc(OC)cc2)cc1)O*,,0.34505988,,,
+482933364,*Nc1ccc([C@@H](c2ccccc2)c2ccccc2N*)cc1,,0.34925827,,,
+483443678,*Nc1ccc(-c2ccc(N*)c(-c3ccc(C)cc3C)c2-c2ccc(C)cc2C)cc1,,0.37968656,,,
+483503968,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5c(c(-c6ccccc6)c(-c6ccc(-c7c(-c8ccccc8)c8c(c(-c9ccccc9)c7-c7ccccc7)C(=O)N(*)C8=O)cc6)c(-c6ccccc6)c5-c5ccccc5)C4=O)c3)c2)c1,,0.38403855,,,
+483514243,*/C=C/CCCCCCCCCC*,,,0.4142,0.822074244,20.61494369
+483787501,*C(=O)c1ccc(N=Nc2ccc(C(=O)N3C(=O)N(*)C(C)(C)C3=O)cc2)cc1,302.3547996,,,,
+484741548,*CC(c1ccccc1)C1C(=O)N(c2ccc(Cl)cc2)C(=O)C1*,168.825845,,,,
+484824618,*c1ccc(C(=O)Nc2cccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5cccc(NC(=O)c6ccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)cc6)c5)cc4)cc3)c2)cc1,,0.3546746,,,
+484906065,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.3421229,,,
+484910075,*C(=O)Nc1cccc(NC(=O)c2ccc3[nH]c(-c4ccc(-c5nc6cc(*)ccc6[nH]5)cc4)nc3c2)c1,,0.37664927,,,
+484914577,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(-c6ccc(*)s6)cc5C(F)(F)F)cc4)c4ccccc4-c4ccccc43)cc2)c(C(F)(F)F)c1,,0.43261591,,,
+485094036,*OCCCCOc1ncnc(*)n1,,0.35576743,,,
+485407995,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCC(*)=O,,0.32762443,,,
+485725497,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(C)CC(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc43)cc1)C2=O,,0.40280655,,,
+486162047,*Oc1ccc(C(C)(c2ccccc2)c2ccc(OC(*)=S)cc2)cc1,,0.36499297,,,
+487578819,*CCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.34744921,,,
+487601527,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(-c4ccc(Oc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37208574,,,
+488191156,*CCCCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1,,0.34353259,,,
+488617798,*c1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(-c4nc5ccc(-c6ccc7nc(*)[nH]c7c6)cc5[nH]4)cc3)cc2)cc1,358.667269,,,,
+489644096,*CCCNC(=O)C(NC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NC(C(=O)NCCCOCCOCCO*)C(C)C)C(C)C,,0.34899724,,,
+489823970,*c1cccc(N2C(=O)c3ccc(Oc4c(C)cc(Cc5cc(C)c(Oc6ccc7c(c6)C(=O)N(*)C7=O)c(C)c5)cc4C)cc3C2=O)c1,,0.39109264,,,
+489861944,*c1ccc(Oc2ccc(-c3ccc(-c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccccc6)C7=O)cc5)c(C(F)(F)F)c4)s3)cc2C(F)(F)F)cc1,,0.4015453,,,
+490110901,*CC(O)CN(*)c1ccc(N=Nc2ccc(C(=O)O)cc2)cc1,,0.3475583,,,
+491061236,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4[nH]c(-c5cc(-c6nc7cc(*)ccc7[nH]6)cc(N6C(=O)c7ccccc7C6=O)c5)nc4c3)cc2)cc1,,0.37434137,,,
+491271299,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C(C)C)cc(C(C)(C)c4cc(C(C)C)c(*)c(C(C)C)c4)cc3C(C)C)cc2)cc1,,0.41746736,,,
+491291994,*CCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,0.37814224,0.396,,
+492004375,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c2)cc1,,0.35547349,,,
+492439302,*Nc1ccc2c(c1)C(c1ccc(C)c(C)c1)(c1ccc(C)c(C)c1)c1cc(N*)ccc1-2,,0.38916246,,,
+492508713,*Nc1ccccc1C(c1ccccc1N)(c1ccccc1N)c1ccccc1N*,,0.36419174,,,
+492534708,*Nc1ccc(-c2ccc(N*)cc2-c2cccc3ccccc23)cc1,,0.35766068,,,
+492635161,*CC(*)(C)C(=O)OCCCCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.35070234,,,
+492640026,*C(=O)Nc1ccc2c(c1)Cc1cc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)ccc1-2,,0.34865411,,,
+493007317,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O,,0.37514628,,,
+493762918,*CC(COC(C)C)O*,,0.38129236,,,
+494086930,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(C(F)(F)F)cc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.38566004,,,
+494165732,*c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)C(C)C3)CC1C)C2=O,,0.39920256,,,
+494602606,*CCCCCCCNC(=O)CCCCC(=O)N*,,0.3410138,0.323,,
+494975376,*c1cccc(C(c2ccccc2)(c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)C(F)(F)F)c1,,0.38438296,,,
+495203055,*c1ccc(Cc2ccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.39061892,,,
+495342241,*C1C(*)C2CC1C(COCCCc1ccccc1)C2COCCCc1ccccc1,,0.36101933,,,
+495385866,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(Oc7ccc(Oc8cccc9c8C(=O)N(*)C9=O)cc7)cc6)c5C4=O)cc3)c2)cc1,,0.36047551,,,
+496258709,*CC(*)(C)c1ccc(C(O)C(F)(F)F)cc1,,0.35571661,,,
+496344217,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N5C(=O)CC(=O)N(*)C5=S)cc4)cc3)cc2)cc1,,0.38334542,,,
+496407382,*Nc1c(F)c(F)c(-c2c(F)c(F)c(N*)c(F)c2F)c(F)c1F,,0.33298496,,,
+496740168,*c1ccc(-c2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.39934746,,,
+496873266,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(N=Nc3ccccc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccccc1,,0.35725453,,,
+497236966,*CC(*)c1cccc(C)c1,,,0.1973333333333333,,
+497317849,*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc([N+](=O)[O-])cc2)cc1,,0.35625446,,,
+497633362,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1-c1cccc(C(F)(F)F)c1,,0.35258781,,,
+498302991,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(S(=O)(=O)c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.3773528,,,
+498538377,*c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(C=Cc4ccc([N+](=O)[O-])cc4)cc3)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.41204926,,,
+498645722,*CC(*)c1cc(-c2ccc(C(=O)OC(C)CC)cc2)ccc1-c1ccc(C(=O)OC(C)CC)cc1,,0.39103863,,,
+499146119,*O[Sn](CCCC)(CCCC)OC(=O)c1ccc(C(*)=O)cc1,,0.36080704,,,
+499802239,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(C)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.36654157,,,
+499947957,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)c2ccccc2C(=O)O)cc1,,0.37386948,,,
+500257329,*C(=O)Nc1ccc(C2(c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Sc6ccc(Oc7ccc(Sc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)cc6)cc4)C5=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.38040953,,,
+500559499,*CCOCCOC(=O)CCC(=O)O*,,0.3219175,,,
+501088576,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(OCCCCCCCC)cc2)cc1,,0.36549965,,,
+501489325,*CCNC(=O)c1cccc(C(=O)NCCOc2ccc3c(O*)cccc3c2)c1,,0.33057891,,,
+502239610,*Nc1ccc2cccc3c2c1C=C(CC=C)[C@@H]3N*,,0.33480182,,,
+502600207,*N[C@H]1CC[C@H](C[C@H]2CC[C@H](N*)CC2)CC1,,0.3557302,,,
+503030513,*CCCCCCOc1ccc(C=Cc2ccc(O*)c3ccccc23)cc1,,0.36429456,,,
+503126715,*CC(*)(C)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,,0.0735,1.482039287,12.88558494
+503535995,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8c(C)cc(C(C)(C)c9cc(C)c(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)c(C)c9)cc8C)cc7C6=O)cc5)cc4)CC4CC3C3CCCC43)cc2)cc1,,0.39469659,,,
+503739413,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1,,0.42386607,,,
+503770614,*CC(*)c1ccc(COc2ccc(-c3ccc(C#N)cc3)cc2)cc1,,0.3628679,,1.065712208,12.90310493
+503797851,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(C(c9ccc(*)cc9)(C(F)(F)F)C(F)(F)F)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36856171,,,
+504470036,*CCN(CCOC(=O)CCC#CC#CCCC(=O)O*)c1ccc(N=Nc2c(C)cc(N=Nc3ccc(C#N)cc3)cc2C)cc1,,0.3810089,,,
+504498894,*CCCCCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.36312778,,,
+504683650,*Oc1c(C)cc(*)cc1-c1ccccc1,,0.40029131,0.1935,1.002505763,15.57570307
+504741555,*CCC(=O)Nc1ccc(Oc2ccc(NC(=O)CCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33487756,,,
+505042674,*CC(OC(C)=O)C1C(=O)N(c2ccccc2)C(=O)C1*,157.3759721,,,,
+505139445,*CC(CC)(CC)C(=O)O*,,0.3595269,,,
+505192232,*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.39219302,,,
+505306090,*c1ccc2c(c1)C(=O)N(c1ccc(OCCN(CCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c3ccccc3)cc1)C2=O,,0.35771697,,,
+505372159,*Nc1ccc(C2(c3ccc(N*)cc3)C3=C(C=CCC3)c3ccccc32)cc1,,0.3719327,,,
+505451918,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C(C)(C)C)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)c(C(F)(F)F)c1)C2=O,,0.41829634,,,
+505548035,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(*)cc5)c4)cc3)cc12,,0.35312849,,,
+505708528,*CC(*)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32462009,,,
+506323355,*CCCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.33556239,,,
+506576027,*CCCCCCN(C)C(=O)CCCCCCCCCCCCCCCCC(=O)N(*)C,49.34222836,,0.3525,0.898624404,19.92649972
+506639284,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(C(F)(F)F)cc5)C6=O)cc4)cc3)cc2)cc1,,0.38171979,,,
+506748374,*CCCCC(=O)NCc1ccc(CNC(=O)CCCCO*)cc1,,0.3396223,,,
+506781855,*c1ccc2[nH]c(*)nc2c1,,0.47901269,,,
+506857730,*C1C(=O)N(c2ccc(COC(C)(C)C)cc2)C(=O)C1*,122.5873684,,,,
+506874673,*c1ccc(CC(=O)Nc2ccc(-c3ccc(NC(=O)Cc4ccc(-c5sc(*)c(-c6ccccc6)c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.37860211,,,
+506900433,*c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(C#N)cc3)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.42132012,,,
+507068449,*Nc1ccc([C@H](C)c2ccc([C@H](C)c3ccc([C@@H](C)c4ccc([C@@H](C)c5ccc(N*)cc5)cc4)cc3)cc2)cc1,,0.36551543,,,
+507545745,*C=C1CCCC(=Cc2ccc(-c3cc(OCCCCCC)c(-c4ccc(*)cc4)cc3OCCCCCC)cc2)C1=O,,0.3857122,,,
+507783555,*CC(*)(C#N)C(=O)OC(C)C,,0.36628241,,,
+507898117,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(*)cc4C3=O)cc(C(F)(F)F)c1)C2=O,,0.38109173,,,
+508336740,*CC(*)c1ccc(C(=O)CCCC)cc1,,0.38199885,,,
+508339615,*Nc1ccc(C2(c3ccc(N*)cc3)CCCC[C@H]2C[C@H]2CC[C@H](CCCCC)CC2)cc1,,0.37129511,,,
+508597876,*Oc1ccc(Oc2ccc(C(=O)c3ccccc3-c3ccccc3C(=O)c3ccc(*)cc3)cc2)cc1,,0.37522129,0.2205,1.125774179,17.8866103
+509167762,*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5CC=CCC5C4=O)c3)cc2)cc1,,0.34814745,,,
+509179600,*c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(C(c3ccccc3)(c3cc(C(C)C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)C(F)(F)F)cc1C(C)C)C2=O,,0.44811081,,,
+509220897,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(N)c4)cc3N)cc2)cc1,,0.37586152,,,
+509241405,*CCCCCCN1C(=O)C(=Cc2ccc(C=C3SC(=S)N(*)C3=O)cc2)SC1=S,35.37444876,,,,
+509577164,*CC(*)(C)C(=O)Oc1ccc(C)cc1,126.1154692,,0.1545,0.951958497,12.66351417
+509659753,*c1cccc(C(O)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.36066207,,,
+509959736,*CC(*)c1ccc(-c2cccc3nsnc23)cc1,,0.37001956,,,
+509992676,*Nc1ccc(/C=C/c2ccc(-c3ccc(/C=C/c4ccc(N*)cc4)cc3)cc2)cc1,,0.35190828,,,
+510587206,*CCOCCOCCOC(=O)C=CC(=O)NCCCCCCNC(=O)C=CC(=O)O*,,0.32787972,,,
+510665412,*OC(=O)C(CCSC)NC(=O)CCCCCCCCC(=O)NC(CCSC)C(=O)OC1COC2C(*)COC12,,0.35394584,,,
+510669742,*c1ccc(CCc2ccc(N3C(=O)C(=O)N(*)C3=O)cc2)cc1,,0.36028471,,,
+510815019,*CC(C)(C)COC(=O)NCCCCCCNC(=O)O*,47.60248847,,,,
+510908370,*CC(*)c1cc(C(C)(C)C)ccc1C,,,0.1636666666666666,0.849065812,12.50033571
+511605655,*Nc1cccc(-c2ccc(-c3ccc(-c4cccc(N*)c4)cc3)cc2)c1,,0.34297147,,,
+512074662,*OP(=O)(Oc1ccc(Br)cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.36041928,,,
+512260776,*CC(*)c1ccc(C(O)C(F)(F)F)cc1,,0.35930976,,,
+512380282,*CC(*)(C)C(=O)OC1CCCC(C)C1,,0.38017657,0.1545,0.881548744,10.34584016
+512910152,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1CCC(C)CC,,0.36764401,,,
+513101692,*c1ccc(-c2ccc(-c3ccc(*)n3C)n2C)cn1,256.5965094,,,,
+513259550,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(-c5nc(*)nc(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.39322876,,,
+513674501,*Oc1ccc(C(=O)OCCOCCOC(=O)c2ccc(OC(*)=O)cc2)cc1,,0.32931388,,,
+513989706,*Nc1ccc(C(c2ccc(N*)cc2)=c2ccc(=C(c3ccc(N)cc3)c3ccc(N)cc3)cc2)cc1,,0.37954577,,,
+514517104,*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3cccc(N*)c3)c2)cc1,,0.35536977,,,
+514560887,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(N=Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37509009,,,
+515010874,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OC)cc1,,0.33673792,,,
+515242470,*CC(*)Cc1ccccc1,,,0.1963333333333333,,
+515253113,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc(C(*)=O)cc8)cc7)CCC(C(C)(C)C)CC6)cc5)cc4)cc3)C3CC4CC(C3)CC2C4)cc1,,0.38086878,,,
+515420444,*CC(*)C(=O)OCCCCn1c2ccccc2c2ccccc21,,0.35334507,,,
+515584536,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36493909,,,
+516007514,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc(NC(=O)CN6C(=O)c7ccc(C(c8ccc9c(c8)C(=O)N(CC(=O)Nc8ccc(Oc%10ccc%11nc(-c%12ccccc%12)c(*)nc%11c%10)cc8)C9=O)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)ccc4nc3-c3ccccc3)cc2)cc1,,0.37474439,,,
+516038607,*CC(F)(F)C(F)=C(*)Cl,,0.36169864,,,
+516307046,*CC(=O)NCCCCCCNC(=O)CO*,,0.32129189,,,
+516348325,*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc(F)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C,,0.37338433,,,
+516368509,*c1ccc2c(c1)C(=O)N(c1ccc(OC(C)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.36566453,,,
+516575920,*c1ccc2c(c1)c1cc(-c3sc(-c4ccc(-c5cc(CCCCCC)c(*)s5)c5nsnc45)cc3CCCCCC)ccc1n2C(CCCCCCCC)CCCCCCCC,,0.42806849,,,
+516647275,*OC(*)C(Cl)(Cl)Cl,,,0.066,1.539478709,18.35760963
+517724366,*CCCCOC(=O)CCCCCCCCC(=O)O*,,0.35670902,,,
+517814653,*N=P(*)(OCCOCCOCC)OCCOCCOCC,,0.36520289,,,
+518335451,*CC(CS(C)(=O)=O)O*,,0.36145145,,,
+518421541,*O[Si](C)(C)CCCCCCOC(=O)C(C)Oc1ccc(OC(=O)c2ccc(-c3ccc(OCCCCCCCCCC[Si](*)(C)C)cc3)cc2)cc1,,0.38168515,,,
+518799909,*CCN(*)C,,0.382623,,,
+519498812,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1,,0.34952395,,,
+519962205,*CCOCCOC(=O)/C=C/C(=O)O*,,0.3158043,,,
+520233682,*CCCCCC(*)CCCCCCCC,,0.40682888,,0.794468482,14.0622995
+520243279,*Oc1ccc(OC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2-c2ccccc2)c2ccccc12,,0.36166198,,,
+520514597,*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.39304879,,,
+520557915,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCCC(*)=O,,0.33709965,,,
+520586980,*NC1=C2C(=CC=CC2(N)N*)[C@@H](c2ccc(N)c3ccccc23)C=C1,,0.3475975,,,
+520661031,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c1)C2=O,,0.37598194,,,
+520740305,*Oc1ccc(Oc2ccc(C(=Nc3ccccc3)c3ccc(Oc4ccc(Oc5cccc(*)n5)cc4)cc3)cc2)cc1,,0.37099152,,,
+521246166,*Nc1cc(=O)c(N*)c(N)[nH]1,,0.2918774,,,
+521384312,*Oc1ccc(S(=O)(=O)c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)cc1,,0.34883911,,,
+521718706,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(S(=O)(=O)c7ccc(Oc8ccc(C=C9CCCCC(=Cc%10ccc(*)cc%10)C9=O)cc8)cc7)cc6)cc5)CCCCCC4)cc3)cc2)cc1,,0.37546992,,,
+522844869,*CC(*)OC(=O)CCCC1CCCCC1,,0.36748858,,,
+522894499,*Nc1ccccc1-c1cccc2c(-c3ccccc3N*)cccc12,,0.35396879,,,
+523250559,*OC(C)CCCC(C)C(*)=O,,0.36757334,,,
+523651487,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.34682404,,,
+523668736,*CC(*)(C)C(=O)OCCCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.34820779,,,
+523674609,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nnc(-c3ccccn3)o2)c1,,0.37590991,,,
+523947346,*Oc1cc(C)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.35222541,,,
+524444446,*CCCCCCCC(=O)OCC1(C)COC(*)OC1,,0.35863844,,,
+525351897,*c1ccc(-c2nnc(-c3cccc(-c4nnc(*)o4)c3)o2)cc1,,0.44804899,,,
+525571828,*C(=O)c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(*)s5)cc4)c4ccccc4-c4ccccc43)cc2)cc1,,0.39472012,,,
+525996171,*Oc1cccc(NC(=O)CCCCCCCCC(=O)Nc2ccc(*)cc2)c1,,0.34695098,,,
+526128388,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.36085743,,,
+526195593,*C(=O)Nc1cccc(Oc2cccc(Oc3cccc(Oc4cccc(Oc5cccc(NC(=O)c6ccc7c(c6)C(=O)N(c6cccc(Oc8cccc(Oc9cccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)c9)c8C#N)c6)C7=O)c5)c4C#N)c3)c2C#N)c1,,0.36067219,,,
+526647748,*Nc1ccc(-c2cc3ccc2CCc2ccc(c(-c4ccc(N*)cc4)c2)CC3)cc1,,0.35898594,,,
+527877967,*Nc1ccc(C2(c3ccc(N*)c(C)c3)CCCCC2)cc1C,,0.36323819,,,
+527882654,*CC(*)(C)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)F,,0.3268589,0.095,,
+527894949,*Nc1ccc(-c2ccc(N*)c3c(N)cccc23)c2cccc(N)c12,,0.35447146,,,
+528293396,*CCCCCCCCCCNC(=O)C(CCCCCCCCCCCC)C(=O)N*,-0.868265132,,0.35,0.888311048,16.27222852
+528559472,*CCC[Si](*)(C)c1cccc(C)c1,,0.40156037,,,
+528637709,*Nc1ccc(Oc2ccc(N*)cc2)cc1,,0.33692082,,,
+528781081,*CC(*)(C)C(=O)OCCNC(=O)N(CCCCCC)CC(O)C(O)C(OC1OC(CO)C(O)C(O)C1O)C(O)CO,23.2402228,,,,
+528835439,*Nc1ccc2c(c1N*)C(C)(C)CC2(C)C,,0.36728055,,,
+528964340,*CCC[Si](C)(C)O[Si](C)(C)CCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.37133662,,,
+530057519,*c1ccc(Oc2ccc(Sc3ccc(Oc4ccc(-c5ccc(*)s5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.40843871,,,
+530220535,*Oc1ccc2c(c1)C(C)(c1ccc(OC(=O)c3ccc(C(*)=O)cc3)cc1)CC2(C)C,,0.37005888,,,
+530546443,*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35627754,,,
+530898286,*Nc1ccc(-c2cccc(-c3cc(-c4cccc(-c5cccc(N*)c5)c4)cc(-c4cccc(-c5ccccc5N)c4)c3)c2)cc1,,0.35285908,,,
+531021171,*c1ccc(Oc2ccc(-c3cnc4ccc(-c5ccc6nc(*)cnc6c5)cc4n3)cc2)cc1,,0.40000868,,,
+531044594,*C(C)C(*)O,113.5665564,,,,
+531093523,*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OCCCC)cc1,,0.36060882,,,
+531126908,*CCCCCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.35125346,,,
+531544469,*Nc1ccc(-c2ccc(N*)c(C)c2)cc1C,,0.348612915,,,
+531780740,*CC(*)c1c(F)c(F)c(C(F)(F)F)c(F)c1F,,0.36531928,,,
+531904856,*Nc1ccc(C2=CC3=CC[C@@H]2CC[C@@H]2C=CC(=C(c4ccc(N*)cc4)C2)CC3)cc1,,0.35807069,,,
+531967957,*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(Oc4cccc(Cc5cccc(*)c5)c4)cc3)c2)cc1,,0.36716519,,,
+532718186,*Nc1c(/C=N\O)cc(/C=N\O)c(N*)c1N,,0.30882264,,,
+532722806,*CCOC(=O)O*,,0.3043208,,,
+533889632,*NC1=C(C2=C(N*)NS(=O)(=O)N=C2N)C(N)=NS(=O)(=O)N1,,0.31382982,,,
+533973730,*CC(*)(C)C(=O)OCC(=O)N1CCOCC1,,0.32527676,,,
+534049242,*OC(C)(CC(*)=O)C(F)(F)F,,0.32631701,,,
+534118680,*CC(*)c1cc(F)ccc1F,,,0.1403333333333333,1.086573844,14.49605377
+534605075,*Oc1cc(CCCCCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.37154084,,,
+535310373,*c1ccc(NC(=O)CCCCC(=O)Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.33651,,,
+535364463,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=O)NNC(=O)c4ccc(NC(=O)c5ccc(*)cc5)c(O)c4)cc3)cc2)cc1,111.457133,,,,
+535689766,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(F)cc5)C6=O)cc4)cc3)cc2)cc1,,0.37834526,,,
+535937381,*CC(CSC(C)C)O*,,0.41844328,,,
+536002998,*CC(*)(Cl)C(=O)OCCCC,,,0.178,1.074741397,13.37410592
+536465827,*CC(*)c1ccc(C([Sn](C)(C)C)[Sn](C)(C)C)cc1,,0.37911502,,,
+536792438,*Nc1ccc2c(c1)-c1ccccc1C21c2cc(N)ccc2-c2ccc(N*)cc21,,0.3825956,,,
+537109947,*CC(*)(C)C(=O)OCCCCCCOc1ccc(-c2ccc(OC)cc2)cc1,,,0.2175,1.04192343,12.60793073
+537336269,*CC(*)(C)C(=O)OCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OCC)cc2)cc1,,0.34042214,,,
+537664135,*CCCCCCOC(=O)OCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.34845785,,,
+538207206,*CC(c1ccccc1)C1C(=O)OC(=O)C1*,,0.33148973,,,
+538524881,*c1cc(-c2ccc(C(=C3C=C(C(C)(C)C)C(=O)C(C(C)(C)C)=C3)c3cc(C(C)(C)C)c(OC(C)=O)c(C(C)(C)C)c3)cc2)c(*)s1,,0.45309822,,,
+538540896,*CC(C)(C)C1C(=O)N(c2ccccc2)C(=O)C1*,,0.35392644,,,
+538764320,*c1cccc(C(=O)Nc2ccc(Oc3ccc(-c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.35494179,,,
+538774618,*C1(F)OC(C(F)(F)F)(C(F)(F)Cl)OC1(*)F,,0.36536352,,,
+539017358,*C(=O)c1ccc2nc(-c3ccc(-c4nc5ccc(*)cc5nc4-c4ccccc4)cc3)c(-c3ccccc3)nc2c1,,0.44667573,,,
+539302432,*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.38528717,,,
+539590566,*CC(OCCCC)C(C(=O)OC)C(*)C(=O)OC,,0.3557316,,,
+540095012,*CCCCCCOC(=O)OCCCCCCOC(=O)OCCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.33877986,,,
+540297296,*CC(*)(CC(=O)OCCCCCC)C(=O)OCCCCCC,,0.38570941,0.2319999999999999,0.927808816,10.01915881
+540769722,*Nc1ccc2c3c4ccccc4c(c2c1N*)CC3,,0.35026083,,,
+541086746,*NC1=C(N)[C@@H](N*)N[C@H](O)N1,,0.28340014,,,
+541170005,*Nc1ccc(C(c2ccc(N*)cc2)c2cc(C(C)C)cc(C(C)C)c2)cc1,,0.37601699,,,
+541335135,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)c(S(=O)(=O)O)c2)cc1S(=O)(=O)O,,0.36521953,,,
+541484216,*CC(=O)NCC(=O)O*,,0.27858992,,,
+541941802,*CCCCCC(=O)NCCNC(=O)CCCCCOC(=O)c1ccc(C(=O)O*)cc1,81.65245915,,,,
+541986160,*CC(*)c1ccc(COCC(CC)CCCC)cc1,,,0.268,0.902459296,11.12376829
+542632051,*Nc1cc(C)c(Cc2ccc(CCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35730996,,,
+543084242,*c1ccc2c3ccc(-c4sc(-c5ccc(-c6cc(CCCCCC)c(*)s6)c6nsnc56)cc4CCCCCC)cc3n(C(CCCCCCCC)CCCCCCCC)c2c1,,0.42631402,,,
+543416509,*CC(*)c1ccccc1O,,0.35680883,,,
+543539170,*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4cccc(C)c4)cc3)C(C2)C1*,,0.38569582,,,
+543597205,*Nc1ccc2c(N*)cccc2c1,,0.33581452,,,
+543958331,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C)cc1,,0.35345752,,,
+544070601,*CCOC(=O)CCCCSCCCCC(=O)O*,,,0.212,1.07074619,19.93042453
+544223047,*CC(CC)N(*)C(=O)CCCC,,0.37465155,,,
+544292910,*Nc1cccc2c1-c1ccc(N*)c(-c3ccccc3-c3ccccc3)c1C2(c1ccccc1)c1ccccc1,,0.38258457,,,
+544581036,*C(C#N)=Cc1cc(OCCCCCCCCCC)c(C=C(C#N)c2cc(OCCCCCCCCCC)c(*)cc2OC)cc1OC,,0.39346704,,,
+544904323,*N=P(*)(OCCCc1ccccc1)OCCCc1ccccc1,,0.36697361,,,
+545195322,*CC(C)C(=O)O*,,0.33755497,,,
+545255610,*Nc1ccc(C2=CC[C@](c3ccccc3)(c3ccc(N*)cc3)C=C2)cc1,,0.35602121,,,
+545546596,*C=Cc1ccc(*)n1CCCCCC,-15.03969582,,,,
+545845623,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccccc2)cc1,,0.3472806,,,
+546152235,*c1ccc2oc3ccc(S(*)(=O)=O)cc3c2c1,,0.39759334,,,
+546427927,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc(C)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.36541949,,,
+546647857,*c1ccc(OCCCCCCCCCCCOC(=O)c2cccc(C(=O)OCCCCCCCCCCCOc3cccc(-c4nnc(*)s4)c3)c2)cc1,,0.36894733,,,
+547172331,*Nc1ccc([C@@H]2[C@H](c3ccc(N)cc3)[C@@H](c3ccc(N)cc3)[C@H]2c2ccc(N*)cc2)cc1,,0.36720973,,,
+547602790,*OC(=O)Nc1ccc(C)c(NC(=O)OC2CCN(c3ccc(C=CC4=CC(=C(C#N)C#N)C=C(C=Cc5ccc(N6CCC(*)CC6)s5)O4)s3)CC2)c1,,0.37611035,,,
+547840653,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(OCCCCCCCC)cc2)cc1,,0.38665475,,,
+548011241,*C=C(C#N)C(=O)OCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCC,,0.37348873,,,
+548292823,*NNC(=O)c1cccc(C(*)=O)c1,232.2266265,,,,
+549125416,*Oc1ccc(-c2ccc(OC(*)(Oc3ccccc3)Oc3ccccc3)cc2)cc1,,0.36489769,,,
+549389508,*CC(*)(C)C(=O)OCCN(CC)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)cc1,,,0.19,1.122445545,12.26891849
+549579953,*C(=O)Nc1cccc2cccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)c12,,0.34205138,,,
+549581828,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCC(c%10ccccc%10)CC9)cc8)cc7C6=O)cc5)cc4)CCC(C(C)(C)C)CC3)cc2)cc1,,0.38206234,,,
+549614488,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(-c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)cc2)cc1,,0.36966262,,,
+550069426,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)c7)cc6C5=O)cc4)cc3)cc2)cc1,,0.35950007,,,
+550478997,*Nc1ccccc1-c1cccc2c1C=C[C@H]2[C@@H]1c2ccccc2-c2c(-c3ccccc3N*)cccc21,,0.36637349,,,
+550776469,*c1ccc(-c2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)cc1,,0.43978712,,,
+550796562,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O,,0.4050413,,,
+551339545,*CCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37531301,,,
+551390778,*C=CC1CC(*)C(C(=O)OC)C1,,0.3605929,,,
+551461476,*OP(=O)(Oc1ccc([N+](=O)[O-])cc1)Oc1c(C)cc(S(=O)(=O)c2cc(C)c(*)c(C)c2)cc1C,,0.36156233,,,
+551862261,*CCCCCCOC(=O)Nc1cc(NC(=O)OCCCCOC(=O)Nc2ccc(C)c(NC(=O)OCCCCCCOc3ccc(-c4ccc(O*)cc4)cc3)c2)ccc1C,41.49405569,,,,
+552032340,*OC(C)COC(=O)c1ccc(C(*)=O)cc1,,0.34490108,,,
+552315395,*OC(F)(F)COC(=O)c1cccc(C(=O)OCC(*)(F)F)c1,,0.32071779,,,
+552340314,*Nc1cccc(NC(=O)CCCCCC(*)=O)c1,-12.07623639,,,,
+552823663,*CC(*)(CC(=O)OCCCCCCCCCC)C(=O)OCCCCCCCCCC,75.04044311,,,,
+553001434,*Oc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(*)cc3)cc(C(=O)OCCOC(=O)C=Cc3ccc(N(C)C)cc3)c2)cc1,,0.3451955,,,
+553487456,*Nc1ccc(Cc2cc(Cc3ccc(N)c(C(C)(C)C)c3)cc(C(C)(C)C)c2N*)cc1C(C)(C)C,,0.38558649,,,
+553498968,*Oc1ccc(C2(c3ccc(OC(=O)CCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36989282,,,
+553763789,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35973835,,,
+553989373,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.3424812,,,
+554199455,*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1O)C2=O,,0.37227168,,,
+554208834,*Nc1ccc(NC(=O)c2cccc(C(*)=O)c2)cc1,,0.33774959,,,
+554219011,*CC(O)COc1ccc(C2(C)CC(C)(C)c3cc(O*)ccc32)cc1,,0.362067,,,
+554452518,*Nc1ccc(C2(c3ccccc3N*)c3ccccc3-c3ccccc32)cc1,,0.36926653,,,
+554573258,*c1cccc(C(=O)Nc2ccc(Oc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)c4)cc3)cc2)c1,,0.35212357,,,
+555027003,*CCN(*)C(=O)CCCCC,,0.36643228,,,
+555056741,*C(=O)OCCOCCOCCOCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.33294368,,,
+555131075,*Oc1ccc(C(c2ccccc2)c2ccc(OC(*)=O)cc2)cc1,,0.36406756,,,
+555208717,*Oc1c(C)cc(C2(c3cc(C)c(OC(=O)c4cccc(C(*)=O)c4)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.414103,,,
+555363148,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(N(C)C)n3)c1)C2=O,,0.37799185,,,
+555506282,*Oc1c(C)cc(C2(c3cc(C)c(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.41226899,,,
+555542733,*C1C(*)C2CC1C(C(=O)OCCc1ccccc1)C2C(=O)OCCc1ccccc1,,0.35586688,,,
+555601753,*CC(*)c1ccc(C)nc1,,0.41719903,,,
+555803922,*C=CC1CC(*)C(C(=O)OCCN(C)C)C1,,0.37093089,,,
+556338128,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34396152,,,
+556552879,*OC1CCC(OC(=O)OCCCCCCOC(*)=O)CC1,,0.343459,,,
+556865522,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3c(-c4ccc(-c5ccccc5)cc4)cc(-c4ccc(OCCCCCCCCCCOc5ccc(-c6cc(-c7ccc(-c8ccccc8)cc7)c(-c7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c(-c7ccc(-c8ccccc8)cc7)c6)cc5)cc4)cc3-c3ccc(-c4ccccc4)cc3)cc1)C2=O,,0.36823501,,,
+557037726,*CC(O)COc1ccc(C(C)(C)C2CCC(C)(c3ccc(O*)cc3)CC2)cc1,,0.35740346,,,
+558140001,*CCCCCCOC(=O)OCCCCCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.35587164,,,
+558319108,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(Sc8ccc(Sc9ccc(Sc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)cc8)cc7C6=O)cc5)s4)s3)s2)cc1,,0.3770355,,,
+558482820,*CCCCCCNC(=O)c1cc(NC(=O)C(C(C)CC)N2C(=O)c3ccccc3C2=O)cc(C(=O)N*)c1,,0.33948967,,,
+558675719,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(C(c8ccc(N9C(=O)c%10ccc(C(=O)Nc%11ccc(Oc%12ccc%13nc(-c%14ccc([N+](=O)[O-])cc%14)c(*)nc%13c%12)cc%11)cc%10C9=O)cc8)(C(F)(F)F)C(F)(F)F)cc6)C7=O)cc5)ccc4nc3-c3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.38115297,,,
+558794697,*c1ccc(-c2nc3cc(Oc4ccc5nc(*)c(-c6ccccc6)nc5c4)ccc3nc2-c2ccccc2)cc1,,0.43112064,,,
+559219019,*CC(OCC(C)C)C(C(=O)OC)C(*)C(=O)OC,,0.36220084,,,
+559323860,*Oc1ccc(C2(c3ccc(OC(=O)CCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36295763,,,
+559874694,*Oc1cc(O*)cc(Oc2ccc(C#N)c(*)c2)c1,,0.35031084,,,
+559980070,*Oc1ccc(Oc2ccc(C(=Nc3ccc(N=C(c4ccccc4)c4ccc(*)cc4)cc3)c3ccccc3)cc2)cc1,,0.38545938,,,
+560279885,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.39564613,,,
+560554259,*CC(*)(C)C(=O)OCC1CC(NC(=O)C2CC(NC(=O)OC(C)(C)C)CN2C(=O)OC(C)(C)C)CN1C(=O)C1CC(NC(=O)OC(C)(C)C)CN1C(=O)OC(C)(C)C,,0.35571836,,,
+561158280,*NNC(=O)c1ccccc1C(=O)NCCCCCCNC(=O)c1ccccc1C(*)=O,,0.33644066,,,
+561427423,*CC(*)c1ccccc1C(=O)OC(C)C,,0.36610214,0.1736666666666666,,
+562022603,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Sc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)cc1)C2=O,,0.40157319,,,
+562176358,*CCCCSS*,,0.4654699,0.175,,
+563610141,*OC(=O)OCC(OC)C(COC(=O)OC1COC2C(*)COC12)OC,,0.32510823,,,
+563697228,*CC(*)OCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.36910342,,,
+563771441,*c1cc(C(=O)OC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.36013659,,,
+563896548,*c1cc(C(=O)OCCC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.36325394,,,
+564164424,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)cc1,,0.351749,,,
+564537590,*CC(*)(C)C(=O)OC1COC(CP(=O)(OCC)OCC)OC1,,0.34922258,,,
+564783421,*c1cc(C(c2ccc(OCCN(c3ccccc3)c3ccc(C=CC4=CC(=C(C#N)C#N)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(c1ccccc1)c1ccc(C=CC2=CC(=C(C#N)C#N)CC(C)(C)C2)cc1,,0.38518834,,,
+564916558,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(OCCCCCC)cc2)cc1,,0.35590587,,,
+565093443,*c1cc(C(c2ccc(OCCN(C)c3ccc(C=CC4=CC(=C(C#N)C#N)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(C)c1ccc(C=CC2=CC(=C(C#N)C#N)CC(C)(C)C2)cc1,,0.3816004,,,
+565283885,*Nc1ccc(-c2cccc(C)c2-c2ccc(N*)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)c(-c2ccc(C)cc2)c1-c1ccc(C)cc1,,0.38573102,,,
+565371765,*c1ccc2c(c1)C(=O)N(C1CC(C)(C)CC(C)(CN3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)C1)C2=O,,0.37756534,,,
+565873116,*CC(*)(C)C(=O)O[Si](C)(C)C,,0.42890458,,,
+566342268,*Oc1ccc(NC(=O)c2ccc(-c3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(C)c6ccc(*)cc6)cc5)cc4)c(C)c3)cc2C)cc1,,0.36556689,,,
+566400652,*CC(*)(Cl)C(=O)OC(C)C,,,0.1465,1.054014289,15.80245006
+566589613,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(C4(*)c5ccccc5Oc5ccccc54)cc3)cc2)cc1,,0.3739272,,,
+567353481,*c1ccc(C(c2ccc(N3C(=O)c4ccc(Oc5cccc6c(Oc7ccc8c(c7)C(=O)N(*)C8=O)cccc56)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.38375006,,,
+567577514,*CC(*)OC(=O)OCC(Cl)(Cl)Cl,,0.35918927,,,
+567731832,*Cc1cc2cc(C(=O)Nc3ccc(Cc4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.32474874,,,
+567984921,*c1ccc(Oc2ccc(NC(=O)c3ccc(Oc4cccc5c4C(=O)N(*)C5=O)cc3)cc2)cc1,,0.35386681,,,
+567993142,*Nc1ccc(/C=C/c2ccc(N*)cc2S(=O)(=O)O)c(S(=O)(=O)O)c1,,0.32549076,,,
+568593240,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)c(OC)c5)cc3OC)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35923607,,,
+569949305,*CCCCCCCCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.35472454,,,
+570358353,*c1ccc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(c4ccc6[nH]c(*)nc6c4)C5=O)cc3C2=O)cc1,,0.39933775,,,
+571029938,*CCCCCCCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.38002887,,0.937930772,19.25364485
+571882834,*CCCCCCCCCCSSCCCCCCSS*,,,0.2295,1.014541659,21.63979729
+571931187,*Oc1ccc(C(C)(CC)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.3617297,,,
+572496153,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc([Si](C)(C)c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.37212317,,,
+572616024,*OC1CCC(OC(=O)OCCCCOC(*)=O)CC1,,0.33295317,,,
+573332233,*Nc1c(-c2ccccc2)cc(C(CC)c2cc(-c3ccccc3)c(N*)c3ccccc23)c2ccccc12,,0.37493532,,,
+573587884,*NNC(=O)CCCCC(=O)NNC(=O)CCCCCCCC(*)=O,101.0022623,,,,
+573601315,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(CC(*)=O)cc3)cc2)cc1,,0.36097789,,,
+573843297,*Nc1ccccc1C(C)(c1ccccc1)c1ccccc1N*,,0.3501796,,,
+574000870,*CC(*)(C)C(=O)OCC(CC)CCCC,,0.38032702,0.1935,0.866688073,11.16194662
+574282474,*c1ccc2c(c1)C(=O)N(c1cccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.34319652,,,
+574709027,*C(=O)NCCCCCCNC(=O)c1ccc(*)nc1,,0.34059316,,,
+574749285,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,,0.37114599,,,
+574869526,*Cc1cc(C23CC4CC(CC(C4)C2)C3)cc(CN(*)C)c1O,,0.36943154,,,
+575252558,*Oc1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(OC(*)=O)c2)c1,,0.33862324,,,
+575704525,*CC(*)c1ccc(C(=O)OCCCC)cc1,,0.37961206,,,
+575774487,*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.3578244699999999,,,
+575975970,*CC(OC(C)=O)C(C(=O)OCCCC)C(*)C(=O)OCCCC,,0.36399896,,,
+576074717,*CC(C)CC(*)(C)C(=O)OC,,0.34800535,,,
+576198194,*CC(*)C(CC)CC,38.96888215,,0.1883333333333333,,
+576207142,*C(=O)Nc1ccc(Cc2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.35705225,,,
+576510280,*c1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(-c4nnc(*)o4)cc3)c(Oc3ccccc3)c2)cc1,,0.37065873,,,
+576593207,*CCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.33250755,,,
+576673479,*OC(C)COC(=O)/C=C\C(*)=O,,0.31850118,,,
+577459185,*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2CC=C)cc1,,0.3491245,,,
+578229955,*C(=O)NCCCCCCNC(=O)c1ccc(*)s1,42.90894132,,,,
+578399710,*c1ccc(Nc2c(F)c(F)c(-c3c(F)c(F)c(Nc4ccc(S(*)(=O)=O)cc4)c(F)c3F)c(F)c2F)cc1,,0.38004082,,,
+578503320,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.36351703,,,
+578621214,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc12,,0.35811295,,,
+579082920,*Oc1ccc(N=CC=Nc2ccc(OC(=O)Nc3cc(NC(*)=O)ccc3C)cc2)cc1,,0.35139659,,,
+579371916,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(C)CC2)cc1,,0.35879292,,,
+579657278,*Nc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(NC(=S)NC(=O)c4ccc(C(=O)NC(*)=S)cc4)c3)cc2)c1,,0.34165595,,,
+579662449,*C(=O)Nc1ccc(NC(=O)c2ccc(NC(=O)c3ccc(-c4nc5cc(*)ccc5[nH]4)cc3)cc2)cc1,,0.355364,,,
+579955066,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)cc1,,0.39692359,,,
+580099798,*CC(*)C(=O)Oc1ccc([N+](=O)[O-])cc1,,0.38054744,,,
+580261809,*C=C[Si](*)(c1ccccc1)c1ccccc1,,0.39366166,,,
+580635980,*Oc1ccc(NC(=O)CCCCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.34425173,,,
+580687337,*CC(O)COC(=O)CCCCC(=O)OCC(O)COc1ccc(C=C(C)c2ccc(O*)cc2)cc1,,0.34074081,,,
+581074933,*Nc1ccc2c(c1)C(Cc1ccccc1)c1cc(N*)ccc1-2,,0.35149388,,,
+581609876,*c1ccc(Oc2ccc(-c3cnc4cc5nc(*)cnc5cc4n3)cc2)cc1,,0.4131264,,,
+581660606,*CC(*)(C)C(=O)OCCCCCOc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.33969728,,,
+582946121,*CC(*)(C)C(=O)OCCCCn1c2ccccc2c2ccccc21,,0.3510259,0.1694999999999999,1.063120281,11.45732375
+583194891,*O[Si](*)(C)CCCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2C)cc1,,0.37464171,,,
+583338677,*CC(*)(CO)C(=O)OCC,22.36004964,,,,
+584085881,*Cc1ccc(C(C)c2ccc(CN3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1,,0.35279512,,,
+584252027,*CCCCCCCCCCOC(=O)O*,,0.36904474,,,
+584265872,*CCN(*)C(=O)c1ccc(F)cc1F,,0.34618853,,,
+584442637,*CC(OC)C(C(=O)O)C(*)C(=O)O,,0.27178974,,,
+584754403,*CC(CO*)(CS(C)(=O)=O)CS(C)(=O)=O,,0.37089814,,,
+584859367,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(C(C)(C)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.39689064,,,
+585087137,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.33992756,,,
+585151885,*CCC(=O)Nc1ccc(Oc2cccc(NC(=O)CCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.33304131,,,
+585470216,*C(=O)OCCOC(=O)c1ccc(*)o1,,0.34230194,,,
+586106490,*C=CCCCCCCC(Cl)CCCCCC*,,0.40278252,,,
+586189423,*CC(CSCC)O*,,0.41785443,,,
+587160342,*Oc1ccc(S(=O)(=O)c2ccc(Oc3cc(C)c(C(c4cc(C(C)C)c(*)cc4C)c4ccccc4C(=O)O)cc3C(C)C)cc2)cc1,,0.39980521,,,
+587553432,*Oc1ccc(-c2ccc(NC(=O)c3cc(C(=O)Nc4ccc(-c5ccc(Oc6ccc(C(c7ccc(*)cc7)(C(F)(F)F)C(F)(F)F)cc6)c(C(F)(F)F)c5)cc4)cc(C(C)(C)C)c3)cc2)cc1C(F)(F)F,,0.37054632,,,
+588836303,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(-c4ccccc4)n3)c1)C2=O,,0.37465693,,,
+589424672,*Nc1ccc(-c2ccc(N*)c(C(=C)c3ccccc3N)c2C(=C)c2ccccc2N)cc1,,0.36948301,,,
+589547196,*Nc1ccc2c(c1N*)[C@](C)(c1ccccc1)CC2(C)C,,0.36474877,,,
+589661282,*Nc1ccc(-c2ccc(N*)c3c4ccc(cc4)c23)cc1,,0.34352989,,,
+589948553,*C1C(=O)N(C(C)(C)C)C(=O)C1*,,0.39509539,,,
+590016095,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C(C)(C)c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.37687293,,,
+590232504,*CC(*)(CC(=O)Oc1ccc(OCCCCC)cc1)C(=O)Oc1ccc(OCCCCC)cc1,,0.36823532,,,
+590474607,*Nc1ccccc1CCc1ccccc1NC(*)=O,207.655323,,,,
+590866562,*Oc1ccc(OC(=O)c2ccc(OCCCCCCOc3ccc(C(*)=O)cc3)cc2)cc1N=Nc1ccc(C#N)cc1,,0.35843895,,,
+590983717,*Cc1cccc(CNC(=O)c2cc(C(=O)N*)cc(C(C)(C)C)c2)c1,,0.3510241,,,
+591078724,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.37935493,,,
+591131573,*Nc1cccc(N*)c1,,0.3236545,,,
+591186983,*N=P(*)(Oc1cccc(C(F)(F)F)c1)Oc1cccc(C(F)(F)F)c1,,0.39150603,,,
+591253647,*c1cccc(-c2cc(C(=O)Nc3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc(-c3cccc(N4C(=O)CC(C5C=C(C)C6C(=O)N(*)C(=O)C6C5)C4=O)c3)c2)c1,,0.36971595,,,
+592007373,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C4(c5ccc(*)cc5)c5ccccc5-c5ccccc54)cc3)cc2)c2ccccc12,,0.4002848,,,
+592083344,*O[Si](*)(CCCOCCOCCOCCOC)CCCOCCOCCOCCOC,,0.36549731,,,
+592184260,*Cc1ccc(*)cc1,,,0.267,,
+592652494,*OCCOC(=O)c1ccc(C(=O)OCCOc2nc(-c3ccc(OCCCC)cc3)nc(*)c2-c2ccc(OC)cc2)cc1,,0.35798582,,,
+592669794,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(-c7cccc8c7C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.38179177,,,
+592686193,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c3C#N)cc1)C2=O,,0.37962339,,,
+593164560,*Cc1ccc(-c2ccc(CSC(=O)CCCCC(=O)S*)cc2)cc1,-14.32353196,,,,
+593363809,*C(F)(F)C(=O)NCCCCCCNS(*)(=O)=O,,0.33449777,,,
+593654363,*Nc1ccc(NC(=O)CCCCCCCC(*)=O)cc1,,0.34227902,,,
+594032602,*CC(COCC=C)S*,,0.39134513,,,
+594346248,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccccc8)[nH]c7*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38623156,,,
+594528040,*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.34941735,,,
+594735990,*CC(*)C(=O)OCCCCCCCCn1c2ccccc2c2ccccc21,,0.36038485,,,
+594803927,*OC(C)C(=O)OCC(*)=O,,0.31804756,,,
+595568506,*CCCCCCOC(=O)C(CCCCCCCCCCP(=O)(O)O)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.35805437,,,
+595580163,*CC(*)(C)C(N)=O,,0.25657854,0.145,1.041991601,16.36913311
+595611477,*CCCCCCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.36922015,,,
+595928255,*CC(*)CO,,0.29464648,,,
+595965977,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(OCc4ccccc4)c(*)cc3OCc3ccccc3)ccc1-2,,0.39814654,0.184,0.926806566,21.32352082
+596712589,*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.363877,,,
+597209826,*Oc1ccc(C(=O)NCC(C)(C)CCCNC(=O)c2ccc(*)cc2)cc1,,0.34465365,,,
+597331938,*OC(=O)c1ccc(C(=O)Oc2ccc3c(c2)Oc2cc(*)ccc2C32c3ccccc3-c3ccccc32)cc1,,0.38655989,,,
+597719463,*Oc1cccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)c1,,0.36602466,,,
+598302444,*CCSSCCOCO*,,0.39441419,,,
+598348393,*Nc1ccc(NC(=O)c2cc(NC(=O)c3ccccc3)cc(C(*)=O)c2)cc1,,0.33713633,,,
+598697113,*CCCCCCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.37037228,,0.934436591,17.25139441
+598835608,*CC(*)(C)C(=O)NC(=O)OC(C)COc1c(Br)cc(S(=O)(=O)c2cc(Br)c(OCC(C)O)c(Br)c2)cc1Br,,0.37079246,0.15,1.587307778,12.5994675
+599456393,*Cc1ccc(CNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*)cc1,,,0.359,,
+599659861,*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCC(=Cc5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1,,0.3667682,,,
+599773122,*C1CC(*)(C#N)C1,,0.41545048,,,
+599869327,*CC(c1ccccc1)C1C(=O)N(c2ccccc2)C(=O)C1*,,0.37701942,,,
+600169729,*C(=O)OCCCCCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.35218627,,,
+600653180,*c1ccc(Oc2cc3ccccc3cc2Oc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.36873488,,,
+601022774,*CCN(*)C(=O)c1ccc(F)cc1,,0.3464689,,,
+601302526,*NCCNCCN*,,0.31717722,,,
+601597696,*Oc1ccc(C(=O)NNC(=O)c2ccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)cc2Oc2ccccc2)cc1,,0.34244327,,,
+601947802,*NC(CCC(=O)OCCCC)C(*)=O,,0.34392678,,,
+602240929,*c1cccc(-c2cc(C(=O)Nc3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc(-c3cccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.3719363,,,
+602268285,*CCNC(=O)c1ccc(C(=O)NCCOc2ccc3ccc(O*)cc3c2)cc1,,0.32844046,,,
+602457926,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37554378,,,
+602531963,*CCCOC(=O)CCCCCCC(=O)O*,,0.34528198,,,
+603228174,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6c5)cc4C3=O)cc2)cc1,,0.36486297,,,
+603751130,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccc(N=Nc2ccc(F)cc2)cc1,,0.35292509,,,
+603788368,*c1cccc(C(=O)Nc2ccc(Oc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7cccc8c7C(=O)N(*)C8=O)cc6C5=O)c4)cc3)cc2)c1,,0.35298703,,,
+604020908,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(Oc7cccc8c7C(=O)N(*)C8=O)cc6)c5C4=O)cc3)c2)cc1,,0.35919355,,,
+604200639,*CC(*)C(=O)OCCN(CC)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2Cl)cc1,,0.36808555,,,
+604209273,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3cccc(-c4cccc5ccccc45)c23)c2c(-c3cccc4ccccc34)cccc12,,0.37421726,,,
+604265550,*CCOCCOC(=O)C1CCC(C(=O)O*)CC1,,0.33324128,,,
+604677160,*c1ccc(Oc2ccc3ccc(Oc4ccc(-c5nc(*)nc(-c6ccccc6)n5)cc4)cc3c2)cc1,,0.40348828,,,
+604776418,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc(C(*)=O)c2)cc1,,0.34786426,,,
+605651145,*c1ccc(-c2nc3cc(Oc4ccc(NC(=O)c5cc(C(=O)Nc6ccc(Oc7ccc8nc(-c9ccccc9)c(*)nc8c7)cc6)cc(N6C(=O)c7ccccc7C6=O)c5)cc4)ccc3nc2-c2ccccc2)cc1,,0.38397838,,,
+605771725,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc(C)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C,,0.37339461,,,
+605793172,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc1)C2=O,,0.36745826,,,
+605814238,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCC)cc1,,0.33942304,,,
+606001217,*CCCCCCCCCCNC(=O)CCCCCCCCCCCCC(=O)N*,,,0.342,0.911164148,18.99975515
+606308469,*CC(*)C(=O)OCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.3538219,,,
+606742148,*CCN*,,0.40888608,0.351,0.992409385,16.50971705
+607683819,*NCCOCCOCCN*,,0.31484972,,,
+607770016,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1,,0.36424669,,,
+607998522,*CCCCCCS(=O)(=O)CCCCCS(*)(=O)=O,,,,1.153509737,18.44065947
+608351613,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)C(C)C(C)C(*)=O,,0.33232876,,,
+608511939,*=Cc1ccc(C(=O)CCCCC(=O)c2ccc(C=C3CN(C)CC(=*)C3=O)o2)o1,95.14114513,,,,
+608604797,*Nc1ccc([C@H]2CCC[C@H](c3ccc(N*)cc3)C2)cc1,,0.35086316,,,
+609443471,*Nc1cc2cc(c1)N(CC)C(=O)c1cc(N)cc(c1)N(CC)C(=O)c1cc(N*)cc(c1)N(CC)C2=O,,0.35869989,,,
+609699139,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7ccc(-c8nc(*)c(-c9ccccc9)[nH]8)cc7)[nH]c6-c6ccccc6)cc5)cc4)c3)cc2)cc1,,0.39401881,,,
+610205689,*CC(*)c1ccccc1C(=O)OCC(C)C,,0.37502808,0.1696666666666666,,
+610301577,*CCOCCOCCOC(=O)CCCCCC(=O)O*,,0.33929278,,,
+610345428,*CCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.35738719,,,
+610467941,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc(NC(=O)c6cc(C(=O)Nc7ccc(Oc8ccc9nc(-c%10ccccc%10)c(*)nc9c8)cc7)cc(N7C(=O)c8ccccc8C7=O)c6)cc5)ccc4nc3-c3ccccc3)cc2)cc1,,0.37759682,,,
+611038154,*OC1C(CO)OC(*)C(NC(C)=O)C1O,,0.3018663,,,
+611092350,*Nc1ccc2c(c1N*)C(C)(C)C(C)(C)C2(C)C,,0.36414557,,,
+611474327,*CC(*)c1cc(C(F)(F)F)ccc1C(F)(F)F,,0.37028401,,,
+611875813,*c1cc(-c2ccc(C(=C3C=C(C(C)(C)C)C(=O)C(C(C)(C)C)=C3)c3cc(C(C)(C)C)c(O)c(C(C)(C)C)c3)cc2)c(*)s1,,0.4542834,,,
+612139638,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(C=Cc2cc[n+](CCCC)cc2)cc1,,0.35814643,,,
+612238315,*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCCC(=Cc5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1,,0.36489082,,,
+612321381,*Oc1ccc(C=CC(=O)c2ccc(Oc3ccc(C(C)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.37609357,,,
+613718745,*CC(*)C(=O)NCCC[N+](C)(C)CCCS(=O)(=O)O,,0.4782126,,,
+614253049,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.34821284,,,
+614398604,*CCCCCCNC(=O)CCCCCCC(=O)N*,,0.34822579,0.345,,
+615042961,*c1ccc(Oc2cccc3c(NC(=O)c4ccc(Oc5ccc(C(=O)Nc6cccc7c(Oc8ccc(-c9nnc(*)o9)cc8)cccc67)cc5)cc4)cccc23)cc1,,0.36893685,,,
+615903439,*CCc1ccc(*)o1,,0.3766254,0.2784999999999999,1.10299243,21.29011365
+616073908,*CC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CN4C(=O)c5ccc(C(c6ccc7c(c6)C(=O)N(*)C7=O)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc2)cc1,,0.34249238,,,
+616177559,*Sc1ccc(*)cc1,64.586946,,,1.231097312,21.1652389
+616276017,*c1ccc(NC2CC(=O)N(c3ccc(-c4sc(-c5ccc(N6C(=O)CC(Nc7ccc(-c8nc(-c9ccc(C=Cc%10ccc([N+](=O)[O-])cc%10)s9)[nH]c8*)cc7)C6=O)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)C2=O)cc1,,0.39689813,,,
+616326539,*CC(*)n1cc2ccccc2n1,,0.37138173,0.2075,1.124957708,13.9727997
+616797364,*NC1=CC(CC)=C(N*)[C@H]2C(=O)c3c(N)ccc(N)c3C(=O)[C@@H]12,,0.33783648,,,
+616888672,*CC(*)(C)C(=O)OCC#CO[Si](C)(C)C,,0.41218435,,,
+617569770,*Oc1ccc(C=Nc2ccc(N=Cc3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.39135122,,,
+617741611,*C(=O)c1ccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(C(=O)c4ccc(C(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc2)C3=O)cc1,,0.37260592,,,
+617827761,*C(=O)Nc1cccc(NC(=O)c2cccc(NC(=O)c3ccc(-c4nc5cc(*)ccc5[nH]4)cc3)c2)c1,,0.35742387,,,
+618105578,*c1cc(CC(=O)OCCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)c(*)s1,,0.37112026,,,
+618173505,*CC(*)c1ccc(F)cc1C,,,0.1413333333333333,0.994546299,15.55720188
+618916633,*Oc1ccc(NC(=O)CCCCC(=O)Nc2ccc(OC3COC4C(*)COC34)cc2)cc1,,0.33012557,,,
+619288860,*c1cccc(N2C(=O)c3cccc(Oc4ccc(Sc5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.36869837,,,
+619459444,*Nc1cc(NC(=O)CCCCC(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1,,0.35404898,,,
+619582212,*Oc1ccc(N=CCCCC=Nc2ccc(OC(=O)NCCCCCCNC(*)=O)cc2)cc1,,0.34722952,,,
+620886467,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.36486218,,,
+621166590,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(NC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5C4=O)cc3)n2)cc1,,0.35975912,,,
+621229234,*N=Nc1ccc(Nc2ccc(C(=O)c3ccc(Nc4ccc(*)cc4)cc3)cc2)cc1,,0.39319261,,,
+621730557,*c1cc(Br)c(Oc2c(Br)cc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2Br)c(Br)c1,,0.43253856,,,
+622020530,*C1C2CC(CCCCCCCCC(=O)OCC)C(C2)C1*,,0.36983985,,,
+622295554,*CCCCCCCCC(*)Cl,,0.40010889,,,
+622583757,*NC1CCC(CC2CCC(NC(=O)CCCCCCCCCCCCC(*)=O)CC2)CC1,,0.36779404,,,
+622739506,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.376155,,,
+622744419,*C=Cc1cc(OCC(CC)CCCC)c(*)cc1OC,,0.39086339,,,
+622964705,*CCCOC(=O)CCCCCCCCCCCCCCCCC(=O)O*,,,0.364,0.922711633,21.46640792
+623331916,*CCCCCCOC(=O)C(CCCCCCOc1ccc(-c2ccc(OCc3ccc([N+](=O)[O-])cc3)cc2)cc1)C(=O)O*,17.95743357,,,,
+623402372,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35685059,,,
+623537081,*OP(=O)(Oc1ccc(NC(=O)Oc2ccc(C(C)(C)c3ccc(OC(=O)Nc4ccc(*)cc4)cc3)cc2)cc1)c1ccccc1,,0.35222708,,,
+624096781,*Nc1cc2c(cc1N*)[C@H]1c3ccccc3[C@@H]2c2cc(N)c(N)cc21,,0.38373071,,,
+624193573,*CC(*)C(=O)OCCC#N,,0.35583972,0.195,1.065811773,14.52014103
+624503496,*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCCCCCC(*)=O)cc1C=Cc1ccc(N(C)C)cc1,,0.35550969,,,
+624653441,*CCCCCCCCCCOc1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)c(-c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(-c6c(-c7ccc(-c8ccccc8)cc7)cc(-c7ccc(O*)cc7)cc6-c6ccc(-c7ccccc7)cc6)cc5)cc4)cc3)c(-c3ccc(-c4ccccc4)cc3)c2)cc1,,0.36612407,,,
+624903152,*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)c2)cc1,,0.3840852,,,
+625017301,*CCCNC(=O)CCCCC(=O)NCCCN(*)C,,0.34164847,,,
+625314216,*c1ccc(OC(=O)Nc2ccc(-c3ccc(NC(=O)Oc4ccc(-c5nc(-c6ccc(-c7nc(-c8ccc([N+](=O)[O-])cc8)c(-c8ccc([N+](=O)[O-])cc8)[nH]7)cc6)[nH]c5*)cc4)cc3OC)c(OC)c2)cc1,,0.37903045,,,
+625438419,*CCC1CC(=O)N(*)C1=O,,0.31621723,,,
+625535971,*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)c2)cc1,,0.3509688,,,
+626242594,*Nc1ccc(C2(c3ccc(NC(=O)c4ccc(C(*)=O)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.37325981,,,
+626677373,*Nc1ccc2c(C)cc(N*)c(C)c2c1,,0.3446566,,,
+626841881,*Nc1cc2c(cc1N*)C1(c3ccccc3-c3ccccc31)c1cc(N)c(N)cc1-2,,0.38997416,,,
+627271746,*c1ccc(Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2Br)c(Br)c1,,0.40757312,,,
+627600990,*CCCCCCCCCCOCO*,,,0.297,,
+627652902,*C(=O)C1C2C=CC(C2)C1*,,0.34482539,,,
+628380881,*C(=O)Nc1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(NC(=O)c5ccc(*)nc5)cc4)cc3)cc2)cc1,,0.35989736,,,
+628657458,*c1ccc2c3ccc(-c4ccc(-c5ccc(-c6ccc(*)s6)c6nsnc56)s4)cc3n(C(CCCCCCCC)CCCCCCCC)c2c1,,0.42868905,,,
+628680153,*CC(*)(C)C(=O)OCCCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.36048254,,,
+629088925,*Nc1ccc(-c2ccc(NC(=O)c3cc(NC(=O)c4ccc(NC(=O)C(C)N5C(=O)c6ccccc6C5=O)cc4)cc(C(*)=O)c3)cc2)cc1,,0.33619542,,,
+630629376,*Oc1ccc(Cc2ccc(OC(=O)OCC3CCC(COC(*)=O)CC3)cc2)cc1,,0.35373109,,,
+630773371,*OP(=O)(Oc1ccccc1)Oc1c(Br)cc(C(c2cc(Br)c(*)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,,0.39155263,,,
+630928117,*CC(*)(C)C(=O)OCc1c(F)cccc1F,,0.33933326,,,
+631598953,*CCOCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.33590503,,,
+631618960,*CNC(=O)OCC(OC(=O)NCc1ccc(C(CCC)c2ccc(*)o2)o1)c1ccco1,,0.35326235,,,
+631651015,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)CCCCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,,0.33693236,,,
+633021524,*N=P(*)(OCCCCCCCC)OCCCCCCCC,,0.41038222,,,
+633348725,*CC(CS(=O)(=O)CCCCC)O*,,0.39727249,,,
+633555318,*CCCCCCCCc1nc2cc(NC(=O)CCCCCCCCC(=O)Nc3ccc4oc(*)nc4c3)ccc2o1,,0.35982282,,,
+633682320,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(N=Nc3ccccc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccc(N=Nc2ccccc2)cc1,,0.36198377,,,
+633891368,*CCN(CCOC(=O)CCCCC(=O)O*)c1ccc(C=NN(c2ccccc2)c2ccccc2)cc1,,0.36249532,,,
+634005101,*OC(=O)c1cccc(C(*)=O)c1,,0.31383194,,,
+634098628,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(C(=O)c3ccccc3)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37719699,,,
+634363683,*c1ccc2[nH]c3ccc(*)cc3c2c1,246.5898577,,,,
+634587841,*CCCCCCOc1ccc(C(=O)N(C(=O)c2ccc(O*)cc2)c2ccccc2)cc1,,0.36864893,,,
+634645972,*CCC1C(=O)N(CCCCCCCCCCCCCCCCCC)C(=O)C1*,,0.39298677,,,
+635174783,*C(=O)Nc1c(C)cc(C)c(NC(=O)c2ccc3c(c2)C(=O)N(c2c(C)cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c2C)C3=O)c1C,,0.38812467,,,
+635384570,*OCCOC(=O)CCCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1,,0.35402393,,,
+635778401,*C(=O)Nc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36004645,,,
+636010546,*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(O)c3)(C(F)(F)F)C(F)(F)F)cc1O)C2=O,,0.3866973,,,
+636356107,*C(F)(F)C(*)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.37519669,,,
+636526191,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1OC,,0.34293786,,,
+636697748,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(-c3nc4ccccc4o3)cc2)cc1,,0.35811225,,,
+636769312,*Oc1ccc(OC(C)COCCCCCCCOc2ccc(-c3ccc(C(*)=O)cc3)cc2)c([N+](=O)[O-])c1,,0.34838594,,,
+636957306,*Nc1ccc(C(c2ccc(C)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.37785275,,,
+637091082,*Nc1ccccc1CCc1ccccc1N*,,0.338535005,,,
+637512395,*c1ccc(-c2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)c(C(C)(C)C)c2)cc1,,0.40636639,,,
+637669265,*CC(*)C(=O)NCCC[N+](C)(C)C,,0.68705709,,,
+638093915,*Nc1ccc(Cc2ccc(CCCc3ccc(Cc4ccc(N*)cc4)cc3C)c(C)c2)cc1,,0.35703055,,,
+638503428,*CCCCCCCCCCOC(=O)CCCCCCCC(=O)O*,,,0.3325,0.932306919,23.93933283
+638750992,*CCOP(=O)(N=Nc1ccc(COC(=O)c2cc(C(=O)OCc3ccc(N=NP(=O)(O*)OC)cc3)cc(C(C)(C)C)c2)cc1)OC,,0.35548,,,
+638889974,*Nc1ccc(-c2ccc(N*)cc2Cc2ccc(C)cc2)c(Cc2ccc(C)cc2)c1,,0.35865855,,,
+639451609,*CC(*)(C)C(=O)OCc1c(Cl)cccc1Cl,,0.35787475,,,
+639553637,*Nc1cccc(-c2ccc(Cc3ccc(-c4cccc(N*)c4)cc3)cc2)c1,,0.34598803,,,
+639559230,*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)c2)cc1,,0.3548653,,,
+639745269,*Nc1ccc(-c2ccc(N*)cc2C)c(C)c1,,0.357459635,,,
+639748056,*C(=S)Nc1ccc(Oc2ccc(NC(=S)N3C(=O)c4ccc(C(c5ccc6c(c5)C(=O)N(*)C6=O)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc2)cc1,,0.3660155,,,
+639770063,*CC(*)(C)C(=O)OCCC1CC[N+](C)(CCCCCCOc2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)CC1,,0.36107045,,,
+639796745,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5ccc(NC(=O)Nc6ccc(Cc7ccc(NC(=O)Nc8ccc(C(=O)Nc9ccc(Oc%10ccc(NC(=O)c%11ccc(N%12C(=O)c%13ccc(*)cc%13C%12=O)cc%11)cc%10)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc1)C2=O,,0.34916316,,,
+639845225,*c1ccc(-c2ccc3nc(Oc4cc(-c5ccccc5)c5cc(*)ccc5n4)cc(-c4ccccc4)c3c2)cc1,,0.39441053,,,
+639994792,*OC(C)C(*)=O,,0.34796841,,,
+640271931,*c1ccc(Oc2ccc(-c3c(-c4ccccc4)nc4ccc(Oc5ccc6nc(-c7ccccc7)c(*)c(-c7ccccc7)c6c5)cc4c3-c3ccccc3)cc2)cc1,,0.39811449,,,
+640368237,*C=C(C#N)c1ccc(-c2ccc(C=CC3=CC(=C(C#N)C#N)C=C(C=Cc4ccc(-c5ccc(C(C#N)=Cc6cc(C=C(C#N)c7ccc(-c8ccc(C=CC9=CC(=C(C#N)C#N)C=C(C=Cc%10ccc(-c%11ccc(C(C#N)=C*)cc%11)s%10)O9)s8)cc7)c7c(c6)C(CC)(CC)c6cc(*)ccc6-7)cc5)s4)O3)s2)cc1,,0.40990352,,,
+640431640,*OS(=O)(=O)c1ccc(-c2ccc(S(=O)(=O)Oc3c(Br)cc(C4(c5cc(Br)c(*)c(Br)c5)CCCCC4)cc3Br)cc2)cc1,268.4647521,,,,
+640690307,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cccc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)c1)C2=O,,0.38683576,,,
+641501724,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCC,,0.35299755,,,
+642211921,*NC[C@H]1CC[C@H](CN*)CC1,,0.33898524,,,
+642330071,*Oc1ccc(-c2ccc(OC(=O)c3cc(N=Nc4ccc(OCC)cc4)ccc3-c3ccc(N=Nc4ccc(OCC)cc4)cc3C(*)=O)cc2)cc1,,0.37270172,,,
+642473337,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c1)C2=O,,0.35936185,,,
+642535209,*CCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35003142,,,
+642639550,*c1nc(C)nc(N(CCCCN(*)c2ccccc2)c2ccccc2)n1,,0.39040149,,,
+642668949,*Oc1cccc(NC(=O)c2cccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)c2)c1,,0.35172352,,,
+642673445,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)CCCCCCC(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.33977526,,,
+643189465,*Nc1c(-c2ccccc2)cc(-c2ccc(-c3cc(-c4ccccc4)c(NC(=O)c4ccc(C(*)=O)cc4)c(-c4ccccc4)c3)cc2)cc1-c1ccccc1,,0.38237528,,,
+643385335,*CC(*)OC(F)(C(F)(F)F)C(F)(F)F,,0.33430951,,,
+643400948,*O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.42793636,,,
+643664826,*CCC(C)CCOC(=O)CCCCC(=O)O*,,0.34352817,,,
+644108438,*CC(OC(=O)OCC)C(COC(=O)O*)OC(=O)OCC,,0.32380857,,,
+644505079,*Oc1ccc(S(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.37231355,,,
+644711726,*CC(*)C(=O)Oc1c(F)c(F)c(F)c(F)c1F,,0.33973517,,,
+644898772,*Nc1ccc(CC(c2ccc(N)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36495838,,,
+645076897,*NC(=O)c1cc(C(=O)Nc2nc(*)nc(-c3ccccc3)n2)c(C(=O)OC(=O)Nc2cc(NC(=O)OCCCCCCCC)ccc2C)cc1C(=O)O,-1.691479041,,,,
+645165602,*CC(*)(C)C(=O)OCCCCCCCCCCCCCC,,0.38812321,0.3115,0.862307366,11.14771595
+645691711,*CCCCCCCCCCOC(=O)c1ccc(C(=O)NCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,76.72929425,,,,
+646292703,*c1ccc(-c2ccc(-c3ccc(-c4ccc(*)n4CCCCCCCCCCCC)s3)s2)s1,,0.41937735,,,
+646573750,*Oc1ccc(C(C)=Nc2ccc(N=C(C)c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38124481,,,
+646596041,*Nc1cc(C2(c3ccc(O)c(N*)c3)c3ccccc3-c3ccccc32)ccc1O,,0.3549773,,,
+646682941,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O,,0.36114859,,,
+647076607,*CC(O)CN(C)S(=O)(=O)c1cccc(S(=O)(=O)N(C)CC(O)COc2ccc(S(=O)(=O)c3ccc(O*)cc3)cc2)c1,,0.33964291,,,
+647273188,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCCCC)cc1,,0.37509747,,,
+647435977,*C(=O)NCCCCCCNC(=O)c1ncn(C)c1*,,0.33524849,,,
+647484326,*CCCCCCOC(=O)c1cc(OCCCCCCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)cc(C(=O)OCCCCCCN2C(=O)c3ccc(S(=O)(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.35510347,,,
+648079347,*CC1CCCC(CN2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)C1,,0.35269829,,,
+648145091,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2)cc1,,0.35833955,,,
+648159856,*Nc1ccc2c(c1)C1(c3ccccc3-c3ccccc31)c1c-2cc(C(C)(C)C)c(N*)c1C(C)(C)C,,0.39847901,,,
+648267860,*CCOCCOCCOC(=O)NCCCCCCNC(=O)O*,,0.33323045,,,
+648602021,*c1ccc(Nc2ccc(C3(c4ccc(Nc5ccc(S(*)(=O)=O)cc5)c(F)c4)c4ccccc4-c4ccccc43)cc2F)cc1,,0.40886141,,,
+648833303,*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.34689579,,,
+649508995,*CCCCCOC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.31700681,,,
+649600195,*CCCCCCCCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.35477028,,,
+650082699,*Oc1c(C)cc(*)cc1C(C)CCCCCC,,0.41283959,,,
+650711958,*CC(CO*)(CSCC)CSCC,,0.41686273,,,
+651742804,*CC(=O)NCCCCCCNC(=O)COC(=O)CCCCC(=O)O*,,0.32189128,,,
+651985897,*OS(=O)(=O)c1cc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCCC3)cc2)ccc1C,,0.36712523,,,
+652006110,*Nc1cc(N*)c2ccc3cccc4ccc1c2c43,,0.33628882,,,
+652579546,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)n3)cc2)cc1,,0.35683183,,,
+652611709,*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(Oc4ccc(C5(c6ccc(*)cc6)CC6CCC5C6)cc4)cc3)c2)cc1,,0.36723415,,,
+652803633,*Nc1ccc(/C=C/c2c3ccccc3c(/C=C/c3ccc(N*)cc3)c3ccccc23)cc1,,0.3630577,,,
+652821562,*CCCCCCCCCOC(=O)CCC(=O)O*,,0.3543665,,,
+654743416,*C(=O)Nc1ccc(-c2ccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)cc2)cc1,,0.35106689,,,
+655941482,*Oc1ccc(OC(=O)c2ccc(C(C)(C)c3ccc(C(*)=O)cc3)cc2)cc1,,0.35709066,,,
+656702107,*Oc1ccc(P(C)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.37833818,,,
+657146499,*CCCCCCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.35494054,,,
+657445738,*CCOCCOCCOCCOCCOCCOC(=O)c1cc(OCCCCCCOc2ccc(-c3ccc(OC)cc3)cc2)cc(C(=O)O*)c1,,0.34770487,,,
+658598412,*N=P(*)(C)C,,0.35255216,,,
+658967063,*C(=O)NCCN(CCNC(=O)c1cccc(N2C(=O)c3ccc(*)cc3C2=O)c1)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34009397,,,
+659061902,*C/C=C\C[Si](*)(C)c1ccccc1,,0.37739733,,,
+659538579,*Nc1cc(-c2ccccc2N*)cc(-c2cc3cc4ccccc4cc3c3cc4ccccc4cc23)c1,,0.36115778,,,
+659864655,*CCCCCCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.35035919,,,
+660205165,*Nc1ccc2c(c1-c1c(N*)ccc3c1CCCC3)CCCC2,,0.36667006,,,
+660253930,*c1ccc(-c2sc(-c3ccc(-c4cc(CCCC)c(*)s4)cc3)cc2CCCC)cc1,,0.50077803,,,
+660384405,*Nc1ccc(-c2ccc(C3(c4ccc(-c5ccc(N*)cc5)cc4)CCCCC3)cc2)cc1,,0.36004409,,,
+660515467,*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)(C)C)c3)cc1C(C)(C)C)C2=O,,0.41254221,,,
+661619581,*C=Cc1sc(-c2ccc(-c3ccc(-c4sc(C=CC5=CC(=C(C#N)C#N)C=C(*)O5)c(CCCCCC)c4CCCCCC)s3)s2)c(CCCCCC)c1CCCCCC,,0.42318746,,,
+661822184,*CC(=O)C(*)c1ccccc1,,0.36460846,,,
+661865874,*C(C)=C(*)[Si](C)(C)CC[Si](C)(C)C,,0.4237153,,,
+662156764,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35745831,,,
+662815164,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1-c1cccc(C)c1,,0.38304917,,,
+663037039,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc4c(=O)n5c6cc(Oc7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.38434287,,,
+663206655,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1-c1cc(C)cc(C)c1,,0.38469279,,,
+663317313,*c1sc(*)c(OCCCCCCC)c1C,32.04437108,,,,
+663564627,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c(OCCCCCCS(=O)(=O)c2ccc(C=Cc3ccc(N(C)C)cc3)cc2)c1,,0.3564308,,,
+663877510,*c1sc(*)c(OCCCCCCCCCCCCCC)c1C,,,0.43675,0.944296325,14.84412514
+663963917,*C(=O)NNC(=O)c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(C(=O)NNC(=O)c3ccc(*)o3)cc2)cc1,,0.37010873,,,
+664013994,*NCCc1c2ccccc2c(N*)c2ccccc12,,0.33811641,,,
+664173456,*CC(*)(C)C(=O)OCC(=O)Nc1cc(C)on1,,0.3312576,,,
+664256549,*Nc1ccc(C2(c3ccc(N*)cc3)CCC([C@H]3CC[C@H](C)CC3)CC2)cc1,,0.36506372,,,
+664408041,*C(=O)Nc1cccc(NC(=O)c2ccc3[nH]c(-c4cc(-c5nc6cc(*)ccc6[nH]5)cc(N5C(=O)c6ccccc6C5=O)c4)nc3c2)c1,,0.36836215,,,
+664447199,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23,,0.41112209,,,
+664683374,*Nc1ccc2cc3cc4cc5ccccc5cc4cc3cc2c1N*,,0.34387937,,,
+664875746,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(OCCCCCCCC)cc2)cc1,,0.3596616,,,
+664999383,*CCCCCCCCCCCCOc1ccc(C(C)Cc2ccc(-c3ccc(O*)cc3)cc2)cc1,,0.38201502,,,
+665327981,*C=C(*)C(CCCCC)[Si](C)(C)C,,0.42424985,,,
+665803754,*CCCCCCCNC(=O)N*,,0.34708922,,,
+666199912,*C([2H])([2H])C(*)([2H])C([2H])([2H])[2H],,0.3861434,,,
+666249832,*CC=CC[Si](*)(C=C)C=C,,0.40464632,,,
+666714004,*Oc1c(C)cc(-c2cc(C)c(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)c(C)c2)cc1C,,0.40345957,,,
+666813875,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(-c2nc3ccc([N+](=O)[O-])cc3o2)cc1,,0.34475093,,,
+666970760,*C(=O)Nc1ccc(NC(=O)c2ccc3[nH]c(-c4cccc(-c5nc6cc(*)ccc6[nH]5)c4)nc3c2)cc1,309.3374253,,,,
+667460496,*CCCCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.34741734,,,
+667477556,*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)c3cc(C)ccc23)cc1,,0.36429835,,,
+667584010,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(Sc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)cc2)cc1,,0.36605169,,,
+667591489,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.34402774,,,
+667840086,*CCCCCCCCCCOC(=O)CCCCCNC(=O)CCCCC(=O)NCCCCCC(=O)O*,,0.35533448,0.2955,0.988248519,19.25074729
+668141599,*Oc1ccc(OC(=O)Oc2ccc(C(*)=O)cc2)cc1,,0.33148318,,,
+668621779,*Cc1cccc(CNC(=O)CCC2(CCC(=O)N*)c3ccccc3-c3ccccc32)c1,,0.34266557,,,
+668704885,*c1ccc(Oc2ccc(-c3c(-c4ccccc4)c4c(c(-c5ccccc5)c3-c3ccccc3)C(=O)N(c3ccc(N5C(=O)c6c(c(-c7ccccc7)c(-c7ccccc7)c(*)c6-c6ccccc6)C5=O)cc3)C4=O)cc2)cc1,,0.41683897,,,
+668824734,*CC(C)(CO)CO*,,0.33612908,,,
+668903098,*C1CCC(CC2CCC(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C(C)C2)CC1C,,0.37632492,,,
+669620499,*CCSCc1ccc(Cc2ccc(CSCCOC(=O)CSCc3ccc(Cc4ccc(CSCC(=O)O*)cc4)cc3)cc2)cc1,,0.36564834,,,
+669636470,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccccc7Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36159124,,,
+669639800,*NC1CCC(CC2CCC(NC(=O)CCCCC(*)=O)CC2)CC1,,0.35191825,,,
+670004959,*OC(COC(=O)c1ccc(C(*)=O)c(OC)c1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35609742,,,
+670279906,*C(=O)Nc1ccc(Oc2cc(C(C)(C)C)c(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)CC8CC7C7CCCC87)cc6)cc4)C5=O)cc3)cc2C(C)(C)C)cc1,,0.38634779,,,
+670455816,*CCOCO*,,0.34034968,,,
+670472475,*c1cccc(N2C(=O)c3ccc(Oc4cc5ccccc5cc4Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37256174,,,
+670589484,*Nc1ccc2cccc(-c3cccc4ccc(N*)cc34)c2c1,,0.35323413,,,
+670939419,*CCc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(CCOC(=O)c4cccc(C(=O)O*)c4)cc3)c2)cc1,,0.34340117,,,
+671093250,*c1ccc(Oc2ccc(C(C)(c3ccccc3)c3ccc(Oc4ccc(-c5nc(-c6ccccn6)nnc5*)cc4)cc3)cc2)cc1,,0.39778842,,,
+671188865,*CC(*)(C)C(=O)OCc1ccc(C=CC(=O)Oc2ccc(OC)cc2)cc1,,0.33755795,,,
+671342841,*CCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.35113383,,,
+671423635,*CC(*)(C)C(=O)OCCCCCCn1c2ccccc2c2ccccc21,,0.35236027,,,
+671507896,*OC(CCCCC)CC(*)=O,,0.36904255,,,
+671591388,*N/C1=N/OC(O)(O)O/N=C(/N*)C/C(N)=N/OC(O)(O)O/N=C(\N)C1,,0.28254764,,,
+671751803,*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)C(=O)N(N3C(=O)c5ccc(*)cc5C3=O)C4=O)cc2)cc1,,0.38648084,,,
+672322123,*c1ccc(OCCCCCCCCCCCOC(=O)c2ccc(C(=O)OCCCCCCCCCCCOc3ccc(-c4nnc(*)s4)cc3)cc2)cc1,,0.37335529,,,
+672610446,*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(Oc4ccc(-c5nc6cc(*)ccc6nc5-c5ccccc5)cc4)cc3)nc2c1,,0.41566433,,,
+673060988,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(C(c4cc(C)c(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(C)c4)c4cccc5ccccc45)cc3C)cc1)C2=O,,0.40356882,,,
+673094713,*Nc1ccc(-c2cccc(-c3ccccc3N*)c2)cc1,,0.34307779,,,
+673172746,*Oc1ccc(C(c2ccc(OC(=O)c3ccccc3-c3ccccc3C(*)=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.3557624,,,
+673214221,*C=CC1CC(*)C2C(=O)N(c3cccc(F)c3)C(=O)C12,,0.37532295,,,
+673225351,*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(F)c4)c4ccccc4-c4ccccc43)cc1F)C2=O,,0.42499356,,,
+673445970,*CC(*)c1cc(C(=O)OCCCCCCCCCCCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.38816333,,,
+673499218,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.33962987,,,
+673704172,*CC(*)(C)C(=O)OCCOCCOC,,0.33828721,,,
+673883343,*C[Si](*)(CCCC)CCCC,,0.40581493,,,
+673984159,*CC(C(C)C)C(COC(=O)c1ccc(C(=O)O*)cc1)C(C)C,,0.35713078,,,
+674610445,*Nc1c(N*)c(-c2ccccc2)c(-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.37324371,,,
+674648279,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36239685,,,
+674773828,*Nc1ccc(/C=C/c2ccc(NC(=C(C#N)C#N)c3ccc(C(*)=C(C#N)C#N)cc3)cc2)cc1,,0.4055685,,,
+674973559,*C(=O)OC1CC2CC(OC(=O)N3CCN(*)CC3)CC(C1)O2,,0.33921521,,,
+675621867,*OC(=O)c1ccc(CCCCc2ccc(C(*)=O)cc2)cc1,,0.35446575,,,
+676068988,*CCCCCCCCNC(=O)CCCCCCCCC(=O)N*,,0.35653288,0.392,,
+676116191,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCC(*)=O,,0.32224016,,,
+676718973,*Nc1ccc2c(-c3ccccc3)c3cc(N*)ccc3cc2c1,,0.35297374,,,
+676741869,*c1cccc(-c2nc3cc(-c4ccc5c(c4)nc(*)n5CCCS(=O)(=O)O)ccc3n2CCCS(=O)(=O)O)c1,208.6363648,,,,
+677190542,*c1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(N4C(=O)c5ccc(NC(=O)Nc6cccc(NC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)n6)cc5C4=O)cc3)ccc3ccccc23)cc1,,0.35781631,,,
+677732142,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1,,0.35682307,,,
+677771115,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.34677139,,,
+677895437,*c1cc(-c2sc(-c3cc(CCCC)c(*)s3)cc2CCCC)c2cccccc1-2,,0.45832341,,,
+678137113,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37813384,,,
+678912517,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(C)cc2)cc1,,0.3526035,,,
+678917308,*CC(C)(C)C(=O)O*,,0.3478783,,,
+679096847,*c1cc(-c2sc(-c3cc(CCCCCCCCCCCCCC)c(*)s3)cc2CCCCCCCCCCCCCC)c2cccccc1-2,,0.42191423,0.316,,
+679122345,*OP(=O)(Oc1ccc(Cl)cc1)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl,,0.35896209,,,
+679270138,*Nc1ccc2c(c1)C1(c3cc(N*)ccc3-2)c2cc(C(C)(C)C)ccc2-c2ccc(C(C)(C)C)cc21,,0.401012485,,,
+679274532,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.36028075,,,
+679404420,*c1ccc(-c2ccc(N3C(=O)c4c(c(-c5ccccc5)c(-c5ccc(Oc6ccc(-c7c(-c8ccccc8)c8c(c(-c9ccccc9)c7-c7ccccc7)C(=O)N(*)C8=O)cc6)cc5)c(-c5ccccc5)c4-c4ccccc4)C3=O)cc2)cc1,,0.41331877,,,
+679533117,*Nc1ccc([C@@H](c2ccccc2)c2ccc(N*)c([C@@H](c3ccccc3)c3ccc(N)cc3)c2)cc1,,0.36527705,,,
+679561961,*CCCC(*)(C)C,,0.4001286,0.236,0.780478615,15.7837124
+679722974,*c1ccc(Oc2ccc(P(=O)(c3ccccc3)c3ccc(Oc4ccc(-c5ccc(*)nc5)cc4)cc3)cc2)cc1,,0.38782954,,,
+679930104,*Oc1ccc(NC(=O)c2cc(-c3ccc(C(=O)O)c(C(=O)Nc4ccc(*)cc4)c3)ccc2C(=O)O)cc1,,0.32759097,,,
+680389374,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OP(=O)(Oc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c3ccccc3)c1)C2=O,,0.36051533,,,
+680463465,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.37707713,,,
+680864768,*Nc1ccc([C@H](CCC)c2ccc(C3(c4ccc([C@@H](CCC)c5ccc(N*)cc5C)cc4)CCC(CC)CC3)cc2)c(C)c1,,0.37733794,,,
+680937306,*c1ccc(Oc2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)c(-c7ccccc7)c6c5)cc4c(-c4ccccc4)c3-c3ccccc3)cc2)cc1,,0.41401327,,,
+680984529,*CC(*)(C)C(=O)OCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OCCCCCC)cc2)cc1,,0.35200857,,,
+681027587,*Nc1ccc2c(c1)C(C)(C)C[C@@]2(C)c1ccc(C)c(N*)c1,,0.36931693,,,
+681043697,*N[C@H]([C@H](N)CO)[C@@H](C=O)N*,,0.29472279,,,
+681148438,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccccc4Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.35765062,,,
+681382026,*CC(c1ccccc1)C1C(=O)N(CCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.3562068,,,
+681607778,*CCN(CCOC(=O)NCCCCCCNC(=O)O*)c1ccc(N=Nc2ccc(C)cc2)cc1,24.09354398,,,,
+682136136,*CC1(CO*)CC2CCC1CC2,81.08225465,,,,
+682392464,*CC(OCC(C)C)C(C)(C(=O)OC)C(*)(C)C(=O)OC,,0.36938061,,,
+682727526,*C1C(=O)N(c2ccccc2C(=O)OCC)C(=O)C1*,,0.37540296,,,
+682848245,*c1cccc(Oc2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.36290404,,,
+683188751,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.35282718,,,
+683707675,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc([Si](C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.37681981,,,
+684755734,*OP(=O)(Oc1ccccc1)Oc1ccc(-c2ccc(*)cc2)cc1,,0.34952529,,,
+684934727,*Nc1ccc(/C(C)=C(/CCC=C)c2ccccc2-c2ccc(N*)cc2)cc1,,0.36161485,,,
+685115474,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.39820678,,,
+685261613,*Nc1ccc2c(c1)C(CC[C@@H](C)CCCC(C)C)(CC[C@@H](C)CCCC(C)C)c1cc(N*)ccc1-2,,0.3674647,,,
+685427135,*OC(CCc1ccccc1)CC(*)=O,,0.35135423,,,
+685579898,*Oc1c(C)cc(-c2cc(C)c(Oc3ccc(C(=O)c4ccccc4-c4ccccc4C(=O)c4ccc(*)cc4)cc3)c(C)c2)cc1C,,0.40252214,,,
+686272948,*c1ccc(NC(=O)c2cc(C(=O)Nc3ccc(-c4nnc(*)o4)cc3)cc(N3C(=O)c4ccccc4C3=O)c2)cc1,,0.36231124,,,
+686526509,*CCCCCCCCCCCCCCCC[N+](C)(C)CCCCCC[N+](*)(C)C,,,0.408,0.872184477,20.26505078
+686833175,*/C=C(/*)C#CCCCCCCCCCCCCCCCCCCCCC(=O)O,,,0.4105,0.885736749,15.06400186
+687447085,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7ccc(-c8nc(*)c(-c9ccccc9)[nH]8)cc7)[nH]c6-c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.40004414,,,
+687545221,*CCC=C(*)c1ccccc1,,0.38403531,,,
+687772920,*CCN(CCCCCOc1ccc(N=Nc2ccccc2)cc1)CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1,,0.35497879,,,
+687864744,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)Oc5ccc(C(C)(C)c6ccc(OC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)CCC(C(C)(C)C)CC2)cc1,,0.37364551,,,
+688101886,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.37198635,,,
+688122210,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(CCC(=O)O)c4ccc(*)cc4)cc3)cc2)cc1,,0.36717423,,,
+688609690,*c1ccc(Cc2ccc(N3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)CCCCCCCCCCC6)cc5)cc4C3=O)cc2)cc1,,0.38469309,,,
+688616302,*CCCCCCCCNC(=O)NCc1ccc(CNC(=O)N*)cc1,,0.33930014,,,
+688656899,*SCC(=O)NN=Cc1ccc(OCCCCCCCCCCOc2ccc(C=NNC(=O)CSc3nnc(*)s3)cc2OC)c(OC)c1,,0.36310288,,,
+688760840,*OC(COC(=O)c1cccc(C(*)=O)c1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35979025,,,
+689174669,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3cc(F)c(*)cc3F)ccc1-2,,0.41547343,,,
+689311182,*CC(O)COC(=O)C1CCC(C(=O)OCC(O)COc2ccc(C(C)(C)c3ccc(O*)cc3)cc2)CC1,,0.334916,,,
+689497002,*CC(*)C(=O)OCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.3457648,,,
+689573357,*CC(*)c1ccc([Si](O[Si](C)(C)C)(O[Si](C)(C)C)O[Si](C)(C)C)cc1,,0.48916637,,,
+689602556,*c1ccc2cc3cc(-c4sc(-c5cc(CCCCCCCCCCCC)c(*)s5)cc4CCCCCCCCCCCC)ccc3cc2c1,,0.4098993,,,
+689672575,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c(C)c2)cc1C,,0.38990238,,,
+690246774,*CC(O)COC(=O)c1cccc(C(=O)OCC(O)COc2ccc(C(C)(C)c3ccc(O*)cc3)cc2)c1,,0.33980018,,,
+690444653,*c1cccc(-c2nc3cc(-c4ccc5[nH]c(*)nc5c4)ccc3[nH]2)c1,,0.41970878,,,
+690772081,*CCOCCOC(=O)CCCCC(=O)O*,,0.33115638,,,
+691002570,*c1ccc(-c2ccc(-c3cc(-c4ccc(Oc5ccc(-c6cc(*)nc7ccccc67)cc5)cc4)c4ccccc4n3)cc2)cc1,,0.39515201,,,
+691154706,*CC(*)(CC(=O)OCCCC1CCCCC1)C(=O)OCCCC1CCCCC1,,0.37003556,0.199,,
+691219107,*CCCCCCCCCCSS*,,0.43577618,,,
+691290005,*C(c1ccccc1)C(*)[N+](=O)[O-],173.9913454,,,,
+691340990,*c1ccc(-c2nc3cc4nc(*)[nH]c4cc3[nH]2)cc1,340.5865983,,,,
+691396928,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)c1ccc(C(*)=O)cc1,,0.3307198,,,
+691744465,*CCCCCCN(C)c1ccc(C(=O)c2ccc(N(*)C)cc2)cc1,,0.37816076,,,
+691831381,*CC(*)(C)C(=O)Oc1ccc(C=Cc2ccccc2)cc1,,0.36180049,,,
+691986630,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.36858029,,,
+692037059,*CC(*)c1cc(C)c(C)cc1C,,0.40919806,0.2095,,
+692373242,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1Cl,119.6241841,,,,
+692434994,*C1C(=O)N(C(C)CC)C(=O)C1*,,0.38386027,,,
+692810892,*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6CCC(C6)C5C4=O)c3)cc2)cc1,,0.3598038,,,
+693011985,*Nc1ccc(-c2ccc(N*)c(N)c2)cc1N,,0.33019375,,,
+695348815,*/C=C/c1cc(CCCCCC)c(/C=C/c2ccc(*)cc2)cc1CCCCCC,51.04706134,,,,
+695374943,*CC(*)(CC(=O)Oc1ccc(OC)cc1)C(=O)Oc1ccc(OC)cc1,,0.33732889,,,
+695536292,*O*CCCCCCCCCC(=O)Oc1ccc(-c2ccc(C(=O)Oc3ccc(Cl)c(OC(=O)c4ccc(-c5ccc(OC(=O)CCCCCCCCC(*)CO*)cc5)cc4)c3)cc2)cc1,,0.38046147,,,
+695925726,*CC/C(C)=C(/*)C,,,,0.817744643,14.53648643
+695940452,*CCCCCSS*,,0.45235831,,,
+696483165,*Oc1ccc(C(=Cc2ccc(-c3ccc(C=C(c4ccccc4)c4ccc(OC(=O)CCCCCCCCC(*)=O)cc4)cc3)cc2)c2ccccc2)cc1,,0.3779872,,,
+696705390,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=O)c4cc(Br)cc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1,,0.379535,,,
+696713776,*c1ccc2c(c1)C(=O)N(c1cc(OCCN(CC)c3ccc(/N=N/c4ccc([N+](=O)[O-])cc4)cc3)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,,0.154,1.205461036,17.1881696
+698037323,*CC(*)OC(=O)C(C)(CC)C(C)(C)C,,0.37558012,,,
+698053626,*CC(*)(C)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)F,,0.32595019,0.113,,
+698200988,*C(=O)Nc1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(-c2nc3ccccc3[nH]2)c1,,0.36147355,,,
+698615321,*C=Cc1cc(OCC(CC)CCCC)c(C=Cc2cc(OC)c(*)cc2OC)cc1OC,,0.3762763,,,
+699007218,*C#CC#Cc1cc(C(C)(C)C)cc2c3cc(C(C)(C)C)cc(*)c3n(CCCCCCCCCCCCCCCC)c12,,0.41367738,,,
+699175207,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(C(=O)O)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.3614755,,,
+699184717,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4c(C)ccc5c(C(=O)c6ccc(*)cc6)c(C)ccc45)cc3)cc2)cc1,,0.39186106,,,
+699462327,*Oc1ccc(Oc2c(F)c(F)c(-c3c(F)c(F)c(*)c(F)c3F)c(F)c2F)cc1S(=O)(=O)O,,0.36032726,,,
+699667055,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(C(C)(C)c9ccc(*)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.37043796,,,
+699908244,*CCCCCC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CCCCCN4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.3404124,,,
+699928185,*OP(=O)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl,,0.36757265,,,
+700020481,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.39550318,,,
+700619853,*CC(*)(C)C(=O)OCCc1ccc(N=Nc2ccc(OC(F)(F)F)cc2)cc1,,0.35475551,,,
+700771684,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(-c5cnc6ccc(-c7ccc8ncc(*)nc8c7)cc6n5)cc4)cc3)cc2)cc1,,0.38386656,,,
+700931497,*ON(C(F)(F)F)C(F)(F)C(*)(F)F,,0.30488476,0.0905,1.732295687,17.31090487
+700994258,*c1ccc(OCOc2ccc(-c3nc(-c4ccc([N+](=O)[O-])cc4)oc3*)cc2)cc1,,0.38196728,,,
+701170718,*CC(*)c1ccccc1C(=O)OCCN(C)C,,0.36119268,0.1853333333333333,,
+701980913,*Sc1cccc(Sc2ccc(Sc3ccc(*)cc3)cc2)c1,,0.39236432,,,
+702351726,*CC(*)(C)C(=O)OCCN(CCCC)c1ccc(N=Nc2ccc(S(=O)(=O)Nc3cc(C)on3)cc2)cc1,,0.37056907,,,
+702495781,*Nc1cccc(C(C)(c2ccccc2)c2cccc(N*)c2)c1,,0.35162648,,,
+702860275,*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O,,0.38497663,,,
+703506417,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OCCOc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.34759445,,,
+703616126,*Oc1ccc(NC(=C(C#N)C#N)c2ccc(-c3ccc(C(Nc4ccc(*)cc4)=C(C#N)C#N)cc3)cc2)cc1,,0.41214769,,,
+704321363,*c1ccc(-c2ccc(-c3cnc4cc(-c5ccc6nc(*)cnc6c5)ccc4n3)cc2)cc1,,0.40589633,,,
+704562669,*CCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.3465094,,,
+704596053,*CC(C)CO*,,0.38408676,,,
+704720758,*Nc1ccc(C2=CC(c3ccccc3)=C[C@@](c3ccccc3)(c3ccc(N*)cc3)[C@@H]2c2ccccc2)cc1,,0.37453464,,,
+704923125,*COC(=O)Nc1ccc(Cc2ccc(NC(=O)OCc3cocc3*)cc2)cc1,,0.33693682,,,
+705117177,*CCOCCOCCOC(=O)CCCCCCCC(=O)O*,,,0.267,,
+705738216,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5c(c(-c6ccccc6)c(-c6ccc(Oc7ccc(-c8c(-c9ccccc9)c9c(c(-c%10ccccc%10)c8-c8ccccc8)C(=O)N(*)C9=O)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)C4=O)c3)c2)c1,,0.39052632,,,
+705844179,*c1c(C)cc(C)c(N2C(=O)c3ccc(Oc4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3C2=O)c1C,,0.3880841,,,
+705855235,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(C)(C)C)c2)cc1,,0.36167594,,,
+707258420,*CC(*)C(=O)OCCCOCC,,0.3584849,,,
+707285446,*Oc1cccc2cc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)ccc12,,0.35254861,,,
+707686102,*Nc1ccc(Oc2ccc(Oc3ccc(N*)cc3)c3c2[C@H]2c4ccccc4[C@@H]3c3ccccc32)cc1,,0.37675561,,,
+707792324,*CC(*)(C)C(=O)OC(COc1ccc(-c2ccccc2)cc1)COc1ccc(-c2ccccc2)cc1,,0.34956652,,,
+707895073,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5cccc(C(F)(F)F)c5)C6=O)cc4)cc3)cc2)cc1,,0.37753065,,,
+708266844,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1,,0.36753658,,1.20752232,22.21547246
+708515334,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.41414112,,,
+708745541,*C=CC(C)C(*)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.3828539,,,
+708888125,*CC1(C)CC(*)(C)C(=O)OC1=O,,0.30782553,,,
+709065178,*NC1=c2ccc3ccc(N*)c4ccc(c2c34)CC1,,0.33334422,,,
+709226489,*c1cc2sc(-c3sc(-c4cc(CCCCCCCCCCBr)c(*)s4)cc3CCCCCCCCCCBr)cc2s1,24.95900908,,,,
+709267633,*c1ccc(Oc2ccc(-c3cnnc(-c4cccc(-c5ncc(*)nn5)n4)n3)cc2)cc1,,0.41536896,,,
+709856096,*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc(OC(F)(F)F)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C,,0.37184486,,,
+709886400,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nc3ccccc3[nH]2)c1,,0.35887119,,,
+710418627,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.3506977,,,
+711711106,*c1ccc(Oc2c(C)cc(Cc3cc(C)c(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)c(C)c3)cc2C)cc1,,0.3857049,,,
+712008066,*CC(*)OC(=O)c1nn(C(C)=O)c2ccccc12,,0.34764523,,,
+712061261,*CC(CS(=O)(=O)CCCC)O*,,0.38279045,,,
+712113333,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(Cc3cc(C(C)C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C(C)C)c3)cc1C(C)C)C2=O,,0.43503278,,,
+712239619,*CCCCCCCCNC(=O)CCCCCCCCC(=O)NCCCCCCCCNC(=O)C(=O)N*,,,0.3315,0.973462542,22.44231118
+712424232,*c1ncnc(N(CCCCCCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.38355919,,,
+712525184,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCC)cc1,,0.36539229,,,
+712532019,*Nc1ccc(N*)c(-c2cccc3ccccc23)c1,,0.34706164,,,
+713020314,*C=CCC*,,0.41866094,,,
+713163686,*N=P(*)(OCc1ccccc1)OCc1ccccc1,,0.35649541,,,
+713393124,*CC(*)C(=O)N1CC[NH+](CC)CC1,,0.6509068,,,
+714476191,*c1ccc(CC(=O)Nc2ccc(NC(=O)Cc3ccc(-c4sc(*)c(-c5ccccc5)c4-c4ccccc4)cc3)cc2)cc1,,0.38234707,,,
+714511257,*c1ccc(-c2nc3ccc(Oc4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1,,0.45462761,,,
+714806677,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(Oc3c(C)cc(-c4cc(C)c(Oc5cc(N6C(=O)c7ccc(*)cc7C6=O)cc(C(F)(F)F)c5)c(C)c4)cc3C)cc(C(F)(F)F)c1)C2=O,,0.38618508,,,
+714836323,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(-c3nc4ccccc4[nH]3)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.39603335,,,
+715482561,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)cc1,,0.3574338,,,
+716027443,*CCCCCCOC(=O)Nc1cccc(P(=O)(c2ccccc2)c2cccc(NC(=O)O*)c2)c1,,0.35310551,,,
+716481605,*Nc1c(N*)c2c3ccccc3cc3ccc4cccc1c4c32,,0.34230926,,,
+716683288,*Oc1ccc(Oc2ccc(S(=O)(=O)c3ccc(*)cc3)cc2)cc1,,0.36840915,,,
+717229286,*N=P(*)(OCCC(=O)C=C)OCCC(=O)C=C,-42.12432011,,,,
+717633419,*O[Si](C)(C)c1ccc2cc([Si](*)(C)C)ccc2c1,,0.41010806,,,
+717936443,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)c1cccc(C(*)=O)c1,,0.33111951,,,
+718095822,*CCCCCCCCCCCCOC(=O)CC/C=C/CCC(=O)O*,,,0.274,,
+718575580,*NCc1ccc(N*)c2ccccc12,,0.32862128,,,
+719137941,*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(N=Nc3ccc(N=Nc4cc(C)c([N+](=O)[O-])c(C)c4)cc3)cc2)cc1,,0.3672374,,,
+719507082,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3ccc4c(c3)c3cc(*)ccc3n4CCCCCC#N)cc2)cc1,,0.39438801,,,
+719751111,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2cccc(C)c2)c(-c2cccc(C)c2)c1-c1ccccc1,,0.38052241,,,
+720344171,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(*)cc3)cc2)cc1,,0.34491184,,,
+720567395,*O*CCCCCCCCCC(=O)Oc1ccc(-c2ccc(C(=O)Oc3ccc(C(=O)OC)c(OC(=O)c4ccc(-c5ccc(OC(=O)CCCCCCCCC(*)CO*)cc5)cc4)c3)cc2)cc1,,0.38046147,,,
+720781054,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc4ccccc4cc3Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37047012,,,
+720835147,*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)CCCCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.33696818,,,
+721329227,*Oc1ccc(Nc2ccc(C(=O)c3ccc(C(=O)c4ccc(Nc5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38639477,,,
+721426023,*OC(COC(=O)c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(C(*)=O)c(F)c3F)cc2CCCCCCCC)c(F)c1F)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37990658,,,
+721715227,*Sc1ccc(P(=O)(c2ccccc2)c2ccc(Sc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1,,0.39670171,,,
+721925849,*Nc1cc2ccccc2c(C(N*)(c2ccccc2)c2ccccc2)c1N,,0.36049549,,,
+722303181,*C=Nc1ccc(N=Cc2ccc(Sc3ccc(*)o3)o2)cc1,95.64631957,,,,
+722323887,*CCN(*)C(=O)c1ccccc1,,0.35014456,,,
+722337542,*CC(CCCCCC)O*,,0.40228779,,,
+722624089,*CC(*)(C)C(=O)OCCCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.36422503,,,
+722656060,*/C=C/C(C)C(*)C,,,,0.801255092,16.53077558
+722657419,*CC(COc1ccc(C2CCCC(CC)(c3ccc(O*)cc3)C2)cc1)OC(C)=O,,0.35641137,,,
+722671179,*CCCCCCNC(=O)NCc1ccc(CNC(=O)N*)cc1,,0.32915874,,,
+722858579,*Nc1ccc(-c2ccc(N*)c(-c3ccc(-c4ccccc4)cc3)c2-c2ccc(-c3ccccc3)cc2)c(-c2ccc(-c3ccccc3)cc2)c1-c1ccc(-c2ccccc2)cc1,,0.36435294,,,
+722954203,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O)cc5C4=O)cc3)cc1)C2=O,,0.366311,,,
+722996762,*C(=O)Nc1ccc(C(=O)Nc2ccc(NC(=O)c3ccc4[nH]c(-c5ccc(-c6nc7cc(*)ccc7[nH]6)cc5)nc4c3)cc2)cc1,,0.37298287,,,
+723086841,*C(=O)c1ccc(C(=O)N(C)c2ccc(Cc3ccc(N(*)C)c(C)c3)cc2C)cc1,167.9642319,,,,
+723328836,*Oc1cc(Oc2ccc(NC(=O)c3cc(NC(=O)C45CC6CC(CC(C6)C4)C5)cc(C(=O)Nc4ccc(*)cc4)c3)cc2)ccc1C12CC3CC(CC(C3)C1)C2,,0.36497894,,,
+723637794,*OS(=O)(=O)c1ccc(Oc2ccc(S(=O)(=O)Oc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1,190.021745,,,,
+723638059,*NCC(C)(C)CN*,,0.32817407,,,
+723903020,*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.38669859,,,
+725291191,*Nc1ccc(C2(c3ccc(N*)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.381850225,,,
+726324276,*CC(*)(C)C(=O)OCCS(=O)(=O)c1ccc(N=Nc2ccc(N(CC)CC)cc2)cc1,,0.37486297,,,
+726632865,*NCCNCCNCCN*,,0.32616627,,,
+726852979,*CCCNC(=O)CCCCCCCCC(=O)NCCCOCCOCCO*,,0.3490113,,,
+726901134,*Oc1ccc(C2(c3ccc(OC(*)=S)cc3)CCC(C)CC2)cc1,,0.36588296,,,
+727287922,*Oc1ccc(C(C)(C)c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)cc1,,0.3613817,,,
+727715310,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)cc4)cc2)C3=O)cc(-c2ccccc2)c1,,0.366746,,,
+727880096,*Nc1c(-c2cccc3ccccc23)ccc(-c2cccc3ccccc23)c1N*,,0.35630163,,,
+727931995,*c1sc(*)c(OCCCCCCCCCCCC)c1C,,,0.37875,0.949355697,16.11371346
+727964996,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccccc5)c(-c5ccc6ccccc6c5)c(-c5ccc6ccccc6c5)c4-c4ccccc4)cc3)cc2)cc1,,0.39702899,,,
+728252718,*Oc1ccc(C(C)(C)c2ccc(OC(=O)C3CCC(C(*)=O)CC3)cc2)cc1,,0.3540751,,,
+728371459,*C(=O)c1ccc(Oc2ccc(-c3ccc(Oc4ccc(C(=O)c5ccc6c(c5)C(=O)N(c5ccc(N7C(=O)c8ccc(*)cc8C7=O)cc5)C6=O)cc4)cc3)cc2)cc1,,0.37364241,,,
+728455428,*C=CC1CC(*)C2C(=O)N(c3ccccc3Br)C(=O)C12,,0.37993611,,,
+729133742,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(*)=O)cc2)C(=O)OCC)cc1,,0.34116188,,,
+729574569,*CC(*)(C)C(=O)OC(C(F)(F)F)C(F)(F)F,,0.33395408,0.0755,1.277149707,12.6767809
+729669366,*Oc1ccc(C(c2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35569196,,,
+730173328,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36098706,,,
+730265166,*c1c(C)cc(C)c(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1C,,0.42202821,,,
+730340221,*Oc1ccc(Sc2ccc(OC(*)=O)cc2)cc1,,0.35903581,,,
+730399756,*CC(*)C(=O)OC(C)CCCCCC,,,0.245,0.877762738,11.2218737
+730567526,*c1ccc([Si](*)(C)c2ccc(CN(C)C)cc2)cc1,12.90627629,,,,
+730591910,*c1nnc(-c2ccc(Oc3ccc(C(c4ccc(Oc5ccc(*)c6ccccc56)cc4)(C(F)(F)F)C(F)(F)F)cc3)c3ccccc23)o1,,0.39620569,,,
+731184698,*C(=O)OCC(C)(C)COC(=O)N1CCN(*)CC1,,0.33768571,,,
+731417662,*CCCCCCCCCCC1CC(CCC2CC(*)OC2=O)C(=O)O1,84.1573412,,,,
+731595810,*CC(c1ccccc1)C1C(=O)N(c2ccc(Oc3ccccc3)cc2)C(=O)C1*,,0.367645,,,
+732208665,*c1ccc2nc(-c3ccc4c(c3)C(=O)N(c3cc(C(*)(C(F)(F)F)C(F)(F)F)ccc3O)C4=O)oc2c1,,0.39736804,,,
+732280868,*Nc1cc(N*)c2ccc3cc(C(C)(C)C)cc4ccc1c2c43,,0.35494944,,,
+732578696,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)c3ccc([Si](C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.37223437,,,
+732613388,*Nc1cccc2c(NC(=O)Nc3cc(NC(*)=O)cc(C(=O)Nc4cccc5c4C(=O)c4ccccc4C5=O)c3)cccc12,,0.33149666,,,
+732957114,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(C(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.34135559,,,
+733015366,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nc(*)nc(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.40568051,,,
+733146174,*c1ccc(Oc2ccc(N3C(=O)c4ccc(C(c5ccc6c(c5)C(=O)N(c5ncc(*)s5)C6=O)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc2)cc1,,0.4074392,,,
+734539627,*c1cccc(P(C)(=O)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(C(c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4C3=O)c2)c1,,0.34534828,,,
+734958593,*CC(*)c1ccccc1COC,,0.37399571,0.2066666666666666,,
+735019484,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c(-c3ccccc3)c2)cc1,,0.37852395,,,
+735028416,*CC(*)c1ccccc1C(=O)OCCCCC,,0.37483446,0.1923333333333333,,
+735082604,*C(=O)c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(*)s4)cc3)cc2)s1,,0.37910073,,,
+735143665,*CCCCCCCCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.35290149,,,
+735307697,*CCC(CC)CC(C)CNC(=O)c1ccc(C(=O)N*)cc1,,0.34406893,,,
+735659533,*CC(*)OCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1,,0.36822065,,,
+736152348,*Nc1ccc2ccccc2c1Cc1c(N*)ccc2ccccc12,,0.34468979,,,
+736727472,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(S(=O)(=O)c4ccc(O*)c(C)c4)cc3C)cc2)cc1,,0.35738719,,,
+736792055,*c1cccc(-c2cccc(C(F)(F)C(F)(F)C(*)(F)F)c2)c1,,0.36208143,,,
+736908890,*OC(=O)OCCOCCOCCOCCOC(=O)OC1COC2C(*)COC12,,0.32639137,,,
+737341727,*CC(*)n1c2ccc(Br)cc2c2cc(Br)ccc21,115.6170166,,,,
+737356509,*c1ccc(N2C(=O)c3c(c(-c4ccccc4)c(-c4ccc(-c5c(-c6ccccc6)c6c(c(-c7ccccc7)c5-c5ccccc5)C(=O)N(*)C6=O)cc4)c(-c4ccccc4)c3-c3ccccc3)C2=O)cc1,,0.42520451,,,
+737362009,*COC1OC(*)C(O)C(O)C1O,,0.28194212,,,
+738034100,*Nc1cccc(NC(=O)CCCCC(=O)Nc2cccc(NC(=S)NC(=O)CCCCCCCCC(=O)NC(*)=S)c2)c1,,0.34083376,,,
+738593262,*Nc1ccc(NC(=O)c2ccc(NC(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.35850742,,,
+738625870,*Nc1cccc(-c2cccc(-c3ccccc3)c2-c2ccccc2)c1N*,,0.36058761,,,
+738887961,*C1C(*)C2CC1C(COCc1ccccc1)C2COCc1ccccc1,,0.35376741,,,
+739506612,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O,,0.36896466,,,
+739562740,*c1ccc(C2(c3ccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.4105138,,,
+739643942,*CCCS(=O)(=O)c1ccc(N=Nc2ccc(N(CCCC)CC(=O)O*)cc2)cc1,,0.37775099,,,
+739659224,*CCC(C)C(*)(C)C(=O)OC,,0.35037365,,,
+739937375,*CCN(CCOC(=O)c1ccc(C(=O)O*)cc1)c1ccc(-c2nc3ccc([N+](=O)[O-])cc3o2)cc1,,0.35125653,,,
+740224734,*Nc1cc2c(cc1N*)[C@]1(C)c3c(ccc(N)c3N)[C@]3(C)c4cc(N)c(N)cc4[C@@]2(C)[C@@H]31,,0.39437895,,,
+741750852,*C=CC1CC(*)C(C(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1C(=O)OCCCCCCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.37717295,,,
+742319182,*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4ccc(C(C)C)cc4)c3-c3ccc(C(C)C)cc3)cc2)cc1,,0.3784415,,,
+742571113,*c1cc(*)c(O)c(C=Nc2ccc(F)cc2)c1,-121.5212841,,,,
+742772976,*c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(-c4ccc(-c5sc(-c6ccc([Si](*)(CCCC)CCCC)s6)cc5CCCCCCCC)s4)s3)s2)s1,,0.42518997,,,
+743402006,*CCCCCCCCNC(=O)CC(=O)N*,,0.33840091,,,
+744301389,*OC(COC(=O)C1=C(C(=O)OCC(COC(=O)C2=C(c3ccccc3)C3C=CC2C3)OC(=O)c2ccc(C(*)=O)cc2)C2C=CC1C2)COC(=O)C1=C(c2ccccc2)C2C=CC1C2,,0.34889157,,,
+744751157,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccccc1,,0.34387073,,,
+745130197,*OC(=O)CCCCCC(*)=O,,,0.237,1.084856799,17.5417392
+745501666,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(-c3ccc(OC)cc3)cc2)cc1,,0.35115071,,,
+745635632,*Nc1ccc2ccccc2c1-c1c(N*)ccc2ccccc12,,0.35275725,,,
+745676028,*Oc1ccc(Oc2ccc(NC(=O)c3cccc(C(=O)Nc4ccc(*)cc4)c3)cc2)cc1C(C)(C)C,,0.36499914,,,
+745925185,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccc(C)cc3)ccc1-2,,0.39958205,,,
+746006701,*CC(C#N)C(CCC(C)(O)C=C)C(*)(C)C,,0.3589437,,,
+746033942,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8cccc(N9C(=O)c%10ccc(*)cc%10C9=O)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36211978,,,
+746099992,*Oc1ccc(C2(c3ccc(Oc4ccc(-c5ccc(-c6ccc(*)c(C(F)(F)F)c6)cc5)cc4C(F)(F)F)cc3)c3ccccc3-c3ccccc32)cc1,,0.40015499,,,
+746373374,*CCCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.34895438,,,
+746810021,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.35629781,,,
+746817611,*CCOC(=O)Nc1ccc(NC(=O)O*)cc1,134.2908917,,,,
+746903081,*C/C=C/COC(=O)NCCCCCCNC(=O)O*,,0.33443961,,,
+747034836,*c1cc(N=Cc2ccc(F)cc2)c(O)c(*)c1C,,0.39302909,,,
+747510254,*Nc1cccc2c1ccc1c3cccc(N*)c3ccc21,,0.33361002,,,
+747579493,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C,,0.43809694,,,
+748222139,*CCCCCCNC(=O)CCC1(CCC(=O)N*)c2ccccc2-c2ccccc21,,0.34859909,,,
+748600767,*/C=C/C1CCC(*)C1,,,0.2603333333333333,,
+748602077,*O[Si](*)(C)CCCCCn1c2ccccc2c2ccccc21,,0.37747009,,,
+748679100,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2c(C)cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c2C)C3=O)cc1,,0.37301692,,,
+749003022,*Sc1ccc(-c2ccc(SC(=O)CCCCC(*)=O)cc2)cc1,13.2621556,,,,
+749288648,*c1ccc(-c2ccc(-c3ccc(-c4cc(-c5ccccc5)c5cc(-c6ccc7nc(*)cc(-c8ccccc8)c7c6)ccc5n4)cc3)cc2)cc1,,0.41180869,,,
+749314033,*OC(=O)C(C)NC(=O)CCCCCCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.33769731,,,
+749316999,*Nc1cccc(C(c2ccccc2)(c2ccccc2)c2ccccc2)c1N*,,0.36834025,,,
+749375446,*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)C45CC6CC(CC(C6)C4)C5)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35504782,,,
+749548087,*C(=O)Nc1cccc(P(=O)(c2ccccc2)c2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)c2)c1,,0.36969182,,,
+749662533,*Nc1ccc(/C(C)=C\C(C)(C)c2ccc(N*)cc2)cc1,,0.35473258,,,
+749826349,*c1ccc(NC(=O)Nc2ccc(NC(=O)Nc3ccc(-c4nc(-c5ccc([N+](=O)[O-])cc5)[nH]c4*)cc3)cc2)cc1,,0.35066768,0.269,1.201196246,19.2258376
+750049504,*c1ccc(-c2sc(-c3ccc(-c4cc(SCCCC)c(*)s4)cc3)cc2SCCCC)cc1,,0.51197804,,,
+750380744,*O*CCCCCCCCCC(=O)Oc1ccc(-c2ccc(C(=O)Oc3cccc(OC(=O)c4ccc(-c5ccc(OC(=O)CCCCCCCCC(*)CO*)cc5)cc4)c3)cc2)cc1,,0.38046147,,,
+750442059,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)CCCCCCCCCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,,0.34759943,,,
+750805819,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.35420645,,,
+750893336,*CCCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.35432247,,,
+751017152,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4C)cc3)cc2)cc1C,,0.36216317,,,
+751223406,*Oc1ccc(N=C(c2ccccc2)c2ccc(Oc3ccc(-c4ccc(Oc5ccc(C(=Nc6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.38227472,,,
+751286665,*CC(Cc1ccc(-c2ccccc2)cc1)C[Si](*)(C)C,,0.37356757,,,
+751321800,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)c([N+](=O)[O-])c2)cc1[N+](=O)[O-],,0.38190674,,,
+751554332,*Nc1cccc2cc3c(N*)cccc3cc12,,0.34156849,,,
+751973346,*N=Nc1ccc([Te]c2ccc(*)cc2)cc1,,0.35960485,,,
+752114706,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)cc2)cc1,,0.35376689,,,
+752379788,*C1=NC2=CC(*)C=CC2=C1,103.156476,,,,
+753005996,*N=P(*)(NCC)NCC,,0.39858721,,,
+753199362,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c2cccc(C(=O)O*)c2)c1,,0.33541643,,,
+753570442,*CC(*)c1cc(C(F)(F)F)cc(C(F)(F)F)c1,,0.38284106,,,
+754272312,*CC(c1ccccc1)C1C(=O)N(CCCCCCCC)C(=O)C1*,105.6965321,,,,
+754288361,*Nc1ccc(-c2ccc(N*)c(=C\c3ccccc3)/c2=C\c2ccccc2)cc1,,0.36805136,,,
+754541700,*O[Si](*)(C)CCCCCOc1ccc(OC(=O)c2ccc(OCCCC)cc2)cc1,-64.12566312,,,,
+754551833,*CC1CCC(COC(=O)O*)CC1,,0.34179915,,,
+754749806,*CCCOC(=O)CCCC(=O)O*,,0.32464851,,,
+755483998,*CC(*)C(=O)OCC(C)(C)C,,0.38252829,0.2115,0.901190148,13.30665929
+756378273,*c1ccc(-c2ccc(-c3cc(CCCCCCCC)c(-c4ccc(-c5sc(*)cc5CCCCCCCC)cc4)s3)cc2)cc1,,0.4395737,,,
+756437706,*Oc1ccc(Oc2ccc(C=NCCCCCCN=Cc3ccc(*)cc3)cc2)cc1,,0.37498701,,,
+756521912,*CCCC(C)(C)CNC(=O)CCCC(=O)N*,,0.33531412,,,
+756721321,*C(=O)OCCOC(=O)N1CCN(*)CC1,,0.31625475,,,
+756782287,*Nc1cc2c(cc1N*)-c1cc(N)c(N)cc1C2,,0.35872806,,,
+757100562,*Oc1ccc(-c2ccc(Oc3ccc(*)cc3)c3ccccc23)c2ccccc12,,0.39245647,,,
+757588646,*N=P(*)(OCC)OCC,,0.38134489,,,
+757729082,*Oc1ccc(C(=Cc2ccc(-c3ccc(C=C(c4ccccc4)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)cc3)cc2)c2ccccc2)cc1,,0.37719758,,,
+758693518,*C=Cc1ccc(OC(=O)CCCCCCCCC(=O)Oc2ccc(-c3nnc(*)s3)cc2)cc1,,0.36259247,,,
+758756190,*Oc1ccc(Oc2cccc(*)n2)c(C)c1,,0.35844504,,,
+759011226,*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(-c4ccc(*)cc4)cc3)ccc2C(=O)c2ccccc2)c1,,0.38076076,,,
+759255806,*CC(*)(C)C(=O)OCCOc1ccc(-c2ccc(-c3ccc(C#N)cc3)cc2)cc1,,0.34955949,,,
+759347222,*Nc1ccc2c(c1)[C@@H]1c3ccc(N*)cc3[C@@H]3c4ccc(N)cc4[C@H]2C31C,,0.3861986,,,
+759929518,*CCCCCCNC(=O)CCC(C)(C)c1ccc(C(C)(C)CCC(=O)N*)cc1,,0.34678668,,,
+760236787,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C5(c6ccc(*)cc6)C6CC7CC(C6)CC5C7)cc4)cc3)cc2)cc1,,0.36751197,,,
+760704855,*CC(CCC)C(CCC)COC(=O)c1ccc(C(=O)O*)cc1,,0.35986074,,,
+760704971,*c1ccc([Si](*)(C)C)s1,,0.42057371,,,
+760753809,*CCc1cc(C)c(*)cc1C,,0.39678497,,,
+761202167,*c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(-c4sc(*)cc4CCCCCCCC)cc3)s2)cn1,,0.43120083,,,
+761297738,*Nc1ccc(CC2=CC=C[C@@](C)(c3ccc(Cc4ccc(N*)cc4)cc3)C=C2)cc1,,0.34837056,,,
+761520590,*Nc1cccc2c1-c1c(N*)cccc1C2(c1ccccc1N)c1ccccc1N,,0.36322501,,,
+761786133,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(S(=O)(=O)c4ccc(-c5ccc(S(=O)(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.3714925,,,
+761886758,*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCC(C)CC)c(OCC(C)CC)c(OCC(C)CC)c4)cc3)cc2)cc1,,0.37208958,,,
+762219171,*c1cccc(N2C(=O)c3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.37303121,,,
+762368408,*C(=O)Nc1cccc(N2C(=O)CC(N3C(=O)c4ccc(*)cc4C3=O)C2=O)c1,,0.34139674,,,
+762944097,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C4CCC(C(C)(C)c5cc(C)c(*)c(C)c5)CC4C)cc3C)cc2)cc1,250.477212,,,,
+763114241,*C(=O)Nc1ccc(Cc2ccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)cc2)cc1,,0.35665576,,,
+763411577,*Nc1cc2c(cc1N*)[C@H]1CC[C@@H]2CC1,,0.35470749,,,
+763411836,*c1ccc(NC(=O)Nc2ccc(NC(=O)Nc3ccc(-c4nc(-c5ccc(-c6nc(-c7ccc([N+](=O)[O-])cc7)c(-c7ccc([N+](=O)[O-])cc7)[nH]6)cc5)[nH]c4*)cc3)cc2)cc1,,0.37265586,,,
+763726764,*CC(C)(C)C1C(=O)N(C)C(=O)C1*,,0.33146348,,,
+763991123,*Nc1ccc(C2(c3ccc(N*)cc3)C=CC(c3ccccc3)=CC2)cc1,,0.35269546,,,
+764245724,*c1ccc(C(=O)OCCCCCCCCOc2ccc(C=C3CCCC(=Cc4ccc(OCCCCCCCCOC(=O)c5ccc(-c6nnc(*)o6)cc5)cc4)C3=O)cc2)cc1,,0.37367873,,,
+764326592,*C=CCC(C*)(C(=O)OC12CC3CC(CC(C3)C1)C2)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.379025,,,
+764363021,*Oc1ccc(P(=O)(c2ccccc2)c2ccc(Oc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1,,0.40024416,,,
+764478101,*C=Cc1ccc(OC(=O)CCCCCCCCC(=O)Oc2cccc(-c3nnc(*)s3)c2)cc1,,0.35845216,,,
+764504672,*CCCCCCCCCCCCCCNC(=O)CCc1ccc(CCC(=O)N*)cc1,,0.36896475,,,
+764566036,*Nc1ccc(Oc2ccc(Oc3ccc(N*)cc3C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.34410013,,,
+764633726,*CCOCCOCCOCCOC(=O)NCCCCCCNC(=O)O*,,0.3380047,,,
+765055507,*CCCCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.37834566,,,
+765135916,*Nc1ccc(C(CC)=C(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36758067,,,
+765310183,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(*)cc4C3=O)c(O)cc1O)C2=O,,0.3634881,,,
+765454454,*c1ncnc(N(CCCCN(*)c2ccccc2)c2ccccc2)n1,,0.37819273,,,
+765521185,*CCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,0.38269266,0.37175,0.887262341,24.48690078
+765731942,*CCNC(=O)CCP(C)(=O)CCC(=O)N*,143.8466204,,,,
+765785607,*c1ccc(OC(=O)c2cc(Cl)cc(C(=O)Oc3ccc(C4(*)OC(=O)c5ccccc54)cc3)c2)cc1,,0.36120932,,,
+765893959,*Nc1cccc(C2(c3cccc(N*)c3)c3cc(C)ccc3-c3ccc(C)cc32)c1,,0.37704106,,,
+765976138,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6cc(*)[nH]n6)cc5)cc4)c3)cc2)cc1,,0.37462428,,,
+766108648,*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5cccc(C(C)(C)c6ccc(*)cc6)c5)cc4)cc3)c2)cc1,,0.35956065,,,
+766443404,*C(F)(F)C1(*)OC(F)(C(F)(F)F)C(F)(C(F)(F)F)O1,,0.33383626,,,
+766636095,*CCCCCCOC(=O)C(CCCCCP(=O)(OCC)OCC)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.36679836,,,
+766749407,*CC(*)(C)C(=O)OCCCc1ccc(N=Nc2ccc(OC(F)(F)F)cc2)cc1,,0.35850149,,,
+767346074,*CCCCCCCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.36151008,,,
+768384141,*CCCP(CCCCCCCC)CCCNC(=O)CCCCC(=O)N*,,0.3656047,,,
+768467775,*Oc1ccc(Oc2ccc(Oc3cccc(*)n3)cc2)cc1,-19.11943844,,,,
+769383218,*CCOP(=O)(N=Nc1ccc(Oc2ccc(N=NP(=O)(O*)OCC)cc2)cc1)OCC,,0.3620127,,,
+769839159,*Oc1ccc(C(=O)OCCOCCOCCOCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.3367055,,,
+770392100,*CC(*)(C)C(=O)OC1(C)CCOC(=O)C1,,0.31901326,,,
+770474874,*CCCCCNC(=O)CCCCCCOCCCCCCC(=O)NCCCCCO*,,,0.285,0.955888415,19.41025454
+770593247,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C#Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.39815997,,,
+771410628,*C(=O)OCC(COc1ccc(N=Nc2ccc(C#N)cc2)cc1)OC(=O)c1ncccc1*,,0.3600163,,,
+771609530,*CC(*)c1ccc(CCCCCC)cc1,,0.40375659,0.241,,
+771639833,*CC(O)CN(C)S(=O)(=O)c1cccc(S(=O)(=O)N(C)CC(O)COc2ccc(-c3ccc(O*)cc3)cc2)c1,,0.34065553,,,
+771772442,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)Nc3c(C)cc(Cc4cc(C)c(NC(=O)c5ccc(*)cc5)c(C(C)C)c4)cc3C(C)C)cc2)cc1C(C)(C)C,,0.40228306,,,
+772440851,*CC(*)c1ccc(C(=O)Cc2ccccc2)cc1,,0.36666659,,,
+772566639,*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCCCCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35617191,,,
+772591088,*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cc6cc(*)ccc6oc5=O)cc4)cc3)cc2c1,,0.33664324,,,
+773428035,*O[Si](*)(C)CCCOc1ccc(C(=O)OC2CCC3(C)C(=CCC4C3CCC3(C)C(C(C)CCCC(C)C)CCC43)C2)cc1,,0.373350915,,,
+773706830,*Nc1cccc2c(N*)c3ccccc3cc12,,0.34219547,,,
+774116157,*c1ccc(-c2ccc3c(c2)C(CCCCCCBr)(CCCCCCBr)c2cc(*)ccc2-3)cc1,,0.41425239,0.236,1.064142833,29.39678289
+774594190,*Nc1ccc(-c2cc(N*)ccc2-c2ccc(N)cc2)cc1,,0.34901501,,,
+775478514,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O,,0.37412723,,,
+776515253,*C(=O)N(*)CCCCCC,14.34014558,,,,
+777120719,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5ncc(*)o5)cc4C(F)(F)F)cc3)(C(F)(F)F)C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.39644075,,,
+777245562,*Nc1ccc2cc3cccc(N*)c3cc2c1,,0.33634853,,,
+777333329,*c1ccc2c(c1)C(=O)N(c1cc(Oc3c(C)cc(-c4cc(C)c(Oc5cc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc(C(F)(F)F)c5)c(C)c4)cc3C)cc(C(F)(F)F)c1)C2=O,,0.39069947,,,
+777956854,*Nc1ccccc1-c1ccc2c(c1-c1ccccc1N*)Cc1ccccc1-2,,0.359745555,,,
+778024152,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(OCCCOc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,163.1829015,,,,
+778266959,*CC(*)c1ccc(C(C)(O)CCCC)cc1,,0.39130508,,,
+778508668,*CCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.33853108,,,
+779018837,*C=C(*)c1cc(CO)c(OCc2ccc(CNC(C)COCCCCCCCC)cc2)c(CO)c1,65.78481038,,,,
+779245999,*c1ccc(C(*)C)cc1,,0.38776723,,,
+779535649,*CCc1ccc(CCNC(=O)CCCCCCCCCCCCCCCC(=O)N*)cc1,,,0.3515,0.963570323,20.56020443
+779877733,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(OCCCCCCOc4ccc(/C=C/c5ccc(F)cc5)cc4)c3)cc1OCCCCCCOc1ccc(/C=C/c3ccc(F)cc3)cc1)C2=O,,0.36477665,,,
+780149366,*Nc1ccc(-c2ccccc2)c(-c2ccccc2)c1N*,,0.3546781,,,
+780388384,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)cc4)o3)cc2)cc1,,0.48674163,,,
+780433305,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(-c9ccc(*)cc9)cc8)cc7)cc6)cc5)c4)cc3)cc2)cc1,,0.36823106,,,
+780752652,*Nc1cc2c(cc1N*)[C@@]1(C)c3cc(N)c(N)cc3[C@@]3(C)c4cc(N)c(N)cc4[C@]2(C)[C@]31C,,0.40071937,,,
+781281190,*Oc1ccc(NC(=O)c2cc(NC(=O)c3ccc(OC(C)=O)cc3)cc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(*)cc5)cc4)cc3)c2)cc1,,0.34777733,,,
+781306630,*c1cccc(N2C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.3954689,,,
+781325985,*Nc1ccc(Br)cc1-c1ccc(NC(=O)c2ccc(C(*)=O)cc2)cc1,,0.36239471,,,
+781403971,*CC(O)CN(*)c1cc(C)cc(C)c1,,0.38340984,,,
+781605401,*CCCCCCCCCCC(=O)NCCCCCC(=O)N*,,0.35926129,0.296,0.953522466,19.36818383
+782092877,*Nc1nc(N*)n(N)c(=S)n1,,0.31626297,,,
+782678603,*CCCCCCCCCCCCCCCCCCCCOC(=O)CC(=O)O*,,0.38129764,0.318,0.918493366,19.41376744
+783040210,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37602514,,,
+783055378,*c1cccc(C(=O)Nc2cccc(S(=O)(=O)c3cccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)c4)c3)c2)c1,,0.35166575,,,
+783150586,*/C=C(\C#N)C(=O)Nc1cccc(NC(=O)/C(C#N)=C/c2ccc(/C=C/c3ccc(N(c4ccccc4)c4ccc(N(c5ccccc5)c5ccc(/C=C/c6ccc(*)s6)cc5)cc4)cc3)s2)c1,243.9892983,,,,
+783176072,*C(=O)OCCCSCCCCCCSCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.35879978,,,
+783233627,*O[Sn](CCCC)(CCCC)OC(=O)CCCCC(*)=O,,0.36080704,,,
+783252697,*CCCCCCCCCCCCOC(=O)NCc1ccc(CNC(=O)O*)cc1,,0.35707291,,,
+783421229,*c1cccc(P(=O)(c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c1,,0.38638827,,,
+783528107,*c1cc(CCCCCCOC(=O)COC)c(*)s1,,0.36444159,,,
+783741919,*Nc1cc2c(cc1N*)C1(c3cc(N)c(N)cc3-c3cc(N)c(N)cc31)c1cc(N)c(N)cc1-2,,0.37495313,,,
+783812275,*C1CCC(*)C1,,0.37999271,,0.903468969,19.86080991
+784293016,*CC(CC(*)(C#N)C#N)c1ccc(CCl)cc1,,0.3903293,,,
+784363756,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6cc(*)n(-c7ccccc7)n6)cc5)cc4)cc3)cc2)cc1,,0.38790383,,,
+784935951,*CCNC(=O)c1ccc2cc(O*)ccc2c1,,0.33091429,,,
+785079844,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.35773858,,,
+785209471,*OC(=O)C(C)NC(=O)CCCCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.32784472,,,
+785463469,*CCCCCCCCc1nc2cc(-c3ccc4[nH]c(*)nc4c3)ccc2[nH]1,,0.37040662,,,
+785739315,*CC(*)(C)C(=O)OCC(C)(C)C1OCC(C)(C)CO1,,0.36823735,0.1619999999999999,0.904274504,10.65097658
+786535870,*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Nc4ccc(C(=O)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.3924613,,,
+786832504,*Oc1ccc(Sc2ccc(Oc3ccc4c(=O)n5c6cc(Oc7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.37773112,,,
+787172285,*Nc1cc2c(cc1N*)[C@H]1c3ccccc3[C@@H]2c2ccccc21,,0.37667413,,,
+787518160,*CCC(=O)OCCCC=CCCCOC(=O)CCC1NC(=O)C(*)NC1=O,25.37727633,,,,
+787533936,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(Cc4cc(C)c(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)c(C)c4)cc3C)cc1)C2=O,,0.39072114,,,
+788080561,*Oc1ccc(C(C)(C)c2ccc(OC(=O)Nc3cccc(P(=O)(c4ccccc4)c4cccc(NC(*)=O)c4)c3)cc2)cc1,,0.36021287,,,
+788688131,*c1cc(OCCCCCCCCCCCCCCCC)cc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37287134,,,
+789071222,*CC(*)c1ccccc1C(=O)Nc1ccccc1,,0.35753244,0.1636666666666666,,
+789164013,*CC(*)(C)C(=O)OCCCCCCn1c2ccccc2c2cc(N=Nc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])ccc21,,0.3638222,,,
+789200912,*CC(*)c1c(Cl)cccc1Cl,,0.39048668,,,
+789216614,*NCCNCCCNCCN*,,0.3304575349999999,,,
+789218686,*CC(*)(C)C(=O)NC(C)c1ccccc1,,0.3551036,,,
+789469220,*CC(*)(C)C(=O)OCc1ccc(C=CC(=O)C2c3ccccc3C(=O)c3ccccc32)cc1,,0.34725441,,,
+790220540,*Oc1ccc(S(=O)(=O)c2cccc3c(S(=O)(=O)c4ccc(Oc5ccc(-c6ccc(*)cc6)cc5)cc4)cccc23)cc1,,0.36562978,,,
+790422297,*CC(*)(C)C(=O)OCCCCCCOc1cccc(C(=O)Oc2ccc(C#N)cc2)c1,,0.34903264,,,
+790707240,*Nc1ccc(CCc2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,245.2414672,,,,
+790828475,*Nc1ccc(C(c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.3504904,,,
+791518570,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6cccc7c(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cccc67)cc5C4=O)cc3)n2)cc1,,0.35993207,,,
+791544306,*c1ccc(-c2cc(OCCCCCCCCCC)c(*)cc2OCCCCCCCCCC)cc1,61.07976291,,,,
+791643770,*c1ccc(NC(=O)c2ccc(OCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)cc1,,0.34555816,,,
+791644318,*C(=O)Oc1ccc(OCCCCCCCCCCCCOc2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35374697,,,
+791901717,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)CCCCC(=O)O*,,0.31905896,,,
+792165329,*CC(*)c1ccccc1C(=O)OCCC(C)C,,0.37558772,0.1929999999999999,,
+792850218,*CCCCS(*)(=O)=O,-14.41108992,,,,
+793023871,*CC(*)c1cccs1,,0.40399034,0.1995,1.115067014,15.89865681
+793067604,*OC(COC(=O)c1ccc(-c2ccc(C(*)=O)cc2)cc1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35920375,,,
+793720253,*Nc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(NC(=S)NC(=O)CCCCCCCCC(=O)NC(*)=S)c3)c2)c1,,0.34654,,,
+793830629,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35263439,,,
+794214898,*CCCCCCNC(=O)CCc1ccc(C(C)CC(=O)N*)cc1,,0.35062376,,,
+794405907,*Oc1ccc(-c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1-c1ccccc1,,0.35661176,,,
+794427257,*CC(*)(C)C(=O)OCC(F)(F)C(F)(F)F,,0.34535374,,,
+794510468,*Nc1ccc(C(C)(c2ccccc2)c2ccc(N*)c(C)c2C)c(C)c1C,,0.36512516,,,
+794641374,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35417449,,,
+794995355,*C(=O)Nc1ccc(C(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1,,0.36068208,,,
+795727494,*c1cc(N=Nc2ccc(OC)cc2)cc(*)c1O,,0.37316682,,,
+795837749,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(-c5cc(C)c(N6C(=O)c7ccc(*)cc7C6=O)c(C)c5)cc3C)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37779724,,,
+796117252,*Nc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(NC(=O)c5ccc([Si](C)(C)c6ccc(C(*)=O)cc6)cc5)cc4)cc3)cc2)cc1,,0.35830263,,,
+796744400,*Oc1cccc(OC(=O)c2cc(OCCCCC)cc(C(*)=O)c2)c1,,0.35370683,,,
+796832411,*c1ccc(Oc2ccc(N3C(=O)c4c(c(-c5ccccc5)c(-c5ccc(-c6c(-c7ccccc7)c7c(c(-c8ccccc8)c6-c6ccccc6)C(=O)N(*)C7=O)cc5)c(-c5ccccc5)c4-c4ccccc4)C3=O)cc2)cc1,,0.4068871,,,
+796983808,*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4cccc5ccccc45)cc3)C(C2)C1*,,0.37604528,,,
+797241563,*CCCCOC(=O)CCCC(=O)O*,,0.33275554,,,
+797481672,*c1c(C)cc(C)c(N2C(=O)c3cccc(Oc4c(C)cc(Cc5cc(C)c(Oc6cccc7c6C(=O)N(*)C7=O)c(C)c5)cc4C)c3C2=O)c1C,,0.39441657,,,
+798215988,*c1ccc(C(=O)Nc2cccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5cccc(NC(=O)c6ccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)cc6)c5)cc4)cc3)c2)cc1,,0.35874825,,,
+798268482,*c1cc(C(c2ccc(OCCN(CC)c3ccc(C=CC4=CC(=C(C#N)C#N)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(CC)c1ccc(C=CC2=CC(=C(C#N)C#N)CC(C)(C)C2)cc1,,0.38611474,,,
+798438482,*OC1CCC(OC(=O)c2ccc(C(*)=O)cc2)CC1,,0.35770119,,,
+798454930,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.34699309,,,
+798498386,*CC(CC)(CC)COC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.35922882,,,
+798869032,*Nc1ccc([C@]23C[C@H]4C[C@H](C[C@](c5ccc(N*)cc5)(C4)C2)C3)cc1,,0.34941941,,,
+799217349,*C(F)(F)C(F)(F)C(F)(F)C(*)(F)C(F)(F)F,-86.88823628,,,,
+799283066,*CC=CCOC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)O*,,0.35266121,,,
+799636548,*CCCCCCCCCCNC(=O)CCP(C)(=O)CCC(=O)N*,,0.35467591,,,
+799690401,*Nc1cc2c(cc1C)-c1cc(C)c(N*)cc1S2(=O)=O,,0.358207955,,,
+800089698,*CCCC(=O)Nc1ccc(Cc2ccc(NC(=O)CCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33581592,,,
+800130991,*Nc1ccc(C(C)(c2ccc(C)cc2)c2ccc(N*)cc2)cc1,,0.36023204,,,
+800215811,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OC)cc1,,0.33150539,,,
+800676373,*CCN(CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C)c(C)c1,,0.35323912,,,
+800838483,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(O*)c3)cc2)cc1,,0.34411231,,,
+800998431,*CC(=O)Nc1ccc(NC(=O)COc2ccc3ccccc3c2Sc2c(O*)ccc3ccccc23)cc1,,0.33977149,,,
+801063364,*CCOc1ccc(C=C2CCC(=Cc3ccc(OCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC,,0.34641138,,,
+801330021,*CC(*)(C)C(=O)Oc1ccccc1,,,0.184,1.049795923,13.34805903
+801675840,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc3c(c1)C(=O)c1cc(N4C(=O)c5ccc(*)cc5C4=O)ccc1-3)C2=O,,0.3830772,,,
+801689933,*CCCCCCOC(=O)CCCCCCCCC(=O)O*,,0.36054003,,,
+801852409,*Nc1ccc(C(c2ccc(N)c(CC)c2CC)c2ccc(N*)c(CC)c2CC)c(CC)c1CC,,0.39358562,,,
+802299717,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(C(C)(C)c9ccc(*)cc9)cc8)cc7)cc6)cc5)c4)cc3)cc2)cc1,,0.36924917,,,
+802360891,*CCCCCCCCC(=O)NCCCCCCNC(=O)CCCCCCCCO*,,,0.3575,0.930685649,20.81346955
+802506538,*CC(*)(C)C(=O)OCCOCC[N+](CC)(CC)CCCCS(=O)(=O)O,,0.420485,,,
+802993859,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCCCCCCCCCC,,0.37769725,,,
+803106454,*CC(*)(C)C(=O)OCC1CCCO1,,0.34666826,0.172,1.030703138,11.83904224
+803775586,*CC(*)C1CCC(C)CC1,,0.39863974,0.1996666666666666,,
+804037111,*CC(*)C(=O)Oc1ccc(Cl)cc1Cl,,0.36475946,0.119,1.323601324,15.04038649
+804149832,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5ccc(*)s5)cc4C(F)(F)F)cc3)(C(F)(F)F)C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.40617944,,,
+804322446,*c1cc(CC(=O)OCCCCCCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)c(*)s1,,0.37372874,,,
+805227128,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.35687756,,,
+806311584,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OCCOC(=O)C=Cc2ccc(N(C)C)cc2)c1,,0.34026095,,,
+806397458,*CCCCCCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCCCCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC,,0.3656755,,,
+806790733,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37007109,,,
+806978622,*CC(*)O[N+](=O)[O-],,0.77406593,0.1585,1.305301671,17.27231362
+807387712,*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)ccc2C(=O)c2ccccc2)c1,,0.38183506,,,
+807551839,*/C=C/c1ccc(*)c(-c2c(OCC(CC)CCCC)ccc3cc(-c4ccccc4)ccc23)c1,,,0.212,0.984517634,14.07025714
+807875474,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(Cc4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36538681,,,
+808029389,*c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O,,0.41239714,,,
+808269149,*C(=O)Oc1ccc([Si](C)(c2ccccc2)c2ccc(OC(=O)c3ccc(*)s3)cc2)cc1,,0.38407419,,,
+808476294,*c1ccc(Oc2ccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(-c8nnc(*)o8)cc7)cc5)C6=O)cc4C3=O)cc2)cc1,,0.38811487,,,
+808488658,*Nc1cc(C)c2cc3c(C)c(C)c(N*)c(C)c3cc2c1,,0.3653562,,,
+808582678,*NCC[C@@]12C[C@H]3C[C@@H](C1)C[C@](c1ccc(N*)cc1)(C3)C2,,0.33910824,,,
+808879247,*c1cccc(N2C(=O)c3cccc(Oc4ccc(C(C)(C)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.3702511,,,
+810134797,*c1cc(OCCCCCCCCCCCC)cc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.3689144,,,
+810221803,*CCCC(CCNC(=O)c1ccc(C(=O)N*)cc1)C(C)C,,0.34137267,,,
+810464372,*Oc1ccc(-c2ccc(Oc3ccc(Oc4cccc(Oc5ccc(*)cc5)c4C)cc3)c3ccccc23)c2ccccc12,,0.38357509,,,
+810570947,*C(=O)c1ccc(C(C)(CC)c2ccc(C(=O)N3CCN(*)CC3)cc2)cc1,,0.37489095,,,
+810716859,*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.37302009,,,
+810767754,*CCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.3352167,,,
+811102498,*CC(OC)C1C(=O)N(c2ccc(N(C)C)cc2)C(=O)C1*,,0.36226319,,,
+811196388,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccc(CC)cc32)cc1,,0.37736059,,,
+811299446,*NC1CCCC(N*)C1,,0.33385202,,,
+812075542,*Nc1ccc(-c2ccc(-c3ccc(N)cc3)c(-c3ccc(N*)cc3)c2)cc1,,0.35568128,,,
+812088904,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1-c1ccc(Cl)c(C(F)(F)F)c1,,0.38008742,,,
+812162763,*CCCCCCOC(=O)C(CCCCCCCCCCP(=O)(OCC)OCC)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.36595222,,,
+812270433,*Oc1ccc(-c2ccc(NC(=O)c3cc(C(=O)Nc4ccc(-c5ccc(Oc6ccc(C(C)(C)c7ccc(*)cc7)cc6)c(C(F)(F)F)c5)cc4)cc(C(C)(C)C)c3)cc2)cc1C(F)(F)F,,0.37653866,,,
+812457901,*CC(*)c1ccc(C(C)=NO)cc1,,0.35867112,,,
+812618019,*C(*)C,122.3867673,,0.2033333333333333,,
+812811097,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2N)cc1,,0.36447678,,,
+813149599,*Oc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.38193072,,,
+813214787,*Nc1ccc2c(c1N*)-c1ccccc1-c1ccccc1-c1ccccc1-2,,0.36739742,,,
+814144401,*C/C=C\C[Si](*)(C)CCCOc1ccc2ccccc2c1,,0.36773968,,,
+814145084,*Nc1ccc(-c2cc(-c3ccc(N*)cc3)cc(C(C)(C)C)c2)cc1,,0.36221606,,,
+814315691,*CC(*)OC(=O)CCCC(=O)c1ccccc1,,0.3393767,,,
+814898102,*CCCCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37926968,,,
+814967191,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4cccc(Oc5c(C)cc(-c6cc(C)c(Oc7cccc8c7C(=O)N(*)C8=O)c(C)c6)cc5C)c4C3=O)c(C)c2)cc1C,,0.3975525,,,
+815036763,*CCCCOC(=O)CCC(=O)O*,,0.3291051,,,
+815051873,*c1ccc(C(=O)OCCCCCCOC(=O)c2ccc(-c3nnc(*)s3)cc2)cc1,,0.36031607,,,
+815588093,*CCCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.3376709,,,
+815998527,*CCCCCCOP(=O)(N=Nc1ccc(C(=O)c2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.3606996,,,
+816208987,*CC(O)CN(*)c1cc(C)c(N=Nc2ccc(Cl)cc2)c(C)c1,,0.38104863,,,
+816499147,*CCCCCCCCc1nc2ccc(-c3ccc4nc(*)sc4c3)cc2s1,,0.37827781,,,
+816523902,*CC(*)OC(=O)c1ccncc1,,0.34235599,,,
+816594704,*CC(*)C(=O)OCC(F)(F)C(F)(F)F,,0.33021141,,,
+816699374,*CC(*)c1ccc(S(=O)(=O)OCCC)cc1,,0.37741586,,,
+816729790,*Nc1ccc(C(c2ccc(N)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36920269,,,
+816767607,*CCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,,0.4165,0.92091158,20.46088256
+816771422,*CCCCCCCCC(=O)NCCCCCOCCCCCNC(=O)CCCCO*,,,0.287,0.959755769,19.44997486
+817102906,*CC(O)COC(=O)CCCCCCCCC(=O)OCC(O)COc1ccc(C(=O)Oc2ccc(O*)cc2)cc1,,0.33714528,,,
+817179455,*CCN(*)C(=O)CCCCCCCC,-49.55374382,,,,
+817222485,*CCC=C(*)CCCCCCCCCC,,0.40745292,,,
+817646682,*CC(*)(C)C(=O)Oc1ccc(C(=O)C=Cc2ccc(OCc3ccccc3)cc2)cc1,,0.34505383,,,
+817764960,*Oc1ccc(Oc2ccc(Oc3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,136.3718208,,,,
+818720899,*OCCOC(=O)c1cccc(C(=O)OCCOc2nc(-c3ccc(OCCCC)cc3)nc(*)c2-c2ccc(OC)cc2)c1,,0.35974624,,,
+818853032,*CC(C)(CC)COC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35540471,,,
+819286039,*c1ccc2nc3c4cccc5c6nc7ccc(*)cc7nc6c6cccc(c3nc2c1)c6c45,411.97,,,,
+819429551,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Sc8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.37088138,,,
+819812613,*Nc1ccc(Cc2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.37242731,,,
+819863073,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(S(=O)(=O)c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,,0.35983629,,,
+819957053,*Nc1ccc(CC(C)(C)NC(=O)CCC(*)=O)cc1,,0.33397161,,,
+820067576,*CC(O)CN(CC(O)CN(*)c1ccccc1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35876659,,,
+820073046,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,,0.35449352,,,
+820293105,*Oc1ccc(OC(=O)CCCCCCCCC(*)=O)cc1,,0.35187189,0.257,1.048950682,23.48222813
+820510299,*Nc1ccc(NC(=S)NC(=O)c2ccc(C(=O)NC(*)=S)cc2)cc1,220.8197438,,,,
+820601696,*C(=O)c1ccc2ncc(-c3ccc(-c4cnc5ccc(*)cc5n4)cc3)nc2c1,,0.40401109,,,
+820692130,*CC(=O)Nc1ccc(NC(=O)CN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.32771486,,,
+821219964,*CC(*)C(=O)Oc1ccc(C(=O)O)cc1,,0.31040659,,,
+821229257,*Oc1ccc(OC(=O)CCCCCCCC(*)=O)cc1,-6.058372606,,,,
+821537914,*CC(*)c1ccccc1C(=O)OC,,0.35299495,0.1736666666666666,,
+821542361,*Sc1ccc(Nc2ccc(*)cc2)cc1,,0.3997415,,,
+821655324,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCC)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37014915,,,
+821914285,*c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1C)C2=O,,0.39473937,,,
+822072702,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(N4C(=O)c5ccc(*)cc5C4=O)cc2)C3=O)cc1,-71.68107141,,,,
+822079361,*CC(*)C(=O)NCP(=O)(O)O,,0.26739328,,,
+822137197,*CCCCCCCCOC(=O)c1ccc(C(=O)NCCCCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,,,0.261,,
+822757140,*CNC(=O)OC(c1ccco1)C(OC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1)c1ccco1,,0.35185553,,,
+822972602,*NNC(=O)CCC(=O)NNC(=O)CCCCCCCCC(*)=O,64.69850401,,,,
+823244753,*Nc1nc(N*)c(N)nc1N,,0.31946255,,,
+823634116,*c1cccc(P(=O)(c2ccc(C(F)(F)F)cc2)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.38545979,,,
+824512000,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.34911505,,,
+825127348,*c1ccc2cc(-c3nc4ccc(-c5ccc6nc(*)[nH]c6c5)cc4[nH]3)ccc2c1,,0.43811052,,,
+825973293,*c1cc(C)c(Oc2ccc(S(=O)(=O)c3ccc(Oc4cc(C)c(C5(*)OC(=O)c6ccccc65)cc4C)cc3)cc2)cc1C,,0.39578665,,,
+826258963,*C=CC1CC(*)C(C(=O)OCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1C(=O)OCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,27.68661296,,,,
+826441672,*NC1CC(C)(C)CC(C)(CNC(=O)c2cc(NC(=O)C(C(C)CC)N3C(=O)c4ccccc4C3=O)cc(C(*)=O)c2)C1,,0.34594928,,,
+827035363,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCC)cc1,,0.33464221,,,
+827086896,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(-c5ncc(*)o5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.397283,,,
+827590818,*c1ccc(Cc2ccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.37388099,,,
+828069066,*CC(CC(*)(C#N)C#N)c1ccc(F)cc1,,0.39106819,,,
+828078901,*c1ccc(Oc2ccc(P(C)(=O)c3ccc(Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.38109983,,,
+828636202,*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1,,0.3631737,,,
+828717601,*Nc1ccc(CCc2ccc(CCc3ccc(N*)cc3)cc2)cc1,,0.34961202,,,
+828807298,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C(F)(F)F)cc5)n4)cc3)c(C(F)(F)F)c1)C2=O,,0.36226702,,,
+828904716,*CCCCCCNC(=O)CCC(C)CC(=O)N*,,0.33972833,,,
+829968625,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccc(Cl)cc3)c3ccccc23)cc1,,0.36207231,,,
+829981361,*Oc1ccc(C(C)(C)c2ccc(OC(=O)NC(=O)c3cc(C(=O)NC(*)=O)cc(C(C)(C)C)c3)cc2)cc1,,0.3446568,,,
+830475899,*CCCNC(=O)CNC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NCC(=O)NCCCOCCOCCO*,,0.34027118,,,
+831848484,*CCC(C)CC(=O)O*,,0.34423811,,,
+831990105,*CC(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.3239093,,,
+832112945,*CCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.33800885,,,
+832174357,*CCCCCCCCCCCCCCCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,17.27783213,,,,
+832267594,*C1C(=O)N(c2ccc(O[Si](C)(C)C)cc2)C(=O)C1*,,0.43916684,,,
+832293069,*CCN(*)C(=O)c1c(F)cccc1F,,0.34715202,,,
+832907527,*Nc1cccc2c(NC(=O)Nc3cc(NC(*)=O)cc(C(=O)Nc4ccc5c(c4)C(=O)c4ccccc4C5=O)c3)cccc12,,0.32826884,,,
+832948495,*Nc1ccc(-c2cc(-c3ccc(N)cc3)cc(-c3ccc(N*)cc3)c2)cc1,,0.348951605,,,
+833018953,*CCCC(C)(C)CNC(=O)c1ccc(C(=O)N*)cc1,,0.34232189,,,
+833673959,*O[Si](C)(C)CCCCCC[Si](*)(C)C,,0.44650373,,,
+834012774,*CC(*)c1cccc(N(c2ccc(C)cc2)c2ccc(C)cc2)c1,,0.42262302,,0.975794098,10.58513595
+834236236,*Nc1ccc2c(c1)C(C)(C)c1cc3c(N*)c4ccccc4c(N)c3cc1-2,,0.38120825,,,
+834328037,*CCCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.38060796,,,
+834450099,*C(=O)Nc1cc(NC(=O)c2cncc(*)c2)cc(-c2nnc(-c3ccccn3)o2)c1,,0.36789463,,,
+834642680,*c1ccc(-c2ccc(S(=O)(=O)NCCNS(*)(=O)=O)cc2)cc1,173.2454244,,,,
+835449661,*CCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.34163356,,,
+835465429,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccccc2)cc1,,0.3703315,,,
+836337726,*c1ccc2oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(S(*)(=O)=O)cc5)cc4)cc3c2c1,,0.37867656,,,
+836390884,*Nc1cc2c(cc1N*)C(C[C@H](CC)CCCC)(C[C@H](CC)CCCC)c1cc(N)c(N)cc1-2,,0.35441352,,,
+836759737,*CC(C)(CO*)COCC(F)(F)C(F)(F)C(F)(F)F,,0.35888622,,,
+836965269,*CC(=O)Nc1ccc(Oc2ccc(NC(=O)CN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33219113,,,
+837296086,*OP(=O)(Oc1ccc(OC)cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.34736304,,,
+837351512,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3cccc(C)c32)cc1,,0.37209959,,,
+837417544,*Nc1ccc(-c2ccc(N*)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.34397372,,,
+837604570,*CCCCOCCCCOCCCCOC(=O)c1ccc(N=Cc2cc(OCCC(C)CCCC(C)C)c(C=Nc3ccc(C(=O)O*)cc3)cc2OCCC(C)CCCC(C)C)cc1,,0.37606592,,,
+838168073,*C(=O)NCCNC(=O)c1ncn(C)c1*,,0.30881015,,,
+838352971,*Nc1ccc2c(c1)Cc1cc(N*)c(N)cc1-2,,0.35883354,,,
+838510907,*Oc1ccc(C(=O)c2cccc(-c3cccc(C(=O)c4ccc(*)cc4)c3)c2)cc1,,0.36823829,,,
+838925791,*CCCN(C)c1ccc(C(=O)c2ccc(N(*)C)cc2)cc1,,0.37870937,,,
+839165953,*N[C@@H](CCC[C@H](N*)C(=O)[C@@H](N)[C@@H](C)O)C(=O)O,,0.30156557,,,
+839183601,*OC(C)CC(*)=O,,0.33260696,,,
+839463211,*Nc1noc2nc3c(C(=O)OCC)cc(C(=O)c4ccc(C)cc4)c(N)c3c(N*)c12,,0.33167362,,,
+839630987,*c1cccc(OCCCCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.3592549,,,
+839752989,*CCCCCCCCCCC(=O)NCCCCCOCCCCCNC(=O)CCCO*,,,0.303,0.949699751,22.28109939
+839878795,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C5(*)c6ccccc6C(=O)N5C)cc4)cc3)cc2)cc1,,0.39435805,,,
+840186031,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)cc2)cc1,,0.38721196,,,
+840194928,*CCCCCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.37504022,,0.949326163,16.72244264
+840303746,*CC(O)CN(CCO)S(=O)(=O)c1ccc(-c2ccc(S(=O)(=O)N(CCO)CC(O)COc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)cc2)cc1,,0.33980494,,,
+840698807,*CC(*)C(=O)OCC(C)CC,,0.37285766,0.221,0.919640881,13.54986722
+841055334,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.3824439,,,
+841072650,*c1ccc(N(*)CCCCCCC)cc1,110.7170963,,,,
+841081788,*CCCCNC(=O)CC(=O)N*,,0.30389328,,,
+841226784,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccc(-c3ccccc3)cc2)cc1,,0.34391945,,,
+841328550,*c1cc(N2C(=O)c3ccc(Oc4ccc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5c4)cc3C2=O)cc(C(F)(F)F)c1,,0.37446524,,,
+841576889,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(Oc4ccc(C56CC7CC(CC(C7)C5)C6)cc4)cc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.38634637,,,
+842024022,*Oc1ccc(OC2(F)C(*)(F)C(F)(F)C2(F)F)cc1,,0.34021379,,,
+842148313,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3ccccc3C)c2-c2ccccc2C)cc1,,0.37961071,,,
+842167683,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCC(c%10ccccc%10)CC9)cc8)cc7C6=O)cc5)cc4)CC4CCC3C4)cc2)cc1,,0.38120517,,,
+842432665,*CC1CCC(COC(=O)CCCCCCCCCCC(=O)O*)CC1,,,0.279,,
+842879180,*CC(*)(C)C(=O)Oc1ccccc1C,,,0.1905,1.024902598,13.62318206
+843120215,*Oc1c(OC)cc(-c2cc(OC)c(OC(=O)c3cccc(C(*)=O)c3)c(OC)c2)cc1OC,,0.34670291,,,
+843575922,*CCOCCOCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1,,0.34013313,,,
+843758383,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.43055287,,,
+843894109,*Nc1ccc(C(c2ccc(N*)cc2)=c2ccc(=C)cc2)cc1,,0.364906,,,
+844100895,*CCOCCOP(=O)(N=Nc1ccc(-c2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.35788799,,,
+844147775,*CCCCCCNC(=O)C(CCCCCCCCCCCCCCCCC)C(=O)N*,,,0.3505,0.889924513,16.83151528
+844156595,*c1cc(C(=O)OCCCCC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.3651339,,,
+844411055,*c1cccc(P(=O)(c2ccc(Oc3ccc(C45CC6CC(CC(C6)C4)C5)cc3)cc2)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.38257569,,,
+844805086,*c1ccc2oc(-c3ccc(C(c4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)(C(F)(F)F)C(F)(F)F)cc3)nc2c1,,0.42531677,,,
+845322563,*CCCCCCC(=O)N*,,0.35135573,,,
+845810761,*c1ccc(C(=O)c2ccc(C(=O)c3ccc(S(*)(=O)=O)cc3)cc2)cc1,,0.37728545,,,
+845873232,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(C(c5ccc(C(=O)Nc6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc12,,0.35696537,,,
+845994024,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1,,0.35537215,,,
+846108255,*Oc1ccc(Oc2ccc(C(=O)c3ccc(NC(=O)CCCCCCCCC(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.35538717,,,
+846390932,*Nc1ccc(/C=C/c2ccccc2/C=C/c2ccc(N*)cc2)cc1,,0.35311635,,,
+846455978,*Nc1cc(O)c(-c2c(O)c(O)c(N)c(N)c2N*)c(O)c1,,0.31859202,,,
+846469607,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4cccc(-c5cccc(C(=O)c6ccc(*)cc6)c5)c4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37249164,,,
+846662171,*CCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.34909907,,,
+846740244,*Nc1cc(S)c(N*)cc1S,,0.34631119,,,
+846811981,*Nc1ccc(C(C)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36464453,,,
+847044135,*CC(*)C(=O)N(O)c1ccccc1,141.1832818,,,,
+847356042,*CCCCCCOC(=O)OCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.34612362,,,
+848025633,*c1cc(C(c2ccc(OCCN(C)c3ccc(C=CC4=CC(=C(C#N)C(=O)OCCc5ccccc5)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(c1ccccc1)c1ccc(C=CC2=CC(=C(C#N)C(=O)OCCc3ccccc3)CC(C)(C)C2)cc1,,0.37158327,,,
+848202218,*CC(O)COc1ccc(C2CCCC(CC)(c3ccc(O*)cc3)C2)cc1,,0.35559164,,,
+848295596,*Oc1cccc(Oc2ccc(NC(=O)c3ccc(-c4ccc(C(=O)Nc5ccc(*)cc5)c(C)c4)cc3C)cc2)c1,,0.35683995,,,
+848393860,*Nc1ccc([C@@]2(c3ccccc3)c3ccccc3-c3cc(N*)ccc32)cc1,,0.37057896,,,
+848462258,*CC(*)OC(=O)c1cccc(Cl)c1,,0.35874129,,,
+849184713,*Oc1c(Cl)cc(*)cc1Br,,0.4236255,,,
+850250800,*c1c(C)cc(C(c2cccnc2)c2cc(C)c(N3C(=O)CC(Nc4cccc(NC5CC(=O)N(*)C5=O)c4)C3=O)c(C)c2)cc1C,,0.38678861,,,
+850843223,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O,,0.36985522,,,
+851867509,*CC(*)C(=O)Oc1cccc(C)c1,,0.35823799,0.2055,1.071947231,13.42046484
+852036166,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)Oc5c(C)cc(C(C)(C)c6cc(C)c(OC(=O)c7ccc(*)cc7)c(C)c6)cc5C)cc4)cc3)CCC(C(C)(C)C)CC2)cc1,,0.39731792,,,
+852138859,*CCCCCCCCCCCCN1C(=O)c2ccc3c4c(ccc(c24)C1=O)C(=O)N(*)C3=O,101.4116865,,,,
+852180145,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37352321,,,
+852184111,*c1cc(C)c(*)s1,,0.5168642,,,
+852432684,*Nc1ccc(Cc2ccc(NC(=O)/C=C/c3ccc(/C=C/C(*)=O)cc3)cc2)cc1,,0.3481612,,,
+852454383,*Nc1ccc(/C=C2/c3ccccc3-c3ccc(N*)cc32)cc1,,0.3508840749999999,,,
+852757055,*Nc1cc(C)c(C)c2c(N*)cc(C)c(C)c12,,0.34685576,,,
+852866777,*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)c5ccc(-c6ccc([Si](C)(C)c7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.38577325,,,
+853471397,*/C=C/CCCCCCC(Cl)CCCCCC*,,,0.3405,0.903154394,19.54188854
+854050780,*CCOCCOP(=O)(N=Nc1ccc(Oc2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.35616921,,,
+854325702,*O[Si](*)(CCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOC,,0.36409015,,,
+854626537,*Nc1ccc(-c2c(N*)ccc3c2[C@@H](C)CC3(C)C)cc1,,0.36596567,,,
+855265813,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(C)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38349717,,,
+856310175,*CC(*)(CC(=O)OC)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2,,0.37886731,,,
+857493941,*C=C(*)CCCOc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.33826779,,,
+857690226,*CCN(*)C(=O)C(CC)CCCC,,0.38443623,,,
+858163366,*CCCCCCNC(=O)C(CC)CC(CC)C(=O)N*,,0.35751221,,,
+858271129,*N=Nc1ccc(C(=O)NCCCCNC(=O)c2ccc(*)cc2)cc1,,0.34984295,,,
+858364876,*Nc1cc2c(cc1N*)C(C)(C)CC2(C)C,,0.36750858,,,
+858663171,*CCC(=O)O*,,0.3077545,,,
+859360973,*Nc1ccc(-c2cccc(-c3cccc(-c4ccc(N*)cc4)c3)c2)cc1,,0.34516599,,,
+859487043,*C(=O)Oc1ccc(C(=O)Oc2cccc(OC(=O)c3ccc(OC(=O)c4ccc(*)nc4)cc3)c2)cc1,,0.33873144,,,
+859606185,*Cc1ccc(CNC(=O)c2cc(C(=O)N*)cc(C(C)(C)C)c2)cc1,,0.34874019,,,
+859644583,*CCCCCCCCCCCCCCOC(=O)C/C=C/CC(=O)O*,-41.10158883,,,,
+859659419,*CC(CSCCCC)O*,,0.41461382,,,
+859939433,*Nc1ccc([C@]23C[C@@H]4C[C@@H]5[C@@H]6C[C@H](C[C@H]52)C[C@@]3(c2ccc(N*)cc2)[C@@H]6C4)cc1,,0.35828475,,,
+860122882,*Nc1ccc([C@@]23C[C@@H]4C[C@@H](C[C@@](N*)(C4)C2)C3)cc1,,0.34323604,,,
+860336451,*CCCCCCOC(=O)C(CCCCP(=O)(O)O)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.34888897,,,
+860722279,*O[Si](C)(CCC(F)(F)F)O[Si](C)(CCC(F)(F)F)O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.38225238,,,
+860898971,*CCOCCOCCOCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.34018016,,,
+861130931,*CCCCCCCCCCCCCCCCCCCCC(*)COCCOCCOCCOCCOCCCCCC,,,0.39925,0.886368922,18.76168306
+861281692,*Oc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.40111229,,,
+861558907,*OCCOC(=O)c1cc(N=Nc2ccc(OCC)cc2)ccc1-c1ccc(N=Nc2ccc(OCC)cc2)cc1C(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1,,0.36727846,,,
+861681123,*COC(=O)NCc1ccc(CNC(=O)OCc2ccc(*)o2)o1,,0.32330608,,,
+862321368,*CC(*)c1cc(C(=O)OCCCCCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.38417048,,,
+862498467,*C(=O)Oc1ccc(C23CC4CC(CC(C4)C2)C3)c(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(-c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C)c(C)c2)C3=O)c1,,0.38353529,,,
+862699408,*Oc1cc(C(C)(C)c2ccccc2)c(OC(=O)c2cccc(C(*)=O)c2)cc1C(C)(C)c1ccccc1,,0.38262051,,,
+862879139,*Nc1ccc(-c2ccccc2)cc1-c1c(N*)ccc2ccccc12,,0.34947854,,,
+863587457,*CC(O)COC(=O)CCCCC(=O)O*,,0.31523099,,,
+863625275,*Nc1c(C)cc(C2(c3cc(C)c(N*)c(C)c3)c3cccc(C)c3-c3c(C)cccc32)cc1C,,0.38998404,,,
+864305544,*CC(*)(C)C(=O)OC(F)(C(F)(F)F)C(F)(F)F,,,0.069,1.327370903,15.38423682
+864842890,*CCCCCCCCCCCCOc1ccc(C=NN=Cc2ccc(O*)cc2O)c(O)c1,,0.36634153,,,
+865092427,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(C4(*)c5ccccc5C(=O)N4C)cc3)cc2)cc1,,0.36865057,,,
+865332656,*CCCCCCCCCCCC(=O)N(*)C,,0.37628119,,,
+865769090,*CC(*)(F)C(C)=O,,0.32677748,,,
+865858202,*Oc1ccc(C(=O)CNc2ccc(S(=O)(=O)c3ccc(NCC(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.36395079,,,
+865879594,*CC(*)CNc1ccc([N+](=O)[O-])c(C(F)(F)F)c1,,0.3854775,,,
+865948061,*c1ccc(-c2ccc(-c3ccc(-n4c(=O)c5cc6c(=O)n(*)c(=O)c6cc5c4=O)cc3)cc2)cc1,278.5221779,,,,
+866279797,*Nc1ccc(C(=O)c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)cc1,,0.36605303,,,
+866677131,*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(OC)cc2)cc1,,0.37913945,,,
+866841862,*Nc1ccc(C(C)=C(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36759676,,,
+866951127,*CC(*)CCCC(C)C,5.980301023,,0.2573333333333333,,
+867336767,*Nc1ccccc1C(C)(C)c1ccc(-c2ccc(C(C)(C)c3ccccc3N*)cc2)cc1,,0.35983878,,,
+867393548,*CCCCCCCCCCCCCCNC(=O)NCCCCCCNC(=O)N*,,0.36160098,0.4,0.953783995,18.42693992
+867402833,*CC(*)C(=O)c1ccccc1,,0.36539681,,,
+867714785,*C(C)=C(*)[Si](C)(C)CCCC,152.5242236,,,,
+867758699,*c1nc(-c2ccccc2)nc(N(CCCCCCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.38458096,,,
+869392310,*NC1=CC=C2C=Cc3c(N*)ccc4c3[C@H]2[C@@H]1C=C4,,0.33488674,,,
+869406421,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4cccc(Oc5c(C)cc(Cc6cc(C)c(Oc7cccc8c7C(=O)N(*)C8=O)c(C)c6)cc5C)c4C3=O)c(C)c2)cc1C,,0.40218453,,,
+869708404,*c1ccc2c(c1)C(=O)N(c1cc(NC(=O)OCCN(CCOC(=O)Nc3cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)ccc3C)c3ccc(N=Nc4ccc([N+](=O)[O-])cc4)cc3)ccc1C)C2=O,,0.35814265,,,
+870443283,*c1ccc(Nc2nc(Nc3ccc(-c4nc5cc(-c6ccc7[nH]c(*)nc7c6)ccc5[nH]4)cc3)nc(N(c3ccccc3)c3ccccc3)n2)cc1,,0.4152424,,,
+870679146,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5c4)cc2)C3=O)c1,,0.35133483,,,
+871001883,*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(O)c2)cc1O,,0.32620307,,,
+871245251,*CCOP(=O)(N=Nc1ccc(C(=O)c2ccc(N=NP(=O)(O*)OCC)cc2)cc1)OCC,,0.36466519,,,
+871482212,*CC(*)OC(=O)C1CCCCC1,,0.36242795,,,
+871802155,*CC(*)(C)C(=O)OCc1ccc(OCCOCC)c(OCCOCC)c1,,0.3637068,,,
+872295488,*CC(*)OC(=O)C(C)(C)C(C)(C)CC,,0.37176287,,,
+873004206,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Sc8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36939171,,,
+873238288,*Nc1cc(N)c(N)c(C)c1C(=O)Oc1cc(N)c(-c2cc(N)c(N)c(C)c2N)c(C)c1N*,,0.32140926,,,
+873401341,*Oc1ccc(NC(=C(C#N)C#N)c2ccc(C(Nc3ccc(*)cc3)=C(C#N)C#N)cc2)cc1,,0.401116,,,
+873453731,*C(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)c3cc(*)ccc3n4C)cc2)cc1,73.83261176,,,,
+873632495,*C#Cc1ccccc1C#C[SiH](*)c1ccccc1,,0.43769633,,,
+873980403,*CC(*)(C)C(=O)OCCOCCOCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35955244,,,
+874837683,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)c3cc(CCCCC)cc(C(*)=O)c3)cc2)cc1,,0.36567786,,,
+875422792,*Oc1ccc2cc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)ccc2c1,,0.36982073,,,
+875480288,*CCCC1CCN(C(=O)c2ccc(C(=O)C3CCN(*)CC3)c(Oc3ccccc3)c2)CC1,131.1821319,,,,
+875491202,*c1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(C4(*)OC(=O)c5ccccc54)cc3)cc2)cc1,292.5925873,,,,
+875508039,*Nc1ccc(-c2ccc(N*)c(-c3ccc4ccccc4c3)c2-c2ccc3ccccc3c2)c(-c2ccc3ccccc3c2)c1-c1ccc2ccccc2c1,,0.3677538,,,
+876188876,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)cc3)CCC3CCCCC3C2)cc1,,0.39821297,,,
+876238099,*c1ccc(N(*)CCCCCC)cc1,-128.6299242,,,,
+877528478,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCCC(=O)O*,,,0.406,0.89731003,22.86240687
+877661391,*CC#CC(C#CCOC(=O)CCCCCCCCC(=O)O*)=Cc1ccc(C#Cc2ccc([N+](=O)[O-])cc2)cc1,,0.37813011,,,
+878287974,*Nc1ccc2c(c1)C1(CC2)CCc2ccc(N*)cc21,,0.3601834849999999,,,
+878623302,*C1CC2CC1C(*)C2CCCCCC,,0.40905206,,0.82757252,10.70862832
+878851923,*Oc1ccc(Oc2ccc(C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38390226,,,
+879566898,*Cc1c(C)c(C)c(CNC(=O)CCCC(=O)N*)c(C)c1C,,0.33742347,,,
+879855559,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(CC)CC2)cc1,,0.35312515,,,
+880146863,*Nc1ccc(-c2ccc(C(CCCC)(c3ccc(-c4ccc(N)cc4)cc3)c3ccc(-c4ccc(N*)cc4)cc3)cc2)cc1,,0.37280295,,,
+880265803,*CC(COC(=O)O*)OCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34702977,,,
+880356503,*CCOCCOC(=O)C(=O)O*,,0.30751302,,,
+880710416,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(C5(c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)NC(=O)c6ccccc65)cc4)cc2)C3=O)c1,,0.36419448,,,
+881168754,*Nc1cccc2c(N*)cccc12,,0.33466696,,,
+881173814,*C1C(=O)N(c2ccccc2)C(=O)C1*,197.1821336,,0.143,1.03911953,14.56697254
+881250244,*c1ccc(-c2nc3cc(-c4ccc5nc(-c6ccccc6)c(-c6ccc(N7C(=O)C8OC9C(=O)N(*)C(=O)C9C8C7=O)cc6)nc5c4)ccc3nc2-c2ccccc2)cc1,,0.4262251,,,
+881286368,*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OCCN(CC)c4ccc(N=Nc5ccc([N+](=O)[O-])cc5)cc4)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OCCN(CC)c1ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc1)C2=O,,0.37188256,,,
+881699959,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3ccc(-c4ccc(C)cc4)c(-c4ccc(C)cc4)c23)c2c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)ccc12,,0.38407296,,,
+882038181,*O[Si](C)(C)c1cccc(C(F)(F)C(F)(F)c2cccc([Si](*)(C)C)c2)c1,,0.40311719,,,
+882891483,*C(=O)Nc1cc(NC(=O)c2cccc(*)n2)cc(-c2nnc(-c3ccccn3)o2)c1,,0.38275614,,,
+884131140,*CCOCCOCCN1C(=O)c2ccc(C(=O)Oc3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)c(-c4ccccc4)c3)cc2C1=O,,0.33757456,,,
+884364741,*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2C3CCC(C3)C2C)cc1,,0.38003376,,,
+884656805,*Nc1nc(CCn2ccnc2C)nc(N*)n1,,0.323466525,,,
+884758912,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5F)C6=O)cc4)cc3)cc2)cc1,,0.37981659,,,
+884792716,*Nc1ccc(C(C)(C)c2ccccc2C(C)(C)c2ccc(N*)cc2)cc1,,0.36215314,,,
+884979712,*c1cc(C(=O)NCCCCCCCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.3677936,,,
+885328080,*CC(*)(CC(=O)OCC)C(=O)O,,0.29105461,,,
+885496474,*CC(*)(C)C(=O)OCCN(CC)c1ccc([N+](=O)[O-])cc1,,0.35415635,,,
+886026826,*c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(-c4ccc(-c5sc(-c6ccc([Si](CCCC)(CCCC)[Si](*)(CCCC)CCCC)s6)cc5CCCCCCCC)s4)s3)s2)s1,,0.42281457,,,
+886271093,*CC(*)(C)C(=O)Oc1ccc(N=Nc2ccc(OC)cc2)cc1,,0.35867178,,,
+886386910,*C=Nc1ccc(C(=O)OCCCCOCCCCOCCCCOC(=O)c2ccc(N=Cc3ccc(*)s3)cc2)cc1,,0.36455788,,,
+886529018,*CCCCCCCCCCC(=O)Nc1ccc(NC(=O)CCCCCCCCCCN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.34716825,,,
+886858918,*CC(*)c1ccc(Oc2ccccc2)cc1,,0.37467295,,,
+887026171,*O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C,,0.42518322,,,
+887350173,*Nc1ccc2c(c1)Oc1cc3c(cc1O2)C1(CC3(C)C)CC(C)(C)c2cc3c(cc21)Oc1ccc(N*)cc1O3,,0.395258,,,
+887653372,*Nc1cc2ccccc2c(-c2c(N*)c(N)cc3ccccc23)c1N,,0.33877583,,,
+887685066,*Nc1cccc2c(N[Se]*)cccc12,,0.38429931,,,
+887804615,*CCOCCOC(=O)c1ccc(NC=Nc2ccc(C(=O)O*)cc2)cc1,,0.34881657,,,
+888408662,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(C(C)(C)c7ccc(Oc8ccc(C(=O)Nc9ccc(*)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36526252,,,
+889509807,*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.3732598,,,
+889670641,*Oc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5cccc(NC(=O)c6ccc(*)cc6)c5)cc4)cc3)c2)cc1,,0.3636819,,,
+889893408,*Nc1cccc(NC(=O)c2ccc(NC(=O)c3ccc([Si](C)(C)c4ccc(C(=O)Nc5ccc(C(*)=O)cc5)cc4)cc3)cc2)c1,,0.35733019,,,
+890037112,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)Cc4cccc(CC(=O)Nc5cccc(O*)c5)c4)c3)cc2)cc1,,0.3397353,,,
+890273694,*C1Oc2ccccc2C1*,,0.40215636,,,
+890286804,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4c3)cc1)C2=O,,0.38094303,,,
+890297179,*c1cc(CCCCCCO[Si](C)(C)C)c(*)s1,,0.42932907,,,
+890414662,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(NC(=O)c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.35268551,,,
+890512503,*CC(*)(C)C(=O)OC1CC(C)CCC1C(C)C,,0.38071685,,,
+890572584,*C=Cc1ccc(*)c(-c2cc(OCCC(C)C)c(OCCC(C)C)cc2-c2ccccc2)c1,,0.39126142,,,
+890993359,*CCCCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)c(C)c1,,0.3439212,,,
+892241258,*Nc1cc(C(=O)O)c(*)cc1OC,93.18891585,,,,
+893298020,*CC(*)c1ccc(COCCOCC)cc1,,0.37466356,,,
+893952822,*CC(*)c1ccc(C(C)=O)cc1,,0.37541315,0.2296666666666666,1.013613515,14.74755617
+894220698,*c1ccc(C(=O)Nc2ccc(-c3nnc(*)o3)cc2)c(Oc2ccccc2)c1,,0.38004401,,,
+895111852,*CC(COC(=O)O*)OCCCCCCCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.35711055,,,
+895255248,*c1cccc(N2C(=O)C(=O)N(CCCCCCN3C(=O)C(=O)N(*)C3=O)C2=O)n1,,0.32153178,,,
+895257711,*CCCCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.38117284,,,
+895302963,*O[Si](*)(CCCOCCOCCOC)CCCOCCOCCOC,,0.37166513,,,
+895368318,*c1ccc(Oc2ccc(N3C(=O)C4CCC(C5CCC6C(=O)N(*)C(=O)C6C5)CC4C3=O)cc2)cc1,,0.37758601,,,
+895465907,*CCCCCCCCNC(=O)c1cccc(C(=O)N*)c1,,0.34757271,,,
+896186812,*Nc1ccc(/C=C2\c3ccccc3-c3ccc(N*)cc32)cc1,,0.352654845,,,
+896500717,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.33599709,,,
+896516520,*Oc1ccc(NC(=O)c2cc(-c3ccc(C(=O)OCC)c(C(=O)Nc4ccc(*)cc4)c3)ccc2C(=O)OCC)cc1,,0.34732657,,,
+896836306,*CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.34876143,,,
+897111350,*CCCCCCCCOc1c(OC)cc(C=Cc2ccc(C=Cc3ccc(C=Cc4cc(OC)c(O*)c(OC)c4)cc3)cc2)cc1OC,,0.36573737,,,
+897856664,*C1C(=O)N(c2ccc(O[Si](CC)(CC)CC)cc2)C(=O)C1*,,0.41373857,,,
+897919146,*CCCCCCCCCCOC(=O)c1ccc(C(=O)NCCCCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,-20.66610996,,,,
+898827717,*C/C=C\COC(=O)CCCCCCCCC(=O)O*,,0.35027613,,,
+899188513,*CC(COc1ccc(-c2ccc(C#N)cc2)cc1)O*,,0.3561426,,,
+899991188,*CCCCCCCCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,0.37055224,0.355,0.905325915,21.08607303
+900206396,*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(Oc4ccc(C56CC7CC(CC(C7)C5)C6)cc4)cc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.3944462,,,
+900377151,*Nc1ccc(/C(=C(/c2ccccc2)c2ccc(N*)cc2)c2ccccc2)cc1,,0.3773241,,,
+900477091,*OC(COC(=O)c1ccccc1-c1ccccc1C(*)=O)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.3636633,,,
+901091575,*C(=O)OCCCCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.34926735,,,
+901146079,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccccc6Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.35799953,,,
+901361482,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCC)cc1,,0.3687618,,,
+901629107,*CCCCOC(=O)/C=C/C(=O)O*,,0.32664054,,,
+902068777,*CCCCCCOC(=O)CCCCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(CCCCCCCCCCC(=O)OCCCCCCN4C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C4=O)C5=O)cc3)cc2C1=O,,0.34746977,,,
+902689817,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)c2ccccc12,,0.36856069,,,
+902705671,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.35587178,,,
+902763521,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(-c5nnc(*)c6c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c56)cc4)cc3)cc2)cc1,,0.4099229,,,
+903431099,*CC(*)(C)C(=O)OCc1ccc(Cl)cc1,,0.35830867,,,
+903930104,*c1ccc(OCCCCCCCCCCCOC(=O)CCCCC(=O)OCCCCCCCCCCCOc2ccc3nc(*)sc3c2)cc1,-17.21827415,,,,
+904061966,*CC(*)(C)C(=O)Oc1ccc(N=Nc2ccc(OCCCCCCCCCCCOC(=O)c3ccc4c(c3)OCCOCCOCCOCCO4)cc2)cc1,,0.35926185,,,
+904517028,*O[Si](C)(C)c1ccc([Si](C)(C)Oc2c(C)cc(-c3cc(C)c(*)c(C)c3)cc2C)cc1,,0.39864657,,,
+904673850,*CCCCCCNC(=O)CC(C)c1ccc(C(C)CC(=O)N*)cc1,,0.35386702,,,
+904679252,*Oc1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1-c1ccc(S(=O)(=O)O)c(C)c1,,0.37255113,,,
+904704494,*CCN(*)C(=O)CCC,,0.35159195,,,
+904791008,*CCCCCCCCCCCCCCC(=O)O*,,0.38218205,0.3595,0.893283062,18.2412139
+905076848,*CC(*)(C)C(=O)Oc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37991064,,,
+906179701,*c1ccc(-c2ccc(-c3ccc(-c4cc(-c5ccccc5)c5cc(Oc6ccc7nc(*)cc(-c8ccccc8)c7c6)ccc5n4)cc3)cc2)cc1,,0.40373085,,,
+906210584,*CC1CCC(COP(=O)(N=Nc2ccc(Oc3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)CC1,,0.36114969,,,
+906778852,*Oc1cccc(NC(=O)CCCCC(=O)Nc2ccc(*)cc2)c1,,0.33664117,,,
+906799787,*c1cnc(C2C(c3ccccc3)C(*)C2c2ccccc2)cn1,,0.39156002,,,
+907119862,*CCC(C)CC(C)(C)CNC(=O)c1ccc(C(=O)N*)cc1,,0.3423688,,,
+907128555,*CC(O)COC(=O)CCCCC(=O)OCC(O)COc1ccc(C(=O)Oc2ccc(O*)cc2)cc1,,0.32619562,,,
+907537660,*CC(*)(C)C(C)=O,,,0.1669999999999999,0.887508896,15.25957674
+907618399,*c1cccc(P(=O)(c2ccccc2)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5c(Br)cc(C(C)(C)c6cc(Br)c(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)c(Br)c6)cc5Br)cc4C3=O)c2)c1,,0.37634912,,,
+907710212,*Oc1c(C)cc(C(=O)c2ccc(*)cc2)cc1C,,0.4003208,,,
+908333095,*C(=O)Nc1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nnc(-c5ccccn5)o4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(-c2nnc(-c3ccccn3)o2)c1,,0.38905414,,,
+908476662,*c1ccc(C(C)(C)c2ccc(N3C(=O)c4ccc(Oc5cccc6c(Oc7ccc8c(c7)C(=O)N(*)C8=O)cccc56)cc4C3=O)cc2)cc1,,0.38628112,,,
+908638014,*CCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.37087012,,,
+908754108,*CC(*)C(=O)OCF,,0.30797623,,,
+908812975,*Oc1ccc(N(c2ccc(OC)cc2)c2ccc3ccc(N(c4ccc(*)cc4)c4ccc(OC)cc4)cc3c2)cc1,,0.41219974,,,
+908837792,*Nc1ccc(CCCCCCc2ccc(N*)cc2)cc1,,0.34643324,,,
+909382922,*Oc1ccccc1S(=O)(=O)c1ccc(*)cc1,,0.36770444,,,
+909509106,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C,,0.42702035,,,
+909673553,*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C#N)cc1,,0.40108774,,,
+909955996,*C(=O)CCC1(CCC(=O)N2CCN(*)CC2)c2ccccc2-c2ccccc21,,0.35082586,,,
+910389413,*Nc1cc(C)c2c(N*)cccc2c1,,0.34129811,,,
+911111741,*Nc1ccc(/C=C/c2cccc(/C=C/c3ccc(N*)cc3)c2)cc1,,0.35180608,,,
+911981082,*CC(*)c1ccc(C(=O)OCC(C)C)cc1,,0.38415931,,,
+912214142,*Oc1c(C)cc(*)cc1C(C)CCCC,,0.42545276,,,
+912864989,*CC(*)(Cl)C(=O)OC1CCCCC1,,,0.1365,1.054643244,12.33320291
+912967034,*CCc1ccc(CCNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*)cc1,,,0.3195,0.936212185,22.57031329
+913368014,*CC(OC)C(C(=O)OC)C(*)C(=O)OC,,0.33997342,,,
+913582816,*CCCCCCCCCCCCCCCCCCCCCCOC(=O)Cc1ccccc1CC(=O)O*,,,0.367,,
+913768396,*Oc1ccc(C(=O)OCCOCCOCCOC(=O)c2ccc(OC(=O)Nc3cc(NC(*)=O)ccc3C)cc2)cc1,100.4455143,,,,
+914204004,*c1cc(OCCCC)c(*)cc1OCCCC,,0.42056313,,,
+914466551,*CC(*)OC(=O)c1ccc(-c2ccccc2)cc1,,0.35360889,,,
+914573947,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCC(c%10ccccc%10)CC9)cc8)cc7C6=O)cc5)cc4)CC4CC3C3CCCC43)cc2)cc1,,0.37595307,,,
+915211571,*c1ccc(-c2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)c(C(C)(C)C)c2)cc1,,0.41001219,,,
+915222222,*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4ccccc4)cc3)C(C2)C1*,,0.37725216,,,
+915365516,*Nc1ccc2c(c1)[C@H]1c3ccccc3[C@@H]2c2ccc(N*)cc21,,0.37877881,,,
+915411038,*Oc1ccc(N=CCCCC=Nc2ccc(OC(=O)Nc3ccc(Cc4ccc(NC(*)=O)cc4)cc3)cc2)cc1,,0.35326814,,,
+915653393,*Oc1ccc(C2(C)CCC(C(C)(C)c3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)CC2)cc1,,0.38061245,,,
+915714545,*Nc1ccc(-c2ccc(N*)c(C)c2-c2cccc(C)c2)cc1C,,0.36515861,,,
+915798099,*Oc1ccc(C2(c3ccc(OC(=O)CCC(*)=O)cc3)c3cc(N(C)C)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.38013673,,,
+915909655,*CC(*)(CC(=O)OC)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.37783312,,,
+916135247,*Oc1ccc(NC(=O)c2ccc(*)cc2)cc1,,0.35267198,,,
+916461490,*CCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12,,0.36210647,,,
+916566560,*Nc1ccc([C@]2(C)CC(C)(C)c3cccc(N*)c32)cc1,,0.36101129,,,
+917149837,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4cccc5c(N6C(=O)c7ccc(*)cc7C6=O)cccc45)cc2)C3=O)n1,,0.35579291,,,
+917240080,*c1ccc(C(c2ccc(-c3cc(-c4ccccc4)c4cc(Oc5ccc6nc(*)cc(-c7ccccc7)c6c5)ccc4n3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.40660468,,,
+917545259,*CCC(F)(F)C(*)(F)F,,0.34634024,,,
+917692864,*C(F)(F)C(*)(F)Cl,,,0.139,,
+917837982,*c1ccc(Oc2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2C(C)(C)C)cc1,,0.40671678,,,
+917938501,*CC(*)OC(=O)OCC(F)(F)F,,0.32040893,,,
+918157319,*c1c(C)c(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c(C)c2c1C(C)(C)CC2,325.2939228,,,,
+918244660,*c1cccc(NC(=O)c2ccc(OCCOCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)c1,,0.34339353,,,
+918773447,*c1ccc(-c2ccc(N3C(=O)c4cc5c(cc4C3=O)C(C(F)(F)F)(C(F)(F)F)c3cc4c(cc3O5)C(=O)N(*)C4=O)cc2C(F)(F)F)c(C(F)(F)F)c1,472.25,,,,
+919063854,*CC(*)CC1CCCC1,,,0.2283333333333333,,
+919712594,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.34179586,,,
+919768604,*Nc1ccc(/C(C)=C(\C)C(C)(C)c2ccc(N*)cc2)cc1,,0.360922,,,
+920265814,*c1cccc(C(=O)Nc2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.36163095,,,
+920561799,*O[Si](C)(C)O[Si](C)(C)Oc1ccc(-c2ccc(*)cc2)cc1,,0.40651802,,,
+920814890,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(N7C(=O)c8ccc(*)cc8C7=O)cccc56)cc3)C4=O)c(OC)c2)cc1OC,,0.35702577,,,
+921026823,*C=C[Ge](C=C[Si](*)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1,,0.39366166,,,
+922945428,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(C(C)(C)c5cccc(C(C)(C)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)c5)cc4)cc3)cc2)cc1,,0.36520777,,,
+922969981,*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(*)cc4)cc3)(P3(=O)Oc4ccccc4-c4ccccc43)P3(=O)Oc4ccccc4-c4ccccc43)cc2)cc1,,0.36147849,,,
+922972840,*CC(*)(C)C(=O)OCCCCCCCCCCCCCCC(=O)NC1OC(COC(C)=O)C(OC2OC(COC(C)=O)C(OC(C)=O)C(OC(C)=O)C2OC(C)=O)C(OC(C)=O)C1OC(C)=O,,0.34215996,,,
+923029677,*CCCCCC(=O)S*,,0.39191905,,,
+923105283,*c1ccc(OC(=O)c2ccc([Si](c3ccccc3)(c3ccccc3)c3ccc(C(=O)Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.37881873,,,
+923146158,*CCCCCCCCCCCCCCNC(=O)NCCCCCCCCCCNC(=O)N*,,0.37604443,0.344,,
+923568845,*Oc1ccc(N(c2ccc(OCCCCCC)cc2)c2ccc(-c3ccc(N(c4ccc(*)cc4)c4ccc(OCCCCCC)cc4)cc3)cc2)cc1,,0.40556896,,,
+924221537,*c1ccc(-c2nc3cc(-c4ccc5nc(*)c(-c6ccccc6)nc5c4)ccc3nc2-c2ccccc2)cc1,,0.44967432,,,
+924258388,*CC(*)OC(=O)c1ccc(OC)cc1,,0.34230372,,,
+924456640,*CCC(c1ccccc1)[Si](*)(C)C,,0.39149503,,,
+924584081,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc(C(c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4C3=O)cc2)cc1,,0.39334445,,,
+924846340,*Nc1ccc2c(c1)-c1cc(N*)ccc1C21c2ccccc2-c2ccccc21,,0.38016064,,,
+925076863,*CCCCCC(*)CCC,,0.40474828,,,
+925163617,*C(=O)c1cc(C(=O)c2ccc3[nH]c4ccc(*)cc4c3c2)c(C(=O)O)cc1C(=O)O,106.1795053,,,,
+925500331,*c1ccc(CC(=O)Nc2cccc3c(NC(=O)Cc4ccc(-c5sc(*)c(-c6ccccc6)c5-c5ccccc5)cc4)cccc23)cc1,,0.38341143,,,
+925984041,*CCCCCCCCOC(=O)CCCCCNC(=O)CCCCC(=O)NCCCCCC(=O)O*,,,0.293,,
+925989264,*Oc1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1-c1ccccc1,,0.3777411,,,
+926324198,*c1ccc(-c2nnc(*)o2)c(OCCCCCCCC)c1,,0.41320631,,,
+926346453,*C(=O)NCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.32267936,,,
+926490158,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccccc3)ccc1-2,,0.39925527,,,
+926565348,*Oc1ccc(Oc2ccc(Oc3ccc(S(=O)(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37216814,,1.200029536,24.89017712
+926565511,*CC(*)(C)C(=O)OCCCCCCN(C)c1ccc(N(C)C)cc1,,0.36989914,,,
+926851553,*Cc1c(C)c(C)c(CSC(=O)CCCCC(=O)S*)c(C)c1C,44.80678229,,,,
+926945718,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc4c(c3)C(CCCCCCCCCCCC)(CCCCCCCCCCCC)c3cc(*)ccc3-4)ccc1-2,37.51200969,,,,
+927050868,*CCCCCOC(=O)c1ccc(C=Cc2ccc(C(=O)O*)cc2)cc1,,0.35355098,,,
+927902684,*CC(*)(C)C(=O)OCC1(C)COCOC1,,0.34420519,0.1305,1.010012446,10.74845321
+928008419,*c1cc(-c2sc(-c3cc(CCCCCCC)c(*)s3)cc2CCCCCCC)c2cccccc1-2,,0.44541284,,,
+928940604,*Nc1ccc(N*)c(-c2cccc3ccccc23)c1-c1cccc2ccccc12,,0.36200233,,,
+929171141,*Nc1ccc2c(-c3ccccc3C)c3ccccc3c(-c3ccccc3C)c2c1N*,,0.36841027,,,
+929535825,*Nc1ccccc1-c1c(N)ccc2cc3c(-c4ccccc4N)c(N*)ccc3cc12,,0.35412155,,,
+930553513,*Nc1cccc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(-c4cccc(N*)c4)ccc2-3)c1,,0.36800418,,,
+930567238,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5c(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc(C(C)(C)C)cc5C(C)(C)C)cc4C3=O)cc2)cc1,,0.39862825,,,
+930831236,*Nc1ccc(NC(=O)c2cc(-c3ccc(C(=O)O)c(C(*)=O)c3)ccc2C(=O)O)cc1,,0.31148404,,,
+931370343,*CC(*)c1ccc(-c2c3ccccc3cc3ccccc23)cc1,,0.37620542,,,
+931521712,*NC1=CC=C2C=CC=C[C@H]2[C@@H]1c1c(N*)ccc2ccccc12,,0.34641044,,,
+931723898,*Nc1ccc(C(c2ccccc2)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.37211398,,,
+931906273,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(S(=O)(=O)c4ccc(-c5ccc(-c6ccc(S(=O)(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.37649648,,,
+932177649,*C(=O)Nc1ccc(C2(c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4cccc(N6C(=O)c7ccc(*)cc7C6=O)c4)C5=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.37103025,,,
+932213408,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCSCCCCC(=O)O*,,,0.322,0.926144559,18.71887364
+932596908,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(S(=O)(=O)c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.37755482,,,
+933005938,*CC(COc1ccc(O*)cc1)OC(C)=O,,0.33587551,,,
+933916766,*/C=C/*,59.5588378,,0.394,0.775000418,34.672905605
+934242824,*CNC(=O)OCCOC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1,,0.33830674,,,
+934350191,*c1cccc(S(=O)(=O)NCCCNS(*)(=O)=O)c1,,0.33288923,,,
+934378848,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.33698124,,,
+934720972,*Nc1cc2cc3c4cc5ccccc5cc4c4cc5ccccc5cc4c3cc2c(N)c1N*,,0.34084911,,,
+934750353,*Nc1ccc(C(C)(c2ccccc2)c2ccc(N*)cc2)cc1,,0.35460063,,,
+935609923,*CC(*)(C)c1ccc2ccccc2n1,,0.38344332,,,
+935627680,*CC(*)c1ccc(C(=O)c2ccccc2)cc1,,0.37525609,,,
+935941337,*CCCCCCCCNC(=O)C(CCCCCCCCCCCC)C(=O)N*,,,0.331,,
+935965318,*NCCCCCCCN*,,0.33180826,,,
+935976337,*c1ccc2c(c1)C(=O)N(c1cc(C(=O)O)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.37985486,,,
+936348759,*CC(*)C(=O)OCC(F)(F)C(F)C(F)(F)F,,0.32043251,,,
+936419032,*Oc1ccc(OC(=O)c2ccc(OCCOc3ccc(C(*)=O)cc3)cc2)cc1C,68.01369706,,,,
+936563267,*C(=O)c1ccc(Oc2ccc(-c3ccc(Oc4ccc(C(=O)c5ccc6c(c5)C(=O)N(c5ccc(Cc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.37592453,,,
+937218293,*CC(*)OC(=O)C1CCCC1,,0.36011903,,,
+937233675,*Oc1ccc(C(C)(c2ccccc2)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.36891791,,,
+937312139,*CCC1CC(OC(=O)CCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)CC1*,,0.36926813,,,
+937429768,*C(=O)Nc1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4cc(C(F)(F)F)cc(N6C(=O)c7ccc(*)cc7C6=O)c4Oc4ccc(C(F)(F)F)cc4)C5=O)cc3)ccc3ccccc23)cc1,,0.37258957,,,
+937600317,*O[Si](*)(C)CCCCCOc1ccc(/C=C/c2ccc(OC)cc2)cc1,,0.36872385,,,
+938001500,*Nc1ccc(C2=C(c3ccc(N*)cc3)[C@](C)(C(C)C)[C@@](C)(C(C)C)C(C)=C2C)cc1,,0.3722569,,,
+938126554,*CC/C=C(/*)C(C)(C)C,,,0.20825,0.831876241,13.577310245
+938471508,*CCCCCCN1C(=O)c2ccc(C(=O)Nc3ccc(S(=O)(=O)c4ccc(NC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34497827,,,
+938632884,*Nc1ccc(C)c(-c2c(C)ccc(N*)c2C)c1C,,0.36618089,,,
+938927224,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7c(C)cc(Cc8cc(C)c(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)c(C)c8)cc7C)cc6C5=O)cc4)cc3)cc2)cc1,,0.38112345,,,
+939175685,*CC(*)C(=O)OC(CC)CC,,,0.213,0.928441574,12.41393583
+939569581,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)C(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.33163835,,,
+940363363,*c1ccc(Cc2ccc(N3C(=O)c4cc(-c5ccc(C(C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc(C(C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.41910129,,,
+940574386,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.34201016,,,
+940977758,*c1ccc(Oc2ccc(-c3cc(-c4ccccc4)c4cc(C5(c6ccc7nc(*)cc(-c8ccccc8)c7c6)c6ccccc6-c6ccccc65)ccc4n3)cc2)cc1,,0.42211202,,,
+941025862,*C(=O)Nc1ccc2c(c1)-c1cc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)ccc1C2,,0.36796455,,,
+941113349,*Nc1ccc2ccc3c(N*)c(-c4cccc(-c5cccc(C6(c7ccccc7)c7ccccc7-c7ccccc76)c5)c4)cc4ccc1c2c43,,0.36666503,,,
+941524634,*CC(*)(C)C(=O)OCc1ccsc1,,0.35367667,,,
+941929262,*Oc1ccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.3613444,,,
+942234134,*Oc1ccc(C(c2ccc(OP3(Oc4ccc(C(=O)OC)cc4)=NP(Oc4ccc(C(=O)OC)cc4)(Oc4ccc(C(=O)OC)cc4)=NP(*)(Oc4ccc(C(=O)OC)cc4)=N3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.34107289,,,
+942714544,*Nc1c(-c2ccccc2)cc(-c2ccccc2)cc1C1(c2cc(-c3ccccc3)cc(-c3ccccc3)c2N*)c2ccccc2-c2ccccc21,,0.3863404,,,
+942731516,*Nc1ccccc1CCc1ccc(NC(=O)Nc2ccc(CCc3ccc(NC(*)=O)cc3)cc2)cc1,,0.35341699,,,
+942798505,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.33608462,,,
+943239154,*Oc1cccc(NC(=O)CCCCCCC(=O)Nc2ccc(*)cc2)c1,,0.34067938,,,
+943356153,*OC(=O)C(C)NC(=O)CCCCCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.33413155,,,
+943955993,*c1cc(*)c(O)c(/C=N/c2ccc(Cl)cc2)c1,,,0.141,1.17680677,13.61558599
+944555071,*CCCCOc1ccc(OCCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1,,0.36004783,,,
+945028672,*c1cccc(-c2nc3cc(-c4ccc5nc(*)c(-c6ccccc6)nc5c4)ccc3nc2-c2ccccc2)c1,,0.42889461,,,
+945057179,*c1ccc(Oc2c(C)cc(C(c3cc(C)c(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)c(C)c3)c3cccc4ccccc34)cc2C)cc1,,0.39971724,,,
+945096755,*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc2)C(=O)N(CCc2ccccc2)C3=O)cc1,,0.37134878,,,
+945247998,*C1C(=O)OC(=O)C1C1C2CC(COCCC)C(C2)C1*,,0.3385676,,,
+945652954,*CC(*)C(=O)OC(Cl)C(Cl)(Cl)Cl,,0.36754584,,,
+945771244,*N=P(*)(N1CCCCC1)N1CCCCC1,,0.4443714,,,
+945861213,*Oc1ccc(Oc2cccc(C(=O)Oc3ccc(OC(=O)c4cccc(*)c4)cc3)c2)cc1,,0.35315362,,,
+946366980,*CC(*)C(=O)OCCO,,0.30069281,,,
+946712155,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OC(=O)C=Cc4ccccc4)c3)cc1OC(=O)C=Cc1ccccc1)C2=O,,0.36819149,,,
+946893976,*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)Oc2ccc(Oc3ccc(C#N)c(C#N)c3)cc2)c1,,0.34874023,,,
+947147165,*N/N=C(\N*)c1nc2c(N)nc(-c3ccccc3)nc2nc1N,,0.35087811,,,
+947769028,*Oc1ccc(C2(c3ccc(Oc4ccc(C=NN=Cc5ccc(*)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.39319174,,,
+948546936,*C(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5C)c(C)c3)C4=O)cc2)cc1,,0.36840705,,,
+948650769,*c1ccc(C(=O)NCCNC(=O)c2ccc(S(*)(=O)=O)cc2)cc1,205.0712987,,,,
+948765285,*CC(*)(C)C(=O)Oc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.37332413,,,
+948991314,*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(F)C(F)(F)C1(F)F,,0.31519849,,,
+949058568,*Nc1cc2c(cc1N*)C(CCCCCC)(CCCCCC)c1cc(N)c(N)cc1-2,,0.35765063,,,
+949058569,*CC(CC)O*,,0.38603228,,,
+949132343,*OC(CC)CC(*)=O,,0.34556434,,,
+949159115,*Nc1cccc(-c2ccc(-c3ccccc3)c(-c3ccccc3)c2-c2cccc(C)c2)c1N*,,0.36795124,,,
+949343034,*C(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c6C#N)cc4)C5=O)cc3)c2)cc1,,0.35717581,,,
+949719355,*OP(=O)(Oc1ccc(Br)cc1)Oc1c(Br)cc(C(c2cc(Br)c(*)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,,0.39848019,,,
+949756192,*NNC(=O)c1ccc(NC(=O)c2cc(C(*)=O)cc(N3C(=O)c4c(Cl)c(Cl)c(Cl)c(Cl)c4C3=O)c2)cc1,133.1528291,,,,
+949789711,*Oc1ccc(C2(c3ccc(OC(=O)CCCCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36424024,,,
+950036559,*N=Nc1ccc(*)cc1,144.6951233,,,,
+950252228,*Oc1c(Br)cc(S(=O)(=O)c2cc(Br)c(OC(*)=O)c(Br)c2)cc1Br,,0.41564538,,,
+950647553,*CC(*)c1ccc(CCC)cc1,,0.40348963,0.2345,0.892912233,13.03650471
+950692936,*c1cccc(S(=O)(=O)NCCCCCNS(*)(=O)=O)c1,,0.34341367,,,
+950984280,*c1ccc(OCCOc2ccc(C=Cc3ccc([N+](=O)[O-])cc3)c(OCCOc3ccc(-c4nc5ccc(Oc6ccc7nc(*)c(-c8ccccc8)nc7c6)cc5nc4-c4ccccc4)cc3)c2)cc1,,0.37078887,,,
+951030953,*c1ccc(Cc2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C(C)(C)C)c2)cc1C(C)(C)C,,0.4120274,,,
+951186365,*C(=O)OCCCSCCCCSCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.35877417,,,
+951464832,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34769384,,,
+951628759,*CCOCCOC(=O)CCCCCCCCCCCCCCCCC(=O)O*,,0.36953742,0.3485,0.948193246,23.57437713
+951639938,*CC(c1ccccc1)C1C(=O)NC(=O)C1*,,0.33021579,,,
+951662507,*Nc1ccc(C(C#N)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.35848755,,,
+951933381,*C=CC1CC(*)C2C(=O)N(CCCCCCCCCCCC)C(=O)C12,,0.3769725,,,
+951940704,*c1ccc(-c2nc3cc(S(=O)(=O)c4ccc5nc(*)c(-c6ccccc6)nc5c4)ccc3nc2-c2ccccc2)cc1,,0.43534029,,,
+952029062,*C(=O)Nc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)c(NC(=O)c2ccc3c(c2)C(=O)N(c2c(C)cc(-c4cc(C)c(N5C(=O)c6ccc(*)cc6C5=O)c(C)c4)cc2C)C3=O)c1,,0.37389366,,,
+952776097,*C(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)c(C)c5)cc3C)C4=O)cc2)cc1,87.17702398,,,,
+952804603,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C,,0.39230829,,,
+952898097,*CC(=O)Nc1cccc(NC(=O)COc2ccc3ccccc3c2Sc2c(O*)ccc3ccccc23)c1,,0.33988537,,,
+953208758,*Nc1ccc(/C=C/c2c(-c3ccc4ccccc4c3)c(-c3ccc4ccccc4c3)c(N*)c(-c3ccc4ccccc4c3)c2-c2ccc3ccccc3c2)cc1,,0.36715532,,,
+953356077,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1CBr,98.31320425,,,,
+953833723,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3C=C)c2-c2ccc(-c3ccccc3)cc2)cc1,,0.36143904,,,
+954090484,*C(C)=C(*)[Si](C)(C)C,,0.39006159,,,
+954258008,*CCCCCCN(CC(F)(F)C(F)(F)F)C(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)N(*)CC(F)(F)C(F)(F)F,,0.33102861,,,
+954755494,*CCCCOC(=O)NCCSCCCCCSCCNC(=O)O*,3.000872148,,,,
+954781354,*Nc1cc(N)c(-c2cccc3cc4ccc5cc6cc7ccccc7cc6cc5c4cc23)c(N*)c1,,0.34724713,,,
+954804864,*CC(CCl)(CCl)COC(=O)c1ccc(C(=O)O*)cc1,11.46555203,,,,
+954903716,*C(=O)OCCCCCCCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.3516794,,,
+955200653,*Oc1cccc(OC(=O)CCC(*)=O)c1,,0.32498132,,,
+955951687,*Nc1ccc2c3cccc4c(N*)ccc(c5cccc1c52)c43,,0.33296652,,,
+956184132,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)CC(=O)N(*)C4=S)cc3)cc2)cc1,,0.37482752,,,
+956462664,*Oc1ccc(NC(=C(C#N)C#N)c2ccc(-c3ccc(-c4ccc(C(Nc5ccc(*)cc5)=C(C#N)C#N)cc4)cc3)cc2)cc1,,0.39506825,,,
+956860993,*CC(COc1ccc(C)cc1)O*,-55.31696784,,,,
+957017762,*C1C(=O)N(c2ccc(C)cc2)C(=O)C1*,,,0.1405,0.95394828,12.83180759
+957944731,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(C4(*)OCc5ccccc54)cc3)cc2)cc1,,0.36114179,,,
+958448882,*CCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12,,0.36234593,,,
+958708723,*CC(*)(C)C(=O)OC1CCOCC1,,0.34938977,,,
+958992897,*CCOc1ccc(C=C2CCC(=Cc3ccc(OCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC,,0.34158729,,,
+959078750,*CCCCCC(*)CCCC,,0.4045687,,,
+959788863,*CC(*)(C)C(=O)OCCCCC1CCCCC1,,0.36717544,,,
+960238804,*CCN(CCOC(=O)c1cc(OCCC2CCN(c3ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc3)CC2)cc(C(=O)O*)c1)c1ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.34501773,,,
+960557401,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(-c6cccc7c6C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.37743403,,,
+960896880,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCC,,0.357945,,,
+961207214,*CCCCCCCCCCOc1cccc(OC(=O)c2ccc(C(=O)Oc3cccc(O*)c3)cc2)c1,20.92794041,,,,
+961272139,*Nc1ccc(C2=C(c3ccccc3)/C(=C/c3ccccc3)[C@@H](N*)/C(=C/c3ccccc3)C2)cc1,,0.38000782,,,
+961432489,*NNC(=O)C=CC(=O)Nc1cccc(C=C2CCC(=Cc3cccc(NC(=O)C=CC(*)=O)c3)C2=O)c1,,0.3318831,,,
+961504352,*O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.43360758,,,
+961636865,*OP(=O)(Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl,,0.36344351,,,
+961938708,*OC(=O)NC1CCC(CC2CCC(NC(=O)OC3CC4CC(*)CC(C3)O4)CC2)CC1,,0.35624612,,,
+961980503,*CC(*)(C)C(=O)OCCC#N,,0.35439971,0.138,1.036075809,14.2239278
+962173929,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3cccc(C(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3)cc1)C2=O,,0.38044047,,,
+962326024,*C=Cc1ccc2c(c1)C(CC(CC)CCCC)(CC(CC)CCCC)c1cc(*)ccc1-2,,0.40667606,,,
+962879741,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3Br)c(Br)c1)C2=O,,0.40438149,,,
+962995886,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.33730096,,,
+963631367,*Nc1ccc(-c2cc(C=C)c(N*)c(-c3ccccc3)c2-c2ccc(-c3ccccc3)cc2)cc1,,0.3632724,,,
+963881444,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCC(*)=O,,0.32881448,,,
+963896306,*CCN(CCCCCCOc1ccc(N=Nc2ccccc2)cc1)CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1,,0.35226534,,,
+963961682,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccc(N)cc32)cc1,,0.36566131,,,
+965296987,*C(C)C(*)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2,,0.39846139,,,
+965501269,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.36576682,,,
+965594112,*CC(*)c1ccc(C(=O)C(F)(F)F)cc1,,0.3627692,,,
+965608760,*Oc1ccc(NC(=O)CCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.33920775,,,
+966245387,*c1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(S(*)(=O)=O)cc3)c2)cc1,,0.34764197,,,
+966864975,*c1ccc(-c2nc3cc(-c4ccc5[nH]c(*)nc5c4)ccc3[nH]2)cc1,423.6341908,,,,
+966953045,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCCCCCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCCCCCCCCCCCC)cc1,,0.39477312,,,
+967532674,*CC(*)OCC,,,0.2465,0.912688006,15.66973123
+967548475,*Nc1ccc(-c2cc(C/C=C/c3ccccc3)c(N*)c(C/C=C/c3ccccc3)c2-c2ccccc2)cc1,,0.36169428,,,
+967673328,*Nc1ccc(-c2c(N*)ccc(-c3ccccc3)c2-c2ccccc2)cc1,,0.35943481,,,
+967943450,*Nc1ccc2c(ccc3c4ccccc4ccc23)c1N*,,0.33046619,,,
+967962139,*CCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.33858806,,,
+967963503,*C1C(=O)OC(=O)C1C1C2CC(C(=O)OC3CCCCC3)C(C2)C1*,,0.34535226,,,
+968134863,*Nc1ccc([C@H](CCCC)c2ccc(CC(C)Cc3ccc([C@@H](CCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36829564,,,
+968431878,*Oc1ccc(NC(=O)c2cc(NC(=O)c3ccncc3)cc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.33905768,,,
+968537124,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1C1CC2CCC1C2,,0.3689794,,,
+968666725,*c1cc(C(=O)OCCCCCC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.35771333,,,
+968949443,*c1ccc2c(c1)nc1c3cc4c(=O)n5c6cc(*)ccc6nc5c4cc3c(=O)n21,384.637936,,,,
+969077485,*Cc1cc(CNC(=O)c2cccc(C(=O)N*)c2)cc(C(C)(C)C)c1,,0.35193217,,,
+969180408,*CC(*)S(=O)(=O)c1ccccc1,,0.3645261,,,
+969338627,*C1CCCC(S(*)(=O)=O)C1,,0.35779851,,,
+970030032,*CC(*)OC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.3287593,,,
+970285384,*CCN(*)C(=O)c1cc(F)c(F)c(F)c1F,,0.33523927,,,
+970455714,*CCCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1,,0.33788415,,,
+970503486,*Nc1cccc(NC(=O)c2cc(NC(=O)c3ccncc3)cc(C(*)=O)c2)c1,,0.32910892,,,
+971170976,*Oc1ccccc1-c1ccccc1Oc1ccc2c(=O)n3c4cc(-c5ccc6c(c5)nc5c7ccc(*)c8cccc(c(=O)n65)c87)ccc4nc3c3cccc1c23,,0.3792992,,,
+972068859,*CCCCCCOC(=O)CCCCCCCCCCCCCCCCCCC(=O)OCCCCCCN1C(=O)c2ccc(S(=O)(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.36103038,,,
+972337368,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc(OCCCC)cc2)cc1,,0.36838691,,,
+972362080,*CC(*)OCCOCCOCCOC,,0.35815042,,,
+972393663,*Nc1ccc(C2(c3ccc(N*)cc3)Cc3ccccc3C2)cc1,,0.35273195,,,
+972435079,*CCOCCOCCOCCOC(=O)C=CC(=O)NCCCCCCNC(=O)C=CC(=O)O*,,0.32877992,,,
+972454148,*Nc1cc2c3cccc4cccc(c5cccc(c1N*)c52)c43,,0.32584641,,,
+972522646,*c1ccc(OC(=O)Oc2ccc(C3(*)OC(=O)c4ccccc43)cc2)cc1,,0.35641972,,,
+972638379,*c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(C(C)(C)c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O,,0.39659641,,,
+972642110,*Nc1ccc2ccc3c(N*)c(-c4ccc(C5(c6ccccc6)c6ccccc6-c6ccccc65)cc4)cc4ccc1c2c43,,0.36945993,,,
+972695180,*CC(C)(CC)CS*,,0.39492528,,,
+972818761,*Cc1ccccc1[Si](*)(C)C,,0.37867068,,,
+973091046,*CCCCCCCCCCCCNC(=O)C(=O)N*,,,0.309,,
+973188497,*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OC(C)=O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OC(C)=O)C2=O,,0.3749767,,,
+973722160,*NC1=C(N)[C@@H](N*)NC(=O)N1,,0.28374372,,,
+974102332,*CC(*)C(=O)Nc1cccc(Cl)c1,,0.34957383,,,
+974171598,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCCCCCC(=O)O*,,,0.276,,
+974543233,*C1C(=O)N(c2ccc(O[Si](C)(C)C(C)(C)C)cc2)C(=O)C1*,,0.42394222,,,
+974608076,*Oc1c(-c2ccccc2)cc(*)cc1-c1cccc(-c2ccccc2)c1-c1ccccc1,,0.40445891,,,
+974829630,*NCCCCCCCCCCCCN*,,0.34669796,,,
+974951002,*CC(CC)C(CC)COC(=O)c1ccc(C(=O)O*)cc1,,0.35322539,,,
+974977615,*CCCCCC(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)CCCCC2)cc1,,0.35101165,,,
+975186441,*C(=O)OCCOC(=O)C=Cc1cccc(N2C(=O)c3ccc(*)cc3C2=O)c1,,0.33326323,,,
+975316837,*CCCCC=CCCCCC1CC(CCC2CC(*)OC2=O)C(=O)O1,,0.35075006,,,
+975372229,*SC(=O)OCCCCOC(=O)SN1CC(C)N(*)CC1C,-30.512105,,,,
+975391754,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCC)cc1,,0.33368013,,,
+975529928,*Nc1ccc2c(c1)[C@@H]1c3ccc(N*)cc3[C@H]2c2c(N)cccc21,,0.38124543,,,
+975679689,*NC(CCC(=O)Nc1ccc(N(c2ccccc2)c2ccccc2)cc1)C(*)=O,,0.36156406,,,
+975874706,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(OC)c(Br)c2)cc1,,0.3611176,,,
+975942451,*Nc1nc2c(c(=O)n1N*)Cc1nc(N)n(N)c(=O)c1C2,,0.34327027,,,
+977134165,*C(=O)c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(*)cc5C4=O)CC3)CC1)C2=O,,0.36459442,,,
+977446169,*CCCCCCCCCCCCCCCCOC(=O)CCCCCCCCCCCCC(=O)O*,,,0.297,,
+977496062,*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)CCCCCCCCC(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1,,0.35461809,,,
+977560848,*Nc1ccc(/C(=C(\c2ccccc2)c2ccc(N*)cc2)c2ccccc2)cc1,,0.374407055,,,
+977724395,*CC(*)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1,,0.35501894,0.2245,1.095853414,13.394951
+977861855,*Nc1ccccc1CCc1ccc(NC(=O)NCCCCCCNC(*)=O)cc1,,0.3465376,,,
+978144128,*CNC(=O)OCC(OC(=O)NCc1ccc(*)o1)c1ccco1,,0.32715503,,,
+978251240,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.36289032,,,
+978860750,*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(N*)c(C)c3)c2)cc1C,,0.3642123,,,
+979011291,*CCCCCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12,,0.3642299,,,
+979179304,*CCOC(=O)NCC1(C)CC(NC(=O)O*)CC(C)(C)C1,,0.33606063,,,
+979211683,*CC(*)c1ccc(C(=O)OCC)cc1,,0.36916022,,,
+979410672,*CC(*)N1CCCC1=S,,0.39052842,,,
+979464287,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.3736609,,,
+979533481,*CCCCCCCCCCNC(=N*)c1ccccc1,,0.3881679,,,
+979588845,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(Oc3ccccc3)cc2)cc1,,0.34706136,,,
+980179181,*/C=C/C(C)(C)C(*)(C)C,,,,0.820729949,15.50648001
+980398576,*c1cccc(N2C(=O)c3cccc(Oc4cccc(Oc5cccc6c5C(=O)N(*)C6=O)c4)c3C2=O)c1,,0.35986815,,,
+980411522,*CC(CCCCCCCCCCCCCCCC)C1C(=O)N(CCCCCCCCCCCC)C(=O)C1*,,0.39088075,0.335,0.867649177,11.65178394
+980661216,*CC(*)(OC(C)=O)c1ccc(OC(=O)CC)cc1,2.162388076,,,,
+980673012,*c1ccc(*)c(C(=O)c2ccc(F)cc2)c1,,0.40977621,,,
+980966583,*C#Cc1cc(OC(COCCOCCOCCOC)COCCOCCOCCOC)c(C#Cc2cc(OCCOCCOCCOCCC(=O)O[Na])c(*)cc2OCCOCCOCCOCCC(=O)O[Na])cc1OC(COCCOCCOCCOC)COCCOCCOCCOC,-43.36051195,,,,
+981778956,*c1cc(CCOC(=O)C(C)Br)c(*)s1,,0.40626564,,,
+981880572,*CCCCCC(=O)Oc1ccc2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2c1,,0.3370471,,,
+982011392,*Oc1ccc(C(=Cc2cc(OC)c(C=C(c3ccc(*)cc3)c3ccc(F)cc3)cc2OC)c2ccc(F)cc2)cc1,,0.38359708,,,
+982052578,*CC(*)(CC(=O)OCC(C)C)C(=O)OCC(C)C,,0.38664522,,,
+982081375,*c1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(-c7cc(-c8ccc(-c9ccccc9)cc8)cc(-c8ccc(NC(=O)Nc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)n7)cc6)cc5C4=O)cc3)ccc3ccccc23)cc1,,0.36383462,,,
+982170716,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.35031486,,,
+982392154,*CC(*)C=C(C)C,,,,0.779950443,12.78661329
+982448073,*CCCCCCOC(=O)C(=O)O*,,0.33558333,,,
+982641733,*CCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.33048829,,,
+982644871,*Nc1cccc(NC(=O)CCCCCCCCC(=O)Nc2ccc(C(=O)NC(*)=S)cc2)c1,,0.34266836,,,
+982652113,*c1ccc(Oc2ccc(C(=O)c3ccc(Cc4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccccc8)[nH]c7*)cc6)cc5)cc4)cc3)cc2)cc1,,0.39839746,,,
+982823848,*Oc1c(OC)cc(*)cc1OC,,0.35493117,,,
+982899341,*Nc1cc2ccc3ccc(N*)c4ccc(c1)c2c34,,0.33737081,,,
+982900215,*Nc1ccc(-c2ccc(NC(=O)Cc3cc(CC(*)=O)c(C)cc3C)cc2)cc1,,0.33947658,,,
+982959217,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)C(c5ccccc5)=C(c5ccc(C6=C(c7ccccc7)C(=O)N(*)C6=O)cc5)C4=O)cc3)cc2)cc1,,0.37787317,,,
+983140270,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(P(C)(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37865073,,,
+983336786,*Nc1nc(CCC2OCC3(COC(CCc4nc(N)nc(N)n4)OC3)CO2)nc(N*)n1,,0.32807349,,,
+983359271,*Oc1ccc(C(c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.34987919,,,
+983408527,*CCCCCCOC(=O)OCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.34233697,,,
+983924337,*CCCCCCCCCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.35722894,,,
+984649058,*CCCCCCCCCCCCCCCCCCNC(=O)Cc1ccc(CC(=O)N*)cc1,,0.36969623,,,
+984722345,*C(=O)OCCCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.34843599,,,
+985031681,*C(=O)OCCCCCCCCCCCCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.35686622,,,
+985113271,*CCOC(=O)Nc1ccc(CCCc2ccc(NC(=O)O*)cc2)cc1,,0.33911152,,,
+985128268,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cc8ccccc8cc7Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36958426,,,
+985248291,*Nc1c(C)cc(C2(c3cc(C)c(N*)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.39543139,,,
+985685560,*c1ccc(-c2nc3cc(-n4c(=O)c5cc6c(=O)n(*)c(=O)c6cc5c4=O)ccc3[nH]2)cc1,456.35,,,,
+985716101,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(-c4ccc(OCCCC#N)cc4)cc(-c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)n3)cc1)C2=O,,0.3862986,,,
+985931014,*CCCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.3520318,,,
+986199632,*OC(=O)C(C)NC(=O)CCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.32344295,,,
+986628745,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCC)cc1,,0.35082054,,,
+987143129,*CC(*)C(=O)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl,,0.37010428,0.0849999999999999,1.469086914,15.64579264
+987280139,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5nc(-c6ccccc6)[nH]c5*)cc4)cc3)cc2)cc1,,0.39015986,,,
+988391543,*Nc1c(C)c(Cc2ccccc2)c(Cc2ccccc2)c(C(Cc2ccccc2)Cc2ccccc2)c1N*,,0.36124078,,,
+988399583,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.44221475,,,
+988455037,*c1ccc(C(=O)Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(OC(=O)c4ccc(N5C(=O)CC(Nc6cccc(NC7CC(=O)N(*)C7=O)c6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.3727379,,,
+988551958,*CCC(C)S*,,0.42340205,,,
+988557841,*c1ccc(Oc2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)cc1,,0.46496815,,,
+989355394,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1ccc(OCCCCC)c(C(=O)O*)c1,,0.34033362,,,
+989396731,*CCCc1nc2cc(-c3ccc4[nH]c(*)nc4c3)ccc2[nH]1,,0.36451297,,,
+990486279,*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1C)C2=O,,0.39717883,,,
+990726873,*Nc1cc(NC(=O)c2cc(OCCCN(C)c3ccc(N=Nc4ccc(S(C)(=O)=O)cc4)cc3)cc(C(*)=O)c2)cc(C(=O)OCCCN(C)c2ccc(N=Nc3ccc(S(C)(=O)=O)cc3)cc2)c1,,0.3639981,,,
+990755656,*NC1=C[C@@](O)(N*)N=C(N)N1,,0.29677001,,,
+990844091,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5)C6=O)cc4)cc3)cc2)cc1,,0.38038292,,,
+990921781,*CC(*)(C)C(=O)OCC(=O)N1CCCC1,,0.33084137,,,
+991150899,*OC(=O)OC1COC2C(*)COC12,,0.32110167,,,
+991486562,*CCC(C)C(*)C,,0.38748444,0.2329999999999999,,
+991497754,*Oc1ccc(-c2ccc(-c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.37801095,,,
+991575324,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.3668584,,,
+991645843,*Nc1cccc2c1[C@H]1c3ccccc3[C@@H]2c2cccc(N*)c21,,0.3714413,,,
+991677197,*CCCCCCCCCCOC(=O)c1ccc(C(=O)NCCCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,,,0.307,,
+991734116,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)C5CCC(C6CCC7C(=O)N(*)C(=O)C7C6)CC5C4=O)cc3)cc2)cc1,,0.36741646,,,
+991748625,*c1ccc(Oc2ccc(-c3nc4cc(S(=O)(=O)c5ccc6nc(-c7ccccc7)c(*)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.4167925,,,
+991749301,*CC(*)(C)C(=O)OCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.3553822,,,
+991912273,*c1ccc2c(c1)c1cc(-c3c4ccc(C=Cc5ccc(N(CCCCCC)CCCCCC)cc5)cc4c(*)c4ccc(C=Cc5ccc(N(CCCCCC)CCCCCC)cc5)cc34)ccc1n2CCCCCCCC,,0.39672462,,,
+992359277,*CC(*)(C)C(=O)OCCF,,,0.1679999999999999,1.091984203,14.10579773
+992375016,*CCCCOc1ccc(C(=O)OCCOC(=O)c2ccc(O*)cc2)cc1,,0.34108064,,,
+992491355,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.39495463,,,
+992696585,*CC(*)C(=O)N(CCCC)CCCC,,,0.211,0.875138172,10.87629214
+992902333,*/C=C/c1cc(OCC2CC3CC2C2CCCC32)c(*)cc1OCC1CC2CC1C1CCCC21,,,0.24,1.004037835,17.43472846
+993287384,*CCOC(=O)N(Cc1ccccc1)c1ccc(C)c(N(Cc2ccccc2)C(=O)O*)c1,,,,1.109114286,
+993495758,*c1cc2c(cc1C)-c1cc(C)c(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc1S2(=O)=O,,0.41132119,,,
+993642509,*CC(O)CN(C)S(=O)(=O)c1ccc(-c2ccc(S(=O)(=O)N(C)CC(O)COc3ccc(O*)cc3)cc2)cc1,,0.33724691,,,
+994147149,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(-c3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.36037645,,,
+994176414,*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc21,,0.38372241,,1.04019662,15.18496739
+994235992,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(C(=O)c7ccc(Oc8cccc9c8C(=O)N(*)C9=O)cc7)cc6)c5C4=O)cc3)c2)cc1,,0.36092418,,,
+994355655,*C(=O)c1ccc2c(c1)C(=O)N(c1c(CC)cc(Cc3cc(CC)c(N4C(=O)c5ccc(*)cc5C4=O)c(CC)c3)cc1CC)C2=O,,0.4118037,,,
+994657756,*CC(CC)S*,,0.42316146,,,
+995410408,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCN(CC)c4ccc(C(C#N)=C(C#N)C#N)cc4)c3)cc1OCCN(CC)c1ccc(C(C#N)=C(C#N)C#N)cc1)C2=O,,0.37956411,,,
+995559885,*C(*)(F)Cl,,0.33776563,,,
+995697393,*OC(CC(*)=O)C(C)(Cl)Cl,,0.35072691,,,
+996300632,*Oc1ccc(Oc2ccc(NC(=O)c3cc(C(=O)Nc4ccc(*)cc4)cc(C(C)(C)C)c3)cc2)cc1,,0.36524897,,,
+996351908,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c(C(C)(C)C)c3)cc1)C2=O,,0.41502916,,,
+996538100,*Oc1ccc(C(c2ccccc2)c2ccc(OC(*)=O)cc2C)c(C)c1,,0.37510529,,,
+997201189,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(c6ccccc6)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O,,0.41024924,,,
+997252615,*CC(*)OC,,0.36080303,0.2614999999999999,0.965989074,16.476876
+997287969,*c1ccc(C(c2ccc(N3C(=O)c4ccc(OCCN(CCOc5ccc6c(c5)C(=O)N(*)C6=O)c5ccc(C=Cc6ccc([N+](=O)[O-])cc6)cc5)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35788304,,,
+997443668,*CO*,,,0.246,1.241962384,21.59688826
+997638129,*CC(*)C=O,,,0.2425,1.074977648,19.22062685
+998478267,*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)c3)nc2c1,,0.42780861,,,
+998917125,*OC(CC(*)=O)C(Cl)Cl,110.2326669,,,,
+999068142,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(S(*)(=O)=O)cc4S(=O)(=O)O)cc3)cc2)c(S(=O)(=O)O)c1,,0.37255612,,,
+999116161,*C(=O)Nc1ccc(-c2cc(-c3ccccc3)cc(-c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(NC(=O)Nc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc4)C5=O)cc3)n2)cc1,,0.35865584,,,
+999660395,*C(=O)NCCN(CCNC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34863371,,,
+999723050,*CCOC(=O)C1C2C=CC(C2)C1C(=O)O*,,0.33382733,,,
+999774546,*c1ccc(OCCCCCCOc2ccc(C3C4C(=O)N(c5ccc(N6C(=O)C7ON(C)C(*)C7C6=O)cc5)C(=O)C4ON3C)cc2)cc1,,0.35427898,,,
+999910921,*CC(*)(C)C(=O)OC1CCC2CCCCC2C1,,0.37667899,,,
+1000606738,*CCCCCCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCCCCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC,,0.3684349,,,
+1000610070,*Nc1ccc(C(=O)c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34522858,,,
+1000940979,*c1ccc(Oc2ccc(N3C(=O)C(Cl)=C(Oc4ccc(C(c5ccc(OC6=C(Cl)C(=O)N(*)C6=O)cc5)(C(F)(F)F)C(F)(F)F)cc4)C3=O)cc2)cc1,,0.36044402,,,
+1001186326,*CCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.34666187,,,
+1001439258,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C(C)(C)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2C#N)cc1,,0.37485348,,,
+1002053152,*CCCCCCOC(=O)OCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.34762282,,,
+1002421560,*OC(C)COC(=O)CCCCC(*)=O,-15.95518318,,,,
+1002611290,*c1ccc(Oc2ccc(N3C(=O)c4cccc(Oc5ccc(Oc6ccc(Oc7cccc8c7C(=O)N(*)C8=O)cc6)cc5)c4C3=O)cc2)cc1,,0.36415634,,,
+1003276848,*NC1=Cc2cccc3ccc(N*)c1c23,,0.33737221,,,
+1003322685,*CC(*)(C)C(=O)OC1(C)CCCN(c2ccc(N=Nc3ccc(C=C(C#N)S(C)(=O)=O)cc3)cc2)C1,,0.37311394,,,
+1003416473,*C(Cl)=C(*)c1ccccc1,,0.41584019,,,
+1003531277,*CC(*)C(=O)N1CC[N+](C)(CCCCCCCCCCCC)CC1,-81.38297384,,,,
+1003609082,*c1ccc(C#Cc2cc(OCCCCCCCC)c(C#Cc3ccc(-c4nnc(*)o4)cc3)cc2OCCCCCCCC)cc1,,0.44177748,,,
+1003747074,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)C5CCC(C6CCC7C(=O)N(*)C(=O)C7C6)CC5C4=O)cc3)c2)cc1,,0.36411353,,,
+1004036255,*c1ccc(Oc2ccc(N3C(=O)C4C5C=CC(C6C(=O)N(*)C(=O)C56)C4C3=O)cc2)cc1,,0.40046874,,,
+1004048009,*=C=C=C(CCCCOC(=O)NC(=O)OCCCC)C(=*)CCCCOC(=O)NC(=O)OCCCC,27.51087357,,,,
+1004172510,*CCCCCCCCNC(=O)CCP(C)(=O)CCC(=O)N*,,0.34556001,,,
+1005319308,*Nc1ccc2c(c1N*)C1(c3ccccc3-c3ccccc31)c1ccccc1-2,,0.37725763,,,
+1005375030,*O[Si](Oc1ccc(C2(c3ccc(*)cc3)c3ccccc3-c3ccccc32)cc1)(c1ccccc1)c1ccccc1,,0.41378255,,,
+1006229444,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc6cc(Oc7ccc8c(c7)C(=O)N(*)C8=O)ccc6c5)cc4C3=O)cc2)cc1,,0.368655,,,
+1006972816,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3cc(C)ccc32)cc1,,0.37206432,,,
+1007721072,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.35428669,,,
+1007994898,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.41642982,,,
+1008054582,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(Oc7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.37350253,,,
+1008455726,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C8(c9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)CCC(C(C)(C)C)CC8)cc7)cc6C5=O)cc4)cc3C)c(C)c2)cc1,,0.38855303,,,
+1009402745,*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c1)C2=O,,0.39081756,,,
+1010071702,*Nc1ccc(-c2ccc(N*)cc2CCCCCCc2cc(N)ccc2-c2ccc(N)cc2)cc1,,0.34872521,,,
+1011327767,*NNc1ccc(-c2ccc(NN*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.37163024,,,
+1011731362,*c1ccc([Si](CCCC)(CCCC)[Si](*)(CCCC)CCCC)s1,,0.44335751,,,
+1011779904,*CC(CC(Cl)(Cl)Cl)O*,,0.37859237,,,
+1011876528,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)c(C)c2)cc1C,,0.37553512,,,
+1011928148,*CC(*)(C)C(=O)OCC1(C)COC(C)(C)OC1,,0.35893262,0.1525,0.956557271,11.46248639
+1011964069,*Nc1ccc(C2(c3ccc(N*)c(C)c3C)c3ccccc3-c3ccccc32)c(C)c1C,,0.37955034,,,
+1012063352,*C=Cc1nc(C=Cc2ccc3c(c2)c2cc(*)ccc2n3CCCCCC)c(C#N)c(C=Cc2ccc(N(c3ccccc3)c3ccccc3)cc2)c1C#N,,0.40041543,,,
+1012066552,*c1ccc(Cc2ccc(N(c3ccc(C)cc3)c3ccc(-c4ccc(N(*)c5ccc(C)cc5)cc4)cc3)cc2)cc1,,0.43960081,,,
+1012240056,*c1ccc(C(c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.38527977,,,
+1012244611,*c1ccc(N2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6nc7cc(-c8ccc9nc(-c%10ccccc%10)c(*)nc9c8)ccc7nc6-c6ccccc6)cc4)C5=O)cc3C2=O)cc1,,0.41170662,0.224,1.081627625,22.18257973
+1012389657,*CCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1,3.697542049,,,,
+1012615960,*C1CCC(CC2CCC(N3C(=O)C4C5C=CC(C6C(=O)N(*)C(=O)C56)C4C3=O)CC2)CC1,,0.37857284,,,
+1012673977,*c1nc(-c2ccccc2)nc(N(C)CCCCCCN(*)C)n1,,0.39607619,,,
+1012813731,*CC(*)c1ccc(C(=O)c2ccc(OC)cc2)cc1,,0.35659947,,,
+1013332010,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5ncc(*)o5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.40096044,,,
+1013677154,*Oc1ccc(C(=O)OC(C)COC(C)COC(C)COC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34810342,,,
+1013955085,*NCCN(C)CCN*,,0.3165625749999999,,,
+1014178298,*c1ccc(C(=O)OCCCCCCCCOC(=O)c2ccc(-c3nnc(*)s3)cc2)cc1,,0.36108206,,,
+1014358426,*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCCCCCCCC,,0.45700932,,,
+1015002552,*CCCCCCCCCCOc1ccc(OC(=O)c2ccc(OCCOCCOCCOc3ccc(C(=O)Oc4ccc(O*)cc4)cc3)cc2)cc1,,0.34939934,,,
+1015530423,*CC(*)(F)C(=O)OCC(F)(F)C(F)F,,0.31321391,,,
+1015804720,*Nc1ccc2cc3cc4ccccc4cc3cc2c1N*,,0.34122991,,,
+1015995822,*c1ccc(S(*)(=O)=O)cc1,,0.37182941,,,
+1016001230,*N=P(*)(N(C)C)N(C)C,,0.38583819,,,
+1016053436,*CC1CC(COC(=O)c2ccc(C(=O)O*)cc2)CC(C(C)(C)C)C1,,0.36365524,,,
+1016427513,*CC(O)CN(C)S(=O)(=O)c1ccc(-c2ccc(S(=O)(=O)N(C)CC(O)COc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)cc2)cc1,,0.34670882,,,
+1016483191,*c1cccc(N2C(=O)c3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.36206097,,,
+1016549467,*Nc1nc(CCn2ccnc2CCCCCCCCCCC)nc(N*)n1,,0.3488479,,,
+1016554396,*Nc1ccc2c(c1)cc(-c1ccccc1)c1cc(N*)ccc12,,0.34652388,,,
+1016682022,*CC(*)c1ccccn1,,,0.196,0.996326132,14.84520894
+1017422838,*Nc1cccc(-c2cccc(N*)c2-c2cccc3ccc4cc5ccccc5cc4c23)c1-c1cccc2ccc3cc4ccccc4cc3c12,,0.36507338,,,
+1017533864,*CC(*)c1ccc(C(=O)OC(C)C)cc1,,0.37933569,,,
+1017786656,*Nc1ccc(Cc2ccccc2C(c2ccccc2Cc2ccc(N)cc2)c2ccccc2Cc2ccc(N*)cc2)cc1,,0.36342323,,,
+1017876939,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5c(C(=O)c6ccc(*)cc6)c(-c6ccc(F)cc6)c(-c6ccc(F)cc6)c(-c6ccc(F)cc6)c5-c5ccc(F)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.40790835,,,
+1017988364,*CCCCCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.35605283,,,
+1018289608,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5ccc(-c6cccc7c6C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.35957648,,,
+1018372810,*Nc1ccc(CC(Cc2ccc(N)cc2)Cc2ccc(N*)cc2)cc1,,0.34584685,,,
+1018492887,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc(C(*)=O)cc8)cc7)CCC(C(C)(C)C)CC6)cc5)cc4)cc3)CCCCCCCCCCC2)cc1,,0.38180644,,,
+1018993474,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)cc4)cc3)c1)C2=O,,0.36515817,,,
+1019146699,*CNC(=O)CCCCCCCCC(=O)NCC1COC(*)CO1,,0.33667776,,,
+1019302045,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(C(C)(C)C)c1,,0.37683798,,,
+1019871749,*NC[C@@]12C[C@H]3C[C@@H](C1)C[C@](c1ccc(N*)cc1)(C3)C2,,0.34037658,,,
+1020201493,*CC(*)C(=O)OCCCSCCC#N,,0.38516817,0.219,1.082808936,12.13020166
+1020575007,*Nc1ccc(-c2ccc(N*)c(CC=C)c2CC=C)c(CC=C)c1CC=C,,0.35962472,,,
+1021238306,*NNC(=O)c1ccc(C(*)=O)cc1OCCCCCCCC,,0.3601387,,,
+1021483975,*c1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8cccc(-c9nc(*)c(-c%10ccccc%10)[nH]9)c8)[nH]c7-c7ccccc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.39957908,,,
+1022466130,*CCOC(=O)Nc1ccc(CCc2ccc(NC(=O)O*)cc2)cc1,,0.33796445,,,
+1022768745,*CCCCCCCNC(=S)N*,,0.37564095,,,
+1022782104,*c1ccc2oc(-c3ccc(Oc4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)cc3)nc2c1,,0.41979595,,,
+1022909256,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4cccc5c(N6C(=O)c7ccc(*)cc7C6=O)cccc45)cc2)C3=O)cc1,,0.36136438,,,
+1022982762,*C1CCC(CC2CCC(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C(C)C2)CC1C,,0.37118198,,,
+1023161233,*CC(*)(C)C(=O)OC1CC2CC1C1C3CCC(C3)C21,,0.37657118,,,
+1023305390,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(C)cn2)C3=O)cc(C(=O)NNC(=O)C(*)=O)c1,,0.33035557,,,
+1024068572,*CC(*)c1ccc(C(=O)CCCCCCC)cc1,,0.39176934,0.2663333333333333,,
+1024345756,*CC(CO*)(CSCCCCC)CSCCCCC,,0.4182603,,,
+1024792036,*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(*)=O)cc21,,0.39692149,,1.005045691,12.92707658
+1025306032,*CC1CCC(COC(=O)c2ccc(C(=O)O*)cc2)CC1,,0.35960828,,,
+1025433212,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2N)c(N)c1,,0.37039035,,,
+1025691953,*Oc1cccc(Oc2ccc(C(=O)O)c(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7cc(*)ccc7C(=O)O)cc6)cc5)cc4)cc3)c2)c1,,0.34519508,,,
+1025699725,*CCCCCCSCCS*,,,0.234,,
+1026017017,*CC(*)C(=O)Oc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.3750338,,,
+1026090267,*CCCCCCNC(=O)CC(=O)N*,,0.32199305,,,
+1026468393,*CC(*)(C)C(=O)OCC(C)Oc1ccc(-c2ccc(-c3ccc(C#N)cc3)cc2)cc1,,0.35257191,,,
+1026585512,*Nc1ccc(CCNC(=O)CCCCC(*)=O)cc1,,0.33119767,,,
+1026999069,*N/N=N\c1ccc2ccccc2c1-c1c(NN*)ccc2ccccc12,,0.35871072,,,
+1027170347,*Oc1ccccc1Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.3645491,,,
+1027277278,*C=Cc1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(C=Cc3nc(*)c(C#N)c(C=Cc4ccc(N(c5ccccc5)c5ccccc5)cc4)c3C#N)ccc1-2,,0.40662582,,,
+1027526557,*Oc1cc(OCCOC)c(OC(=O)c2ccc(C(*)=O)cc2OCCOCC)cc1OCCOC,,0.34810915,,,
+1027925897,*CCOCCOCCOC(=O)CCSCCC(=O)O*,,0.34274002,,,
+1028383994,*COc1ccc(C(C)(C)c2ccc(Oc3ccc(*)n3-c3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.35710014,,,
+1028416033,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C,,0.43005336,,,
+1028677935,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)cc1)C2=O,,0.37383074,,,
+1028942320,*CCCCCC(Cl)C(*)Cl,,0.3900218,,,
+1029035301,*Oc1c(F)c(F)c(-c2c(F)c(F)c(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(F)c2F)c(F)c1F,,0.36997043,,,
+1029595953,*CCCCCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,64.73496741,,,,
+1030451978,*Nc1ccc([C@]23C[C@@H]4[C@H]5C[C@]6(c7ccc(N*)cc7)C[C@H]([C@H]5C2)[C@H](C3)[C@H]4C6)cc1,,0.3645152,,,
+1031120219,*CC(COC(C)(C)C)O*,,0.3876684,,,
+1031229001,*Nc1ccc(Oc2ccc(-c3cccc4c3[C@H]3c5ccccc5[C@@H]4c4ccccc43)c(Oc3ccc(N*)cc3)c2)cc1,,0.37154684,,,
+1032049018,*CC(*)C(=O)OC(CCC(CC)CCCC)CC(C)C,,,0.2319999999999999,0.858171779,10.23167095
+1032380700,*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCC(*)=O)cc1C=Cc1ccc(N(C)C)cc1,,0.34629729,,,
+1032824878,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(C)C6=O)cc4)cc3)cc2)cc1,,0.3747633,,,
+1032864427,*c1cccc(N2C(=O)c3ccc(Oc4ccc5c(c4)C4(CC(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc64)CC5(C)C)cc3C2=O)c1,,0.38956456,,1.109968877,20.26112885
+1033105320,*CCCCCC(*)CCCCCCCCCCCC,,,0.381,,
+1033189892,*C=Cc1ccc(N(C)CCOC(=O)Nc2ccc(C)c(NC(=O)OCCN(C)c3ccc(C=CC4=CC(=C(C#N)C#N)C=C(*)O4)s3)c2)s1,,0.37243706,,,
+1033278937,*c1ccc(-c2ccc(C(*)(C)C(F)(F)F)cc2)cc1,,,0.194,1.086916359,21.06056085
+1033351470,*C(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C)c(C)c4)C5=O)cc3)CCC(C(C)(C)C)CC2)cc1,,0.38006135,,,
+1033553223,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6cc7ccccc7cc6Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.36802502,,,
+1033716962,*CC1CCCC(CN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)C1,,0.36071936,,,
+1033918415,*c1cc(OCCCCCCCC)cc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37173532,,,
+1033946861,*CC(*)C(=O)OCCSCC#N,,0.37675973,0.183,1.158953952,13.88465277
+1034104971,*CCOC(=O)NC1CCC(CC2CCC(NC(=O)OCCOc3ccc(C4(c5ccc(O*)cc5)c5ccccc5-c5ccccc54)cc3)CC2)CC1,,0.35609978,,,
+1034123279,*CC(*)(C)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2,,0.38728627,0.122,0.88188115,10.05911169
+1034236621,*C(=Cc1cc(OC)c(C=C(c2ccccc2)c2ccc(-c3ccc(*)cc3)cc2)cc1OC)c1ccccc1,,0.3885182,,,
+1034741718,*CCCS*,,0.44308089,,,
+1034918215,*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(NC(=O)c4ccccc4)cc(C(*)=O)c3)cc2)cc1,,0.35251392,,,
+1034983348,*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(C#N)cc3)cc2)cc1,,0.38002629,,,
+1035028820,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc4c(c3)C(c3ccc(-c5nnc(-c6ccc(C(C)(C)C)cc6)o5)cc3)(c3ccc(-c5nnc(-c6ccc(C(C)(C)C)cc6)o5)cc3)c3cc(*)ccc3-4)ccc1-2,,0.43188759,,,
+1035315076,*c1ccc(Oc2ccc(-c3cc(-c4ccccc4)c4cc(-c5ccc6nc(*)cc(-c7ccccc7)c6c5)ccc4n3)cc2)cc1,,0.41044986,,,
+1035856874,*CC(*)CC(C)CC,,,0.202,,
+1036138133,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C7(c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)CCC(C(C)(C)C)CC7)cc6)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.38499973,,,
+1036220346,*Oc1ccc(C=CC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.37255278,,,
+1036443335,*NNC(=O)C=CC(=O)NCCCCCCNC(=O)C=CC(*)=O,,0.30930644,,,
+1036702537,*C(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Sc6ccc(Oc7ccc(Sc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)cc6)cc4)C5=O)cc3)c2)cc1,,0.36243783,,,
+1037214507,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37003696,,1.184644856,21.96383699
+1037247055,*Nc1ccc2ccccc2c1/N=N/c1ccc(-c2ccc(/N=N/c3c(N*)ccc4ccccc34)c(C)c2)cc1C,,0.35713398,,,
+1037483608,*C=CC1CC(*)C(C#N)([Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)C1,,0.44205828,,,
+1037753254,*COC(=O)OCC1OC(C)(C)OC1*,,0.33690966,,,
+1037907132,*CC(*)C(=O)Oc1ccc(N=Nc2ccc(OC)cc2)cc1,,0.35769038,,,
+1037933162,*Oc1ccc(OC(=O)c2ccc(OC(=O)c3ccc(C(=O)Oc4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.33585548,,,
+1038226153,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,,0.204,1.135056188,22.20715615
+1038295245,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.3576415,,,
+1038630240,*c1ccc2c(c1)C(CC(CC)CCCC)(CC(CC)CCCC)c1cc(*)ccc1-2,,0.41460255,,,
+1038723100,*Nc1cc2cc3cc(C)c(N*)cc3cc2cc1C,,0.353293,,,
+1038983275,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.36221772,,,
+1039049249,*Oc1cc(C)c(C(c2cc(C(C)C)c(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2C)c2ccccc2C(=O)O)cc1C(C)C,,0.41139,,,
+1039097467,*CCC(C)(C)CC(C)COC(=O)c1ccc(C(=O)O*)cc1,,0.34932417,,,
+1039985158,*CCCCC(C)CC(=O)N*,,,0.254,,
+1040974624,*c1cccc(-c2nc3cc4nc(-c5ccccc5)c(*)nc4cc3nc2-c2ccccc2)c1,,0.45725149,,,
+1041000545,*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4ccc(C)cc4)c3-c3ccc(C)cc3)cc2)cc1,,0.37262427,,,
+1041165828,*CCCCCC(=O)Nc1ccc(Cc2ccc(NC(=O)CCCCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33846216,,,
+1042275963,*C=CC(C)C(*)C(=O)OCCCCCCN(C)c1ccc(N(C)C)cc1,,0.38084203,,,
+1042390014,*Oc1ccc(Oc2ccc(OC(=O)c3cc(OCCCCC)cc(C(*)=O)c3)cc2)cc1,,0.35782168,,,
+1042430628,*Nc1cc(C)c(Cc2ccc(CCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.36377279,,,
+1042780856,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OC)c1)c1ccc(N=Nc2ccc(-c3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.36008701,,,
+1043405335,*c1ccc(-c2sc(-c3ccc(-c4cc(CCCCCCCC)c(*)s4)cc3)cc2CCCCCCCC)cc1,,0.44716761,,,
+1044196654,*C(F)(F)C1(*)OC(F)(F)C(F)(F)O1,,0.3876684,,,
+1044241223,*NC(CCC(=O)OCCCCCCCCCC)C(*)=O,,0.37124704,,,
+1044295958,*OC(=O)C(Cc1ccccc1)NC(=O)CCCCC(=O)NC(Cc1ccccc1)C(=O)OC1COC2C(*)COC12,,0.33895751,,,
+1044700576,*CC(*)OC(=O)C=Cc1ccccc1,,0.35329706,,,
+1044740151,*CCN(*)C(=O)c1c(F)cc(F)c(F)c1F,,0.34348957,,,
+1044945612,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38948605,,,
+1045029623,*CCCCCCNC(=O)CCc1ccc(CC(=O)N*)cc1,,0.34201824,,,
+1045248903,*Nc1ccc(CCCc2cccc(CCCc3ccc(N*)cc3)c2)cc1,,0.34755922,,,
+1045265904,*CCOC(=O)CCSCCC(=O)O*,,0.33737751,,,
+1045484392,*Nc1ccc(-c2ccc(NC(=O)c3cc(NC(=O)c4ccncc4)cc(C(*)=O)c3)cc2)cc1,,0.33973104,,,
+1045667225,*CC(=O)NCCCCCCNC(=O)COC(=O)CCCCCCCCCCC(=O)O*,,0.34246271,,,
+1046009311,*C(C)=C(*)SCC,,0.42702088,,,
+1046027555,*Oc1ccc(Oc2ccc(C=NCCCCN=Cc3ccc(*)cc3)cc2)cc1,,0.36995005,,,
+1046064945,*C=C(*)c1ccccc1C[Si](C)(C)C,,0.43642908,,,
+1047211829,*Nc1ccccc1NC(=O)CCCCCCC(*)=O,71.92438381,,,,
+1047488413,*Nc1ccc([C@@H](C)/C=C2/C=CC/C(=C\[C@H](C)c3ccc(N*)cc3)C2)cc1,,0.36075305,,,
+1047573880,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4C(F)(F)F)cc3)(C(F)(F)F)C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.3742406,,,
+1047587622,*CCC(*)(C)C,,0.39620363,0.1956666666666666,,
+1047765125,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(Br)c(Oc3c(Br)cc(N4C(=O)c5ccc(*)cc5C4=O)cc3Br)c(Br)c1)C2=O,,0.42586088,,,
+1048177525,*CC(CCl)(CCl)COC(=O)CCCCC(=O)O*,2.83724036,,,,
+1048219303,*Oc1ccc2c(c1)C(C)(C)CC2(C)c1ccc(Oc2ccc(C3(C)CC(C)(C)c4ccc(*)cc43)cc2)cc1,308.5311609,,,,
+1048356010,*Oc1ccc(Oc2ccc(NC(=C(C#N)C#N)c3ccc(-c4ccc(C(Nc5ccc(*)cc5)=C(C#N)C#N)cc4)cc3)cc2)cc1,270.2905197,,,,
+1048411136,*Oc1c(C)cc(*)cc1C(C)CCC,,0.42324434,,,
+1048769739,*Nc1ccc(N*)c(Cc2ccc3ccccc3c2)c1,,0.34211504,,,
+1048831581,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCC,,0.35634052,,,
+1049514979,*Nc1ccc([C@H](C)c2ccc(C(C)(c3ccc([C@@H](C)c4ccc(N*)cc4C)cc3)C3CCCCC3)cc2)c(C)c1,,0.37519578,,,
+1049869017,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccc(N)c(N)c32)cc1,,0.377461295,,,
+1050100593,*c1ccc(C(=O)Nc2ccc(NC(=O)c3ccc(-n4c(=O)c5sc6c(=O)n(*)c(=O)c6sc5c4=O)cc3)cc2)cc1,327.2441987,,,,
+1050347337,*Oc1ccc(N=Cc2ccc(Oc3ccc(C(c4ccc(Oc5ccc(C=Nc6ccc(Oc7ccc(-c8ccc(*)cc8)cc7)cc6)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.36915045,,,
+1050360528,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)CCCCCC2)cc1,,0.35365375,,,
+1050435822,*CCCCCCNC(=O)N*,,,0.328,1.021142597,23.54088095
+1050763719,*c1cccc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37088821,,,
+1050868721,*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(Cl)C1(F)F,,0.3357861,,,
+1051120785,*OC(=O)CCSCCC(*)=O,6.950276755,,,,
+1051267871,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(C(C#N)=C(C#N)C#N)cc1,,0.35653071,,,
+1051950887,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35265054,,,
+1052347491,*CCNC(=O)c1ccccc1O*,,0.32695077,,,
+1052403305,*CC(*)C(=O)OCCCCCn1c2ccccc2c2ccccc21,,0.3540605,,,
+1053128614,*Nc1ccc2ccc3c(N*)c(-c4cccc(C5(c6ccccc6)c6ccccc6-c6ccccc65)c4)cc4ccc1c2c43,,0.3681964,,,
+1053654075,*CC(C)(C)COC(=O)c1ccc(C=Cc2ccc(C(=O)O*)cc2)cc1,,0.36382705,,,
+1053689640,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)C(F)(F)F)cc1)C2=O,,0.39510719,,,
+1053749106,*CCCCCCCCCCSSSS*,,0.45331783,,,
+1054068496,*CCCCCCCCCCC(=O)NCc1ccc(CNC(=O)CCCCCCCCCCS*)cc1,,,0.285,0.976393146,21.13686578
+1054663737,*CCOCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1,,0.33617998,,,
+1054699439,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1cccc([Si](*)(C)C)c1,,0.44425689,,,
+1055096978,*C(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C)c(C)c4)C5=O)cc3)CCCCCCCCCCC2)cc1,,0.36900879,,,
+1055238521,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCCCCCC)cc1,,0.37774913,,,
+1055403183,*CC(CSCCCCC)O*,,0.4202301,,,
+1055512615,*CC(*)c1ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.36402861,,,
+1056241646,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3C#N)cc1)C2=O,,0.37263888,,,
+1056427666,*CC(*)(C)C(=O)OCC(F)(F)C(F)F,,0.33748264,,,
+1056745854,*CCSS*,,0.48456382,,,
+1056838688,*CC(*)OCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36475321,,,
+1056941097,*CCOCCOCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1,,0.33695594,,,
+1057142863,*CCCCSCS*,1.410869343,,,,
+1057428367,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2C#N)c1,,0.36471825,,,
+1057546694,*CC(*)C1CCC1,,,0.209,,
+1057577802,*c1ccc(NC(=O)c2ccc(OCCOCCOCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)cc1,,0.34464658,,,
+1057831981,*CCN(CCOC(=O)NCC1(C)CC(NC(=O)O*)CC(C)(C)C1)c1ccc(C=C(C#N)C#N)cc1,,0.35418826,,,
+1058058469,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc4c(c3)C3(CCC(C(C)(C)C)CC3)c3cc(*)ccc3-4)ccc1-2,,0.4177397,,,
+1058114006,*CC(*)(F)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)F,,0.31210556,,,
+1058146720,*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)c1ccccc1C(=O)O*,,0.34980137,,,
+1058557408,*CCOC(=O)C1CCC(C(=O)O*)CC1,,0.32751388,,,
+1058865431,*NCCCN*,,0.311097505,,,
+1058915923,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(S(=O)(=O)c7ccc(Oc8ccc(C=C9CCC(=Cc%10ccc(*)cc%10)C9=O)cc8)cc7)cc6)cc5)CCCC4)cc3)cc2)cc1,,0.37436911,,,
+1058961949,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7cc(C(C)(C)C)c(Oc8cccc9c8C(=O)N(*)C9=O)cc7C(C)(C)C)c6C5=O)cc4)cc3)cc2)cc1,,0.39017325,,,
+1059090867,*Nc1cc(NC(=O)Nc2ccc(NC(*)=O)cc2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1,,0.32639066,,,
+1059116091,*Oc1ccc(C(=O)Nc2cc(C(c3ccc(C)c(NC(=O)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)c3)(C(F)(F)F)C(F)(F)F)ccc2C)cc1,,0.34683518,,,
+1059315394,*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(-c4nc5cc(*)ccc5nc4-c4ccccc4)cc3)nc2c1,,0.4322761,,,
+1059383045,*Oc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(Oc5nc(*)nc(Sc6ccccc6)n5)cc4)cc3)cc2)cc1,,0.35642575,,,
+1059454282,*C(Cl)=C(*)CCCCCCCC,,0.40192369,,,
+1059658423,*CC(*)CCCC(C)(C)C,,,0.216,,
+1059731090,*c1ccc(Oc2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.38213277,,,
+1059955409,*C(C)=C(*)[Si](C)(C)C[Si](C)(C)C,,0.41481827,,,
+1060516600,*Nc1ccc([C@H](C=C)c2ccc(C3(c4ccc([C@@H](C=C)c5ccc(N*)cc5)cc4)CCCCC3)cc2)cc1,,0.36338058,,,
+1060757060,*CC(C)=CC(C)S(*)(=O)=O,,0.38497626,,,
+1060916262,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7c(C)cc(C(C)(C)c8cc(C)c(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)c(C)c8)cc7C)cc6C5=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38730912,,,
+1061550399,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.42334602,,,
+1061842940,*Oc1cccc(OC2(F)C(*)(F)C(F)(F)C2(F)F)c1,,0.33739946,,,
+1063041507,*Oc1ccc(N=C(c2ccccc2)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(C(=Nc6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.38185283,,,
+1063440128,*CC(*)C(=O)OCCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(-c3cccc(OC(=O)c4ccc(OC(=O)c5ccc(OCCCCCCCCCCCC)cc5)cc4)c3)cc2)cc1,,0.36748449,,,
+1064087880,*OC1C(C)(C)C(OC(*)=O)C1(C)C,,0.37459599,,,
+1064576585,*CCOCCOc1ccc(NC=Nc2ccc(O*)cc2)cc1,,0.35570592,,,
+1064842298,*c1ccc(OCCCCCCCCCCCOC(=O)CCCCCCCCC(=O)OCCCCCCCCCCCOc2ccc(-c3nnc(*)s3)cc2)cc1,,0.37562329,,,
+1064934987,*CC(*)c1ccc([SiH](C)C)cc1,,0.42154947,,,
+1065372195,*NCCCC(C)CN*,,0.32866479,,,
+1065435792,*CC(*)(C)C(=O)OCCCCCCCCCCn1c2ccccc2c2cc(N=Nc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])ccc21,,0.36431526,,,
+1065541315,*CCCCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1,,0.34386571,,,
+1065659422,*Nc1ccc(Cc2ccc(NC(=O)Nc3ccc(CCc4ccc(NC(*)=O)cc4)cc3)cc2)cc1,183.1975298,,,,
+1065912423,*c1ccc(Cc2ccc(N3C(=O)CC(Nc4ccc(NC5CC(=O)N(*)C5=O)cc4)C3=O)cc2)cc1,248.5034267,,,,
+1065918379,*Oc1ccc(OC(=O)c2cc(NC(=O)c3ccc(NC(=O)C(C)N4C(=O)c5ccccc5C4=O)cc3)cc(C(*)=O)c2)cc1,58.7319127,,,,
+1066071241,*c1ccc(-c2ccc(C(*)(c3ccc(F)cc3)C(F)(F)F)cc2)cc1,,,0.185,1.113444185,20.76961788
+1066274431,*OP(=O)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)OC1CCCCC1,,0.36508508,,,
+1066470436,*c1cc(CCCCCCCCCCCCCCCCC)c(*)s1,,,0.396,,
+1066912127,*CC(C)(CC)CO*,,0.38345634,,,
+1067785609,*Nc1ccc(Nc2ccc(C(O)(c3ccccc3)c3ccc(C(O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.39313145,,,
+1067787070,*c1ccc(*)c(C(=O)c2ccccc2)c1,,0.39891755,,,
+1068100442,*CC(*)(C)C(=O)NC(C)c1cccc2ccccc12,,0.35801124,,,
+1068153413,*Oc1ccc(Oc2cccc(*)n2)cc1,,0.3485515,,,
+1068186328,*CCOCC(CCl)O*,,0.36652669,,,
+1068353755,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccc(Br)cc2)c1,,0.35980816,,,
+1068808459,*C=Cc1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(F)c(C(F)(F)F)c1,,0.39040099,,,
+1068978203,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)c4cccc(C(=O)Nc5cccc(O*)c5)c4)c3)cc2)cc1,,0.34174788,,,
+1069092654,*C[Ge](C)(C)Cc1cocc1*,,0.3589821,,,
+1069141321,*Nc1ccc(C(CC[C@](c2ccccc2)(c2ccc(N*)cc2)c2cccc(N)c2)(c2ccccc2)c2ccc(N)cc2)cc1,,0.37535492,,,
+1070036927,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.37048176,,,
+1070310585,*Nc1ccc(-c2cc(-c3ccc(N)c(C)c3)cc(-c3ccc(N*)c(C)c3)c2)cc1C,,0.36074996,,,
+1070320227,*c1ccc(-c2ccc(-c3cc(-c4ccc(Oc5ccc(Oc6ccccc6)cc5)cc4)c4cc(-c5ccc6nc(*)cc(-c7ccc(Oc8ccc(Oc9ccccc9)cc8)cc7)c6c5)ccc4n3)cc2)cc1,,0.38429931,,,
+1070754450,*NC1=C[C@@H]2[C@H]3c4cc(N*)ccc4[C@H](c4cc(N)ccc43)[C@@H]2C=C1,,0.37404178,,,
+1071227644,*c1cc(C(c2ccc(OCCN(C)c3ccc(C=CC4=CC(=C(C#N)S(C)(=O)=O)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(C)c1ccc(C=CC2=CC(=C(C#N)S(C)(=O)=O)CC(C)(C)C2)cc1,,0.37777031,,,
+1071356842,*Oc1ccc(C(=O)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.35694077,,,
+1071493956,*CCCCCCCCCCCSCCCCCCS*,,,0.3145,0.923953778,21.98769917
+1071638401,*c1ccc(Oc2c(C)cc(-c3cc(C)c(Oc4ccc(N5C(=O)c6ccc(Oc7ccccc7Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4C(F)(F)F)c(C)c3)cc2C)c(C(F)(F)F)c1,,0.38084473,,,
+1071860128,*O[Si](*)(C)CCCCCCCCCCCn1c2ccccc2c2ccccc21,,0.38480246,,,
+1071877788,*CCCCCCCC(=O)OCCCCC1COC(*)O1,,0.35831824,,,
+1071999103,*O[Si](c1ccccc1)(c1ccccc1)c1ccc(-c2ccc([Si](*)(c3ccccc3)c3ccccc3)cc2)cc1,,0.40332327,,,
+1072790069,*Nc1c(CC(=O)O)cc2c(N*)nc(N)nc2c1C=O,,0.32104988,,,
+1073329534,*Oc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(NC(=O)c6ccc(*)cc6)cc5)cc4)cc3)c2)cc1,,0.36368772,,,
+1073330794,*Oc1ccc(C(=O)c2cccc(NC(=O)CCCCCCCCC(=O)Nc3cccc(C(=O)c4ccc(*)cc4)c3)c2)cc1,,0.35027949,,,
+1073733808,*CC(*)OC(=O)c1cccc(C)c1,,0.36484403,,,
+1074067336,*CC(*)C(=O)OCCCSC,,0.38841806,0.211,1.075871128,12.36347326
+1074560123,*CCCCCCCCNC(=O)CCC(=O)N*,69.22130195,,,,
+1074625910,*NCCCCCN*,,0.320050415,,,
+1075276432,*Oc1ccc(-c2ccc(Oc3ccc(Oc4ccc(Oc5ccc(*)cc5)cc4)cc3)c3ccccc23)c2ccccc12,,0.37716624,,,
+1075404096,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37879971,,,
+1075461516,*CC(O)COC(=O)CCCCCCCCC(=O)OCC(O)COc1ccc(C=C(C)c2ccc(O*)cc2)cc1,,0.35077016,,,
+1075544965,*c1ccc2c(c1)C(=O)N(c1cccc(C(C)(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c1)C2=O,,0.3925623,,,
+1075748489,*Oc1cccc(Oc2cccc(OC(*)=O)c2)c1,,0.34159568,,,
+1075922952,*Nc1ccc(-c2cc(C)c(-c3ccc(N*)cc3)c(C)c2N)cc1,,0.36789723,,,
+1076149389,*CC(*)OC(=O)C(CC)(CC)CC,20.41787387,,,,
+1076285178,*CCCCCCCCCC(=O)N*,,0.36456285,,,
+1076325582,*CC(*)(C)C(=O)OC1CC2CCC1(C)C2(C)C,,0.40517863,0.143,0.87251011,10.2622674
+1076737435,*Oc1ccc(C(=O)Nc2ccc(NC(=O)c3ccc(*)cc3C(=O)O)cc2)c(C(=O)O)c1,216.6231471,,,,
+1076766601,*CC(*)OC(=O)c1ccc(OCC)cc1,,0.35412177,,,
+1077387611,*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nnc(-c5ccc(Oc6ccc(-c7nnc(*)o7)cc6)cc5)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.36777032,,,
+1077715415,*C=Cc1cc(OCCCCCC)c(*)cc1OC,,0.38047762,,,
+1077770059,*C(=O)Nc1c(CC)cc(Cc2cc(CC)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N=Nc5ccc(C)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(CC)c2)cc1CC,,0.3890601,,,
+1077976008,*CCc1ccc(CCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*)cc1,,0.36974923,0.368,0.942820572,20.09079341
+1078277401,*Nc1cccc(NC(=O)CCCCC(=O)Nc2ccc(C(=O)NC(*)=S)cc2)c1,,0.33377673,,,
+1078510278,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccc(C)cc3)c3ccccc23)cc1,,0.36498491,,,
+1078694994,*Oc1ccc(P(=O)(c2ccccc2)c2ccc(*)cc2)cc1,,0.38958964,,,
+1079075328,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)c(C(F)(F)F)c1)C2=O,,0.38478301,,,
+1079079241,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1C1CCCCC1,,0.37641146,,,
+1079236284,*Nc1ccc(-c2ccc(C3=CC[C@](N*)(c4ccccc4)C=C3)cc2)cc1,,0.34703022,,,
+1079254349,*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6CCC(C6)C5C4=O)c3)cc2)cc1,,0.36179092,,,
+1079860530,*OC(CCOc1ccccc1)CC(*)=O,,0.34230265,,,
+1080075202,*CCCCCCCCCCCCCCCCOC(=O)CC/C=C/CCC(=O)O*,,0.37777688,0.304,,
+1080185947,*CCC1CC(*)C2C3CCC(C3)C12,,0.38101564,,0.933540935,15.81211052
+1080677483,*C=CC1CC(*)C2C(=O)N(c3ccccc3)C(=O)C12,,0.3604348,,,
+1080857742,*c1ccc(C(c2ccccc2)c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.39612833,,,
+1081105585,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(C)c(N3C(=O)c4ccc(*)cc4C3=O)c1C)C2=O,,0.40883151,,,
+1081227608,*C[Si](*)(C)CCCN(CC)CC,,0.38730657,,,
+1081296468,*O[Si](*)(C)OCCOCCOCCOCCOCCOCCOCCOCCOC,,0.36497131,,,
+1081913704,*Cc1ccccc1CSS*,,0.39101963,,,
+1082020725,*O[Si](C)(C)c1ccc([Si](C)(C)O[SiH](*)CCC(F)(F)F)cc1,,0.41587885,,,
+1082166960,*C(F)(F)C(*)(F)OC(F)(F)F,,0.30823837,,,
+1082266101,*CCCCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.35264374,,,
+1082761945,*NC[C@H](N*)C(=O)[C@@H](N)[C@H](N)C=O,,0.30524997,,,
+1082852638,*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc21,,0.38802883,,1.024454484,16.97613627
+1083086006,*CCCCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)c(C(=O)OCC=C)c2)cc1,,0.34241314,,,
+1083192261,*CCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.3510293,,,
+1083208752,*C(=O)Nc1ccc(Oc2cccc3c(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc7c(N8C(=O)c9ccc(*)cc9C8=O)cccc67)cc4)C5=O)cccc23)cc1,,0.36673597,,,
+1083322172,*CCN(*)C(=O)CCCCCCCCCCCCCC,,,0.309,,
+1083424600,*CC(*)C(=O)OCCCCCCn1c2ccccc2c2ccccc21,,0.35821095,,,
+1084524118,*c1ccc(Oc2ccc(-c3nc4cc5sc(*)nc5cc4s3)cc2)cc1,,0.47726356,,,
+1084974257,*Nc1c(CCCCC)cc2c(ccc3c4ccccc4ccc23)c1N*,,0.34568189,,,
+1085199184,*CCNC(=O)c1cccc(C(=O)NCCOc2ccc3ccc(O*)cc3c2)c1,,0.32973712,,,
+1085340471,*Nc1c2nc(C)n(N)c(=O)c2cc2c(=O)n(N*)c(C)nc12,,0.36303676,,,
+1085397410,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.35082943,,,
+1085462355,*Oc1ccc(C(C)(c2ccccc2)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.36571135,,,
+1085625486,*CCCCCCCCN(C)c1ccc(C(=O)c2ccc(N(*)C)cc2)cc1,,0.38912701,,,
+1085727848,*CCC(=C(*)c1ccccc1)c1ccccc1,,0.39996108,,,
+1085767747,*Oc1ccc(C2(c3ccc(OC(*)=O)c(Cl)c3)CC3CCC2C3)cc1Cl,,0.37867206,,,
+1086507513,*N[C@H]1CC[C@H](C(C)([C@H]2CC[C@H](N*)CC2)C(C)(c2ccc(N)cc2)c2ccc(N)cc2)CC1,,0.36049082,,,
+1086648178,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)N(C)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)c1)C2=O,,0.36412971,,,
+1086813009,*Oc1ccc(SC(=O)Sc2ccc(*)cc2)cc1,,0.3635142,,,
+1087155239,*CCOCCOC(=O)CCCCCCCCC(=O)O*,,0.34936993,0.229,,
+1087298590,*CCN(*)C(=O)C1(c2ccc(Cl)cc2)CCC1,,0.37883274,,,
+1087567752,*C=C1CCC(=Cc2ccc(Sc3ccc(*)o3)o2)C1=O,102.2883172,,,,
+1087727668,*Nc1ccc(C(Cc2ccccc2)c2ccc(N*)cc2)cc1,,0.35034415,,,
+1087863432,*CCC[Sn](C)(C)CCCOC(=O)C(CCCCCCCCOc1ccc(-c2ccc(OCc3ccc(C#N)cc3)cc2)cc1)C(=O)O*,,0.34246271,,,
+1087874446,*CC(*)(C)C(=O)OCCCCCCCCCC,,0.38783787,0.259,0.880799375,10.58766356
+1088069103,*CC(*)c1ccc(CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.37120433,,,
+1088166568,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.33766319,,,
+1089099608,*CCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)c(OCCCCC)c2)cc1,,0.34588903,,,
+1089343278,*CC(*)(C)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.39138472,0.1245,0.905497494,10.82663282
+1089446070,*Nc1cc2c(cc1N*)-c1ccccc1C2,,0.34184722,,,
+1089600845,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCCCCCCCCCC9)cc8)cc7C6=O)cc5)cc4)CC4CCC3C4)cc2)cc1,,0.38133865,,,
+1090041949,*c1ccc(Oc2c(C)cc(C(C)(C)c3cc(C)c(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C8(c9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)CCC(c9ccccc9)CC8)cc7)cc6C5=O)cc4)c(C)c3)cc2C)cc1,,0.38650416,,,
+1090062634,*CC(*)(Cl)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32985562,,,
+1090620461,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5nc(-c6ccccc6)[nH]c5*)cc4)cc3)cc2)cc1,,0.38724079,,,
+1090704353,*CC(*)COCC1COC(=O)O1,,0.31094849,,,
+1090817229,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6cccc(Oc7cccc8c7C(=O)N(*)C8=O)c6)c5C4=O)cc3)c2)cc1,,0.3559394,0.165,1.216100189,17.29130819
+1091045706,*Oc1ccc(-c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.37742789,,,
+1091424818,*Nc1ccc2c(C)c(-c3ccccc3)c(-c3ccccc3)c(N*)c2c1N,,0.35215178,,,
+1091676819,*c1ccc(C(C#N)(C(=O)OC)C(*)(C#N)C(=O)OC)cc1,,0.40250352,,,
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+1092008018,*Nc1ccc([C@@H]2c3ccccc3-c3ccc(N*)c(N)c32)cc1,,0.35996312,,,
+1092288968,*Nc1ccc2c(c1)[C@@H]1c3ccc(C)cc3[C@H]2c2cc(N*)ccc21,,0.38323804,,,
+1092404802,*CCCCNC(=O)Cc1ccc(C(=O)N*)cc1,,0.32910711,,,
+1092542084,*c1ccc(-n2c(=O)c3c(F)c4c(=O)n(*)c(=O)c4c(F)c3c2=O)cc1,337.816724,,,,
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+1092652620,*CC(*)c1ccc(N(c2ccccc2)c2ccccc2)cc1,,0.4131707,,,
+1092863578,*N=P(*)(OCCCCCCCCC)OCCCCCCCCC,,0.40563512,,,
+1093111808,*c1ccc2oc(-c3cc(OCCCCCCCCCC)c(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cc3OCCCCCCCCCC)nc2c1,,0.39288084,,,
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+1093757238,*Oc1cccc(OC(=O)c2ccc(C(C)(C)c3ccc(C(*)=O)cc3)cc2)c1,,0.36093522,0.222,1.102151001,21.991583
+1093999589,*Nc1ccc([C@H](CCCC)c2ccc(C(CC[C@H](CCC)C3CCCCC3)c3ccc([C@@H](CCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.37019133,,,
+1094849192,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.40384519,,,
+1095118388,*Nc1cc(NC(=O)CCCCCCCCC(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1,,0.36358328,,,
+1096169476,*CCCCCCCCNC(=O)CCCCCCCCCCCCC(=O)N*,,,0.381,,
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+1097037876,*C(=O)Nc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C8(c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)NC(=O)c9ccccc98)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.3628852,,,
+1097383443,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1,,0.35891323,,,
+1097591808,*c1ccc(-c2cc(-c3ccc(OCCCCCC)cc3)cc(-c3ccc(-c4ccc5c(c4)C(CCCCCC)(CCCCCC)c4cc(*)ccc4-5)cc3)c2-c2ccc(OCCCCCC)cc2)cc1,,0.39702352,0.29,0.887994076,19.66529502
+1098253429,*C(=Cc1cc(OC)c(C=C(c2ccc(*)cc2)c2ccc(F)cc2)cc1OC)c1ccc(F)cc1,,0.39273614,,,
+1098329316,*CC(CCCCCCCCCCCC)COC(=O)NC(=O)c1cccc(C(=O)NC(=O)O*)c1,,0.34491692,,,
+1098458595,*Nc1cc(NC(=O)c2cc(OCCCS(=O)(=O)c3ccc(N=Nc4ccc(N(C)C)cc4)cc3)cc(C(*)=O)c2)cc(C(=O)OCCCN(C)c2ccc(N=Nc3ccc(S(C)(=O)=O)cc3)cc2)c1,,0.36246198,,,
+1098493024,*CCCCCCOc1ccc(C(=O)N(C(=O)c2ccc(O*)cc2)c2cc(C)cc(C)c2)cc1,,0.37589953,,,
+1098503912,*Nc1ccc(CCCC=C(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.35438758,,,
+1099489498,*CC(*)C(=O)Oc1ccccc1C(C)(C)C,,0.36674405,0.175,0.949266643,11.30667741
+1099529039,*C1C(=O)N(CCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.34826627,,,
+1099611212,*CC(C)(C)CO*,,0.38553594,,,
+1099687723,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(NC(=O)Nc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)cc1,,0.33834111,,,
+1100016656,*O[Si](C)(C)CCCC(=O)Oc1ccc(C=Nc2ccc(Oc3ccc(N=Cc4ccc(OC(=O)CCC[Si](*)(C)C)cc4)cc3)cc2)cc1,,0.37725697,,,
+1100237859,*CC(C)(COC(=O)O*)C(=O)OCc1ccccc1,,0.34012243,,,
+1100342042,*Nc1ccc2c(c1)[C@]1(c3ccccc3-2)c2cc(N*)ccc2-c2ccc(C(C)(C)C)cc21,,0.3907835,,,
+1100790418,*Oc1ccc(-c2ccc(Oc3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(*)cc6)cc5)cc4)cc3)c3ccccc23)c2ccccc12,,0.3844144,,,
+1100941404,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4[nH]c(-c5cccc(-c6nc7cc(*)ccc7[nH]6)c5)nc4c3)cc2)cc1,,0.37607565,,,
+1101351580,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccccc5)c(-c5ccc6ccccc6c5)c(-c5ccc6ccccc6c5)c4-c4ccccc4)cc3)cc2)cc1,,0.3993001,,,
+1101626993,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37953912,,,
+1101869163,*CC(*)C(=O)Oc1ccc(C(=O)OCCCC)cc1,,0.35302021,0.2329999999999999,1.072941358,12.11326982
+1102089845,*CC(*)(CC(=O)OCCOCCOCCOCCOCCOCCOCCOC)C(=O)OCCOCCOCCOCCOCCOCCOCCOC,,0.34860054,,,
+1102195850,*Cc1ccc(CNC(=O)CCCCCCCCC(=O)N*)cc1,,0.34218034,,,
+1102959825,*c1ccc(-c2nc3cc(S(=O)(=O)c4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)cc1,,0.42858574,,,
+1103272237,*CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)OCCOc3ccc(O*)cc3)cc2)cc1,,0.33635449,,,
+1103276909,*CC(*)C(=O)OC(F)(C(F)(F)F)C(F)(F)F,,0.31831745,0.1119999999999999,1.502297049,14.21742857
+1103729487,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(C(c4cc(C)c(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)c(C)c4)c4cccc5ccccc45)cc3C)cc1)C2=O,,0.4076565,,,
+1104001182,*Nc1ccc(Nc2ccc(N*)cc2)cc1,,0.31540535,,,
+1104150399,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccc(OC)cc3)ccc1-2,,0.38956004,,,
+1104675070,*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2CC3CC2C2C4CCC(C4)C32)cc1,,0.37873472,,,
+1104783164,*c1ccc(-c2ccc(-c3ccc(-c4ccc(-c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C(C)(C)c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.37765587,,,
+1104839860,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7c6)cc5C4=O)cc3)cc2)cc1,,0.36627913,,,
+1105092944,*Nc1ccccc1-c1c(N)ccc2c(-c3ccccc3N)c(N*)ccc12,,0.35001421,,,
+1105455152,*C(=O)OCCOC(=O)N1CC(C)N(*)CC1C,60.10633691,,,,
+1105607471,*CCCCOC(=O)/C=C\C(=O)O*,,0.31922537,,,
+1105655721,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(C)c(N5C(=O)c6ccc(*)cc6C5=O)c3C)C4=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.37354476,,,
+1105688396,*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(Br)cc3)cc2)cc1,,0.35394137,,,
+1106240394,*C(=O)C1CC1C(=O)N1CCN(*)CC1,100.5840527,,,,
+1106363407,*Nc1c(-c2ccccc2)cc(-c2cccc3ccccc23)c(-c2ccccc2)c1N*,,0.36326423,,,
+1106578418,*Oc1ccc(CNC(=O)c2ccc(C(=O)NCc3ccc(OC4COC5C(*)COC45)cc3)cc2)cc1,,0.33322541,,,
+1106718991,*c1cccc(N2C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.37305754,,,
+1106744054,*CC(CS(=O)(=O)CCC)O*,,0.37543167,,,
+1106849268,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7c(C)cc(Cc8cc(C)c(Oc9cccc%10c9C(=O)N(*)C%10=O)c(C)c8)cc7C)c6C5=O)cc4)cc3)cc2)cc1,,0.3774632,,,
+1108103978,*CC(*)c1ccccc1COCCCCC,,0.38189308,0.186,0.935560059,11.1484409
+1108143190,*c1cccc(-c2nc3cc(-c4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)c1,,0.41818532,,,
+1108204726,*c1cc(*)c(O)c(/C=N/c2ccc(N3CCOCC3)cc2)c1,,,0.266,1.120600809,15.17923007
+1108782775,*CCCCCCOC(=O)OCCCCCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.339636,,,
+1108992243,*CC(=O)C(*)c1ccc(C)cc1,67.57313464,,,,
+1109762567,*c1ccc(Oc2cccc(N3C(=O)c4ccc(Oc5ccc6cc(Oc7ccc8c(c7)C(=O)N(*)C8=O)ccc6c5)cc4C3=O)c2)cc1,,0.36778289,,,
+1109873532,*C(=O)OCCOC(=O)c1ccccc1N1C(=O)c2ccc(*)cc2C1=O,,0.34278751,,,
+1110214736,*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1OCCCCCCOc1ccc(-c3ccc(OCC)cc3)cc1)C2=O,,0.3582791,,,
+1110372599,*CCCCCCOC(=O)CCCCCCC(=O)OCCCCCCN1C(=O)c2ccc(S(=O)(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.34654201,,,
+1110669170,*C(=O)c1ccc(N=Nc2ccc(C(=O)N3C(=O)N(*)C(c4ccccc4)(c4ccccc4)C3=O)cc2)cc1,183.844758,,,,
+1110735297,*CC(*)(C)C(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.33305735,,,
+1110869363,*c1ccc(OCC2CCC(COc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)CC2)cc1,,0.36298735,,,
+1110958155,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3C(C)(C)C)c2)cc1,,0.36107989,,,
+1111539989,*Cc1ccc(OCc2ccc(COc3ccc(*)c4cccnc34)cc2)c2ncccc12,200.3538526,,,,
+1112220366,*CC(*)(C)C(=O)OCc1cccc(Cl)c1Cl,,0.35611555,,,
+1112287021,*Oc1ccc(N=Nc2ccc(S(=O)(=O)c3ccc(N=Nc4ccc(OC(=O)c5ccc(N=Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1,,0.36582704,,,
+1112294940,*OP(=O)(Oc1c(Cl)cc(Cl)cc1Cl)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl,,0.36280983,,,
+1112362061,*CCc1ccc(*)cc1,,0.38563218,,,
+1112494306,*C=CC1CC(*)C2C(=O)N(CCCC)C(=O)C12,,0.36281424,,,
+1112748716,*OS(=O)(=O)c1ccc(-c2ccc(S(=O)(=O)Oc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1,,,,1.24578392,23.22098571
+1112902230,*NC(CCC(=O)OCC)C(*)=O,,0.32193384,,,
+1113081697,*CCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.36894603,,,
+1113417814,*CC(CCCCCCOc1ccc(N=Nc2ccc(Cl)cc2)cc1)COC(=O)CCCCC(=O)O*,,0.36250896,,,
+1113818194,*Nc1ccc2c(c1)C(C)(C)C[C@@]21CC(C)(C)c2ccc(N*)cc21,,0.37806906,,,
+1114103605,*Oc1c(C)cc(C2(c3cc(C)c(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.39855056,,,
+1114394646,*c1ccc(-c2ccc(-c3ccc(-c4ccc([Si](*)(CCCC)CCCC)s4)s3)s2)s1,,0.43213271,,,
+1114536999,*CC(=O)OC(=O)c1ccc(C(=O)OC(=O)COc2ccc(O*)cc2)cc1,80.44433982,,,,
+1115041145,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(C)c4)cc3C)cc2)cc1,,0.3875115,,1.095765995,20.44800803
+1115283771,*O[Si](C)(C)O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F,,0.38359544,,,
+1115535876,*c1sc(*)c(OCCCCCCCCCCCCCCCC)c1C,,,0.388,,
+1116045273,*CC(*)c1cccc2c1OCO2,,0.35639333,,,
+1116077459,*Oc1c(-c2ccccc2)cc(*)cc1-c1ccc(C(C)(C)C)cc1,,0.41314664,,,
+1116322713,*C([2H])([2H])C(*)(C(=O)OC([2H])([2H])C([2H])([2H])[2H])C([2H])([2H])[2H],,0.35523733,,,
+1116382939,*C(CCCCCC)=C(*)c1ccccc1,,0.41697419,,,
+1116523292,*CC(*)C(=O)OCCCn1c2ccccc2c2ccccc21,,0.34946013,,,
+1116545880,*CCN(*)C(=O)Cc1ccc(C(F)(F)F)cc1,,0.34471923,,,
+1116644924,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(C(C)(C)CC)CC2)cc1,,0.36254494,,,
+1117693803,*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(OC)c2)cc1OC,,0.34285391,,,
+1118322325,*Cc1ccc(CNC(=O)CCCCCC(=O)N*)cc1,61.73836642,,,,
+1119425310,*O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C,,0.4145335,,,
+1120266201,*c1ccc(*)c(C(=O)c2ccc(C)cc2)c1,,0.40940736,,,
+1120810907,*NCCCCN*,,0.31431622,,,
+1120932055,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7c(C)cc(-c8cc(C)c(Oc9cccc%10c9C(=O)N(*)C%10=O)c(C)c8)cc7C)c6C5=O)cc4)cc3)cc2)cc1,,0.37366019,,,
+1121030015,*CCS*,,0.44850444,0.223,1.187990132,21.83251301
+1121099554,*c1cc(C(=O)NCCCCCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.36819069,,,
+1121162795,*CC(c1ccccc1)C1C(=O)N(C)C(=O)C1*,,0.35994352,,,
+1121663416,*Oc1ccc(N=Cc2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(C=Nc6ccc(Oc7ccc(-c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.37355301,,,
+1121754299,*Nc1cc(-c2ccccc2)c(-c2ccccc2N*)cc1-c1ccccc1,,0.35590858,,,
+1121884614,*CCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.346649,,,
+1122012910,*CC(*)(C)C(=O)OCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.34405824,,,
+1122196297,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)c4ccccc4)cc(C(*)=O)c3)cc2)cc1,,0.34866462,,,
+1122498895,*CCCCCCCCCCCCOC(=O)CCCCCNC(=O)CCCCC(=O)NCCCCCC(=O)O*,,,0.306,0.968420729,19.39078981
+1122802098,*Cc1ccc(CNC(=O)CCCCCCCCCCCCCC(=O)N*)cc1,,0.36199531,,,
+1123352348,*CC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CN4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.34069553,,,
+1123567256,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCC(C)CC(=O)O*,,,0.362,0.897262686,19.1607057
+1123597680,*c1cccc(P(C)(=O)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(P(=O)(c6ccccc6)c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.35188872,,,
+1123733382,*c1cccc(S(=O)(=O)NCCNS(*)(=O)=O)c1,,0.32570227,,,
+1124160654,*CC(*)(C)CC,,0.3612215,0.17,,
+1124320017,*Nc1ccc2c3ccccc3c3ccccc3c2c1N*,,0.33446977,,,
+1125785790,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OC)cc1,,0.34260216,,,
+1125984046,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(Oc7ccc(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)n2)cc1,,0.35547731,,,
+1126266523,*CCCCCCCCCCNC(=O)NCc1ccc(CNC(=O)N*)cc1,,0.34448877,,,
+1126464769,*Oc1cc(OCCOC)c(OC(=O)c2ccc(C(*)=O)cc2-c2ccccc2)cc1OCCOC,,0.35160749,,,
+1126802641,*ON(C(F)(F)F)C(F)(F)C(F)=C(F)C(*)(F)F,,0.30255693,,,
+1127073632,*CCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.34925616,,,
+1127321613,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(-c3nc4cc([N+](=O)[O-])ccc4[nH]3)cc2)cc1,,0.35638392,,,
+1127504526,*CCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.33190974,,,
+1128237994,*Nc1ccc(*)c(Cl)c1Cl,115.5762842,,,,
+1128593254,*Nc1ccc(C2=CC[C@](N*)(c3ccc(-c4ccccc4)cc3)C=C2)cc1,,0.34625627,,,
+1128659903,*Nc1ccc([C@@]2(C)CC(C)(C)c3cc(N*)ccc32)cc1,,0.36572803,,,
+1128762246,*Nc1cccc(-c2cc(-c3ccccc3)c(-c3ccccc3)c(-c3ccccc3)c2-c2ccccc2)c1N*,,0.36487251,,,
+1129166454,*CCCCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.35582087,,,
+1129389664,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C)c(C)c1,,0.3973231,,,
+1129473304,*CCOC(=O)NCCCCCCCCCNC(=O)O*,,0.34415845,,,
+1129746387,*Nc1ccc(/C=C/c2cc3ccccc3cc2/C=C/c2ccc(N*)cc2)cc1,,0.35386503,,,
+1130000577,*Nc1ccc(-c2ccc(N*)c(-c3cccc4ccccc34)c2-c2cccc3ccccc23)c(-c2cccc3ccccc23)c1-c1cccc2ccccc12,,0.37642959,,,
+1130043437,*NCCCCCCCCCCN*,,0.34317069,,,
+1130524840,*C(=O)Nc1ccc(-c2ccc(NC(=O)N3CC(C)N(*)CC3C)cc2)cc1,,0.35679719,,,
+1130989297,*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.37326139,,,
+1131052008,*OC(*)CCCCCC,,0.42182416,,,
+1131219961,*Oc1ccc(C(=O)NNC(=O)c2ccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)cc2)cc1,,0.33957071,,,
+1131234083,*c1ccc(Sc2ccc(-c3nc4cc(-c5ccc6nc(-c7ccccc7)c(*)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.41874626,,,
+1131649373,*CCCCCCOC(=O)NCc1ccc(CNC(=O)O*)cc1,,0.33648246,,,
+1131929043,*Nc1ccc(C(=CC=C)c2ccc(N*)cc2)cc1,,0.35869466,,,
+1132377613,*NC1=CC=C(C(c2ccc(N)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)CC1,,0.35797409,,,
+1132744464,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)CCCCC2)cc1,,0.3621841,,,
+1132763612,*C=Cc1ccc(C=Cc2cc(*)ncn2)cc1,84.80053451,,,,
+1132889582,*Oc1ccc(-c2ccc(OC(=O)c3cccc(Oc4ccc(C(*)=O)cc4)c3)cc2)cc1,89.5930824,,,,
+1132923909,*CCCCCCCCCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.34726599,,,
+1133044696,*CC#CCOC(=O)CCCCCCCCCCC(=O)O*,,0.36542527,,,
+1133390257,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc(NC(=O)c6cc(C(=O)Nc7ccc(Oc8ccc9nc(-c%10ccccc%10)c(*)nc9c8)cc7)cc(N7C(=O)c8c(Cl)c(Cl)c(Cl)c(Cl)c8C7=O)c6)cc5)ccc4nc3-c3ccccc3)cc2)cc1,,0.38426551,,,
+1133455490,*CCCCCCOC(=O)c1cc(OCCCCCCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)cc(C(=O)OCCCCCCN2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.35250141,,,
+1133460389,*CCCCCCOC(=O)C(CCCCCCP(=O)(OCC)OCC)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.3621374,,,
+1133515947,*Nc1cccc(NC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)c1,,0.34788989,,,
+1133631248,*CC(*)(C)C(=O)OCc1ccc2c(c1)OCCOCCOCCOCCO2,,0.34634532,,,
+1134375146,*Nc1cc(N*)c2c(=O)[nH]c3c(N)cc(N)c4c(=O)[nH]c1c2c34,,0.33153693,,,
+1134601529,*CC(*)OC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32618869,,,
+1134713599,*Nc1ccc(N*)c2c(=O)n([C@H]3C(=O)NC(=O)[C@@H](N)C3(N)N)c(C)nc12,,0.34495062,,,
+1134997266,*C(OC)=C(OC)c1ccc(*)s1,,0.371067,,,
+1135072955,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(S(=O)(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.36903562,,,
+1135542546,*CCCC1(CCCNC(=O)c2ccccc2-c2ccccc2C(=O)N*)c2ccccc2-c2ccccc21,,0.35838113,,,
+1135550271,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1,,0.37975744,,,
+1135788027,*CC(*)OC(=O)c1ccccc1OC(C)=O,,0.33486317,,,
+1135838417,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)cc1,,0.34392183,,,
+1136024156,*CCN(CCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1)CCOC(=O)NCCCCCCNC(=O)O*,,0.34321902,,,
+1137226558,*CC(*)(C)C(=O)OCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OCCCCC)cc2)cc1,,0.34489175,,,
+1137323137,*OC(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)OC3CC4CC(*)CC(C3)O4)cc2)cc1,,0.34709392,,,
+1137418125,*C=CCC(C*)c1ccccc1,,0.38727971,,,
+1137724791,*CC(*)c1ccc2c(c1)OCO2,,0.3514226,,,
+1138085523,*C(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35288587,,,
+1138097304,*CC(*)c1ccc(CCCCCCCCCCCCCCCCCC)cc1,,0.40382331,0.3751666666666667,0.844184922,13.54553558
+1138111441,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C,,0.4031994,,,
+1138356248,*Nc1ccc([C@@]2(C)CCc3ccc(N*)cc32)cc1,,0.35627346,,,
+1139097399,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(CCCCCCCCCCCC)C6=O)cc4)cc3)cc2)cc1,,0.37826557,,,
+1139542368,*Nc1ccc(NC(=O)c2ccc(NC(=O)c3ccc([Si](C)(C)c4ccc(C(=O)Nc5ccc(C(*)=O)cc5)cc4)cc3)cc2)cc1,,0.35485235,,,
+1139696241,*Nc1c(N*)c2ccc3ccc(C)c4c(C)c(C)c(c1C)c2c34,,0.34040386,,,
+1139986817,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccc(F)cc2)c1,,0.34608545,,,
+1140194913,*CCCCCS*,-55.37860961,,,,
+1140956631,*CCCCCCN1C(=O)C(C)C(SCCOCCSC2C(=O)N(CCCCCCN3C(=O)CC(C)(SCCOCCSC4(C)CC(=O)N(*)C4=O)C3=O)C(=O)C2C)C1=O,,0.34774274,,,
+1141098203,*CC(*)(C)C(=O)Oc1cc(C)cc(C)c1,,0.36781222,,,
+1141104268,*CCCCNC(=O)CCCCCCC(=O)N*,,,0.2915,1.002012128,18.29004994
+1141363047,*c1ccc(C(=O)OCCCCCCCCCCOc2ccc(C=CC(=O)C=Cc3ccc(OCCCCCCCCCCOC(=O)c4ccc(-c5nnc(*)o5)cc4)cc3)cc2)cc1,,0.36732562,,,
+1141791290,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4cccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)c6)cc5)c4)cc3)c1)C2=O,,0.35755231,,,
+1142382691,*c1ccc(Cc2ccc(N3C(=O)C(Cl)=C(Oc4ccc(C(c5ccc(OC6=C(Cl)C(=O)N(*)C6=O)cc5)(C(F)(F)F)C(F)(F)F)cc4)C3=O)cc2)cc1,,0.36436728,,,
+1142386049,*C=CC1CC(*)C2C(=O)N(c3ccc(Cl)cc3)C(=O)C12,,0.38672628,,,
+1142561667,*CC(*)CNc1ccc([N+](=O)[O-])cc1[N+](=O)[O-],,0.48421101,,,
+1142759458,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4cccc(Oc5ccc(C(C)(C)c6ccc(Oc7cccc8c7C(=O)N(*)C8=O)cc6)cc5)c4C3=O)c(C)c2)cc1C,,0.38334817,,,
+1142909412,*N=P1(*)Oc2ccccc2-c2ccccc2O1,,0.36981013,,,
+1143081598,*O[Si](*)(C)CCCOCC1COC(=O)O1,,0.34240927,,,
+1143098154,*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc2)C(=O)N(CCCCCCCCCCCCCCCC)C3=O)cc1,,0.37283494,,,
+1143195068,*c1ccc(OCCN(CCOc2ccc(-c3nc4ccc(Oc5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3C)cc2)cc1,,0.38224032,,,
+1143212466,*OC1CCC(OC(=O)CCCCCCC(*)=O)CC1,-35.83964331,,,,
+1143308077,*CCCNS(*)(=O)=O,,,,1.368480015,21.72486866
+1143554851,*CC(*)C(=O)OCC(F)(F)C(F)(F)C(F)(F)F,,0.32963325,,,
+1143744739,*Nc1cccc(C(=O)c2ccc(NC(=O)c3cccc(C(=O)c4ccc(C(*)=O)cc4)c3)cc2)c1,,0.35096229,,,
+1144028367,*Nc1ccc(CC(C)(C)NC(=O)CCCCC(*)=O)cc1,,0.34067775,,,
+1144294791,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.34340133,,,
+1144375159,*c1ccc(-c2sc(-c3cc(CCCCCCCC)c(*)s3)cc2CCCCCCCC)cc1,,0.42872185,,,
+1144377193,*C(=O)Nc1cccc(C(=O)Nc2cccc(NC(=O)c3ccc4[nH]c(-c5cccc(-c6nc7cc(*)ccc7[nH]6)c5)nc4c3)c2)c1,,0.36420228,,,
+1144496929,*CC(C)(CO*)COCCOCCOCCOC,,0.36052012,,,
+1144952297,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O,,0.37284844,,,
+1145064910,*Nc1ccc(C2=CC[C@@H](c3ccc(N*)cc3)[C@@H]2c2ccc(C(C)(C)C)cc2)cc1,,0.37225779,,,
+1145617844,*Nc1ccc([C@@H]2C=C(C(C)C)C=C[C@@]2(c2ccc(N*)cc2)C(C)C)cc1,,0.36697228,,,
+1145796233,*Nc1ccc(S(=O)(=O)c2ccc(N*)cc2)cc1,,0.33955438,,,
+1146375572,*CC(*)(CC(=O)OCc1ccccc1)C(=O)OCc1ccccc1,,0.34757807,0.176,1.113470646,10.67770667
+1146618065,*CCCCCCOC(=O)OCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.34934036,,,
+1146876238,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.34824049,,,
+1146896045,*CCCNC(=O)Cc1ccc(C(=O)N*)cc1,,0.32374161,,,
+1147140569,*c1ccc(Cc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37624478,,,
+1147168409,*CCCCCC(=O)N*,,0.34031759,0.301,,
+1148404116,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2Br)c(Br)c1,,0.40092491,,,
+1148596222,*CC(*)(C)C(=O)OCCCOc1ccc(C(=O)Oc2ccc3ccc(=O)oc3c2)cc1,,0.32418426,,,
+1148927533,*CCCCCOC(=O)OCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.33350028,,,
+1148967455,*Nc1ccc2ccc3c(-c4ccc5ccc6ccccc6c5c4N*)cccc3c2c1,,0.35015279,,,
+1149199303,*CC(*)(C)C(=O)OCC=C,,0.34181464,,,
+1149265769,*NCc1cccc(N*)c1,,0.32297138,,,
+1150009994,*CC(*)(CCC(C)C(=O)OC)C(=O)OC,,0.33779719,,,
+1150049230,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C5(c6ccc(*)cc6)C6CC7CC(C6)CC5C7)cc4)cc3)c3ccccc23)cc1,,0.37034563,,,
+1150090312,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nc5ccccc5s4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc1,,0.37132377,,,
+1150209468,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(OCCCCCCCC)c(*)cc3OCCCCCCCC)ccc1-2,,0.40970568,0.291,0.863900052,18.18381296
+1150286039,*Nc1ccc2c(c1N*)[C@H](c1ccccc1)c1ccccc1-2,,0.35924295,,,
+1150417190,*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(N4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(-c8nc9cc(*)ccc9nc8-c8ccccc8)cc6)C7=O)cc5C4=O)cc3)nc2c1,,0.40701998,,,
+1150491517,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)c2ccccc2C(=O)O[Na])cc1,169.1341304,,,,
+1150533909,*O[Si](*)(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.3677149,,,
+1150855035,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OCCCCCC)cc2)cc1,14.67015775,,,,
+1150964358,*Nc1ccc(-c2c(Cc3ccccc3)c(Cc3ccccc3)c(N*)c(Cc3ccccc3)c2Cc2ccccc2)cc1,,0.36853875,,,
+1151260550,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(c5ncc(*)s5)C6=O)cc4C3=O)cc2)cc1,,0.39445531,,,
+1151287804,*c1ccc(-c2ccc(-c3cc(-c4ccc(Oc5ccc(-c6ccccc6)cc5)cc4)c4cc(-c5ccc6nc(*)cc(-c7ccc(Oc8ccc(-c9ccccc9)cc8)cc7)c6c5)ccc4n3)cc2)cc1,,0.38807327,,,
+1151566525,*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(N=Nc3ccc(N=Nc4cc(C)c(C#N)c(C)c4)cc3)cc2)cc1,,0.37555317,,,
+1151772971,*CCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2C)c(C)c1,,0.36167035,,,
+1152245354,*Nc1ccc([C@@H](CCCCC)c2ccc(CCc3ccc([C@H](CCCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36934253,,,
+1152269820,*c1ccc(Oc2ccc(N3C(=O)C(c4ccccc4)=C(c4ccc(C5=C(c6ccccc6)C(=O)N(*)C5=O)cc4)C3=O)cc2)cc1,,0.39159291,,,
+1152453728,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36801205,,,
+1152527958,*CCCOC(=O)c1cc(N=Nc2ccc(OCC)cc2)ccc1-c1ccc(N=Nc2ccc(OCC)cc2)cc1C(=O)O*,,0.36910686,,,
+1153123442,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1C)C2=O,,0.4222629,,,
+1153144775,*Nc1ccccc1-c1ccc(NC(=O)c2cccc(C(*)=O)c2)cc1,,0.34730716,,,
+1153274738,*C(=O)c1ccc(N=Nc2ccc(C(=O)N3C(=O)N(*)C4(CCCC4)C3=O)cc2)cc1,17.25361376,,,,
+1153424706,*Nc1c(C)c(N)c(N)c(NS(=O)(=O)C(C)(CC)CC)c1N*,,0.33683103,,,
+1153616490,*Nc1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(N*)cc3)c3c2[C@H]2c4ccccc4[C@@H]3c3ccccc32)cc1,,0.36285875,,,
+1153834877,*CCCCCCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,0.37018411,0.367,0.913177976,25.33874374
+1153885240,*CC(=O)NCCCCCCNC(=O)CCc1ccc(O*)cc1,,0.33856885,,,
+1154018955,*CC(*)n1c2ccccc2c2c3ccccc3ccc21,,0.36652293,,,
+1154163429,*Oc1ccc(N=Nc2c(C)cc(Cc3cc(C)c(N=Nc4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)c(C)c3[N+](=O)[O-])c([N+](=O)[O-])c2C)cc1,,0.36435194,,,
+1154938499,*CCCP(CCCNC(=O)CCCCC(=O)N*)CC(C)C,,0.35350822,,,
+1155135793,*CC(C)(CO*)COCCCCCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.37119619,,,
+1155257724,*Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N*)cc4)cc3)cc2)cc1,,0.35032863,,,
+1155325628,*CCCCCCCCCCCCCCCCCCCCc1nnc(*)o1,,,0.398,0.88997462,21.82969596
+1155566609,*CCCCCCCCCOC(=O)C(O)C(O)C(=O)O*,,0.33968364,,,
+1155913960,*CCN(C)C(=O)CCCCCCCCC(=O)N(*)C,,0.35452131,,,
+1156088794,*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)c1cccc(C(=O)O*)c1,,0.35593374,,,
+1156094009,*Nc1ccc2c(c1)C1(C(C)=C2C)C(C)=C(C)c2ccc(N*)cc21,,0.39134226,,,
+1156161130,*Nc1ccc(C2=CC[C@@](N*)(c3ccccc3)C=C2)cc1,,0.34164757,,,
+1156532186,*C([2H])([2H])C(*)([2H])c1c([2H])c([2H])c([2H])c([2H])c1[2H],,0.38844688,0.199,0.93861698,15.86735299
+1156634940,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4cccc5cccc(C(=O)c6ccc(*)cc6)c45)cc3)cc2)cc1,,0.37726443,,,
+1156665768,*c1cc(C(=O)Oc2ccc(-c3ccc(O[Si](C)(C)C(C)(C)C)cc3)cc2)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.39199576,,,
+1156796148,*Nc1ccc(C2=CC[C@H](c3ccc(N*)cc3)[C@H]2c2ccccc2)cc1,,0.35771822,,,
+1156980921,*CCc1cccc(*)c1,,0.37970446,,,
+1157376824,*C(=O)OCCCCCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.34823376,,,
+1157422620,*C=Cc1ccc(OC(=O)c2ccc(C(=O)Oc3cccc4ccc(*)nc34)cc2)cc1,,0.35224005,,,
+1157657223,*c1ccc(Oc2ccc(Oc3ccc(-c4cnc5cc(-c6ccc7nc(*)cnc7c6)ccc5n4)cc3)cc2)cc1,,0.39204345,,,
+1158192712,*OC(=O)C(CCSC)NC(=O)CCCCC(=O)NC(CCSC)C(=O)OC1COC2C(*)COC12,,0.34425455,,,
+1158201779,*C(=O)Nc1ccc(NC(=O)c2ccc3[nH]c(-c4cc(-c5nc6cc(*)ccc6[nH]5)cc(N5C(=O)c6ccccc6C5=O)c4)nc3c2)cc1,,0.3726693,,,
+1158220592,*CC(=O)C(*)c1ccc(C(C)(C)C)cc1,,0.38642464,,,
+1158703899,*CCOCCOCCN1C(=O)c2ccc(C(=O)Oc3ccc(C(C)(C)c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34462113,,,
+1158783004,*CC(OC=O)C(COC(=O)O*)OC=O,,0.27866603,,,
+1158854097,*CCCCCCCCCCC(=O)NCCCCCCNC(=O)CCCO*,,,0.3245,0.954616145,20.55029983
+1158896668,*CCOC(=O)CCC(=O)O*,,0.31070608,,,
+1159125442,*CC(*)C(=O)OCC(F)(F)C(F)(F)OC(F)(F)C(F)(F)C(F)(F)F,,0.3160919,,,
+1159254929,*CCCCCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.35013256,,,
+1159550508,*CCCCCCCCOC(=O)NCc1ccc(CNC(=O)O*)cc1,-25.31417235,,,,
+1159696305,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccccc7Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4C(F)(F)F)cc3)(C(F)(F)F)C(F)(F)F)cc2)c(C(F)(F)F)c1,,0.36819358,,,
+1159981352,*Oc1ccc(Oc2ccc(C(C)=Nc3ccc(Oc4ccc(N=C(C)c5ccc(*)cc5)cc4)cc3)cc2)cc1,117.5082044,,,,
+1160560393,*c1cccc(NC(=O)CCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1,,0.34373731,,,
+1160783446,*NC1CC(C)(C)CC(C)(CNC(=O)c2cc(NC(=O)c3ccc(NC(=O)C(CC(C)C)N4C(=O)c5ccccc5C4=O)cc3)cc(C(*)=O)c2)C1,195.2683571,,,,
+1160809525,*CC(*)(C)C(=O)OC1CC(C)CC(C)(C)C1,,0.38218681,0.146,0.860209573,10.34521471
+1161122757,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34547985,,,
+1161267582,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.3933978,,,
+1161429718,*Nc1ccc([C@H](CCC)c2ccc(C[C@H](CC)c3ccc([C@@H](CCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36752092,,,
+1161669930,*C1CC2C3CCC(C3)C2C1*,,,,0.88601111,12.5969108
+1161685710,*Nc1ccc(C(=C)[C@H]2[C@@H]3C[C@H]4C[C@@H](C3)C[C@@H]2C4)cc1N*,,0.35093584,,,
+1161927023,*C(=O)c1ccc2c(c1)C(=Nc1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N=C6OC(=O)c7ccc(*)cc76)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)OC2=O,,0.36045189,,,
+1162303824,*Oc1ccc(C(=O)c2cccc(NC(=O)c3cccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)c3)c2)cc1,,0.35641643,,,
+1162525545,*C=CCCCCCCCCCC*,,0.40688317,,,
+1162986011,*CCCCCCOC(=O)c1cc(OCCCCCCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)cc(C(=O)OCCCCCCN2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.35666694,,,
+1163047718,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4ccc(Oc5c(C)cc(Cc6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)c(C)c2)cc1C,,0.40667295,,,
+1163052769,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1-c1cccc(C(F)(F)F)c1,,0.38193114,,,
+1163216608,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2ccccc2)c1,,0.34066113,,,
+1163273261,*Nc1ccccc1-c1ccc(N*)c(-c2ccccc2)c1,,0.34288507,,,
+1164865014,*CCCCCCCCCCCCCCCCOC(=O)CCCCC(=O)O*,,0.37375344,,,
+1164892547,*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1,,0.35674478,,,
+1165921168,*CC(*)C(=O)OCC(COC(=O)c1ccccc1)(COC(=O)c1ccccc1)COC(=O)c1ccccc1,,0.34152411,,,
+1166008818,*ON(C(F)(F)F)C(F)(Br)C(*)(F)F,,0.34640325,,,
+1166293848,*NC(CCCCNC(=O)NCCCCNC(*)=O)C(=O)OC,0.336556425,,,,
+1166324163,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)C5CCC6C(=O)N(*)C(=O)C6C5C4=O)cc3)cc2)cc1,,0.36688453,,,
+1166598350,*Nc1ccc2cc3ccccc3cc2c1N*,,0.33917986,,,
+1166612863,*c1ccc(CC(=O)Nc2cccc(NC(=O)Cc3ccc(-c4sc(*)c(-c5ccccc5)c4-c4ccccc4)cc3)c2)cc1,,0.3789381,,,
+1166758072,*CCOc1ccc(C2(c3ccc(OCCO[Si](O*)(c4ccccc4)c4ccccc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.36865547,,,
+1167306747,*CC(*)c1ccc(Cl)cc1Cl,,,0.1006666666666666,1.218576983,14.20802348
+1167512472,*CCCN(*)C,,0.38665346,,,
+1168402050,*Oc1ccc(OC(=O)/C=C/c2ccc(/C=C/C(*)=O)cc2)cc1,,0.34258266,,,
+1169343082,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccccc4)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.38545958,,,
+1169485252,*Oc1ccc(Oc2ccc(C=NCCN=Cc3ccc(*)cc3)cc2)cc1,,0.37614959,,,
+1169539258,*CC(*)(CC(=O)OC)C(=O)O,,0.27558304,,,
+1169677958,*CCCCCCOCO*,-41.68464364,,0.278,,
+1169871864,*CC(*)c1ccccc1C(=O)O,,0.32924433,,,
+1169954236,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O,,0.41309361,,,
+1170082688,*CCCCCCCCCCNC(C)=N*,,0.39022876,,,
+1170368720,*Nc1cc(NC(=O)Nc2ccc(CCc3ccccc3NC(*)=O)cc2)ccc1C,,0.34797387,,,
+1170403788,*c1ccc(OCCOCCOCCOc2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.33992834,,,
+1170534949,*Nc1c(C(C)C)cc(C2(c3cc(C(C)C)c(N*)c(C(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)C,,0.41778352,,,
+1171030201,*Oc1ccc(C(C#N)(c2ccccc2)c2ccc(OC(*)=O)cc2)cc1,,0.36601192,,,
+1171188982,*Nc1ccc(N*)c(-c2c(-c3ccccc3)ccc3ccccc23)c1,,0.35686225,,,
+1171923773,*Nc1ccc([C@H](C)c2ccc(C(CCCC)c3ccc([C@@H](C)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36515652,,,
+1171970548,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5C(F)(F)F)cc4)n3)cc2)c(C(F)(F)F)c1,,0.35861377,,,
+1173339618,*CC(*)C(N)=O,,0.23763722,,,
+1173467294,*Cc1ccc(COP(=O)(N=Nc2ccc(C(=O)c3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)cc1,,0.35518082,,,
+1173471549,*CC(*)C(=O)OCCN(C)S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,-148.0297376,,,,
+1173729020,*CC=C(C)CS(*)(=O)=O,,0.37474397,,,
+1174292255,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.37288957,,,
+1174531602,*Nc1ccccc1Sc1ccccc1N*,,0.34070371,,,
+1174602411,*Nc1ccc(Cc2cc(C(C)C)cc(C(C)(C)c3ccc(N*)cc3)c2)cc1,,0.36692867,,,
+1175231244,*C(F)(F)C1(F)C(F)(F)C(*)(F)C1(F)F,,0.34723685,,,
+1175281698,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(C)(c3ccc(C(*)=O)cc3)CC4(C)C)cc2)cc1,,0.37916583,,,
+1175337954,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)(C)C1=O,,0.37117268,,,
+1175753193,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c(-c2ccccc2)c1,,0.35913328,,,
+1175936826,*CCN(CCOC(=O)c1ccc(C(=O)O*)cc1)c1ccc(N=Nc2ccc(-c3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.36628188,,,
+1176059607,*CC(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.33775225,,,
+1176518490,*CC(*)CCCCCCCCC,,,0.3193333333333333,0.79444681,10.8952526
+1177030933,*CC1CC(*)C(=O)OC1=O,,0.29067637,,,
+1177244369,*CCCCCCNC(=O)c1ccccc1-c1ccccc1C(=O)N*,,0.34846301,,,
+1177484221,*c1cc(C(=O)Nc2ccc(-c3nnc(*)o3)cc2)cc(N2C(=O)c3ccccc3C2=O)c1,,0.3700457,,,
+1177560732,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CC(C)CCC(*)=O)cc2)C(=O)OCC)cc1,,0.33783278,,,
+1177876091,*Nc1ccc(C(c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.36614746,,,
+1178071397,*CC(O)CN(*)c1cc(C)c(N=Nc2ccc([N+](=O)[O-])cc2)c(C)c1,,0.36660744,,,
+1178419855,*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3c(C)cc(-c4cc(C)c(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)c(C)c4)cc3C)ccc1C)C2=O,,0.38149095,,,
+1178529764,*CCCCCCCCCCNC(=O)CCCCCCC(=O)N*,,,0.422,0.946533099,22.25983328
+1178789985,*Nc1ccc(-c2ccccc2Cc2ccccc2-c2ccc(N*)cc2)cc1,,0.351946935,,,
+1179116190,*CC(*)(C)C(=O)Oc1ccc(C(C)(C)C)cc1,,0.38346494,0.181,0.896405025,11.99542846
+1179190627,*c1ccc(*)n1C(C(=O)NO)n1ccc2ccccc21,180.3082327,,,,
+1179272125,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.34613604,,,
+1179363034,*C(C)=C(*)SCCCCCCCCCC,,0.40626048,,,
+1179731134,*CCOCCOC(=O)CCCCCCCCCCC(=O)O*,,0.35940014,,,
+1179754286,*OC(=O)OCCCCCCOC(=O)OC1COC2C(*)COC12,,0.33128506,,,
+1180333206,*CCCCNC(=O)CCCCC(=O)N*,,0.32958527,0.302,1.040711554,18.074599
+1180480883,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ncccc2C)C3=O)cc(C(=O)NNC(=O)C(*)=O)c1,,0.32332887,,,
+1180503880,*CCCCCCOC(=O)NC1CCC(CC2CCC(NC(=O)OCCCCCCOc3ccc(CCc4ccc(O*)cc4)cc3)CC2)CC1,,0.36064219,,,
+1180568340,*Oc1ccc(C2(c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)CCCCC2)cc1,,0.36356124,,,
+1180738924,*OP(=O)(Oc1cccc2c(*)cccc12)OC1CCCCC1,,0.36283438,,,
+1181185977,*Nc1ccc2c(CC=C)c3c(CC=C)c(N*)ccc3cc2c1,,0.34493349,,,
+1181407570,*c1cc(Oc2c(C)cc(-c3cc(C)c(Oc4cc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc(C(F)(F)F)c4)c(C)c3)cc2C)cc(C(F)(F)F)c1,,0.38333986,,,
+1181944068,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3c(-c4ccc(-c5ccccc5)cc4)cc(-c4ccc(OCCCCCCOc5ccc(-c6cc(-c7ccc(-c8ccccc8)cc7)c(-c7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c(-c7ccc(-c8ccccc8)cc7)c6)cc5)cc4)cc3-c3ccc(-c4ccccc4)cc3)cc1)C2=O,,0.37259523,,,
+1182108104,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cc(C(F)(F)F)cc(N5C(=O)c6ccc(*)cc6C5=O)c3Oc3ccc(C(F)(F)F)cc3)C4=O)cc2-c2ccccc2)c(-c2ccccc2)c1,,0.3736737,,,
+1182258858,*NN[C@@H](N*)[C@H](O)C(N)N,,0.29561057,,,
+1182569183,*CC(OCC)C(C)(C(=O)OC)C(*)(C)C(=O)OC,,0.35110044,,,
+1182679846,*CCCO*,,,0.296,,
+1182712915,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)c4ccccc4C(=O)O)cc3)cc2)cc1,,0.37097336,,,
+1182822577,*c1ccc(-c2cc(-c3ccccc3)c3cc(-c4ccc5nc(*)cc(-c6ccccc6)c5c4)ccc3n2)cc1,,0.42908191,,,
+1182969887,*c1ccc(C(=O)Nc2ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)cc6)cc5)cc4)cc3)cc2)cc1,,0.35960225,,,
+1183558225,*c1ccc(-c2nc3ccc(Oc4ccc(Oc5ccc(P(=O)(c6ccccc6)c6ccc(Oc7ccc(Oc8ccc9nc(*)c(-c%10ccccc%10)nc9c8)cc7)cc6)cc5)cc4)cc3nc2-c2ccccc2)cc1,,0.40210863,,,
+1183565625,*C=CCCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCCCCCC,,0.39011499,,,
+1183988874,*OC(=O)c1ccc(N=Nc2ccc(C(=O)OC3COC4C(*)COC34)cc2)cc1,,0.35808986,,,
+1184109682,*OC(=O)C(NC(=O)CCCCC(=O)NC(C(=O)OC1COC2C(*)COC12)C(C)CC)C(C)CC,,0.34256683,,,
+1184454690,*CCCNC(=O)CCCCC(=O)NCCCO*,,0.33205833,,,
+1184730423,*Oc1ccc(C2(c3ccc(OC(=O)c4cccc(C(*)=O)c4)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.39753607,,,
+1185510088,*Nc1cc2c(cc1C)[C@H]1c3cc(C)c(N*)cc3[C@@H]2[C@H]2CCCC[C@H]12,,0.3950421,,,
+1185605956,*c1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2)cc1,,0.35953155,,,
+1185913675,*c1ccc(Oc2ccc(N3C(=O)CC(C4C=C(C)C5C(=O)N(*)C(=O)C5C4)C3=O)cc2)cc1,,0.36657642,,,
+1186098894,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(CC3CCCCC3)CC2)cc1,,0.36004283,,,
+1186143208,*CCCCOC(=O)CCCCCNC(=O)O*,47.28425166,,,,
+1186744234,*CCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.4003854,,,
+1186809405,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OC)c1)c1ccc(N=Nc2ccc(C=Cc3nc4ccc([N+](=O)[O-])cc4n3CC)cc2)cc1,,0.35606018,,,
+1187123244,*C/C=C\COC(=O)NCCCCCCNC(=O)O*,,0.32961119,,,
+1187202232,*CC(*)(C(=O)OC)c1ccccc1,,0.36467404,0.1426666666666667,0.964628447,13.63274404
+1187350081,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)NC(=O)c8ccccc87)cc6)cc4)C5=O)cc3)cc2)cc1,,0.36132612,,,
+1188020715,*CCCCCOC(=O)CCCCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(CCCCCCCCCCC(=O)OCCCCCN4C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C4=O)C5=O)cc3)cc2C1=O,,0.34769791,,,
+1188519956,*CC(*)C1CC1,,,0.2175,0.863629462,14.17497209
+1188842959,*CC(*)c1ccc(C)cc1,,,0.1873333333333333,0.869468057,14.54352841
+1188921734,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(N(c4ccccc4)c4ccccc4)n3)c1)C2=O,,0.38475396,,,
+1189256579,*CCOCCOCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.34164497,,,
+1189403071,*CC(O)COc1ccc(C(C)CC(C)(C)c2ccc(O*)cc2)cc1,,0.35423541,,,
+1189744629,*CC1CC1COC(=O)c1cccc(C(=O)O*)c1,10.23490017,,,,
+1190275023,*c1ccc(NC(=O)CCCCCC(=O)Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.3393363,,,
+1190316839,*Oc1ccc(C(=O)OC(C)COC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.33682987,,,
+1190678672,*CC(*)c1ccc([Si](C)(C)C)cc1,,0.43524245,,,
+1190710133,*Nc1ccc(-c2ccc3cc4ccc(-c5ccc(N*)cc5)cc4cc3c2)cc1,,0.34703567,,,
+1190953274,*CCCCCCCCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.36364477,,,
+1191042202,*CC(*)C(=O)OCCn1c2ccccc2c2ccccc21,,0.35947428,,,
+1191663733,*Oc1ccc(Oc2ccc(C(=O)c3cccc4cccc(C(=O)c5ccc(*)cc5)c34)cc2)cc1C(C)(C)C,,0.38686999,,,
+1191731244,*CC=CC(CC)S(*)(=O)=O,,0.38724825,,,
+1191831125,*CC(*)OC(=O)c1ccc(C(C)C)cc1,,0.37812553,,,
+1192232068,*C(=O)Nc1ccc(-c2sc(-c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6sc(-c7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c(-c7ccccc7)c6-c6ccccc6)cc4)C5=O)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.41434744,,,
+1192456163,*Nc1ccc(/C(=C\C/C(=C/C(C)(C)c2ccc(N*)cc2)c2ccc(N)cc2)CC(C)(C)c2ccc(N)cc2)cc1,,0.35963898,,,
+1192553911,*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cn3)nc2c1,,0.44520367,,,
+1192592037,*Oc1cccc(NC(=O)c2ccc([Si](C)(C)c3ccc(C(*)=O)cc3)cc2)c1,,0.36668921,,,
+1192958446,*c1cccc(N2C(=O)c3ccc(Oc4ccccc4Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.36147212,,,
+1193670213,*CCC[Si](*)(c1cccc2ccccc12)C1CCCCC1,,0.38478574,,,
+1193720815,*CC(*)C(C)c1ccccc1,,,0.1506666666666666,,
+1193815432,*Nc1ccc2c(N*)cc3cccc4ccc1c2c43,,0.33523592,,,
+1193899229,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccccc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.40964735,,,
+1194500574,*Nc1cc2c3c(c1N*)C(C)(C)C(C)(C)[C@@]3(C)C(C)(C)C2(C)C,,0.36944886,,,
+1194503224,*CC(*)C(=O)Oc1cccc(N(C)C)c1,,0.36366436,0.221,1.096542432,12.53190731
+1195238736,*Nc1ccc2c(c1)C1(c3ccccc3-c3ccccc31)c1c-2cc(-c2ccccc2)c(N*)c1-c1ccccc1,,0.38640294,,,
+1195485493,*c1cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c(O)cc1O,,0.36305869,,,
+1196350517,*CC(*)(C)C(=O)Oc1cc(C)ccc1C,,0.36692261,,,
+1197069937,*C*,-2.526682925,0.40967205,,0.811251102,24.4363295
+1197104268,*CC/C=C(/*)C,,,0.256,,
+1197180422,*CCCCC(=O)O*,,0.3399073,,,
+1197310078,*CCCCCCCCCCOc1ccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4ccc(O*)cc4)cc3)cc2)cc1,,0.38946721,,,
+1198345617,*OP(=O)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)Oc1c(Cl)cccc1Cl,,0.36463475,,,
+1199431647,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6c5)cc3)C4=O)cc2)cc1,,0.35646665,,,
+1199538165,*Nc1ccccc1C1(c2ccccc2N*)c2ccccc2-c2ccccc21,,0.37077531,,,
+1199796903,*CCCCCCCCC(=O)NCCCCCCNC(=O)CCCO*,,,0.286,0.961251471,18.75749252
+1200344407,*Nc1ccc([C@]2(C)CCc3cc(N*)ccc32)cc1,,0.35515271,,,
+1200465842,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c(C)c2)cc1,,0.37233946,,,
+1200924510,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(Sc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.3674231,,,
+1201052937,*CCCCCNC(=O)O*,-13.55087665,,0.268,1.071824251,20.62252342
+1201572278,*c1ccc(-c2nnc(*)o2)c(OCCCCC)c1,,0.42893237,,,
+1201617221,*c1ccc(-c2sc(-c3cc(SCCCC)c(*)s3)cc2SCCCC)cc1,,0.50761848,,,
+1201779173,*Nc1ccc(C2(c3ccc(N*)cc3)C=CC(c3ccccc3)C=C2)cc1,,0.3550972,,,
+1202036659,*Nc1cccc(-c2c3ccccc3c(-c3cccc(N)c3N)c3ccccc23)c1N*,,0.35466159,,,
+1202039443,*CC(COCC)O*,,0.37418551,,,
+1202357620,*CC(*)C(=O)OCCC(C)OC,,0.35734175,0.212,1.002966511,13.18729468
+1202772120,*Nc1ccc(NC(=O)c2cc(C(=O)c3ccc(C(=O)O)c(C(*)=O)c3)ccc2C(=O)O)cc1,,0.31519377,,,
+1202816034,*Oc1ccc(C(C)=Cc2ccc(OC(=O)CCCCCCCCCCC(*)=O)cc2)cc1,6.458707655,,,,
+1202982181,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.35549527,,,
+1203408030,*Nc1ccc(/C=C/c2ccc(/C=C/c3ccc(N*)cc3)c3ccccc23)cc1,,0.3544734,,,
+1203525380,*CC(*)(CC)C(=O)OCC,,0.36305897,0.145,0.864397975,12.48037065
+1203570548,*Nc1cc2c(cc1N*)C(C)(C)C(C)C2(C)C,,0.36621378,,,
+1203599832,*CCCCCC(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.34816577,,,
+1203618670,*CCCCCCOP(=O)(N=Nc1ccc(Oc2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.36018857,,,
+1204067122,*Sc1ccc(C(=O)c2ccc(C(c3ccc(C(=O)c4ccc(*)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38438273,,,
+1204134441,*CCCCCCOC(=O)CCCCSCCCCC(=O)O*,,,0.224,,
+1204249965,*CC(*)CC(C)(C)C,,0.39627266,0.1735,0.748691234,11.55027129
+1204523273,*c1cc(CCCCOC(=O)CCC)c(*)s1,,0.38073142,,,
+1204736801,*N=Nc1ccc(Nc2ccc(C(=O)c3ccc(C(=O)c4ccc(Nc5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38946168,,,
+1204898498,*Oc1ccc(N=CC=Nc2ccc(OC(=O)NCCCCCCNC(*)=O)cc2)cc1,,0.34892214,,,
+1205044664,*Nc1ccc(C2(c3ccc(N*)cc3C)c3ccccc3-c3ccccc32)c(C)c1,,0.37196959,,,
+1205228260,*c1ccc(-c2ccc(-c3ccc(-c4ccc(-c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36808729,,,
+1205655131,*CC(*)(C)C(=O)OCCCCCCCCCCn1c2ccccc2c2cc(C=C(C#N)C(=O)OC)ccc21,,0.36253093,,,
+1205814834,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C,,0.43174845,,,
+1205844020,*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(F)C1(F)F,,0.31955151,,,
+1205846674,*c1ccc(-c2ccc(-c3nc4cc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(C(c8ccc(N9C(=O)c%10ccc(C(=O)Nc%11ccc(Oc%12ccc%13nc(-c%14ccccc%14)c(*)nc%13c%12)cc%11)cc%10C9=O)cc8)(C(F)(F)F)C(F)(F)F)cc6)C7=O)cc5)ccc4nc3-c3ccccc3)cc2)cc1,,0.38363567,,,
+1206071463,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2ccc(S(C)(=O)=O)cc2)cc1,,0.37573,,,
+1206416017,*CC(*)C(=O)NC(C)(C)C,,0.37998182,0.199,0.889061076,12.90682668
+1206915036,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(Oc4ccc5c(c4)C(C)(c4ccc(Oc6ccc(-c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6C(F)(F)F)cc4)CC5(C)C)c(C(F)(F)F)c3)cc1)C2=O,,0.38854581,,,
+1207045788,*Nc1ccc(C(c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.37321457,,,
+1207110637,*c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.3566638,,,
+1207409378,*C(C#N)=Cc1cc(C=C(C#N)c2ccc3c(c2)C(CCCCCCCC)(CCCCCCCC)c2cc(*)ccc2-3)cc(N(c2ccccc2)c2ccccc2)c1,,0.41240529,,,
+1208726540,*CCCCCCNC(=O)/C=C/CC/C=C/C(=O)N*,2.127453311,,,,
+1208748565,*CC(*)C(=O)OCCN(CC)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36721657,,,
+1208847299,*C(=S)Nc1ccc(Oc2ccc(NC(=S)N3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.35715427,,,
+1208969128,*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(-c2nnc(-c3ccccn3)o2)c1,,0.38233539,,,
+1209031692,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.36250996,,,
+1209050565,*CC(O)C(O)CN(*)c1ccc(N(C)C)cc1,,0.36101121,,,
+1209050682,*CCCCOC(=O)CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC(=O)O*,,0.38882843,0.49475,0.868453809,25.68405963
+1209057611,*C(=O)Nc1ccc(C(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.3459702,,,
+1209084809,*Oc1ccc(-c2cc(-c3ccccc3)c(-c3ccc(OC(=O)c4ccc(C(*)=O)cc4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.38146128,,,
+1209928353,*CCCCCCCCOC(=O)NCCCCCCCCCCNC(=O)O*,24.78605928,,,,
+1210327805,*NC1CCC(CC2CCC(NC(=O)CCCCCCCCCCC(*)=O)CC2)CC1,,0.36732026,,,
+1210773764,*c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(-c4ccc(-c5sc(-c6ccc([Si](*)(C)C)s6)cc5CCCCCCCC)s4)s3)s2)s1,,0.43305383,,,
+1210971861,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)CCC(c7ccccc7)CC6)cc5)cc4C3=O)cc2)cc1,,0.37299608,,,
+1211550700,*c1ccc(Oc2ccc(-c3cc(-c4ccccc4)c4cc(C5(c6ccc7nc(*)cc(-c8ccccc8)c7c6)c6ccccc6C(=O)c6ccccc65)ccc4n3)cc2)cc1,,0.42004547,,,
+1212285406,*NC1(N)C=C(C2=C(c3ccccc3)C(N)(N)[C@@](O)(N*)C(c3ccccc3)=C2)C=C[C@@]1(N)O,,0.3198717,,,
+1212362961,*Oc1c(C)cc(-c2cc(C)c(OC(=O)CCCCC(*)=O)c(C)c2)cc1C,,0.37060213,,,
+1212645329,*OCCCCOc1nc(*)nc(OC)n1,,0.3615296,,,
+1213234132,*COc1ccc(C=C2CCC(=Cc3ccc(OCc4nnc(-c5ccc(-c6ccc(-c7nnc(*)n7-c7ccccc7)cc6)cc5)n4-c4ccccc4)cc3)C2=O)cc1,140.326419,,,,
+1213369367,*CC(*)C(=O)Nc1ccccc1Cl,,0.34811254,,,
+1213893349,*NCC(C)CN*,,0.31971706,,,
+1213899080,*Nc1ccc2c(c1)C1(c3ccccc3-c3cc(C)ccc31)c1cc(N*)ccc1-2,,0.38631578,,,
+1214234223,*CCCCCCCCCCOC(=O)CCCCCCCCCCCCCCCCC(=O)O*,,0.38276992,0.3415,0.8954389,20.54848101
+1214356356,*CCCCCCCCCCCC(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)OC(=O)c3ccccc32)cc1,,0.35534379,,,
+1214422376,*CCN(*)C(=O)CCC(=O)OC,,0.31456612,,,
+1214923559,*CCCCCCCCCCCCCCCCCCCCOC(=O)C(=O)O*,,,0.3545,0.917519076,22.92381656
+1215056104,*Nc1c(N*)c(-c2ccc(-c3c4ccccc4cc4ccccc34)cc2)c(-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.37007258,,,
+1215188002,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(Cc4ccc(Cc5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.37158061,,,
+1215512335,*CCCCCCCCCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.39608248,,,
+1215694607,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4cccc5cccc(C(=O)c6ccc(*)cc6)c45)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37599895,,,
+1215820166,*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7cc8cc(*)ccc8oc7=O)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2c1,,0.34746519,,,
+1216120038,*N=P(*)(OCCOC)OCCOC,,0.3536972,,,
+1216424248,*CCCCCCCCCCOC(=O)c1cccc(C(=O)O*)c1,,0.35874529,,,
+1217132738,*CC(*)(Cl)C(=O)OC(C)CC,,,0.174,1.057163419,14.79567929
+1217226410,*CC(*)C(=O)c1ccc(OC)cc1,,0.34949255,,,
+1217265261,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CCCCC2)cc1,,0.3710893,,,
+1217496894,*NC(CCC(=O)OC)C(*)=O,,0.30960525,,,
+1217545349,*CC(*)C(=O)Oc1ccccc1Cl,,0.35897864,0.166,1.226889668,13.82994352
+1217713342,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c4ccccc4-c4ccccc43)cc1)C2=O,,0.42161644,,,
+1217901314,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.34717027,,,
+1218214236,*COC(=O)OC1C(*)OC2OC(C)(C)OC21,,0.33284068,,,
+1218861482,*OC(C)CCOC(=O)c1cccc(C(*)=O)c1,-42.28619333,,,,
+1219358292,*CCCCCCNC(=O)CC(C)(C)c1ccc(C(C)(C)CC(=O)N*)cc1,,0.35064905,,,
+1219579071,*c1cccc(OCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34538338,,,
+1219791952,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3ccc(-c4cccc5ccccc45)c(-c4cccc5ccccc45)c23)c2ccccc12,,0.37377403,,,
+1220036380,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(-c4ccc(*)cc4)cc3)cc2N)c(N)c1,,0.36266828,,,
+1220421998,*Oc1ccc(Sc2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.36425728,,,
+1220689198,*c1ccc(-c2ccc(*)nc2)cn1,138.2497069,,,,
+1220722345,*CC(*)OC(=O)c1ccccc1OC,,0.33690183,,,
+1220760786,*CCCCCCCCCCCCCCCCCCNC(=O)CCc1ccc(CCC(=O)N*)cc1,,0.37301777,0.3755,0.937104778,21.36882265
+1220908034,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCCCC,,0.36094499,,,
+1221040845,*OP(=O)(Oc1ccc([N+](=O)[O-])cc1)Oc1c(Br)cc(C(c2cc(Br)c(*)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,,0.39706819,,,
+1221113390,*NC(CCC(=O)OCCC)C(*)=O,,0.33535597,,,
+1221138931,*CCCCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.38552553,0.4341428571428571,0.886924555,21.34846534
+1221972864,*C=CC1CC(*)C(C#N)(CC)C1,,0.39311406,,,
+1223012575,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35909297,,,
+1223074288,*Oc1ccc(C(CC)(c2ccccc2)c2ccc(OC(*)=O)cc2)cc1,,0.36890167,,,
+1223077195,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3ccc(-c4ccccc4)c(-c4ccccc4)c23)c2c(-c3ccccc3)c(-c3ccccc3)ccc12,,0.37429399,,,
+1223442975,*CCCCCCCCCCOc1c(OC)cc(/C=C/c2ccc(/C=C/c3cc(OC)c(O*)c(OC)c3)cc2)cc1OC,,0.36539147,,,
+1223466134,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCCCC(*)=O,,0.34302627,,,
+1223535676,*Nc1ccccc1CC(c1ccccc1N)(c1ccccc1N)c1ccccc1N*,,0.34821531,,,
+1223550383,*Oc1ccc(C2(c3ccc(OC(=O)CCCCCCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36635489,,,
+1224319371,*c1ccc2c(c1)C(=O)N(c1ccccc1Oc1ccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1)C2=O,,0.37203471,,,
+1224371344,*CCCCCCCCNC(=O)CCCCC(=O)N*,,0.35113971,0.291,,
+1224631495,*Oc1ccc(S(=O)(=O)c2ccc(-c3ccc(-c4ccc(S(=O)(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.37003296,,,
+1224767743,*OCCON(OCCO*)c1ccc(N=Nc2cc([N+](=O)[O-])ccc2OCCOC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)cc1,,0.33829793,,,
+1225123322,*CC(*)C(=O)OCCCOc1ccc(C(=O)OCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.33201436,,,
+1225921990,*Oc1ccc(C(=O)Nc2cc(SCCC#N)c(NC(=O)c3ccc(*)cc3)cc2SCCC#N)cc1,,0.36223788,,,
+1225989211,*CCCC(=O)Nc1ccc(Oc2ccc(NC(=O)CCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33327208,,,
+1226464299,*Nc1c(C(C)(C)C)cc(C2(c3cc(C(C)(C)C)c(N*)c(C(C)(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)(C)C,,0.41407636,,,
+1226489424,*c1ccc(Oc2ccc(N3C(=O)c4c(F)c(F)c(Oc5c(F)c(F)c(Oc6c(F)c(F)c7c(c6F)C(=O)N(*)C7=O)c(F)c5F)c(F)c4C3=O)cc2)cc1,283.2696326,,,,
+1226552406,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc1,,0.34777302,,,
+1226651340,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.35314116,,,
+1227244872,*CC(*)C(=O)OCCCCCCCCC,,,0.281,0.892016503,10.93715508
+1227492040,*c1cccc(-c2nc3cc(-c4ccc5c(c4)nc(*)n5C)ccc3[nH]2)c1,,0.41641721,,,
+1227953094,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(OCCCC)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37183498,,,
+1229175862,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(*)cc5)c5ccccc5-c5ccccc54)cc3)cc2)cc1,,0.39274981,,1.096450635,19.41986966
+1229640212,*Nc1ccc([C@H](C)c2ccc(C3(c4ccc([C@@H](C)c5ccc(N*)cc5)cc4)CCC(CCCC)CC3)cc2)cc1,,0.36917749,,,
+1230087703,*C=Cc1ccc(-c2nnc(-c3ccc(C=Cc4ccc5c(c4)c4cc(-c6ccc7c(c6)c6cc(*)ccc6n7CCCCCCCC)ccc4n5CCCCCCCC)cc3)n2-c2ccccc2)cc1,,0.39496451,,,
+1230182375,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2cccc(P(=O)(c4ccccc4)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c2)C3=O)C(C)(C)C1=O,138.7903633,,,,
+1231617285,*CC(*)NC=O,,0.27493701,,,
+1231928634,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1c(C)cc(C)c(N3C(=O)c4cc(*)c(Cl)cc4C3=O)c1C)C2=O,,0.41818103,,,
+1233245878,*c1ccc(Oc2ccc(-c3cccc(-c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccccc6)C7=O)cc5)c(C(F)(F)F)c4)n3)cc2C(F)(F)F)cc1,,0.39084929,,,
+1233315694,*C=Cc1ccc(*)cc1,,0.38774885,,,
+1233336012,*CCOCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1,,0.33599238,,,
+1233529700,*CCN(CCOC(=O)C=Cc1ccc(C=CC(=O)O*)cc1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35081029,,,
+1233557380,*CC(*)(C)CCC,,0.39381473,0.174,,
+1234075748,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.36313366,,,
+1234355256,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(C)c6ccc(C(C)(C)c7ccc(Oc8ccc(NC(=O)c9ccc(*)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1,,0.36837941,,,
+1235337223,*NC1=N[C@H](N*)N(CO)C(N)=N1,,0.29102826,,,
+1235772628,*Nc1ccc(-c2ccccc2)c(-c2c(CC)cccc2CC)c1N*,,0.3607122,,,
+1235835504,*Oc1c(Br)cc(C(C)(C)c2cc(Br)c(OC(=O)c3cccc(C(*)=O)c3)c(Br)c2)cc1Br,,0.41973656,,,
+1236281959,*c1ccc(C(C)(C)c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.38484293,,,
+1236372812,*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8c7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36074861,,,
+1236653976,*CCOCCOC(=O)O*,,0.32111694,,,
+1236698324,*c1cccc(C(c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)c1,,0.36984267,,,
+1237680808,*N=P(*)(Oc1ccc(C(C)C)cc1)Oc1ccc(C(C)C)cc1,,0.40872556,,,
+1237730261,*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C=CC4=O)c3)cc2)cc1,,0.34689977,,,
+1237783866,*Oc1ccc(C(c2ccc(Oc3c(F)c(F)c(C(=O)c4c(F)c(F)c(*)c(F)c4F)c(F)c3F)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.3658273,,,
+1237875565,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.36049034,,,
+1238011731,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C=Nc2ccc(C#N)cc2)cc1,,0.3618059,,,
+1238020141,*CC(*)OC(=O)C1(C)CCCCC1,,0.3613498,,,
+1238107253,*c1cc(N2C(=O)c3ccc(Oc4cccc5c(Oc6ccc7c(c6)C(=O)N(*)C7=O)cccc45)cc3C2=O)cc(C(F)(F)F)c1,,0.37819795,,,
+1238169222,*N=P(*)(OCc1ccc(Br)cc1)OCc1ccc(Br)cc1,,0.37799409,,,
+1238226226,*c1ccc(Cc2ccc(N3C(=O)CC(Nc4ccc(Cc5ccc(NC6CC(=O)N(*)C6=O)cc5)cc4)C3=O)cc2)cc1,,0.3630038,,,
+1238987612,*NCc1cccc(CN*)c1,,0.31831938,,,
+1239353537,*C(=O)OCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.3305321,,,
+1239672440,*CCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.34021913,,,
+1239731818,*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)s2)cc1,,0.37246963,,,
+1239746533,*CCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.3442798,,,
+1239868812,*Oc1ccc(-c2ccc(OC(=O)c3ccc(-c4ccc(C(*)=O)cc4)cc3-c3ccccc3)cc2)cc1,,0.35602332,,,
+1240229654,*C1C2CC(C1*)C([Si](C)(C)C)C2,,0.43382057,,,
+1240400626,*C(=O)Oc1ccc(OCCCCCCCCCCCCOc2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35267151,,,
+1240885962,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1-c1ccc(Oc2ccccc2)cc1,,0.37479029,,,
+1241111211,*Nc1cc2c(cc1N*)C(CCCCCCCC)(CCCCCCCC)c1ccccc1-2,,0.36691614,,,
+1241410417,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37368209,,,
+1241617720,*CC(CO*)(CS(=O)(=O)CC)CS(=O)(=O)CC,,0.37003283,,,
+1242192055,*CCCCOC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.31017139,,,
+1242216634,*C(F)C(*)C(F)(F)F,,0.31361691,,,
+1242356842,*Nc1ccc2c3ccc4ccc5ccccc5c4c3c3ccccc3c2c1N*,,0.35285371,,,
+1242684513,*CCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.34203506,,,
+1242973596,*CC(*)(C)C(=O)OC1(C)C2CC3CC(C2)CC1C3,,0.37224912,,,
+1242989659,*CCOCCOCCOC(=O)c1ccc(C(=O)O*)cc1,36.06576581,,,,
+1243367222,*c1ccc(Oc2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(Oc5ccc(Oc6ccc(-c7nc8ccc(-c9ccc%10nc(*)oc%10c9)cc8o7)cc6)cc5)cc4)cc3)cc2)cc1,,0.38892688,,,
+1243527935,*Nc1ccc(Oc2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1,,0.38890239,,,
+1244119715,*/C=C/c1cc(CCCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1CCCCCCCCCCC,,,0.368,0.89127614,15.68183389
+1244604743,*c1cc(O)c(O)cc1*,158.9171015,,0.183,0.986360805,13.85120498
+1244610888,*c1ccc(-c2nc3ccc(Oc4ccc5nc(*)c(-c6ccccc6)c(-c6ccccc6)c5c4)cc3c(-c3ccccc3)c2-c2ccccc2)cc1,,,0.27,0.956272559,25.28802464
+1245028222,*Oc1ccc(Oc2ccc(NC(=C(C#N)C#N)c3ccc(C(Nc4ccc(*)cc4)=C(C#N)C#N)cc3)cc2)cc1,,0.39351854,,,
+1245052771,*c1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c2)cc1,155.8375326,,,,
+1245106816,*Oc1c(C(C)C)cc(*)cc1C(C)C,,0.44422978,,,
+1245265865,*C=CC1CC(*)C2C(=O)N(c3ccc(C)cc3)C(=O)C12,,0.38445256,,,
+1245320293,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1C,,0.37828298,,,
+1245611127,*CC(O*)c1ccccc1,,0.36793308,0.181,1.068349202,12.8198381
+1246412636,*c1cccc(N2C(=O)c3ccc(Oc4c(C)cc(-c5cc(C)c(Oc6ccc7c(c6)C(=O)N(*)C7=O)c(C)c5)cc4C)cc3C2=O)c1,,0.39022826,,,
+1246466408,*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccccc7-c7ccccc7Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.3624363,,,
+1246478630,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.35295865,,,
+1247053205,*Oc1c(Cl)cc(C2(c3cc(Cl)c(OC(*)=O)c(Cl)c3)CC3CCC2C3)cc1Cl,,0.40020648,,,
+1247276415,*CCNC(=O)c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(C(=O)NCCN(*)c3ccc(/C=C/c4ccc([N+](=O)[O-])cc4)cc3)cc2)cc1,,0.35889095,,,
+1247283822,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(P(=O)(c5ccccc5)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38593544,,,
+1247292893,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OCCOCCOc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.34407338,,,
+1247307752,*c1ccc(OCCCCCCCCCCCOc2ccc(-c3ccc4nc(-c5cccc(-c6cc(-c7ccccc7)c7cc(*)ccc7n6)c5)cc(-c5ccccc5)c4c3)cc2)cc1,,0.3894808,,,
+1247311317,*CCC(C)C(C)CCNC(=O)c1cc(C(=O)N*)cc(C(C)(C)C)c1,,0.35663758,,,
+1247317223,*Nc1ccc(-c2cc3ccc2CCc2ccc(c(-c4ccc(N*)c(N)c4)c2)CC3)cc1N,,0.36354697,,,
+1247694906,*C(=O)Nc1cc(C(F)(F)F)ccc1Oc1cccc2c(Oc3ccc(C(F)(F)F)cc3NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cccc12,,0.36667366,,,
+1248082530,*C(=Cc1cc(OCCCCCCCC)c(C=C(c2ccccc2)c2ccc3c(c2)Sc2ccc(*)cc2S3)cc1OCCCCCCCC)c1ccccc1,,0.39856806,,,
+1248304721,*c1ccc(Oc2ccc(-c3cnc4cc(-c5ccc6nc(*)cnc6c5)ccc4n3)cc2)cc1,,0.39789231,,,
+1248717152,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1,,0.36252693,,,
+1248950707,*CCC(=O)NCC(=O)N*,70.42884183,,,,
+1249625777,*CCCNC(=O)CNC(=O)C(NC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NC(C(=O)NCC(=O)NCCCOCCOCCO*)C(C)C)C(C)C,,0.34386538,,,
+1249855570,*c1ccc(C(c2ccc(C(=O)OC)cc2)c2ccc(N(*)c3ccc(C)cc3)cc2)cc1,,0.42975739,,,
+1250128226,*CC(CC)(CC)CO*,,0.3776766,,,
+1250174161,*CC(C)C(*)(C)C,,0.37444531,0.2026666666666666,,
+1250664164,*Nc1cc2cc3cc4cc5ccccc5cc4cc3cc2cc1N*,,0.34863907,,,
+1250759727,*Nc1ccc(-c2c3ccccc3c(-c3ccc(N*)cc3)c3ccccc23)cc1,,0.3582881,,,
+1250976105,*Oc1ccc(NC(=O)/C=C/c2ccc(/C=C/C(=O)Nc3ccc(*)cc3)cc2)cc1,,0.33908362,,,
+1251251952,*C=CC(*)(C)C,,0.41706039,,,
+1251718993,*C(=O)CCCCCCCCC(=O)N1CC(C)N(*)CC1C,,0.35664394,,,
+1251846091,*CCN(CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.34851961,,,
+1251856230,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35606637,,,
+1252158010,*c1ccc(S(=O)(=O)c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37544083,,,
+1252177757,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C7(c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)CCC(c8ccccc8)CC7)cc6)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.38624967,,,
+1252473600,*C(C)C(*)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.38554512,,,
+1252624826,*C[Si](*)(C)CCCCCC,,0.39640493,,,
+1252741517,*Nc1ccc(-c2ccc(N*)c(/C=C/c3ccccc3)c2/C=C/c2ccccc2)cc1,,0.35976311,,,
+1252756161,*SCC(=O)NN=Cc1ccc(OCCCCCCCCCCOc2ccc(C=NNC(=O)CSc3nnc(*)s3)cc2)cc1,,0.36696758,,,
+1252814215,*CC(*)C(=O)NCCCCCCCCCCCCCCCC,,,0.3105,0.864456022,12.35489175
+1252824054,*C=CC1CC(*)C(C(=O)Oc2ccccc2)C1,,0.36298238,,,
+1253121067,*CCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.34210366,,,
+1253393179,*CC(COC(C)OCC)O*,,0.36879886,,,
+1253409024,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c1,,0.35906708,,,
+1253785759,*CC(*)(F)C(=O)OCCC,,0.35411146,,,
+1253961002,*Oc1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(OC(=O)c3ccc4c(c3)Cc3cc(C(*)=O)ccc3-4)cc2)cc1,,0.38005772,,,
+1254056862,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc(C3OCC4(CO3)CC3C=CC4C3)cc2)cc1,,0.3670177,,,
+1254412515,*C#Cc1cccc(C#C[SiH](*)C)c1,,0.48442803,,,
+1254752288,*Nc1ccc(-c2ccc3c(c2)C(C)(C)c2cc(-c4ccc(N*)cc4)ccc2-3)cc1,,0.3647088899999999,,,
+1255030804,*Nc1ccc(-c2cc(N*)cc(-c3ccccc3)c2)cc1,,0.34439898,,,
+1255406268,*c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(Cc3cc(C(C)C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)cc1C(C)C)C2=O,,0.43780015,,,
+1255816024,*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)c5ccc([Si](C)(C)c6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4C3=O)c2)c1,,0.39436632,,,
+1256351112,*Nc1ccc(C2(c3ccc(N*)cc3)c3cccc(C)c3-c3cccc(C)c32)cc1,,0.3731668,,,
+1256508173,*C(=O)Nc1cccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4cccc(NC(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c4)cc3)cc2)c1,,0.35369558,,,
+1256836915,*Nc1ccc(*)cc1OCCCCCCCCCCCCCCCC,,,0.3671666666666667,0.899934311,14.9826959
+1256957523,*CCOP(=O)(N=Nc1ccc(-c2ccc(N=NP(=O)(O*)OCC)cc2)cc1)OCC,,0.35913079,,,
+1256991095,*C=CC1CC(*)C2C(=O)N(CCCCCCCCC)C(=O)C12,,0.37137448,,,
+1257255803,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1,,0.36672584,,,
+1257657215,*c1ccc(Oc2ccc(Oc3ccc(-c4nc(*)nc(-c5ccccc5)n4)cc3)cc2)cc1,,0.40894875,,,
+1257792790,*O[Si](C)(CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.39205725,,,
+1257932675,*CC(*)OC(=O)CC(C)=O,9.819405608,,,,
+1257958128,*CCCCCCCCCNC(=O)CCCCCCCCCC(=O)N*,,,0.312,0.933531017,25.4839371
+1258103517,*Nc1ccc(-c2ccc(N*)c(C)c2-c2ccccc2)cc1,,0.35793789,,,
+1258628140,*Oc1ccc(-c2ccc(OC(*)(Oc3ccccc3)Oc3ccccc3)c(C)c2)cc1C,,0.3726312,,,
+1259156024,*C(=O)Nc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c2)c1,213.4893774,,,,
+1259678348,*CC(*)C(=O)Oc1ccc(-c2ccccc2)cc1,,0.3489094,0.2055,1.091305455,11.54820166
+1259746879,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)cc1,48.20867337,,,,
+1260132541,*c1ccc(Cc2ccc(N3C(=O)c4ccc([Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.39071977,,,
+1260477255,*CCCCCCNC(=O)c1c(Cl)c(Cl)c(C(=O)N*)c(Cl)c1Cl,,0.35512749,,,
+1260660121,*CC/C=C(/*)CCCCCCC,,,0.269,,
+1261008131,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)cc4)cc2)C3=O)cc(C(C)(C)C)c1,,0.37319105,,,
+1261139473,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)c3ccccc3Cc3ccccc32)cc1,,0.3749586,,,
+1261531807,*CCc1ccc(CCNC(=O)CCCCCCCCCCCCC(=O)N*)cc1,,0.36154696,,,
+1261888727,*C(=O)c1ccc(C(=O)N(CCC)c2ccc(Cc3ccc(N(*)CCC)cc3)cc2)cc1,173.7390032,,,,
+1262712163,*OP(=O)(Oc1ccc(-c2ccc(*)cc2)cc1)OC1CCCCC1,,0.36429244,,,
+1263057506,*CC(*)(C)C(=O)OCC#C,,0.35098547,,,
+1263366372,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.33929019,,,
+1263725170,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6cccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)c6)cc5C4=O)cc3)c2)cc1,,0.35641912,,,
+1263773798,*Oc1ccc(C2(c3ccc(OC(=O)CCCCCCCCCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36830388,,,
+1263993270,*Oc1ccc(Oc2ccc(C(=O)c3cccc4cccc(C(=O)c5ccc(*)cc5)c34)cc2)cc1,,0.3769196,,,
+1264073564,*Nc1ccc([C@@H](CC)c2cccc([C@H](CC)c3ccc(N*)cc3)c2)cc1,,0.36105687,,,
+1264214740,*Sc1ccc(Nc2ccc(Nc3ccc(*)cc3)cc2)cc1,,0.4021004,,,
+1264383388,*Nc1ccc(/N=N/c2ccc(/C=C/c3ccc(/N=N/c4ccc(N*)c5ccccc45)cc3)cc2)c2ccccc12,,0.36475136,,,
+1264421130,*CC(*)c1cc(-c2ccc(C(=O)OC(C)CCCCCC)cc2)ccc1-c1ccc(C(=O)OC(C)CCCCCC)cc1,,0.37966374,,0.947782381,10.01820949
+1264684812,*CC(*)c1cc(C(=O)Oc2ccc(OC)cc2)ccc1C(=O)Oc1ccc(OC)cc1,,0.34367981,,,
+1264707088,*CCOC(=O)Nc1ccc(CCCCc2ccc(NC(=O)O*)cc2)cc1,,0.34609329,,,
+1264767069,*Oc1ccc(OC(=O)c2cc(N=Nc3ccc(OCC)cc3)ccc2-c2ccc(N=Nc3ccc(OCC)cc3)cc2C(*)=O)cc1,,0.36951145,,,
+1264867007,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6cc(*)[nH]n6)cc5)cc4)cc3)cc2)cc1,,0.38159758,,,
+1265296504,*c1cc(C(=O)OCC)cc(N2C(=O)CC(C3Cc4ccccc4C4C(=O)N(*)C(=O)C43)C2=O)c1,,0.36068687,,,
+1265311522,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3ccc(C(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O,,0.37431986,,,
+1265853164,*CCSCc1ccc(Cc2ccc(CSCCOC(=O)CCSCc3ccc(Cc4ccc(CSCCC(=O)O*)cc4)cc3)cc2)cc1,,0.36622097,,,
+1266330575,*c1ccc(-c2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.40434518,,,
+1266599294,*CCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.33765741,,,
+1266997255,*CCOC(=O)c1ccccc1C(=O)O*,,0.3271084,,,
+1267045105,*c1cc(C(c2ccc(OCCN(c3ccccc3)c3ccc(C=CC4=CC(=C(C#N)S(C)(=O)=O)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(c1ccccc1)c1ccc(C=CC2=CC(=C(C#N)S(C)(=O)=O)CC(C)(C)C2)cc1,,0.37934659,,,
+1267165499,*Nc1cccc(-c2cccc(/C=C/c3ccccc3)c2/C=C/c2ccccc2)c1N*,,0.35817163,,,
+1267231154,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nc5ccccc5s4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)n1,,0.36524921,,,
+1267496441,*CCCN(*)C(=O)CCCCC,,0.36869304,,,
+1267767457,*CCCCCCCC(=O)OCCOC(=O)CCCCCCCC1OCC(COCC2COC(*)O2)O1,-2.553202642,,,,
+1267983155,*CC(C)(CO*)COCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.33988756,,,
+1268212088,*NC1=C(/C=C/c2ccc(N*)cc2)C=C(Cc2ccccc2)C(N)(N)[C@@H]1Cc1ccccc1,,0.34133831,,,
+1268591883,*C(*)C(=O)OC(C)(C)C,12.73837737,,,,
+1268903954,*CC1CCC(COC(=O)c2ccc(NC(=O)O*)cc2)CC1,148.8519878,,,,
+1269165942,*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(C)cc3)cc2)cc1,,0.34896025,,,
+1269399876,*CC(O)COc1ccc(S(=O)(=O)c2ccc(O*)c(C)c2)cc1C,,0.35246453,,,
+1269445005,*CC(=O)Nc1cccc2c(NC(=O)COc3ccc4ccccc4c3Sc3c(O*)ccc4ccccc34)cccc12,,0.34427885,,,
+1269889405,*CC(*)c1cc(Br)ccc1OCCCC,,0.41686593,0.1493333333333333,1.214722149,12.90254496
+1269952522,*Nc1ccc2c(c1)-c1cc(N*)ccc1C2(c1ccccc1N)c1ccccc1N,,0.36757979,,,
+1270138741,*Nc1ccc2cc3ccccc3cc2c1-c1c(N*)ccc2cc3ccccc3cc12,,0.35802001,,,
+1270448600,*Nc1ccccc1C1(c2cc(C)ccc2N*)CCCCC1,,0.36301614,,,
+1270893836,*CCCCCCCCCOC(=O)c1ccc(C(=O)NCCCCCCNC(=O)c2ccc(C(=O)O*)cc2)cc1,,,0.259,,
+1271079066,*c1ccc(-c2ccc(N(*)c3ccc(CCCC)cc3)cc2)cc1,,0.43625228,,,
+1271134038,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)cc4)cc3)cc12,,0.35493198,,,
+1272587185,*c1ccc(Oc2ccc(-c3cc(-c4ccccc4)c4cc5nc(*)cc(-c6ccccc6)c5cc4n3)cc2)cc1,,0.42862349,,,
+1273620790,*CC(*)C(=O)OCCCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35994367,,,
+1274254712,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(NC(=O)CCCC(=O)Nc4ccc(O*)cc4)cc3)cc2)cc1,,0.3351735,,,
+1275263414,*CCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1,,0.33751561,,,
+1275276173,*Oc1ccc(NC(=O)c2ccc(P(=O)(c3ccccc3)c3ccc(C(=O)Nc4ccc(Oc5ccc(P(=O)(c6ccccc6)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37546944,,,
+1275591013,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.38954407,,,
+1275707146,*Oc1ccc(Oc2ccc(Oc3ccc(Oc4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)cc3)cc2)cc1,,0.35576866,,,
+1275832134,*/C=C/c1cc(CCCCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1CCCCCCCCCCCC,,,0.315,0.863597922,16.18461866
+1276163646,*CCCCCCN(C(=O)C(F)(F)C(F)(F)C(F)(F)C(=O)N(*)C(C)C)C(C)C,,0.35467829,,,
+1276246944,*c1ccc(OC(=O)Nc2ccc(-c3ccc(NC(=O)Oc4ccc(-c5nc(-c6ccc([N+](=O)[O-])cc6)[nH]c5*)cc4)cc3OC)c(OC)c2)cc1,,0.36184068,,,
+1276410560,*c1ccc2c(c1)C(=O)N(c1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.33986713,,,
+1276975260,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OCCC)c1)c1ccc(-c2nc3ccc([N+](=O)[O-])cc3o2)cc1,,0.35306861,,,
+1277787594,*CCCCCCOC(=O)Nc1ccc(C)c(NC(=O)OCCCCCCOc2ccc(-c3ccc(O*)cc3)cc2)c1,,0.3490302,,,
+1277971955,*CC(*)(C)C#N,,,0.146,0.912756425,16.28256988
+1278963276,*c1ccc(Oc2cccc(NC(=O)c3cc(C(=O)Nc4cccc(Oc5ccc(-c6nnc(*)o6)cc5)c4)cc(N4C(=O)c5c(Cl)c(Cl)c(Cl)c(Cl)c5C4=O)c3)c2)cc1,,0.3655579,,,
+1279775850,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCCCCCCCCCC9)cc8)cc7C6=O)cc5)cc4)CCC(C(C)(C)C)CC3)cc2)cc1,,0.38480107,,,
+1279821267,*CC(*)(C)C(=O)OC,,,0.1594999999999999,0.906319536,14.29532166
+1280165390,*Nc1ccc(-c2ccc(N*)c(OC)c2)cc1OC,,0.33411517,,,
+1280502593,*CCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35177391,,,
+1280563800,*CC(*)OC(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32384943,,,
+1282073595,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)cc1,,0.40131849,,,
+1282093980,*CCCCCCNC(=O)c1ccc(C2(C)CC(C)(C)c3ccc(C(=O)N*)cc32)cc1,,0.35883552,,,
+1282732042,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3nc(-c4ccccc4)nc(-c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)n3)cc1)C2=O,,0.40912107,,,
+1282744521,*Nc1ccc(CC2=CC[C@H](Cc3ccc(N*)cc3)C=C2)cc1,,0.34680076,,,
+1283103305,*CC(Cc1cccc2ccccc12)C[Si](*)(C)C,,0.37233408,,,
+1283259076,*CC(*)c1ccc(CCCCCCCC)cc1,,0.40676664,0.2674999999999999,0.86701632,11.37694228
+1284040004,*Nc1ccc([C@H]2[C@H](C)c3cc(N*)ccc3C2(C)C)cc1,,0.36746212,,,
+1284082821,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)CCCCCC(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.33816204,,,
+1284164313,*c1ccc(Oc2ccc(-c3nc4cc(Oc5ccc6nc(*)c(-c7ccccc7)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.41462508,,,
+1285221061,*CCCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.3489851,,,
+1285571721,*CC1CCCC(CNC(=O)CCCCC(=O)N*)C1,,0.33557048,,,
+1285747694,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCCCCC(=O)O*,,,0.3379999999999999,0.892806607,20.14069114
+1286286715,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2nc3ccc([N+](=O)[O-])cc3s2)cc1,,0.37343287,,,
+1286574060,*c1ccc(-c2ccc(-c3c(-c4ccccc4)nc4ccc(Oc5ccc6nc(-c7ccccc7)c(*)c(-c7ccccc7)c6c5)cc4c3-c3ccccc3)cc2)cc1,,0.40808604,,,
+1286616307,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(C6(c7ccc8nc(*)cc(-c9ccccc9)c8c7)c7ccccc7-c7ccccc76)ccc5n4)cc3)c3ccccc3-c3ccccc32)cc1,,0.44097468,,,
+1286748689,*Oc1ccc(-c2ccc(-c3ccc(Oc4ccc(C(c5ccc(*)cc5)(C(F)(F)F)C(F)(F)F)cc4)c(C(F)(F)F)c3)cc2)cc1C(F)(F)F,,0.37834788,,,
+1286920787,*C=C(C#N)C(=O)OCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.36240665,,,
+1287697682,*c1ccc2cc(OC(=O)c3cccc(C(=O)Oc4ccc5cc(-c6nc7ccccc7nc6*)ccc5c4)c3)ccc2c1,,0.37114739,,,
+1287726377,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37938549,,,
+1287850309,*C=C(*)c1ccccc1C(F)(F)F,,0.41894641,,,
+1287920729,*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)C5C6C=CC(C7C(=O)N(*)C(=O)C67)C5C4=O)cc2)C(=O)N(CCc2ccccc2)C3=O)cc1,,0.38062067,,,
+1288275829,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)Nc4cccc(NC(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1,,0.36217731,,,
+1288612893,*Nc1ccc2c(c1)-c1ccc3c4ccc5c6c(ccc(c7ccc(c1c73)C2)c64)Cc1c(N*)cccc1-5,,0.33775178,,,
+1289045435,*Nc1cc(C)c(-c2cccc(C)c2-c2cc(C)c(N*)cc2C)cc1C,,0.37804144,,,
+1289126534,*CCCC1(CCCNC(=O)CCCCC(=O)N*)c2ccccc2-c2ccccc21,,0.34825862,,,
+1289234945,*CC(*)C(=O)OCCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.35313837,,,
+1289453744,*N=P(*)(OCC(F)(F)C(F)(F)C(F)(F)F)OCC(F)(F)C(F)(F)C(F)(F)F,-77.91107652,,,,
+1290046924,*Oc1ccc(NC(=O)c2cc(NC(=O)CCCN3C(=O)c4ccccc4C3=O)cc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.33591306,,,
+1290159505,*C(=O)Nc1cccc2c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(N7C(=O)c8ccc(*)cc8C7=O)cccc56)cc3)C4=O)cccc12,,0.36744912,,,
+1290242331,*Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=O)cc2)cc1,,0.37391919,,,
+1290671546,*Nc1ccc2c(c1)-c1ccccc1-c1cc(N*)ccc1-c1cc(N)ccc1-2,,0.37663236,,,
+1290806312,*c1ccc(Oc2ccc3ccc(Oc4ccc(N5C(=O)CC(=O)N(*)C5=S)cc4)cc3c2)cc1,,0.37149644,,,
+1290822168,*c1cc(N=Nc2ccc(C#N)cc2)cc(*)c1O,,0.39563271,,,
+1291515166,*CC(*)OC(=O)C1(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C1(F)F,,0.33243377,0.086,0.81190569,13.56949767
+1291566068,*CCCCCCCCCCCC(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.357058,,,
+1291851819,*CCCCCCCCCCCCNC(=O)CCCCC(=O)N*,,,0.3745,0.944709172,22.08425305
+1292415850,*C(Cl)=C(*)CCCCCC,,0.40438433,,,
+1292622955,*Nc1ccc(/C=C/c2ccc(N*)c(C)c2C)cc1,,0.35534148,,,
+1293239198,*=c1cc2ccc3cc(=c4c5ccccc5c(=*)c5ccccc45)cc4ccc(c1)c2c34,437.49,,,,
+1293974706,*Nc1cc2c3ccccc3c3ccccc3c2c(N*)c1N,,0.3316594,,,
+1294053022,*C1C2CCC(C2)C1*,,0.39273913,,0.859348965,14.8573309
+1294362796,*CC(*)C(=O)OCCCCCCCCCCCCCC,,,0.3215,0.867171478,11.51022625
+1294421875,*CCCCCCOC(=O)OCCCOC(=O)OCCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.33499957,,,
+1294607317,*Oc1ccc(C(C)c2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2)cc1,76.95210942,,,,
+1295002110,*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.38799659,,,
+1295229741,*CCCCCCOC(=O)CCCCCCCCC(=O)OCCCCCCN1C(=O)c2ccc(S(=O)(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.34948634,,,
+1295293979,*C1C(*)C2CC1C1C=CCC12,,0.43220188,,,
+1295301974,*CC(*)C(=O)OCCOCC(F)(F)C(F)(F)C(F)(F)F,,0.33448798,,,
+1295510505,*CCC[Si](C)(C)O[Si](C)(C)CCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.37495768,,,
+1295536082,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(-c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36892515,,,
+1295850561,*Nc1ccc(CC(CC(C)(C)c2ccc(N*)cc2)=C(C)C)cc1,,0.34957516,,,
+1295880392,*NCCCNCCNCCCN*,,0.33492047,,,
+1296143576,*O[Si](C)(C)CCCN=C1C=CC(=NCCC[Si](*)(C)C)c2c(O)ccc(O)c21,,0.39150617,,,
+1296755065,*c1ccc(*)nc1,322.0959561,,,,
+1297415322,*N=P(*)(OCCCCCC)OCCCCCC,,0.4012116,,,
+1297876567,*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.36105469,,,
+1297972519,*CCCCCCCCCCCCCCCCC(=O)N*,15.38660594,,0.379,,
+1298070198,*CC(*)C(=O)OCC(C)CCC,,,0.217,0.903896392,12.10743643
+1298418799,*NNC(=O)c1ccc(C(*)=O)cc1OCC,,0.33579349,,,
+1298446728,*c1ccc2c(c1)C(=O)N(c1ccc3c(c1)Cc1cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)ccc1-3)C2=O,,0.40300966,,,
+1298577403,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)C5C6C=CC(C7C(=O)N(*)C(=O)C67)C5C4=O)c3)c2)c1,,0.37016836,,,
+1298805440,*CC(*)(C)C(=O)OCCC(C)(C)C,,,0.1895,0.860835583,11.4609216
+1299104612,*CC(*)C(=O)NCCCCCC(=O)O,,0.30692904,0.2495,1.07691203,11.78404643
+1299119036,*Nc1ccccc1-c1ccc(NC(=O)c2ccc(C(*)=O)cc2)cc1,,0.34525764,,,
+1299451372,*Nc1ccc2c(c1)C(C)(C)c1cc3cc4ccccc4c(N*)c3cc1-2,,0.36945143,,,
+1299554017,*CCCCCCCCCCCC(=O)N*,,0.37505502,0.3409999999999999,,21.42021733
+1299836604,*CC(*)C(=O)OCCCCCCCCCCCCCCCCCCCCCC,,,0.3585,0.846770802,13.56836556
+1300048574,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C(C)(C)C)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O,,0.41772006,,,
+1300127001,*CC1(*)CC(=O)OC1=O,143.854555,,,,
+1300383951,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1,,0.46246248,,,
+1300562294,*Oc1ccc(-c2ccc(OC(=O)c3ccc(C(*)=O)cc3Sc3ccc4ccccc4c3)cc2)cc1,,0.35785251,,,
+1300646660,*c1ccc(Oc2ccc(-c3cccc(-c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccccc6)C7=O)cc5)c(C(F)(F)F)c4)c3)cc2C(F)(F)F)cc1,,0.38085344,,,
+1300902354,*Nc1ccc([C@H](C)c2ccc(C3(c4ccc([C@@H](C)c5ccc(N*)cc5C)cc4)CCCCC3)cc2)c(C)c1,,0.37089997,,,
+1300907784,*Sc1ccc(C(=O)c2ccc(C(C)(C)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.40938395,,,
+1301068905,*OC(COC(=O)c1ccc(C(*)=O)cc1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35889758,,,
+1301288779,*CC(*)OC(=O)C(C)(C)C,,0.3821509,,,
+1301620481,*Nc1ccc2c(c1)C(c1ccc(C)cc1)(c1ccc(C)cc1)c1cc(N*)ccc1-2,,0.38337526,,,
+1301630790,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C8(c9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)CCCCCCCCCCC8)cc7)cc6C5=O)cc4)cc3C)c(C)c2)cc1,,0.38784907,,,
+1301948076,*CC(OCC)C1C(=O)OC(=O)C1*,,0.31032484,,,
+1302310440,*c1ccc(CCc2ccc(N3C(=O)C(=O)N(c4ccc(CCc5ccccc5N5C(=O)C(=O)N(*)C5=O)cc4)C3=O)cc2)cc1,,0.35446793,,,
+1302453294,*Nc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(NC(=S)NC(=O)CCCCCCCCC(=O)NC(*)=S)c3)cc2)c1,,0.34740592,,,
+1302563585,*OS(=O)(=O)c1ccc(Oc2ccc(S(=O)(=O)Oc3ccc(C4(c5ccc(*)cc5)CCCCC4)cc3)cc2)cc1,149.9071313,,,,
+1303245845,*Nc1ccc(Cc2ccccc2-c2ccccc2-c2ccc(N*)cc2)cc1,,0.35281368,,,
+1304546254,*C=CC1CC(*)C2C(=O)N(CCCCCCC)C(=O)C12,,0.36908283,,,
+1305600405,*Nc1c(N)c(O)c(C)c(O)c1N*,,0.30904003,,,
+1305829895,*CCCCCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.3476494,,,
+1306591589,*Oc1ccc(Oc2ccc(S(=O)(=O)c3ccc(-c4ccc(S(=O)(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.36986468,,,
+1307322805,*c1ccc(Oc2ccc(Oc3ccc(NC(=O)c4cccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)c4)cc3)cc2)cc1,,0.35725183,,,
+1307515193,*c1ccc(NC2CC(=O)N(c3ccc(-c4sc(-c5ccc(N6C(=O)CC(Nc7ccc(-c8nc(C#N)c(C#N)nc8*)cc7)C6=O)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)C2=O)cc1,,0.40408501,,,
+1307775698,*CCCC1(CCCNC(=O)c2cccc(C(=O)N*)c2)c2ccccc2-c2ccccc21,,0.35132317,,,
+1308167790,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O,,0.37344882,,,
+1308663062,*N[C@@]12C3=CCc4ccccc4[C@]31[C@@]21[C@]2(NC)C3=CCc4ccccc4[C@]321,,0.35949556,,,
+1308684557,*Oc1ccc(-n2on2-c2ccc(OC(=O)CCCCCCCCCCC(*)=O)cc2C)c(C)c1,50.67469492,,,,
+1308745603,*CC(*)(C)C(=O)OCCCCCCCCCCCC,,,0.2665,0.876333907,11.1507459
+1308822610,*Nc1ccc([C@@]2(C)c3ccc(N*)cc3CC2(C)C)cc1,,0.36731857,,,
+1308893158,*c1ccc(-c2ccc(-c3cc(-c4ccc(-c5ccc(Oc6ccccc6)cc5)cc4)c4cc(-c5ccc6nc(*)cc(-c7ccc(-c8ccc(Oc9ccccc9)cc8)cc7)c6c5)ccc4n3)cc2)cc1,,0.38479819,,,
+1309160623,*CCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.3238941,,,
+1309401144,*c1cccc(S(=O)(=O)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.36365297,,,
+1310550432,*CC(OCC(C)C)C1(C)C(=O)OC(=O)C1(*)C,,0.34284139,,,
+1310881775,*C(=O)Oc1ccc(C(c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(-c5cc(C)c(N6C(=O)c7ccc(*)cc7C6=O)c(C)c5)cc3C)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37721251,,,
+1311329501,*[Se]c1c(Cl)c(Cl)c(*)c(Cl)c1Cl,,0.39302909,,,
+1311388075,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.36650764,,,
+1311473361,*Oc1ccc(C(=Cc2cc(OC)c(C=C(c3ccc(*)cc3)c3ccc(C(F)(F)F)cc3)cc2OC)c2ccc(C(F)(F)F)cc2)cc1,,0.3904137,,,
+1312487500,*NC(CC(=O)OCc1ccccc1)C(*)=O,70.10647363,,,,
+1312935547,*CC(*)C(=O)OCCCCCCCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.3602016,,,
+1313130081,*Oc1ccc(NC(=O)c2cc(NC(=O)C34CC5CC(CC(C5)C3)C4)cc(C(=O)Nc3ccc(*)cc3)c2)cc1,,0.35628794,,,
+1313149520,*c1ccc(C(c2ccccc2)(c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)C(F)(F)F)cc1,,0.39310677,,,
+1313165210,*Nc1ccc(C2(c3ccc(N*)c(F)c3)c3ccccc3-c3ccccc32)cc1F,,0.37295561,,,
+1313737543,*O[Si](C)(C)c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc([Si](*)(C)C)c2)c1,,0.39988791,,,
+1313953255,*CC(CC(*)(C#N)C#N)c1ccc(Cl)cc1,,0.39209317,,,
+1314048322,*Nc1cc2ccc3cc4ccccc4cc3c2c(C=C)c1N*,,0.34315656,,,
+1314252641,*CC(O)COc1ccc(C(=O)c2ccc(OCC(O)COc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)cc2)cc1,,0.34542983,,,
+1314700598,*CCCCCCNC(=O)CCP(C)(=O)CCC(=O)N*,,0.33988654,,,
+1314710990,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)Nc4ccc(-c5ccc(NC(=O)c6ccc(*)cc6)cc5C(F)(F)F)c(C(F)(F)F)c4)cc3)cc2)cc1,,0.36383822,0.2685,1.198766828,27.69445684
+1315123551,*O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C,,0.3890851,,,
+1315505306,*Oc1ccc(OC(=O)c2ccc(OCCCCCCCCOc3ccc(C(*)=O)cc3)cc2)cc1C(=O)CC,,0.35362648,,,
+1315760893,*Nc1ccc([C@H](C=C)c2ccc(Cc3ccc([C@@H](C=C)c4ccc(N*)cc4)cc3)cc2)cc1,,0.35508348,,,
+1315872207,*CC(*)(C)C(=O)Oc1ccc(C(=O)C=Cc2ccc(Cl)cc2)cc1,,0.3546963,,,
+1316732452,*OC(=O)OCCCCOC(=O)OC1COC2C(*)COC12,,0.3180929,,,
+1316887618,*CC(*)(C)C1=NC(C)(C)C(=O)O1,,0.35497529,,,
+1318130357,*C=CC1CC(*)C2C(=O)N(c3ccc(Br)cc3)C(=O)C12,,0.39805684,,,
+1318650528,*CCCC(*)C,,0.40163185,,,
+1318766747,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc(B(c3c(C)cc(C)cc3C)c3c(C)cc(C)cc3C)cc2)cc1,,0.34069727,,,
+1318838343,*Nc1ccc(NC(=O)C=CC(*)=O)cc1,115.1255691,,,,
+1319010024,*CC(*)c1cccc(NC(C)=O)c1,,0.3447239,,1.037473199,12.26908267
+1319112543,*c1ccc(-c2c(-c3ccccc3)nc3ccc(-c4ccc5nc(-c6ccccc6)c(*)c(-c6ccccc6)c5c4)cc3c2-c2ccccc2)cc1,,0.39774474,,,
+1319321747,*CC(*)c1ccc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(-c4ccc5c(c4)C(CCCCCC)(CCCCCC)c4ccccc4-5)ccc2-3)cc1,,0.40538104,,0.887288053,12.74028627
+1319410597,*Oc1cccc(OC(=O)c2cc(C(C)(C)C)c(OC(=O)c3ccc(C(=O)Oc4c(C(C)(C)C)cc(C(*)=O)cc4C(C)(C)C)cc3)c(C(C)(C)C)c2)c1,135.2011857,,,,
+1320165565,*Nc1cc(C)c(C2(c3c(C)cc(N*)cc3C)c3ccccc3-c3ccccc32)c(C)c1,,0.3801695,,,
+1320607876,*Oc1ccc(C2(c3ccc(OC(=O)CCCC(*)=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36266451,,,
+1321017513,*Oc1ccc(-c2ccc(C(C)(C)c3ccc(-c4ccc(OC(*)=O)c(F)c4)cc3)cc2)cc1F,,0.36767878,,,
+1321194504,*CC(*)P(=O)(c1ccccc1)c1ccccc1,,0.38231193,,,
+1321346245,*OC1C(COCC)OC(*)C(OCC)C1OCC,,0.37498776,,,
+1321681529,*Oc1ccc(OC(=O)c2cc(OCCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.34952872,,,
+1321705347,*OP(=O)(Oc1ccccc1)OC(CCl)COc1ccc(S(=O)(=O)c2ccc(OCC(*)CCl)cc2)cc1,,0.35156893,,,
+1321934906,*Oc1ccc(N=Nc2ccc(*)cc2)cc1,116.9759489,,,,
+1322145207,*c1cc(/C=N/c2ccc(C)cc2)c(*)c(OC)c1,,,0.215,0.9332806,24.66422713
+1322304083,*Nc1ccc(C(c2ccc(N)cc2)c2cccc(N*)c2)cc1,,0.35836442,,,
+1322409102,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35829726,,,
+1322812178,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8c7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36611158,,,
+1322899924,*c1cc(C(=O)OCCCCCCCCCCOc2ccc(-c3ccc(OC(=O)c4ccc(OCC(F)CCCCCC)cc4)cc3)cc2)c(*)s1,,0.36606782,,,
+1323119594,*OC(=O)C(C)NC(=O)CCCCCC(=O)NC(C)C(=O)OC1COC2C(*)COC12,,0.32870245,,,
+1323225597,*c1cccc(N2C(=O)c3cccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.36430041,,,
+1323245183,*Nc1ccc(C)c(C)c1-c1c(N*)ccc(C)c1C,,0.36698246,,,
+1323525187,*CC(*)C(=O)OCCN(C)C,,0.36492858,,,
+1323609368,*C(=Cc1cc(OC)c(C=C(c2ccc(F)cc2)c2ccc3cc(*)ccc3c2)cc1OC)c1ccc(F)cc1,,0.3940856,,,
+1324036450,*Nc1ccc(-c2cc(N*)ccc2-c2c(-c3ccccc3)ccc(-c3ccccc3)c2-c2ccccc2)cc1,,0.36283476,,,
+1324493760,*Nc1cc(CC)c(C2(c3cc(CC)c(N*)cc3CC)c3ccccc3-c3ccccc32)cc1CC,,0.38916158,,,
+1325509753,*Nc1ccc(C(=O)c2ccc(N*)cc2)cc1,,0.337244965,,,
+1325980765,*CC(*)(C)C(=O)OCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OC)cc2)cc1,,0.3349532,,,
+1326111661,*CC(CCCC)C(CCCC)COC(=O)c1ccc(C(=O)O*)cc1,,0.36856898,,,
+1326151916,*c1ccc(Cc2ccc(N3C(=O)CC(Nc4ccc(N(c5ccc(Nc6ccc([N+](=O)[O-])cc6)cc5)c5ccc(NC6CC(=O)N(*)C6=O)cc5)cc4)C3=O)cc2)cc1,,0.37796488,,,
+1327742614,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)c3ccc([Si](c4ccccc4)(c4ccccc4)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.37668952,,,
+1328109466,*CCCC1CC(=O)N(*)C(=O)C1,,0.32853402,0.23,1.09306772,17.92550338
+1328279815,*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)OCC(C)CC)cc2)cc1,,0.36613468,,,
+1328538029,*N=P(*)(OC)OC,,0.33810683,,,
+1329379844,*CC(*)OC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32895822,,,
+1329476125,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35825823,,,
+1329795537,*CC(O)COC(=O)CCCCCCCCCCC(=O)OCC(O)COc1ccc(O*)cc1,,0.34177602,,,
+1329805417,*c1ccc(-c2sc(-c3cc(CCCC)c(*)s3)cc2CCCC)cc1,,0.49358329,,,
+1330350980,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)CCCCC(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.34097584,,,
+1330491095,*Oc1ccc(C(C)(C)c2ccc(OC(=O)Oc3ccc(Cc4ccc(OC(*)=O)cc4)cc3)cc2)cc1,,0.35217052,,,
+1330672528,*Nc1nc(C)nc(N*)n1,,0.3244865,,,
+1330783970,*CCCCCCNC(=O)C(O)C(O)C(=O)N*,149.1553302,,,,
+1330841310,*CCCCCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35720916,,,
+1330844840,*CC(COCOCC(C)([N+](=O)[O-])[N+](=O)[O-])O*,,0.44890128,,,
+1331449531,*CCC(C(=O)OCC)C(*)C(=O)OCC,,,0.2075,0.983422084,17.46362519
+1332074394,*c1ccc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.38387233,,,
+1332282841,*CC(*)C(=O)OCCCCCCCCCCCn1c2ccccc2c2ccccc21,,0.36992924,,,
+1332783697,*OC(=O)OCC1OC(C)(C)OC1COC(=O)OC1COC2C(*)COC12,,0.32749602,,,
+1332933153,*c1ccc([Si](*)(C)C)cc1,126.7080905,,,,
+1333138019,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCC)cc1,,0.33525127,,,
+1333183317,*Oc1ccc(C(C)=Nc2ccc(N=C(C)c3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38978483,,,
+1333418832,*Nc1ccc(-c2ccc3c4ccc(-c5ccc(N*)cc5)cc4c4ccccc4c3c2)cc1,,0.34206693,,,
+1333567417,*Nc1ccc(C2(c3ccc(N*)cc3)C=C(C(C)C)C=C[C@@H]2C(C)C)cc1,,0.37150212,,,
+1333596225,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3C)c(C)c1)C2=O,,0.39115299,,,
+1333610491,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3-c3cccc4ccccc34)c2)cc1,,0.35656149,,,
+1333766804,*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.33894182,,,
+1333923150,*NCCCNCCCN*,,0.3265421,,,
+1334743519,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(C(c5ccc(C(=O)c6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37426463,,,
+1335169485,*CCc1ccc(*)c(Br)c1,,0.40939985,,,
+1335256384,*Oc1ccc(C(=O)NNC(=O)c2cccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)c2)cc1,,0.33756272,,,
+1335530924,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CCC(CC)CC2)cc1,,0.37532282,,,
+1335818219,*CC(*)c1ccc(CCCCCCCCCCCC)cc1,,0.40128707,0.3035,0.85312948,11.9470464
+1335837736,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccc(N=Nc2ccc(C)cc2)cc1,,0.35543807,,,
+1335930330,*c1cccc(N2C(=O)c3ccc(Oc4cccc5c(Oc6ccc7c(c6)C(=O)N(*)C7=O)cccc45)cc3C2=O)c1,,0.36724296,,,
+1336441380,*CCCOC(=O)c1cccc(C(=O)O*)c1,,0.3342418,,,
+1336532550,*CCN(*)C(=O)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,-52.64825421,,,,
+1336700268,*Oc1ccc(-c2ccc(OC(=O)c3ccc(Oc4ccc(C5(c6ccc(Oc7ccc(C(*)=O)cc7)cc6)CCC(C(C)(C)C)CC5)cc4)cc3)cc2C)c(C)c1,,0.37755445,,,
+1336943329,*C(*)F,,0.33115728,,,
+1336999615,*Nc1ccc(-c2ccc(N*)c(Cc3ccccc3)c2Cc2ccccc2)cc1,,0.35426981,,,
+1337045352,*CCCCCCOC(=O)OCCCCCCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.36434074,,,
+1337142448,*Oc1ccc(NC(=O)c2ccc([Si](C)(C)c3ccc(C(*)=O)cc3)cc2)cc1,,0.36897255,,,
+1337289078,*Oc1ccc(S(=O)(=O)c2ccc(-c3ccc(*)cc3)cc2)cc1,,0.37072306,,,
+1337317565,*CC(*)(C)C(=O)O,,0.2715841,,,
+1337375382,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35862232,,,
+1337675110,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2c1,,0.35681519,,,
+1337696117,*CCCCCCCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,0.37340882,0.389,,
+1337772240,*c1ccc(OC(=O)c2cccc(C(=O)Oc3ccc(C4(*)C(=O)Nc5c(Cl)cc(Cl)cc54)cc3)c2)cc1,,0.3679968,,,
+1338251164,*OC(COC(=O)c1ccccc1C(*)=O)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35887152,,,
+1338688328,*CC(*)(C)C(=O)OC(C)C(C)(C)C,,0.3963458,0.1674999999999999,0.844615787,10.99075229
+1339056719,*c1cccc(N2C(=O)c3ccc(Oc4ccc(Oc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.3655675,,,
+1339302130,*CC(*)C(=O)OCCCC,,,0.2075,0.952753315,12.71172021
+1339379527,*C(C)=C(*)c1ccccc1,,0.41349595,,,
+1340249291,*c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.38859265,,,
+1340852987,*CC(*)(C)C(=O)OCC[N+](C)(C)CCCCS(=O)(=O)O,,0.45104433,,,
+1341096524,*C=CC1CC(*)C2C(=O)N(CCCCCCCC)C(=O)C12,,0.36926721,,,
+1341187910,*Cc1cccc(CNC(=O)CCCCC(=O)N*)c1,,0.32740895,,,
+1341702533,*C=Cc1ccc(C=Cc2ccc3c(c2)Sc2cc(*)ccc2N3c2ccc(OCCCCCCCCCCCC)cc2)cc1,,0.3922989,,,
+1341724908,*Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N*)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.34875187,,,
+1341866550,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35929695,,,
+1342064373,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Sc2ccccc2)cc1-c1ccccc1,,0.36668313,,,
+1342174126,*C(=O)c1ccccc1-c1ccccc1C(=O)N1CCN(*)CC1,,0.36072955,,,
+1342666772,*c1ccc(C(=O)c2ccc(-c3nc4cc(-c5ccc6nc(*)[nH]c6c5)ccc4[nH]3)cc2)cc1,300.9001313,,,,
+1343284955,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cc(C(=O)OC)cc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)c1)C2=O,,0.39142907,,,
+1344066127,*CCCCCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.3784776,,,
+1344203582,*c1ccc(-c2sc(-c3ccc(-c4cc(CCCCCCCCCCCC)c(*)s4)cc3)cc2CCCCCCCCCCCC)cc1,,0.42148672,,,
+1344237352,*c1c(C)cc(C(c2cccc(C(F)(F)F)c2)c2cc(C)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.41905022,,,
+1344927771,*CC(*)C(=O)OCCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35935263,,,
+1345021190,*CCCCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC,,0.36323545,,,
+1345501676,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.33379444,,,
+1345670200,*CC(COc1ccc(C(C)(c2ccccc2)c2ccc(O*)cc2)cc1)OC(C)=O,,0.35362656,,,
+1345726690,*CC#CCOC(=O)CCCCCCC(=O)O*,,0.35175594,,,
+1345866279,*O[Si](*)(C)c1ccc(C)cc1,,0.39948933,,,
+1345870350,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(-c6nc7ccccc7n6-c6ccc(-n7c(*)nc8ccccc87)cc6)cc5)cc4)cc3)cc2)cc1,,0.3802456,,,
+1345870546,*CCCCCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.35143027,,,
+1346046576,*Oc1ccc(C(=O)OCCCCCCOC(=O)c2ccc(*)cc2)cc1,,0.35010483,,,
+1346259301,*C=CC(C)C(*)C(=O)OCCN(C)c1ccc(N(C)C)cc1,,0.37701058,,,
+1346551249,*CC1(*)CCC(C)CC1,,,0.164,,
+1346729731,*Nc1ccc2cc(N*)c(-c3cc4ccccc4c(N)c3N)cc2c1,,0.34931736,,,
+1346910233,*CCCCCCCCCCCCOc1ccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4ccc(O*)cc4)cc3)cc2)cc1,,0.39073827,,,
+1347090486,*CCCCCCCCCCOC(=O)CCCCCCCCC(=O)O*,,0.37098942,0.281,0.930218098,21.56106622
+1347844859,*c1ccc2c(c1)C(=O)N(c1cccc(S(=O)(=O)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.37548521,,,
+1347977204,*CCOC(=O)CC(=O)O*,-90.52916041,,,,
+1349126211,*CC(*)C(=O)Oc1cccc(C(=O)OC)c1,,0.33263806,,,
+1349202654,*CC1CCC(COC(=O)c2cccc(C(=O)O*)c2)CC1,,0.35579865,,,
+1349238031,*CCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.34709523,,,
+1349530452,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.34605005,,,
+1349559067,*CC(CS(=O)(=O)C(C)C)O*,,0.37689062,,,
+1349609988,*Nc1c(N)n(C)c(N*)nc1=O,,0.3003834,,,
+1349614436,*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccccc4Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.35589538,,,
+1349968586,*NC(=O)[C@H](CCC[C@H](N)C(=O)O)N*,,0.29081425,,,
+1350186524,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5cc6ccccc6cc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.38811935,,,
+1350245180,*Nc1ccc(C(CCCC[C@H]2CC[C@H](/C=C/[C@H]3CC[C@H](CCC)CC3)CC2)c2ccc(N*)cc2)cc1,,0.3683838,,,
+1350837084,*CC(C)(CC)C(=O)O*,,0.3539173,,,
+1350968415,*C1C(=O)N(CCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*,,0.35300767,,,
+1351618025,*CC(*)(C)C(=O)OCCOC(=O)c1cc(OC(=O)c2ccc(N=Nc3ccc(OCCCCCCC)cc3)cc2)cc(OC(=O)c2ccc(N=Nc3ccc(OCCCCCCC)cc3)cc2)c1,9.014452923,,,,
+1352932501,*Nc1ccc(-c2ccc(-c3cc(-c4ccc(N*)cc4)cc(-c4ccc(-c5ccc(N)cc5)cc4)c3)cc2)cc1,,0.35536772,,,
+1353112393,*CCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*,,,0.38825,0.884434958,21.19385371
+1353148965,*Nc1cc(N*)c2c3ccccc3c3ccccc3c2c1,,0.33007057,,,
+1353215039,*c1cccc(Oc2ccc(P(=O)(c3ccccc3)c3ccccc3)c(Oc3cccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.3682581,,,
+1354246474,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3C)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.36478693,,,
+1355319996,*C(=O)NCCN(CCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.33834048,,,
+1355567925,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(Oc8ccc(-c9ccc(*)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36733109,,,
+1355819266,*Nc1cccc(NC(=O)CCCCCCCCC(*)=O)c1,,0.34121372,,,
+1356052280,*CCOCCOCCOCCOc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1,,0.35954959,,,
+1356279835,*CC(*)c1ccccc1Cl,,,0.1436666666666667,,
+1356849537,*c1ccc(N(c2ccc(N(C)C)cc2)c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.417039,,,
+1356914950,*c1cccc(N2C(=O)c3cccc(Oc4ccc(-c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.36636392,,,
+1357039642,*Oc1ccc(S(=O)(=O)c2ccc(-c3ccc(S(=O)(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37627805,,,
+1357177247,*c1ccc(-c2ccc(-c3ccc(-c4ccc(N(*)c5ccc(C)cc5)cc4)c4c(C)ccc(C)c34)c3c(C)ccc(C)c23)cc1,,0.41844123,,,
+1358109974,*Nc1ccc(/C=C/c2cc(/C=C/c3ccc(N)cc3)cc(/C=C/c3ccc(N*)cc3)c2)cc1,,0.35610463,,,
+1358861008,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)CC8CC7C7CCCC87)cc6)cc4)C5=O)cc3)cc2)cc1,,0.36692147,,,
+1358943628,*CC(*)(F)C(=O)OC,,0.3195917,,,
+1359050753,*NC1=C(CC=C)[C@H](CCCCC)c2cccc3ccc(N*)c1c23,,0.3464462,,,
+1359082710,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)n5-c5cccc(C(F)(F)F)c5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38188218,,,
+1359130105,*c1cccc(N2C(=O)c3cccc(Oc4ccc(Oc5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1,,0.36431938,,,
+1359436093,*Nc1ccc(-c2ccc(C)cc2)c(-c2ccc(C)cc2)c1N*,,0.36465263,,,
+1359594039,*c1ccc(C=C(c2ccc(OC)cc2)c2ccc(C(=Cc3ccc(N(*)c4ccccc4)cc3)c3ccc(OC)cc3)cc2)cc1,,0.40199668,,,
+1359687224,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.35002992,,,
+1359872176,*CC(*)(C)C(=O)NC(=O)OC(C)COc1ccc(S(=O)(=O)c2ccc(OCC(C)O)cc2)cc1,,0.33219505,,,
+1360256183,*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(C(c5ccc6c(c5)C(=O)N(*)C6=O)(C(F)(F)F)C(F)(F)F)cc4C3=O)o2)o1,,0.35852465,,,
+1361184303,*Nc1ccc(C2(c3ccc(N*)c(-c4ccccc4)c3)c3ccccc3-c3ccccc32)cc1-c1ccccc1,,0.379009425,,,
+1361620762,*C=CC(C)C(*)C(=O)OC,,0.36981772,,,
+1361875495,*c1cc(C(C(=O)OC)C(=O)OC)c(*)s1,,0.39850796,,,
+1362818896,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc(C(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.39211202,,,
+1362829798,*CC(*)P(=O)(OCC)N(C)C,,0.3661564,,,
+1362985746,*C=NN(CCCC)c1ccc(S(=O)(=O)c2ccc(N(CCCC)N=Cc3ccc4c(c3)c3cc(*)ccc3n4CC)cc2)cc1,139.604302,,,,
+1363453372,*Oc1ccc(Oc2ccc(NC(=O)c3cc(C(=O)Nc4ccc(*)cc4)cc(N4C(=O)c5c(Cl)c(Cl)c(Cl)c(Cl)c5C4=O)c3)cc2)cc1,315.5911196,,,,
+1363602163,*CCCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.39697678,,,
+1364091804,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37216814,,,
+1364709079,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.438999,,,
+1365155281,*C#Cc1cccc(C#C[SiH](*)c2ccccc2)c1,,0.45724186,,,
+1365375368,*CC(*)C(=O)Oc1cc(C)cc(C)c1,,0.36603281,,,
+1365608843,*CCCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.33568737,,,
+1365694834,*CCCCCCCC(=O)OCC1COC(*)O1,,0.35131956,,,
+1365820335,*C1OCCCC1C1C(=O)OC(=O)C1*,,0.31521835,,,
+1365848174,*c1ccc(-c2nc3cc4nc(-c5ccccc5)c(*)nc4cc3nc2-c2ccccc2)cc1,,0.51398194,,,
+1365937405,*CC(OC(C)C)C1C(=O)OC(=O)C1*,,0.31948862,,,
+1366065674,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.36983712,,,
+1366196353,*Nc1ccc(-c2ccc(N*)cc2-c2cccc3ccccc23)c(-c2cccc3ccccc23)c1,,0.35926745,,,
+1366250569,*CCCCCCCCCCCCO*,,,0.4307499999999999,0.85333452,24.21423422
+1366419204,*Nc1ccc(C2(c3ccc(N*)c(C)c3)c3cc(-c4ccccc4)ccc3-c3ccc(-c4ccccc4)cc32)cc1C,,0.3801140149999999,,,
+1366538369,*Oc1ccc(OC(=O)c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,176.5386789,,,,
+1366987461,*Nc1ccc(C(=CCC(C)(c2ccc(N)cc2)c2ccc(N*)cc2)c2ccc(N)cc2)cc1,,0.35500045,,,
+1367013502,*N=P(*)(OCCOCCOC)OCCOCCOC,,0.35370177,,,
+1367398148,*CCCOC(=O)O*,,0.31917204,,,
+1367679735,*CC1CCC(COP(=O)(N=Nc2ccc(C(=O)c3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)CC1,,0.36569586,,,
+1367828072,*CC(*)(C)C(=O)Oc1ccc(OCc2ccccc2)cc1,,0.34318377,,,
+1367832773,*c1ccc(N(c2ccccc2)c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.41550047,,,
+1367879345,*CCSC(=O)CCCCC(=O)S*,-21.91188888,,,,
+1368560795,*Nc1ccc2c(-c3ccccc3)c3cc(N*)ccc3c(-c3ccccc3)c2c1,,0.36152182,,,
+1369201997,*C(=O)NC1CCC(CC2CCC(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CC2)CC1,,0.3545699,,,
+1369478865,*CC(*)S(=O)c1ccccc1,137.9705527,,,,
+1369988926,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.38241921,,,
+1370170679,*Oc1ccc(C(CC)(CC)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.36895658,,,
+1370228225,*CCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.35379237,,,
+1370311648,*Oc1cccc(NC(=O)c2ccc(C(c3ccc(C(=O)Nc4ccc(*)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)c1,157.6228264,,,,
+1370577687,*CC(*)C(=O)NCC(N)=O,,0.24561261,,,
+1370610727,*CCCC=C(*)c1ccccc1,,0.38339331,,,
+1370707470,*CCN(*)CC[Si](C)(C)C,,0.41091348,,,
+1370747504,*CCCCCCNC(=O)CCCCCCCCCCCCC(=O)N*,,,0.355,0.933728856,22.40485025
+1370904651,*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2C3CCC(C3)C2c2ccccc2)cc1,,0.36998662,,,
+1371045959,*C1C2CC(C)C(C2)C1*,,0.40953416,,,
+1372092396,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)c(Cl)c2)cc1Cl,,0.37511386,,,
+1372197295,*Oc1c(C)cc(*)cc1C,,,,0.945927386,16.3810139
+1372357517,*CCCCCCOC(=O)O*,,0.34844798,,,
+1372478864,*Oc1ccc(-c2ccc(OC(=O)c3ccc(-c4ccc(C(*)=O)cc4C(F)(F)F)c(C(F)(F)F)c3)cc2C(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)c(C(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)c1,,0.36028801,,,
+1373042522,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34700053,,,
+1373216700,*Nc1ccc2c(c1)C1(c3cc(N)ccc3-2)c2cc(N)ccc2-c2ccc(N*)cc21,,0.38294109,,,
+1373295279,*Nc1ccc(C(c2ccc(N*)cc2)[C@H](CCC)CCCCC(c2ccc(N)cc2)c2ccc(N)cc2)cc1,,0.35865315,,,
+1373519109,*CC(CCCCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)OCC(C)CC)cc2)cc1)O*,-0.17829817,,,,
+1373716942,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.36106386,,,
+1373795866,*Nc1ccc(-c2ccc(-c3ccc(-c4ccc(N*)cc4)c4ccccc34)c3ccccc23)cc1,,0.36481305,,,
+1375025595,*C(=O)Oc1ccc([Si](C)(CC)c2ccc(OC(=O)c3ccc(*)s3)cc2)cc1,,0.3789267,,,
+1375441577,*N/N=N\c1ccc2ccccc2c1-c1c(/N=N/N*)ccc2ccccc12,,0.36086898,,,
+1375874201,*NC1=CC=C(C2(c3ccc(N*)cc3)CCCCC2)CC=C1,,0.35363223,,,
+1376051647,*CC(*)(C)C(=O)Oc1cccc(C)c1C,,0.36564325,,,
+1376172701,*Nc1ccc(C(C)(CCc2cc(N*)ccc2-c2ccc(N)cc2C)c2ccc(N)cc2)cc1,,0.36703698,,,
+1376346368,*c1ccc(-c2ccc(-c3ccc4c(c3)C(CCCCCCCC)(CCCCCCCC)c3cc(-c5ccc(*)s5)ccc3-4)s2)cn1,,0.45492217,,,
+1376684295,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(=O)Nc4ccc(*)cc4)cc3)cc2)cc1,,0.35061579,,,
+1376982255,*CCCCCCCCCCC(=O)Nc1ccc(Oc2cccc(NC(=O)CCCCCCCCCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.34938578,,,
+1377024014,*C(=O)NNC(=O)c1ccc(*)nc1,135.1836518,,,,
+1377176379,*c1ccc(C2(c3ccc(-c4nnc(*)o4)cc3)OC(=O)c3ccccc32)cc1,,0.4359793,,,
+1377849521,*CC(*)c1ccc(COCCO)cc1,,0.34743668,,,
+1378439616,*CCCCCC(=O)Nc1ccc(NC(=O)CCCCCN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.33248324,,,
+1378483093,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(-c5nc6ccc(-c7ccc8nc(*)c(-c9ccccc9)nc8c7)cc6nc5-c5ccccc5)cc4)cc3)cc2)cc1,,0.39796101,,,
+1378557946,*N[C@H]1CC[C@H](C[C@H]2CC[C@H](N*)[C@@H](C)C2)C[C@@H]1C,,0.36416977,,,
+1378818444,*Nc1ccc(C(Cc2ccccc2)(Cc2ccccc2)c2ccc(N*)cc2)cc1,,0.36110645,,,
+1379119982,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C=CC2=C(C#N)C(=C(C#N)C#N)OC2(C)C)cc1OCCCCCC,,0.37360953,,,
+1379290572,*Oc1ccc(NC(=O)Nc2ccccc2CCc2ccccc2NC(=O)Nc2ccc(*)cc2)cc1,,0.35119431,,,
+1380589062,*CC(*)OCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(OCC)cc2)cc1,,0.36990497,,,
+1380807593,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.36329439,,,
+1381195647,*c1ccc(NC2CC(=O)N(c3ccc(Cc4ccc(N5C(=O)CC(Nc6ccc(-c7nc(-c8ccc(C=Cc9ccc([N+](=O)[O-])cc9)cc8)[nH]c7*)cc6)C5=O)cc4)cc3)C2=O)cc1,,0.37470794,,,
+1381337604,*Oc1ccc(C(=Cc2ccc(-c3ccc(C=C(c4ccccc4)c4ccc(OC(=O)CCCCC(*)=O)cc4)cc3)cc2)c2ccccc2)cc1,,0.37196915,,,
+1381529897,*CC(*)(C)C(=O)OCCNC(C)(C)C,,0.36999858,0.184,0.899956342,11.94476747
+1382189340,*CCC=C(*)C(C)C,,0.40099098,,,
+1382529497,*O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1,,0.4683381,,,
+1382532575,*Nc1ccc(C2=CC[C@](N*)(c3ccccc3)C=C2)cc1,,0.343948335,,,
+1382706123,*Oc1c(F)c(F)c(S(=O)(=O)c2c(F)c(F)c(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(F)c2F)c(F)c1F,,0.36236981,,,
+1382769983,*Nc1cccc2c1OC1(Oc3c(cccc3N*)C=C1C)C(C)=C2,,0.35078115,,,
+1383097828,*CC(*)(C)C(=O)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32884638,0.084,1.517768128,12.13515797
+1383379124,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.34693865,,,
+1383381631,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3cccc(N)c32)cc1,,0.368668165,,,
+1383628967,*C(=O)c1ccc(Oc2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.36913731,,,
+1384565570,*CCCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37561448,,,
+1385095283,*Nc1cccc2c1-c1ccc(N*)c3cccc-2c13,,0.33425976,,,
+1385421838,*CCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*,,,0.3944285714285714,0.881825572,22.00194528
+1386193418,*Nc1ccc(C(CC)(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.36099654,,,
+1386252295,*CC(CS(=O)(=O)CC)O*,,0.36974777,,,
+1386318462,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nc(-c6ccccn6)nnc5*)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.39042731,,,
+1386480271,*CC(*)n1c2ccccc2c2cc3ccccc3cc21,,0.38102074,,,
+1386834034,*CCCCCCCCCCCCCCCCOC(=O)NCc1ccc(CNC(=O)O*)cc1,,0.36337534,,,
+1387274327,*OC(C)C(=O)NC(C)C(*)=O,,0.33069722,,,
+1387462410,*CCN(*)C(=O)c1ccccc1F,,0.34924662,,,
+1387474634,*c1cc(C(c2ccc(OCCN(C)c3ccc(C=CC4=CC(=C(C#N)C(=O)OCc5ccc(Oc6c(F)c(F)c(F)c(F)c6F)cc5)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(c1ccccc1)c1ccc(C=CC2=CC(=C(C#N)C(=O)OCc3ccc(Oc4c(F)c(F)c(F)c(F)c4F)cc3)CC(C)(C)C2)cc1,,0.36411083,,,
+1387601413,*CCN(CCNC(=O)c1ccc2c(c1)C(=O)N(*)C2=O)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34587663,,,
+1387608181,*CC(*)CCCCCCCCCCCCCCCCCCCC,21.77937998,,0.4019999999999999,,
+1387610033,*Nc1ccccc1NC(=O)c1ccc(C(*)=O)cc1,231.8080905,,,,
+1387678477,*c1ccc(-c2cnc3cc4ncc(*)nc4cc3n2)cc1,,0.44896941,,,
+1387768222,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c([N-][N+]#N)c4)cc3[N-][N+]#N)cc2)cc1,181.1592195,,,,
+1388155176,*Nc1ccc2c(c1N*)Cc1ccccc1C2,,0.3459074,,,
+1388167094,*C1c2cccc3cccc(c23)C1*,,0.40696221,,,
+1388289215,*CCCCNC(=O)C(=O)N*,,0.29824188,,,
+1388694213,*CC(*)(F)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,-98.70472035,,,,
+1388851619,*Nc1cccc2c1C(CCC)(CCC)c1c(N*)cccc1-2,,0.36616885,,,
+1388941962,*OP(=O)(Oc1ccc([N+](=O)[O-])cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.35035156,,,
+1389124790,*c1cccc(S(=O)(=O)c2cccc(N3C(=O)c4ccc(Oc5ccc(Sc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.36721228,,,
+1389236508,*CC(*)(C)C(=O)OC(COc1cc2ccccc2c2ccccc12)COc1cc2ccccc2c2ccccc12,,0.34422999,,,
+1389502780,*Nc1ccc2cc3c(N*)cccc3cc2c1,,0.3412073,,,
+1389796975,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccccc4-c4ccccc4C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37420624,,,
+1389935777,*CC(*)C(=O)NC,,0.31352975,,,
+1390124643,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.39879809,,,
+1390426236,*CC(*)c1cccc(OC(C)=O)c1,,0.34951316,,1.002961799,14.19064142
+1390442930,*CC(CCCCCCCC)COC(=O)NC(=O)c1cccc(C(=O)NC(=O)O*)c1,,0.33456199,,,
+1390880036,*Oc1ccc(-c2ccc(OC(=O)/C=C/c3ccc(/C=C/C(*)=O)cc3)cc2)cc1,,0.34195777,,,
+1390904524,*CC(*)(C)C(=O)OCC1CC(NC(=O)OC(C)(C)C)CN1C(=O)OC(C)(C)C,,0.36053611,,,
+1391196204,*CC(*)SCCCC,,0.42462566,,,
+1391471000,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5C(F)(F)F)C6=O)cc4)cc3)cc2)cc1,,0.38249401,,,
+1391471674,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ncccc2C)C3=O)cc(C(=O)NNC(=O)CCCC(*)=O)c1,,0.3253017,,,
+1391761944,*Cc1cc(C(C)(C)C)cc(*)c1O,,0.40309705,,,
+1392013159,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37421287,,,
+1392018017,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O,302.652739,,,,
+1392118578,*CCCCCCCCOC(=O)CCCCCNC(=O)c1ccc(C(=O)NCCCCCC(=O)O*)cc1,,,0.276,,
+1392601033,*Nc1ccc(C(=C(c2ccc(N)cc2)c2ccc(N*)cc2)c2ccc(N)cc2)cc1,,0.36763505,,,
+1393144437,*CCOC(=O)c1ccc(C2(C)CC(C)(C)c3ccc(C(=O)O*)cc32)cc1,,0.36910231,,,
+1393145054,*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(C)(C)CCCCC(*)(C)C)cc5C4=O)cc3)cc1)C2=O,,0.36933669,,,
+1393359836,*C(=O)Nc1ccc(Sc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C6(c7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)NC(=O)c7ccccc76)cc5)cc3)C4=O)cc2)cc1,,0.36914488,,,
+1393430812,*Nc1ccc(CC[C@H](c2ccccc2)c2ccc(N*)cc2)cc1,,0.34550971,,,
+1394137835,*O[Si](C)(C)c1ccc2cc3cc([Si](*)(C)C)ccc3cc2c1,,0.407332,,,
+1394335529,*CCCCCCCCCCCCCCCCCCCCCCOC(=O)CC(CC(=O)O*)c1ccccc1,,,0.354,,
+1394769597,*Nc1ccc2ccc3c4ccccc4ccc3c2c1N*,,0.34015504,,,
+1394885564,*c1ccc(-c2ccc(C(*)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38907083,,,
+1394969707,*OC(COC(=O)c1ccc(C)cc1C(*)=O)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.3633577,,,
+1395351360,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(C)c6cccc(C(C)(C)c7ccc(Oc8ccc(C(=O)Nc9ccc(*)cc9)cc8)cc7)c6)cc5)cc4)cc3)cc12,,0.36813986,,,
+1395602302,*Oc1ccc(C(=O)c2ccc(*)cc2)cc1,,0.37786189,,1.182134646,25.66079856
+1395946154,*c1ccc(C(c2ccc(C#N)cc2)c2ccc(N(*)c3ccc(C)cc3)cc2)cc1,,0.45124557,,,
+1396011418,*Oc1ccc2c(c1)C(C)(c1ccc(OC(*)=O)cc1)CC2(C)C,,0.37135373,,,
+1396173463,*NC(=O)O[C@@H]1/C=C\C=C/[C@H](C)[C@@H](/C(C)=C/C(N)(N*)C(N)(N)C[C@]2(O)CC(=O)O[C@@H](CC)[C@H]2C)OC(=O)CC/C=C(\C)[C@H](C)[C@H](O)[C@@H]1C,,0.30358477,,,
+1396280832,*Nc1ccc2c(c1)-c1cc(N*)ccc1C2(C)C,,0.36307576,,,
+1396448482,*Nc1ccc(CCc2cc3ccc2CCc2ccc(c(CCc4ccc(N*)cc4)c2)CC3)cc1,,0.35371722,,,
+1396499237,*Nc1ccc([C@]23C[C@@]4(c5ccc(N)cc5)C[C@](c5ccc(N)cc5)(C[C@](c5ccc(N*)cc5)(C4)C2)C3)cc1,,0.37559858,,,
+1397085325,*Nc1cc(C)c(Cc2ccc(CCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35526555,,,
+1397226441,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)CCCCCCCCCCC6)cc5)cc4C3=O)cc2)cc1,,0.37598187,,,
+1397367740,*O[Si](C)(C)CCCCCCOC(=O)C(C)Oc1ccc(OC(=O)c2ccc(-c3ccc(OCCCCCCCCCC[Si](*)(C)C)cc3)cc2)cc1[N+](=O)[O-],,0.37268989,,,
+1397495008,*c1ccc(Oc2ccc(Oc3ccc(-c4cnc5ccc(-c6ccc7nc(*)cnc7c6)cc5n4)cc3)cc2)cc1,,0.38737398,,,
+1397532955,*CC(F)(F)C1(F)CC(CC(O)(C(F)(F)F)C(F)(F)F)CC1*,,0.32389296,,,
+1397660267,*CCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.34358239,,,
+1397915827,*NCCC[Si](C)(C)O[Si](C)(C)CCCN*,,0.37320765,,,
+1397995354,*c1ccc(Oc2ccc(Sc3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36490067,,,
+1398649969,*c1cc(CCCCCCCCCCCCCCC)c(*)s1,,,0.3742499999999999,0.902739983,16.04613034
+1399574805,*CCCCCCN(C)C(=O)CCCC(=O)N(*)C,,0.35107943,,,
+1399918189,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5-c5ccccc5)C6=O)cc4)cc3)cc2)cc1,,0.38081371,,,
+1399962717,*c1cccc(S(=O)(=O)NCCCCNS(*)(=O)=O)c1,,0.33938166,,,
+1399980984,*CC(*)(C)C(=O)OCCCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.348815,,,
+1400204987,*OC(CCl)COC(=O)C1=C(C(=O)OCC(CCl)OC(=O)c2ccc(C(*)=O)cc2)C2C=CC1C2,,0.35028816,,,
+1400904747,*Nc1cccc(-c2ccccc2-c2cccc3ccccc23)c1N*,,0.35403641,,,
+1401313512,*OP(=O)(Oc1c(Cl)cccc1Cl)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl,,0.35947365,,,
+1401699288,*OC1C(C)(C)C(OC(=O)c2ccc(C(*)=O)cc2)C1(C)C,,0.37886333,,,
+1401767370,*CC(*)C(=O)OCCCSCC,,0.39575862,0.2155,1.049932374,13.79806606
+1401880967,*CC(*)(C)C(=O)OCCCCCCCCCCCCCCCC,,,0.26,,
+1401907543,*C(=C(C#N)c1ccc(C(=O)OC2CCN(*)CC2)cc1)c1ccc(OC)cc1,,0.38086854,,,
+1402707423,*CC(*)(C)C(=O)OCCC(C)CC(C)(C)C,,0.37283397,0.198,,
+1403025307,*CC(*)Cc1ccccc1C,,0.37559758,,,
+1403056181,*Nc1ccc(-c2ccc(N*)cc2-c2cccc(C)c2)cc1,,0.35636161,,,
+1403885669,*C(F)(F)C(F)(F)C1(F)C(*)(F)OC(F)(F)C1(F)F,,,0.1265,1.564699369,19.456651
+1404736558,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCC)cc1,,0.34457254,,,
+1404881179,*Oc1ccc(OC(=O)Oc2ccc(C(C)(C)c3ccc(OC(*)=O)cc3)cc2)cc1,,0.34624319,,,
+1404906780,*CC(*)c1c(C)cc(C)cc1C,,0.40245926,0.1669999999999999,,
+1405342945,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2ccc([N+](=O)[O-])c3ccccc23)cc1,,0.35424799,,,
+1405483472,*c1ccc(NC(=O)c2cc(C(=O)Nc3ccc(S(*)(=O)=O)cc3)cc(S(=O)(=O)c3ccccc3)c2)cc1,,0.35464323,,,
+1405504097,*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35628749,,,
+1405932277,*C1CCC(CC2CCC(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)CC2)CC1,,0.36947651,,,
+1406114427,*Nc1ccc([C@H](C=C)c2ccc(C3(c4ccc([C@@H](C=C)c5ccc(N*)cc5)cc4)CCC(CCCC)CC3)cc2)cc1,,0.36703684,,,
+1407338879,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(C(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)(C(F)(F)F)C(F)(F)F)cc2)C3=O)cc(-c2ccccc2)c1,,0.37734538,,,
+1408229683,*CC(*)(C)C(=O)OCc1ccccc1Cl,,0.35759108,0.159,1.151611779,13.32041855
+1408675476,*Oc1ccc(C(c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,150.9011854,,,,
+1408691704,*Nc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(NC(=S)NC(=O)CCCCC(=O)NC(*)=S)c3)c2)c1,,0.33460461,,,
+1408899479,*CCCCCCCCCCCCCCC(=O)N*,,0.37887581,0.389,0.89024336,23.00931982
+1409017581,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7ccccn7)nnc6*)cc5)cc4)c3)cc2)cc1,,0.37990238,,,
+1409490636,*Nc1ccc(C)c2c(N*)cccc12,,0.33694341,,,
+1409584413,*CC(*)c1ccccc1C(=O)NC,,0.35557991,0.1836666666666666,,
+1409830378,*CC(OC)C1C(=O)OC(=O)C1*,,0.287972,,,
+1409947542,*CC(*)(C)C(=O)OCCC1CCCCC1,,0.36923892,,,
+1410914942,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(c4cc(C)c(*)c(C)c4)(C(F)(F)F)C(F)(F)F)cc3C)cc2)cc1,,0.39642368,,1.103576244,21.11102867
+1411169396,*Oc1cc(CCCCCCCCCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.37339649,,,
+1411192314,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(-c7cc(-c8ccc(-c9ccccc9)cc8)cc(-c8ccc(NC(=O)Nc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)n7)cc6)cc5C4=O)cc3)n2)cc1,,0.3711086,,,
+1411288736,*CC(=O)NCCCCCCNC(=O)CC1COC(*)CO1,,0.32963111,,,
+1411415365,*CCOC(=O)c1cc(C(=O)O*)cc(C(C)(C)C)c1,,0.35812909,,,
+1411771596,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCCC(=Cc5ccc(*)cc5)C4=O)cc3)cc2)cc1,,0.37492274,,,
+1411897079,*c1ccc(C(=O)Oc2ccc(C(C)(C)c3ccc(OC(=O)c4ccc(N5C(=O)CC(SCCOCCSC6CC(=O)N(*)C6=O)C5=O)cc4)cc3)cc2)cc1,,0.34542063,,,
+1412120720,*Nc1cccc(NC(=O)c2cccc(C(=O)Nc3ccc(C(=O)NC(*)=S)cc3)c2)c1,,0.33903798,,,
+1412223212,*CC(*)(C)C(=O)OCc1ccc(OCCOCCOCCOCC)c(OCCOCCOCCOCC)c1,,0.35316611,,,
+1412767415,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(S(=O)(=O)c4ccc(O*)cc4)cc3)cc2)cc1,,0.34762026,,,
+1412954804,*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(C)cc4)c4nsnc34)cc2)cc1,,0.35943603,,,
+1413330755,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6c5)cc3)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35976848,,,
+1413610901,*Oc1ccc(OC(=O)c2cc(OCCCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.35542806,,,
+1413611773,*Oc1cc(C)c(*)c(C)c1Br,,0.42901007,,,
+1413837140,*CC(*)(C)C(=O)OCSC,,0.36623582,,,
+1414225049,*CC(*)C(=O)NCC,55.10481694,,,,
+1415337639,*CCOCCOCCOCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.3414643,,,
+1415406733,*C(=O)CCCCC(=O)c1ccc(C=C2CCC(=Cc3ccc(*)cc3)C2=O)cc1,197.4539358,,,,
+1415457510,*Nc1cc2ccccc2c(-c2cccc(C)c2)c1N*,,0.35472795,,,
+1416739755,*Nc1cccc(C2(c3cccc(N*)c3C)c3ccccc3-c3ccccc32)c1C,,0.371195595,,,
+1417031371,*CC(*)c1cc(Br)ccc1OC(C)C,,0.4237647,0.124,,
+1417043259,*Nc1ccc(C2=CC(C)(C)[C@H](N*)C(C)(C)C2)cc1,,0.36534008,,,
+1417054451,*Nc1ccc(/C=C/c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.36615587,,,
+1417361969,*CC(*)c1ccc(C(=O)N2CCCCC2)cc1,,0.37271412,,,
+1417768053,*Nc1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(NC(=O)c3ccc4c(c3)Cc3cc(C(*)=O)ccc3-4)cc2)cc1,,0.37694297,,,
+1417800703,*Oc1cccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccc(N=Nc5ccc([N+](=O)[O-])cc5)cc4)c4cccc(OC(=O)OCCOCCOC(*)=O)c4)cc3)cc2)c1,,0.37231044,,,
+1417827855,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(N8C(=O)c9ccc(*)cc9C8=O)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36551103,,,
+1418585210,*CC(*)c1cccc(O)c1,,0.34145083,,,
+1418607549,*CC(*)c1ccc(CCl)cc1,,0.38772297,,,
+1418628819,*CC(CO*)(CSC)CSC,,0.41704128,,,
+1419016384,*CC(*)C(=O)OCCN(CC)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)cc1,,,0.2135,1.12679408,12.43938389
+1419552421,*Nc1ccccc1C(c1ccccc1)(c1ccccc1N)c1ccccc1N*,,0.37480672,,,
+1420611006,*CCCC1(C)CCN(C(=O)CCCC(=O)N2CCC(*)(C)CC2)CC1,,0.3501927,,,
+1420737735,*N[C@@H](CCCC(=O)[C@](N)(CS)C(=O)O)C(=O)[C@@](CS)(N*)C(=O)O,,0.30543387,,,
+1420812496,*OC(COC(=O)c1cc(C(*)=O)cc(C(C)(C)C)c1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37044454,,,
+1421209921,*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCC(*)=O,,0.33135982,,,
+1421320527,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.33859086,,,
+1421392945,*CNC(=O)OCC(OC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1)c1ccco1,,0.34356107,,,
+1421653372,*OP(=O)(Oc1ccc(C)cc1)Oc1c(C)cc(S(=O)(=O)c2cc(C)c(*)c(C)c2)cc1C,,0.36875805,,,
+1421658168,*Nc1ccc(-c2ccc(Cc3ccccc3)cc2)c(C)c1N*,,0.34585663,,,
+1421896070,*CCN(*)C(=O)CCCCCCC,,0.37652091,,,
+1421909568,*NCCOCCN*,,0.310244355,,,
+1422009513,*CC(*)C1CCCC1,,,0.2016666666666666,,
+1422047403,*C[Si](C)(C)[Si](C)(C)[Si](C)(C)[Si](*)(C)C,,0.44384338,,,
+1422066716,*c1ccc(-c2ccc(N(c3ccc(OC)cc3)c3ccc4ccc(N(*)c5ccc(OC)cc5)cc4c3)cc2)cc1,,0.43192443,,,
+1422469487,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(C(F)(F)F)cc5)C6=O)cc4)cc3)cc2)cc1,,0.38077033,,,
+1422560601,*NC(C)(C)C(C)(C)N*,,0.338250255,,,
+1423269762,*CCCCCCCC(=O)OCCOC(=O)CCCCCCCC1OCC2(COC(*)OC2)CO1,,0.34857545,,,
+1423401067,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5cccc(*)n5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.39554354,,,
+1423424888,*Nc1c(N*)c2cc3ccccc3cc2c2cc3ccccc3cc12,,0.34595266,,,
+1423530794,*c1ccc(Oc2ccc(N3C(=O)N4C(c5ccc(OC)cc5)N(*)C(=O)N4C3c3ccc(OC)cc3)cc2)cc1,,0.39684923,,,
+1424144967,*c1ccc(C(C)c2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1,307.2645731,,,,
+1424387141,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35411311,,,
+1424467870,*Nc1ccc(Cc2cccc(Cc3ccc(N*)cc3)c2)cc1,,0.3462571,,,
+1426385152,*CCCOc1ccc(OCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1,,0.37021373,,,
+1426651085,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c3)cc1)C2=O,,0.36867528,,,
+1426845091,*Oc1ccc(N=C(C)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(C(C)=Nc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38825618,,,
+1426880410,*CC(*)(F)C(=O)OC(C(F)(F)F)C(F)(F)F,,0.31561364,,,
+1426978607,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.37552366,,,
+1427121961,*CC(C)(N=C=O)C1C(=O)OC(=O)C1*,,0.29181625,,,
+1427163348,*C1C(=O)N(c2cc(Br)c(O[Si](C)(C)C(C)(C)C)c(Br)c2)C(=O)C1*,,0.46658823,,,
+1427715399,*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)(c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)C(F)(F)F)cc1)C2=O,,0.40799156,,,
+1428013665,*CCCCCCCCOC(=O)c1cccc(C(=O)O*)c1,,0.35394853,,,
+1428459269,*CCCP(CCCNC(=O)N*)c1ccccc1,-49.19787298,,,,
+1429766327,*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)c3ccccc23)cc1,,0.35146434,,,
+1429793294,*Nc1cc(N*)c(N)cc1N,,0.32432141,,,
+1430116322,*CC(SS*)c1ccccc1,,0.40293208,,,
+1430315937,*CC(O)COc1cccc(O*)c1,,0.33076501,,,
+1430519110,*C(=O)c1ccc(C(=O)c2ccc(C=C3CCCC(=Cc4ccc(*)cc4)C3=O)cc2)cc1,240.3798309,,,,
+1430804351,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1-c1ccccc1,,0.3631401,,,
+1431061733,*CC(*)c1ccc(Cl)c([N+](=O)[O-])c1,,0.45234816,,,
+1431221596,*c1cccc(NC(=O)CCCCCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1,,0.35368633,,,
+1431937128,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccc(C)cc2)cc1,,0.34694127,,,
+1432698021,*CC(*)OC(=O)c1ccc([Si](C)(C)C)cc1,,0.40244256,,,
+1432821779,*CCN(CCOC(=O)c1ccc([Si](C)(C)c2ccc(C(=O)O*)cc2)cc1)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.3656424,,,
+1432826264,*CCCCOC(=O)c1cccc(C(=O)O*)c1,,0.33869423,,,
+1433887505,*NC1CCC(CC(C)(C)NC(=O)CCCCC(*)=O)CC1,,0.33902943,,,
+1434137991,*CCCCCCCCSSCCCCSS*,,,0.192,,
+1434167997,*/C=C/CCCCCCCCCC(Cl)CCCCCCCCC*,,,0.3419999999999999,0.878956685,20.00761368
+1435049128,*CC(*)(C)C(=O)OCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OCCC)cc2)cc1,,0.34387044,,,
+1435571706,*CC(F)(F)CC(*)(C(F)(F)F)C(F)(F)F,,0.3151687,,,
+1435785371,*CCCCCOC(=O)CCCCSCCCCC(=O)O*,,,0.202,,
+1436177841,*CC(*)C1CCC(C(C)(C)C)CC1,,0.41878097,,,
+1436330682,*c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.40114484,,,
+1436575335,*Nc1ccc2c(c1)C(C)(C)c1cc(N*)ccc1-2,,0.36132753,,,
+1436896741,*CC(*)(C)C(=O)OCCN(CC)c1ccccc1,,0.36250293,,,
+1437172858,*Nc1ccc(NC(=O)c2cc(NC(=O)c3ccncc3)cc(C(*)=O)c2)cc1,,0.33138478,,,
+1437182858,*Nc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(NC(=S)NC(=O)c4ccc(C(=O)NC(*)=S)cc4)c3)c2)c1,,0.33973914,,,
+1437198887,*CCCC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CCCN4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.33944014,,,
+1437455994,*CC(*)c1cccc2ccccc12,,,0.194,0.993182994,12.95937506
+1437522401,*C=CC1=C(*)CC(C(=O)OCC)(C(=O)OCC)C1,,0.37814836,,,
+1437709159,*Nc1ccc2ccccc2c1-c1ccc2ccccc2c1N*,,0.35558527,,,
+1437848848,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(S(*)(=O)=O)cc4)cc3)cc2)s1,,0.3741321,,,
+1438275910,*c1ccc(Oc2ccc(-c3csc(N=Cc4ccc(OCCCCCCCCOc5ccc(C=Nc6nc(*)cs6)cc5)cc4)n3)cc2)cc1,,0.37304106,,,
+1438410833,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.34168737,,,
+1438539748,*CCCOC(=O)OCCCCCCOC(=O)OCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.32872339,,,
+1439560323,*CCCC(=O)NCCCCCCNC(=O)CCCOC(=O)c1ccc(C(=O)O*)cc1,60.02078862,,,,
+1439590900,*Oc1ccc(NC(=O)Nc2ccccc2CCc2ccc(NC(=O)Nc3ccc(*)cc3)cc2)cc1,,0.34891233,,,
+1439796054,*c1ccc(C(c2ccc(N3C(=O)c4cc5c(cc4C3=O)C(C(F)(F)F)(C(F)(F)F)c3cc4c(cc3O5)C(=O)N(*)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.42823132,,,
+1439837660,*C(=O)Nc1ccc(C2(c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c6)cc4)C5=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36700382,,,
+1439947780,*Nc1ccc(C(c2ccc(N*)cc2)c2ccc(C(C)C)cc2)cc1,,0.36575628,,,
+1441154602,*Nc1ccc2ccc3ccc(N*)c4ccc1c2c34,,0.33563497,,,
+1441221742,*CCCOC(=O)CCCCCC(=O)O*,-71.28231613,,,,
+1441373602,*c1ccc(C(=O)Nc2ccc(-c3cc(Br)ccc3NC(=O)c3ccc(S(*)(=O)=O)cc3)cc2)cc1,,0.36672831,,,
+1441731142,*c1ccc(Cc2ccc(NC(=O)c3cccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)c3)cc2)cc1,,0.35744351,,,
+1441736260,*CCCCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.35004101,,,
+1442807010,*CC(*)(F)C(=O)OCC(F)(F)F,,0.31655801,,,
+1443435953,*Nc1ccc2c3cccc4cccc(c5c(N*)ccc1c25)c43,,0.33627702,,,
+1443684978,*c1ccc(-c2ccc(N3C(=O)c4ccc(Oc5ccc(Sc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.36479388,,,
+1443853019,*CC(*)C(=O)NCCCCCCCCCCCCCC,,,0.3746666666666667,0.86728938,11.91385451
+1443895665,*Nc1ccccc1-c1cc(-c2ccccc2N)cc(-c2ccccc2N*)c1,,0.34714968,,,
+1443962559,*CC(=O)NCCCCCCNC(=O)COC(=O)CCCCCCCCC(=O)O*,,0.33901503,,,
+1444001096,*CC(*)C(=O)OCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.35282472,,,
+1444443456,*CC(*)C(=O)OC1CCCCC1,,0.36637018,0.201,0.975831621,11.16519036
+1444497775,*Nc1ccc2c(c1)C(C)(C)c1cc3cc4ccc(N*)cc4cc3cc1-2,,0.366552245,,,
+1444677131,*c1ccc(NC(=O)c2ccc(OCCOCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)cc1,,0.34281464,,,
+1444682939,*Nc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(NC(=S)NC(=O)c4cccc(C(=O)NC(*)=S)c4)c3)c2)c1,,0.34030013,,,
+1444841962,*c1ccc(Oc2ccc(-c3csc(N=Cc4ccc(OCCCCOc5ccc(C=Nc6nc(*)cs6)cc5OC)c(OC)c4)n3)cc2)cc1,104.0626058,,,,
+1446315012,*Nc1ccc2c(C)c3cc(N*)ccc3c(C)c2c1,,0.34480504,,,
+1446335630,*Nc1ccc(C(CCCC[C@H]2CC[C@H](/C=C\[C@H]3CC[C@H](CCC)CC3)CC2)(CCCC[C@H]2CC[C@H](/C=C\[C@H]3CC[C@H](CCC)CC3)CC2)c2ccc(N*)cc2)cc1,,0.376234835,,,
+1446403769,*Oc1ccc(Oc2cccc(*)n2)c(-c2ccccc2)c1,,0.36427467,,,
+1446406615,*Nc1ccc(C2(c3ccc(N*)cc3)c3cccc(N)c3-c3c(N)cccc32)cc1,,0.36840054,,,
+1446517158,*CC(*)c1ccc(C(C)(C)O)cc1,,0.3711129,0.2316666666666666,0.934073122,12.4288407
+1446956621,*CC(*)C(=O)Oc1ccc(C(C)(C)C)cc1,,0.37651726,0.2245,0.958441992,10.3213082
+1447075360,*CCCCCCCCCCCNC(=O)CCCCCCCCC(=O)NCCCCCCCCCCCNC(=O)C(=O)N*,,,0.3195,0.945505999,21.31594048
+1447287020,*CCCOC(=O)c1ccc(C(=O)O*)cc1,,0.33381899,,,
+1447445383,*Oc1ccc(C(C)(C)c2ccc(Oc3nc(*)nc(OC)n3)cc2)cc1,121.2094568,,,,
+1447629123,*Oc1ccc(Oc2ccc(OC(*)=O)cc2)cc1,,0.34396355,,,
+1447631278,*Oc1cccc(Oc2cccc(*)c2)c1C#N,,0.37237337,,,
+1448332716,*CC(*)(CC(=O)OCCCCCCCC)C(=O)OCCCCCCCC,,0.38613046,,,
+1448341292,*Oc1c(-c2ccccc2)cc(*)cc1-c1ccccc1,,,,1.041525617,16.01472431
+1448448374,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.37622546,,,
+1448731732,*Nc1cccc(-c2c3ccccc3cc3ccccc23)c1N*,,0.34840678,,,
+1449297338,*CCCCCCCCCCCCCCCCCCCCOC(=O)COCC(=O)O*,,,0.3249999999999999,0.930690382,21.36408001
+1449481867,*CC(*)C(=O)OCCc1ccccc1,,0.35483589,0.196,1.060028456,12.57995644
+1449543209,*c1cc(CCCCCCCCCCCCCC)c(*)s1,,,0.4182499999999999,0.91341608,16.14848283
+1449552519,*CCCCCOC(=O)c1ccccc1C(=O)O[Cd]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.3810089,,,
+1450236105,*C#Cc1cccc(C#C[Si](*)(C)C)c1,,0.49595595,,,
+1450460730,*Oc1ccc(C(C)(CCC(=O)O)c2ccc(OC(=O)c3cccc(C(*)=O)c3)c([N+](=O)[O-])c2)cc1[N+](=O)[O-],127.0784722,,,,
+1450812051,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1C(F)(F)F,,0.33995878,,,
+1450851242,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OC)cc1,,0.33262746,,,
+1451556354,*CC(*)c1ccc(F)cc1,,,,0.989259062,16.36886102
+1451589071,*c1ccc2cc(OC(=O)c3ccc(C(=O)Oc4ccc5cc(-c6nc7ccccc7nc6*)ccc5c4)cc3)ccc2c1,,0.37059699,,,
+1451687458,*Nc1ccc(-c2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1,,0.35263573,,,
+1451767844,*SC(C)C(*)=O,,0.38098783,,,
+1452233273,*CC(CCCC)(CCCC)C(=O)O*,-17.98562642,,,,
+1452416309,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5nc(-c6cccc(-c7nc(*)c(-c8ccccc8)[nH]7)c6)[nH]c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.39966133,,,
+1452761460,*Cc1cccc(CNC(=O)c2ccccc2-c2ccccc2C(=O)N*)c1,,0.34299947,,,
+1452778640,*CCCOC(=O)C1CCC(C(=O)O*)CC1,,0.33783784,0.222,1.082251488,20.95656384
+1453017209,*C=C(*)c1ccc([N+](=O)[O-])cc1,-12.60583746,,,,
+1453388212,*COC(=O)Nc1ccc(Cc2ccc(NC(=O)OCc3ccc(*)o3)cc2)cc1,,0.3423126,,,
+1453450335,*Oc1cccc(Oc2cccc(NC(=O)c3cc(Oc4ccc(C(=O)O)c(C(=O)Nc5cccc(*)c5)c4)ccc3C(=O)O)c2)c1C#N,,0.33531487,,,
+1453479470,*Nc1ccc(C2(c3ccc(N*)cc3C)CCCCC2)c(C)c1,,0.35510458,,,
+1453811128,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,,0.35651308,,,
+1453894655,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=C)c4ccc(*)cc4)cc3)cc2)cc1,,0.37206608,,,
+1453979051,*Nc1cc(Oc2ccccc2)c(N*)c2c1C(=O)c1c(N)c(Oc3ccccc3)cc(N)c1C2=O,,0.34012349,,,
+1454170504,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(C(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O,,0.39003172,,,
+1454388009,*c1ccc(-c2ccc(N(*)c3ccc(C(F)(F)F)cc3)cc2)cc1,,0.44845876,,,
+1454873499,*Oc1ccc(Oc2ccc(C=Nc3ccc(Oc4ccc(N=Cc5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.37351245,,,
+1455090776,*Nc1ccc(-c2ccccc2)c(-c2ccc(N*)c3ccccc23)c1-c1cccc2ccccc12,,0.36684133,,,
+1455134437,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cccc(P(=O)(c4ccccc4)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c2)C3=O)cc1,,0.3614848,,,
+1455221251,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.34198543,,,
+1455433082,*CCN(*)C(C)=O,,0.32742534,,,
+1455448064,*c1ccc(Oc2ccc(-c3c(-c4ccccc4)nc4ccc(-c5ccc6nc(-c7ccccc7)c(*)c(-c7ccccc7)c6c5)cc4c3-c3ccccc3)cc2)cc1,,0.40050787,,,
+1455477899,*C(=O)CCCCCCCCC(=O)N1CCN(*)CC1,,0.3439724,,,
+1455663460,*Nc1ccc([C@]2(C)c3cc(N*)ccc3[C@@H](C)[C@H]2C)cc1,,0.3637627799999999,,,
+1455763004,*CC(*)C(=O)c1ccc(C)c(C)c1,,0.3826776,,,
+1455946712,*Nc1ccc(-c2cccc3c2[C@@H](c2ccc(N*)cc2)c2ccccc2C3)cc1,,0.36094468,,,
+1456134582,*CCCCCCNC(=O)CCP(=S)(CCC(=O)N*)c1ccccc1,,0.34436665,,,
+1457502415,*c1ccc(Cc2ccc(N3C(=O)c4cc(-c5ccc([Si](C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc([Si](C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.43467136,,,
+1457793219,*CC(CCCCCCOc1ccc(N=Nc2ccc(C(F)(F)F)cc2)cc1)COC(=O)CCCCC(=O)O*,,0.35608461,,,
+1458472862,*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6c5)cc3)C4=O)c2)cc1,,0.35874109,,,
+1458806978,*Nc1c(CC)cc(C2(c3cc(CC)c(N*)c(C(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)C,,0.40355199,,,
+1458835114,*CC(*)c1ccc(C#N)cc1,,0.40396396,,,
+1458927340,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OCCC)c1)c1ccc(N=Nc2ccc(-c3nc4ccccc4o3)cc2)cc1,,0.36708551,,,
+1458930812,*CCCCCCCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.39112847,,,
+1459842972,*N[C@H]1OC(N)(N*)NC1=O,,0.29071263,,,
+1459846963,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.35492786,,,
+1460712253,*CCCCCCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.35120821,,,
+1460910986,*Nc1ccc(-c2c(-c3ccc(C(c4ccccc4)c4ccccc4)cc3)cc(-c3cccc(N*)c3)cc2-c2ccc(C(c3ccccc3)c3ccccc3)cc2)cc1,,0.36459266,,,
+1461036801,*C=C(*)CCCCCCCCCCOc1ccc(-c2ccc(OCC(F)CCCCCC)cc2)cc1,27.34580481,,,,
+1461767577,*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(Oc5cc6ccccc6cc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.36199186,,,
+1461784308,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(C(C)(C)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5)cc4C3=O)cc2)cc1,,0.395355,,,
+1461861414,*c1ccc(-c2nnc(*)o2)c(OCCCCCCCCCC)c1,,0.41425345,,,
+1462340201,*C1CC2CC1C(*)C2CCCC,,0.41628236,,0.793157119,12.17988527
+1462614613,*OP(=O)(Oc1ccc(*)cc1)OC1CCCCC1,,0.35761127,,,
+1462748332,*c1ccc(-c2nc3ccc(-c4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1,,0.53276928,,,
+1462931199,*N=P(*)(OCCCCCCC)OCCCCCCC,,0.40469164,,,
+1463007614,*CCCCCCCCCCCCCCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,,0.4337499999999999,0.890200365,20.23441523
+1463085835,*c1ccc(Oc2cc3ccccc3cc2Oc2ccc(N3C(=O)c4ccc(Oc5ccccc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.35912156,,,
+1463095437,*Oc1ccc(N=CC=Nc2ccc(OC(=O)Nc3ccc(Cc4ccc(NC(*)=O)cc4)cc3)cc2)cc1,,0.35769059,,,
+1463175891,*C([2H])([2H])C(*)(C([2H])([2H])[2H])C([2H])([2H])[2H],85.2671101,,,,
+1463811998,*CCCCCCCCCCOC(=O)C(=O)O*,-65.08541819,,,,
+1464019841,*Oc1ccc(NC(=O)c2ccc(-c3ccc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)CC7CCC6C7)cc5)cc4)cc3)cc2)cc1,,0.3665982,,,
+1464421877,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCCCC)c(C(*)=O)cc2OCCCCCCCCCCCC)cc1-c1ccccc1,,0.38150674,,,
+1464436894,*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(-c5c(-c6ccccc6)c(-c6ccccc6)c(-c6ccc(Sc7ccc(-c8c(-c9ccccc9)c(-c9ccccc9)c(*)c(-c9ccccc9)c8-c8ccccc8)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)cc4C3=O)cc1)C2=O,,0.40791529,,,
+1464819967,*Nc1ccc(-c2c(C)cccc2C2(c3cccc(C)c3-c3ccc(N*)cc3)CCCCC2)cc1,,0.37034703,,,
+1465340291,*CC(*)(C)C(=O)NC(=O)Oc1ccc(S(=O)(=O)c2ccc(O)cc2)cc1,,0.31639128,,,
+1465942783,*CC(*)c1ccc(OC(C)=O)cc1,,0.35340606,0.2005,1.037413892,13.82349277
+1466879267,*CC(*)C(=O)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.31737027,,,
+1467166248,*CC(*)C(=O)OC(C(F)(F)F)C(F)(F)F,,0.32320653,,,
+1467252065,*CC(O)CCCCC(O)CN(*)c1ccc(N(C)C)cc1,,0.35960485,,,
+1468461975,*c1ccc(Oc2ccc(N3C(=O)C4CCC5C(=O)N(*)C(=O)C5C4C3=O)cc2)cc1,,0.36953337,,,
+1468518443,*CC(*)(C)C(=O)OCCCCCC,,,0.184,0.903441723,11.78953051
+1468882159,*CC(CBr)O*,,0.39889944,,,
+1468958338,*Nc1cccc2cc3cccc(N*)c3cc12,,0.33370765,,,
+1469078897,*CCc1ccc(*)c(C)c1,,0.39177788,,,
+1469251580,*Nc1ccc(-c2c(C)cc(C3(c4cc(C)c(-c5ccc(N*)cc5)c(C(C)C)c4)c4ccccc4-c4ccccc43)cc2C(C)C)cc1,,0.4084913999999999,,,
+1469266389,*CC(C)(C)COC(=O)NC1CCC(CC2CCC(NC(=O)O*)CC2)CC1,15.9792949,,,,
+1469368089,*CCN(CCOC(=O)c1ccc(C(=O)O*)cc1)c1ccc(C=C(C#N)C#N)cc1,,0.35440714,,,
+1469495249,*C(=O)Oc1ccc([Si](C)(C)c2ccc(OC(=O)c3ccc(*)s3)cc2)cc1,,0.37656746,,,
+1469675356,*O[Si](C)(C)O[Si](C)(C)O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F,,0.40336035,,,
+1469897580,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.36995915,,,
+1470418130,*c1ccc(Cc2ccc(N3C(=O)N(c4ccc(Cc5ccc(N6C(=O)N(*)C(C)(C)C6=O)cc5)cc4)C(=O)C3(C)C)cc2)cc1,,0.4025765,,,
+1470645091,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.41157762,,,
+1470768847,*CC(*)C(=O)OCCCCCCOc1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36065471,,,
+1470774077,*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.36576424,,,
+1471142716,*C=C(c1ccc(*)cc1)c1ccc(C(F)(F)F)cc1,,0.40375931,,,
+1472109489,*CCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.34468789,,,
+1472118288,*CC(*)(F)C(=O)OCC(F)(F)C(F)(F)F,,0.32568695,,,
+1472271341,*CCCCCCCCCCCCOc1ccc(C(C)(C)c2ccc(OCOc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)cc2)cc1,,0.36755988,,,
+1472751426,*c1cc(N2C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc(C(F)(F)F)c1,,0.3984853,,,
+1473380579,*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(-c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C)c4)cc3C)cc1C)C2=O,,0.42965902,,,
+1473395922,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc%12c(c%11)C(=O)N(*)C%12=O)cc%10)CCCCCCCCCCC9)cc8)cc7C6=O)cc5)cc4)C4CC5CC(C4)CC3C5)cc2)cc1,,0.38174277,,,
+1473570358,*C(=O)c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(C(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.37060615,,,
+1473690215,*Nc1ccc2c(c1)C(c1ccccc1N)(c1ccccc1N*)c1cc(N)ccc1-2,,0.3674954,,,
+1473745481,*C1CCC1*,69.57488221,,0.163,0.823406686,14.41189122
+1474070468,*Oc1c(C(C)(C)C)cc(C(*)=O)cc1C(C)(C)C,206.7951116,,,,
+1474131719,*Oc1ccc(C(C)(C)c2ccc(OC(=O)Sc3ccc(C(C)(C)c4ccc(SC(*)=O)cc4)cc3)cc2)cc1,,0.35298003,,,
+1474494101,*Oc1ccc(Sc2ccc(Sc3ccc(Sc4ccc(*)cc4)cc3)cc2)cc1,,0.38951806,,,
+1474888709,*CC(*)(C)C(=O)OCc1ccc(OCCOCCOCC)c(OCCOCCOCC)c1,,0.35596618,,,
+1474909304,*CCCCCNc1nc(NCCCCCc2nc3cc(-c4ccc5[nH]c(*)nc5c4)ccc3[nH]2)nc(N(CC)CC)n1,,0.37614223,,,
+1474978524,*CCCN(*)C(=O)CC,,0.34854625,,,
+1475222377,*C(=O)Nc1cccc(NC(=O)c2ccc3[nH]c(-c4cccc(-c5nc6cc(*)ccc6[nH]5)c4)nc3c2)c1,,0.37766539,,,
+1475370376,*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)cc4)cc2)C3=O)c1,,0.36035989,,,
+1475746225,*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(OCCCC)cc2)cc1,,0.38608209,,,
+1476123993,*CC=CCOC(=O)C(Cc1ccccc1)NC(=O)CCCCC(=O)NC(Cc1ccccc1)C(=O)O*,,0.34434051,,,
+1476310950,*c1[nH]c(*)c(CC(=O)OCCCCCCCCCCCC)c1CC(=O)OCCCCCCCCCCCC,,,0.2794999999999999,0.913558675,11.77651013
+1476461237,*Oc1ccc(P(=O)(c2ccccc2)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.39008003,,,
+1476641400,*Nc1ccc2c(c1)C(C)(C)CC21CC(C)(C)c2cc(N*)ccc21,,0.380516755,,,
+1476707326,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccccc5-c5ccccc5Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.36139938,,,
+1476896117,*CC(*)c1ccccc1OC,160.10962,,0.1946666666666666,,
+1476896274,*c1cccc(N2C(=O)c3ccc(C(c4ccc5c(c4)C(=O)N(c4cccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c4)C5=O)(C(F)(F)F)C(F)(F)F)cc3C2=O)c1,,0.37976208,,,
+1476946894,*c1cccc(NC(=O)CCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1,,0.34455896,,,
+1477172876,*CC(*)C[Si](C)(C)C,,0.41950012,,,
+1477353024,*N=P(*)(Nc1ccccc1)Nc1ccccc1,,0.38717322,,,
+1477468420,*Cc1ccc(COP(=O)(N=Nc2ccc(Oc3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)cc1,,0.35571352,,,
+1477631774,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc3)C4=O)cc2)cc1,,0.36567383,,,
+1477890619,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4C)cc3)cc2)cc1,,0.35751398,,,
+1478638391,*CC(*)(C)C(=O)OCCOCCOCCOCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35894931,,,
+1479037928,*c1cc(Br)c(OC(=O)c2cccc(C(=O)Oc3c(Br)cc(C4(*)OC(=O)c5ccccc54)cc3Br)c2)c(Br)c1,245.9668197,,,,
+1479528015,*Nc1ccc(-c2ccc(N*)c(-c3ccc(N)cc3)c2)cc1,,0.34495239,,,
+1479577775,*Oc1ccc(N=Cc2ccc(N(c3ccccc3)c3ccc(C=Nc4ccc(*)cc4)cc3)cc2)cc1,,0.39892551,,,
+1479997464,*CCCCCCCNC(=O)CCCCCCCCC(=O)N*,,,0.3235,0.943364885,20.0736371
+1480032186,*Nc1ccc(C2(c3ccc(N*)cc3CC)c3ccccc3-c3ccccc32)c(CC)c1,,0.37129273,,,
+1480316431,*Nc1ccc(C(c2ccccc2)c2ccc(N*)cc2)cc1,,0.35100351,,,
+1480445744,*Nc1ccc(NC(=O)c2ccc(NC(=O)C=Cc3ccc(C=CC(=O)Nc4ccc(C(*)=O)cc4)cc3)cc2)cc1,169.7759737,,,,
+1480784338,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)o4)o3)cc2)cc1,,0.47072462,,,
+1480818126,*C=Cc1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(N(c2ccc(OC)cc2)c2ccc(OC)cc2)cc1,,0.38664092,,,
+1480945963,*CC(CSCCC)O*,,0.41871813,,,
+1481083200,*Oc1ccc(SSCCCCSSc2ccc(*)cc2)cc1,102.260186,,,,
+1481177497,*CCC1C(=O)N(CCC)C(=O)C1*,,0.34544005,,,
+1481191612,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.3609597,,,
+1481448213,*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCC(*)=O)cc1C=Cc1ccc(N(C)C)cc1,,0.35183378,,,
+1481540874,*CCCCCCNC(=O)CCCC(=O)O*,,0.33848118,,,
+1482021173,*CC(C)C(=O)N*,,0.32319229,,,
+1482072500,*c1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(N4C(=O)c5ccc(Oc6cccc7c(Oc8ccc9c(c8)C(=O)N(*)C9=O)cccc67)cc5C4=O)cc3)cc2)cc1,,0.38669783,,,
+1482083544,*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Sc6ccc(Sc7ccc(Sc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)s2)cc1,,0.37801232,,,
+1482120134,*Oc1ccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(S(=O)(=O)c3ccccc3)c2)cc1,,0.35307461,,,
+1482727524,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.36288338,,,
+1482787899,*CCCCCCCCOc1c(OC)cc(/C=C/c2ccc(/C=C/c3cc(OC)c(O*)c(OC)c3)cc2)cc1OC,,0.36086403,,,
+1483068354,*Nc1ccc(-c2cccc3ccccc23)c(-c2cccc3ccccc23)c1N*,,0.36015799,,,
+1483153718,*Nc1ccc([C@H](CCC)c2ccc(C[C@@H](CCCCCC)Cc3ccc([C@H](CCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36041733,,,
+1483304960,*C=CC(C*)OC,,0.3862843999999999,,,
+1483538338,*Nc1cccc(/C(=C(/c2ccccc2)c2cccc(N*)c2)c2ccccc2)c1,,0.37179153,,,
+1483754768,*Nc1ccc(-c2c(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(-c4cc(-c5ccc(-c6ccccc6)cc5)c(-c5ccc(NC(=O)c6ccc(C(*)=O)cc6)cc5)c(-c5ccc(-c6ccccc6)cc5)c4)cc3)cc2-c2ccc(-c3ccccc3)cc2)cc1,,0.37232804,,,
+1484029097,*C(OCOC(*)C(*)(F)F)C(*)(F)F,,0.34668638,,,
+1484271204,*Nc1ccc2ccccc2c1-c1c(N*)c(-c2ccccc2)cc2ccccc12,,0.35903974,,,
+1484621104,*Oc1cccc(Oc2cccc(NC(=O)c3cccc(Oc4cccc(Oc5cccc(C(=O)Nc6cccc(*)c6)c5)c4)c3)c2)c1,,0.35258693,,,
+1484839925,*CCCCCCCCCCOCCCCCCCCCCOCCCCO*,,,0.329,0.882717038,21.18686489
+1484940598,*C=C(C#N)C(=O)OCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCCCCCCCCC,,0.38239556,,,
+1485740570,*CCC=CCCC1CC(CCC2CC(*)OC2=O)C(=O)O1,,0.33942699,,,
+1485822682,*CCCCCCCCCCC(=O)O*,,0.37420675,,,
+1485858652,*CC(*)(C)C(=O)OCC1COC(c2ccccc2)O1,,0.33388652,,,
+1485862700,*c1ccc(-c2nc3cc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(C(c7ccc(N8C(=O)c9ccc(C(=O)Nc%10ccc(Oc%11ccc%12nc(-c%13ccccc%13)c(*)nc%12c%11)cc%10)cc9C8=O)cc7)(C(F)(F)F)C(F)(F)F)cc5)C6=O)cc4)ccc3nc2-c2ccccc2)cc1,,0.38075733,,,
+1486120892,*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.38857369,,,
+1486334179,*CC(*)c1ccc(C(O)CCN(C)C)cc1,,0.39056968,,,
+1486442979,*CCC(O*)(C(F)(F)F)C(F)(F)F,-8.026293088,,,,
+1487203237,*CCCCCCCCCNC(=O)C(CCCCCCCCCCCC)C(=O)N*,,,0.347,,
+1487747929,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(C)(C)c4cc(C)c(*)c(C)c4)cc3C)cc2C(O)(C(F)(F)F)C(F)(F)F)c(C(O)(C(F)(F)F)C(F)(F)F)c1,,0.38564574,,,
+1487920180,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OCCCC)cc2)cc1,,0.35353312,,,
+1488067118,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3c(C)cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c3C)c1)C2=O,,0.39668425,,,
+1488760517,*CCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.34646376,,,
+1488918987,*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.3392001,,,
+1489195327,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1,,0.34828568,,,
+1489614055,*c1ccc(Oc2ccc(C(=O)c3ccc(-c4ccc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccc(-c9nc(*)c(-c%10ccccc%10)[nH]9)cc8)[nH]c7-c7ccccc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.40415832,,,
+1490448831,*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.34642034,,,
+1490582596,*CC(*)CCCCCCCCCCC,,,0.3206666666666666,0.803315138,10.73852636
+1490863909,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCCCCCCCCCCCC,,0.37558543,,,
+1490994278,*Nc1cccc(NC(=O)CCCCC(=O)Nc2cccc(NC(=S)NC(=O)CCCCC(=O)NC(*)=S)c2)c1,,0.33328285,,,
+1491147756,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cc2-c2ccccc2)c(-c2ccccc2)c1,,0.37066383,,,
+1491246883,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(F)cc2)cc1,,0.34953736,,,
+1491854107,*C=CCC(C)(CC*)C(=O)OC,-28.15217408,,,,
+1492808796,*Nc1ccc2c3ccc(cc3)c2c1N*,,0.33320503,,,
+1493417341,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(c4ccc(C(*)=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.36851832,,,
+1493656978,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccc(F)cc6)C7=O)cc5)cc4)c3)cc2)cc1,,0.37299965,,,
+1494135620,*O[Si](C)(C)CCCN=C1C=C(O)C(=NCCC[Si](*)(C)C)C=C1O,,0.39375505,,,
+1494459100,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35893266,,,
+1495114099,*Nc1ccc(Cc2ccccc2Cc2ccc(N*)c(C)c2)cc1C,,0.35364083,,,
+1495561124,*c1cccc(C(=O)Nc2ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.35728456,,,
+1495657690,*CCCCCCNC(=O)CCCCCC(=O)N*,,0.34167967,0.278,,
+1495695702,*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c4ccccc4-c4ccccc43)cc1)C2=O,,0.42357892,,,
+1496106807,*CCCC(C)O[Si](C)(C)O*,,0.4012962,,,
+1496171453,*CC(*)c1ccc(OCCOCCOC(C)=O)cc1,,0.34580144,,,
+1496884432,*CCN(*)C(=O)C1CC1,,0.34943923,,,
+1497450918,*Oc1ccc(OC(=O)c2ccc(Sc3cccc(OC(=O)c4ccc(C(=O)Oc5cccc(Sc6ccc(C(*)=O)cc6)c5)cc4)c3)cc2)cc1C,,0.36045779,,,
+1497637197,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(-c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.35883674,,,
+1497639710,*CC(*)(C)C(=O)OCCCCCCN1CCN(c2ccc(N=Nc3ccc(C#N)cc3)cc2)CC1,,0.37393027,,,
+1498294171,*CC(*)c1ccc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2ccccc2-3)cc1,,0.40118451,,0.899366976,10.82333757
+1498407701,*CCCCCCc1nc2cc(NC(=O)CCCCCCCCC(=O)Nc3ccc4oc(*)nc4c3)ccc2o1,,0.35561284,,,
+1498502031,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(N5C(=O)CC(=O)N(*)C5=S)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.37856211,,,
+1498613996,*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(CCCCCCCC)c(*)cc3CCCCCCCC)ccc1-2,,0.41835338,0.349,0.85154393,17.51524142
+1498770138,*CC(*)(C)C(=O)OCCOCCO,,0.31660238,,,
+1498889771,*CC(*)C(=O)OCCCCCCCCCCCCCCCC,,,0.3626666666666667,0.864622073,12.59659455
+1499027687,*Nc1cccc(-c2ccc(-c3ccccc3-c3ccc(-c4ccccc4)cc3)cc2)c1N*,,0.35096567,,,
+1499293987,*c1ccc(OCCN(CCOc2ccc(-c3nc4ccc(Oc5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.38165004,,,
+1500693468,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)CCCCC6)cc5)cc4C3=O)cc2)cc1,,0.39468168,,,
+1500711821,*CCCCOC(=O)Cc1ccc(CC(=O)O*)cc1,-34.10658315,,,,
+1500837944,*c1ccc(Oc2cc(C(C)(C)C)c(Oc3ccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2C(C)(C)C)cc1,,0.40608449,,,
+1501114922,*c1cccc(N2C(=O)c3ccc(Oc4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3C2=O)c1,,0.36338221,,,
+1501485804,*Nc1ccc(C[C@@H](C)C2(Cc3ccc(N*)cc3)CCCCC2)cc1,,0.3481947,,,
+1501914019,*CC(*)(C)C(=O)OC1CCC(C)CC1,,0.37962423,0.1709999999999999,0.895972858,11.80505526
+1502018022,*C=CC1CC(*)C2C(=O)N(c3cc(C(F)(F)F)cc(C(F)(F)F)c3)C(=O)C12,,0.3593193,,,
+1502215930,*C=Cc1cc(CCCCCCCCCCCCCCCC)c(C=Cc2nc(*)c(C#N)c(C=Cc3ccc(N(c4ccccc4)c4ccccc4)cc3)c2C#N)cc1CCCCCCCCCCCCCCCC,,0.40312645,,,
+1502259897,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c(C(C)(C)C)c2)cc1,,0.38670531,,,
+1502465383,*Nc1ccc(-c2ccc3c(c2-c2ccc(N*)c(C)c2)Cc2ccccc2-3)cc1C,,0.36453901,,,
+1502724295,*CCCCCCOC(=O)OCCCCCCCCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.35846938,,,
+1502897533,*Oc1c(-c2ccccc2)cc(-c2cc(-c3ccccc3)c(OC(=O)CCCCCCCCC(*)=O)c(-c3ccccc3)c2)cc1-c1ccccc1,,0.37181135,,,
+1503069300,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34701241,,,
+1503134682,*Oc1ccc(C(C)(C)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.36526848,,,
+1504210048,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCC(=O)O*,-32.76938912,,0.362,,
+1504320453,*c1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2)cc1,,0.35373697,,,
+1504459006,*CCCCCCCCCCCCCCCCOC(=O)CCCCCCCCC(=O)O*,,,0.442,0.912106929,22.06826429
+1504692554,*Nc1ccc(C2(c3ccc(N*)cc3)CC=CCC2)cc1,,0.35342761,,,
+1504708152,*Cc1ccc(CN(C)C(=O)CCCCCCCCCCCCCCC(=O)N(*)C)cc1,-14.32362541,,,,
+1504763552,*C#CC(Cn1c2ccc(CCCCCCCCCCCCCCCC)cc2c2cc(CCCCCCCCCCCCCCCC)ccc21)=C(*)Cn1c2ccc(CCCCCCCCCCCCCCCC)cc2c2cc(CCCCCCCCCCCCCCCC)ccc21,-2.237818484,,,,
+1505007551,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.36148157,,1.101878575,18.7163871
+1505164160,*Nc1ccccc1-c1c(N)ccc2c1ccc1c(-c3ccccc3N)c(N*)ccc12,,0.35843699,,,
+1505192900,*Oc1cccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4cccc(Oc5nc(*)nc(Sc6ccccc6)n5)c4)cc3)cc2)c1,,0.35247357,,,
+1506883443,*c1ccc(C(c2ccccc2)c2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.40443369,,,
+1506970146,*CC(*)c1ccc(C(C)(O)CC)cc1,,0.37635976,0.2266666666666666,,
+1507536890,*CCCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12,,0.36158697,,,
+1507569319,*c1ccc(NC(=O)c2cc(NC(=O)c3ccc(OC(C)=O)cc3)cc(C(=O)Nc3ccc(-c4nnc(*)o4)cc3)c2)cc1,,0.34832358,,,
+1507772517,*c1ccc(Cc2ccc(N3C(=O)C4CCC(C5CCC6C(=O)N(*)C(=O)C6C5)CC4C3=O)cc2)cc1,,0.38536816,,,
+1508066220,*CC(*)c1ccc(COCCCC)cc1,,0.38801598,,,
+1508126626,*Nc1cccc(C#Cc2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)c1,,0.37889334,,,
+1508181683,*Nc1ccc([C@H](CCCCC)c2ccc([C@@](CCCC)(c3ccc([C@H](CCCCC)c4ccc(N*)cc4C)cc3)C3CCCCC3)cc2)c(C)c1,,0.38359453,,,
+1508221747,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4cc(NC(=O)c5ccc(OC(C)=O)cc5)cc(C(=O)Nc5ccc(*)cc5)c4)cc3)cc2)cc1,,0.34979882,,,
+1508323114,*OC1CCC(OC(=O)CCCCCCCCC(*)=O)CC1,,0.35688418,,,
+1508373353,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(*)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1C(C)(C)C,,0.37989241,,,
+1508770794,*c1ccc(C2(c3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.41356417,,,
+1509674504,*Oc1cccc(OC(=O)c2ccc(OC(=O)c3cccc(C(=O)Oc4ccc(C(*)=O)cc4)c3)cc2)c1,,0.33712765,,,
+1509700944,*c1ccc(Oc2cccc3c(Oc4ccc(N5C(=O)c6ccc(S(=O)(=O)c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cccc23)cc1,,0.38098412,,,
+1509770012,*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccccc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.39499825,,,
+1509773761,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCCCCCC,,0.37281948,,,
+1509897801,*NCC(C)N*,,0.31754129,,,
+1510154304,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccc(C)cc32)cc1,,0.37464154,,,
+1510186873,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(CCCCC)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,204.7640603,,,,
+1510690030,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C#N)cc1,,0.37188038,,,
+1510766002,*Nc1ccc(-c2cccc(-c3ccc(N*)cc3)c2)cc1,,0.34163964,,,
+1510829152,*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)C=Cc1ccco1,,0.3534342,,,
+1510840632,*CC(COC(=O)O*)OCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34394609,,,
+1510846055,*C=C(*)C(CCC)[Si](C)(C)CCCCCC,,0.40434862,,,
+1511018789,*CCc1ccc(*)c(CC)c1,,0.39486528,,,
+1511476999,*c1ccc2c(c1)C(=O)N(c1cc(OCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,,0.38373514,,,
+1511530438,*OC(=O)C(NC(=O)CCCCCCCCC(=O)NC(C(=O)OC1COC2C(*)COC12)C(C)CC)C(C)CC,,0.35095234,,,
+1511995434,*Nc1cccc(-c2cc(-c3ccc(C4(c5ccccc5)c5ccccc5-c5ccccc54)cc3)c(-c3ccc(C4(c5ccccc5)c5ccccc5-c5ccccc54)cc3)c(-c3ccc(C4(c5ccccc5)c5ccccc5-c5ccccc54)cc3)c2N*)c1,,0.38354872,,,
+1512069165,*O*CCCCCCCCCC(=O)Oc1ccc(-c2ccc(C(=O)Oc3ccc(C(=O)OC)c(OC(=O)c4ccc(-c5ccc(OC(=O)CCCCCCCCC(*)CO*)cc5)cc4)c3[N+](=O)[O-])cc2)cc1,,0.38046147,,,
+1512407115,*Nc1ccc(-c2c(-c3ccccc3)cc(N*)c(C)c2-c2ccccc2)cc1,,0.36604674,,,
+1512415132,*Nc1ccccc1-c1cccc(-c2cccc(-c3ccccc3)c2)c1N*,,0.34626289,,,
+1512714339,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccncc2)cc1,,0.36156812,,,
+1513366860,*O[Ca]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)OCCCCCOC(=O)c1ccccc1C(*)=O,,0.3810089,,,
+1513913960,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(-c3ccc(OC)cc3)cc2)cc1,,0.34280149,,,
+1514104618,*CCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,,0.407,,20.46241251
+1514204622,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34424319,,,
+1514327735,*CC(*)c1ccc(C(C)C)cc1C(C)C,,,0.1979999999999999,0.839803273,10.87873099
+1514376697,*CCCC(C)(C)CNC(=O)CCCCCC(=O)N*,,0.3440828,,,
+1514418516,*OC1C(COC(C)=O)OC(*)C(OC(C)=O)C1O,,0.32163018,,,
+1514514964,*C(=O)c1ccc(Oc2c(C)cc(-c3cc(C)c(Oc4ccc(C(=O)c5ccc6nc(*)ccc6c5)cc4)c(C)c3C)c(C)c2C)cc1,,0.40570264,,,
+1514572566,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(Sc7ccc(Sc8ccc(Sc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.37974001,,,
+1514643124,*CC(*)(C)c1ccc(C(C)C)cc1,,0.40109572,0.2013333333333333,0.808542478,12.41984319
+1514751058,*Nc1ccc(-c2cc(N*)cc(-c3ccc(N)cc3)c2)cc1,,0.34788734,,,
+1515428777,*CCCCCCNC(=O)c1cc(C(=O)N*)cc(C(C)(C)C)c1,,0.35198152,,,
+1515861742,*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(S)c2)cc1S,,0.35876694,,,
+1515864887,*NC1CCC(CC2CCC(NC(=O)CCCCCCCCC(*)=O)CC2)CC1,,0.36172537,,,
+1515872647,*CC(*)C(=O)N1CCOCC1,,0.340331,0.212,1.114429035,11.35819502
+1515877635,*CC(*)(C)C(=O)OCC(C)(C)C,,,0.1555,0.810146915,10.91862442
+1515920130,*C(=O)NCCCCCCCCCCNC(=O)c1cccc(*)n1,49.59402876,,,,
+1516275191,*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCC(*)=O)cc1C=Cc1ccc(N(C)C)cc1,,0.34625185,,,
+1516593013,*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cc6cc(*)ccc6oc5=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2c1,,0.34097421,,,
+1516837155,*CCCCCCN(CC(F)(F)C(F)(F)F)C(=O)C(F)(F)C(F)(F)C(F)(F)C(=O)N(*)CC(F)(F)C(F)(F)F,,0.33260446,,,
+1517122573,*CCCP(CCCNC(=O)CCCCC(=O)N*)c1ccccc1,,0.34331894,,,
+1517401237,*CC(*)c1cc(C(=O)OCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.38076446,,,
+1517587457,*CC(*)(Cl)C(=O)OCCC,,,0.173,1.102798912,12.27540357
+1517792443,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c2C#N)cc1,,0.37073474,,,
+1518669456,*CCCCCCCCCCC(=O)N(*)C,,0.37747863,,,
+1518803212,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36893387,,,
+1519364280,*CCC(C)O[Si](C)(C)O*,,0.39779568,,,
+1519582452,*Nc1c(CC(C)(C)C)cc2ccccc2c1-c1c(N*)c(CC(C)(C)C)cc2ccccc12,,0.39483113,,,
+1520136246,*C(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C)c(C)c4)C5=O)cc3)CC3CC2C2CCCC32)cc1,,0.37865395,,,
+1520266361,*c1ccc(-c2nc(-c3cccc(-c4nnc(-c5ccccc5)c(*)n4)n3)nnc2-c2ccccc2)cc1,,0.44423251,,,
+1521487084,*Oc1ccc(P(C)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.38231831,,,
+1522414034,*CC(*)c1cc(C(=O)OCCCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.38549986,,,
+1522625717,*C(C)=C(*)CCCCCCC,,0.40916746,,,
+1523167762,*CC(*)C(=O)NC(C)(C)CC(C)=O,,0.33505306,,,
+1523200168,*CCCCCCCCCCOc1ccc(N=Cc2ccc(C=Nc3ccc(O*)cc3C)cc2)c(C)c1,,0.38083348,,,
+1523230328,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5cccc6c(Oc7ccc8c(c7)C(=O)N(*)C8=O)cccc56)cc4C3=O)c(C)c2)cc1C,,0.39800868,,,
+1523654682,*CC(O)COc1c(C)cc(C(C)(C)c2cc(C)c(OCC(O)COc3c(C)cc(S(=O)(=O)c4cc(C)c(O*)c(C)c4)cc3C)c(C)c2)cc1C,,0.36996727,,,
+1523715614,*CCCCCCCCCCNC(=O)c1ccc(C(=O)N*)cc1,50.1155014,,,,
+1523905334,*Nc1cc(C)c(C2(c3cc(C)c(N*)cc3C)c3ccccc3-c3ccccc32)cc1C,,0.38791749,,,
+1524051176,*CCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.32364481,,,
+1524213012,*c1cc(C=Nc2ccc3c(c2)c2cc(N=Cc4cc(*)c(O)c(*)c4)ccc2n3CCCC)ccc1O,,0.36995322,,,
+1524317479,*CC(*)(CC(=O)OCCOCCOC)C(=O)OCCOCCOC,,0.33439887,,,
+1524856753,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(OC4COC5C(*)COC45)cc3)cc2)cc1,,0.33832234,,,
+1524880405,*c1ccc(OC(=O)c2cccc(C(=O)Oc3ccc(C4(*)OC(=O)c5ccccc54)cc3)c2)cc1,,0.35513866,,,
+1525026548,*Oc1ccc(-c2ccc(-c3cc(-c4ccccc4)c(-c4ccc(-c5ccc(OC(=O)c6ccc(C(*)=O)cc6-c6ccccc6)cc5)cc4)c(-c4ccccc4)c3-c3ccccc3)cc2)cc1,,0.38238683,0.315,0.963593,27.63852935
+1525361018,*CCOC(=O)C1=CCCCC(N2CCCC2)=C1C(=O)O*,,0.35148816,,,
+1525755711,*C=CC1CC(*)C(C(=O)Oc2ccccc2)C1C(=O)Oc1ccccc1,,0.353015,,,
+1526653040,*Oc1ccc(Oc2ccc(OC(=O)c3ccc(C(c4ccc(C(*)=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.35598766,,,
+1526713439,*CCc1ccc(*)s1,,0.39157828,0.288,1.164977581,25.29233638
+1527011099,*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(CC)(CC)C1=O,,0.36982444,,,
+1527133418,*Oc1ccc(NC(=O)CCCCC(=O)Nc2ccc(*)cc2)cc1,,0.3339539,,,
+1527614677,*CCCP(CCCNC(=O)CCCCCCCCC(=O)N*)c1ccccc1,56.20366062,,,,
+1527753234,*c1cccc(C(=O)c2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)cc2)c1,,0.3698895,,,
+1527864576,*c1ccc(Nc2ccc(C3(c4ccc(Nc5ccc(S(*)(=O)=O)cc5)c(C)c4)c4ccccc4-c4ccccc43)cc2C)cc1,,0.42014539,,,
+1528273395,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)cc2)cc1,,0.37524227,,,
+1528295982,*CC(c1ccccc1)C1C(=O)N(CCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)C(=O)C1*,,0.36659752,,,
+1528389333,*CCCCCCCCCCNC(=O)CCCCCCCCC(=O)NCCCCCCCCCCNC(=O)C(=O)N*,82.2677155,,0.4125,0.955200662,21.885982
+1528618359,*Nc1ccc(C(C)(C)c2ccc(N*)cc2-c2ccccc2)cc1,,0.35615321,,,
+1528836681,*CCCCCCCCCCOC(=O)c1cccc(-c2cccc(C(=O)O*)c2)c1,8.531981028,,,,
+1529298649,*C=CC1CC(*)C(C#N)([Si](C)(C)O[Si](C)(C)C)C1,,0.43871287,,,
+1529319759,*c1cc(C(=O)NCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37595452,,,
+1529607957,*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)cc2)cc1,,0.36093422,,,
+1529815158,*Nc1ccc(C[C@@H](c2ccccc2)c2ccc(N*)cc2)cc1,,0.35117651,,,
+1530164061,*CC(*)OC(=O)c1cccc([N+](=O)[O-])c1,,0.37430809,,,
+1530902893,*Oc1cccc(Oc2ccc(Nc3ccc(Nc4ccc(Nc5ccc(Nc6ccc(*)cc6)cc5)cc4)cc3)cc2)c1C(=O)Nc1ccc(N=Nc2ccccc2)cc1,238.4094841,,,,
+1531080761,*c1cccc(OCCCCCCCCCCCOC(=O)c2ccc(C(=O)OCCCCCCCCCCCOc3cccc(-c4nnc(*)s4)c3)cc2)c1,,0.37334678,,,
+1531336843,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(-c4cc(-c5ccccc5)cc(-c5cccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c5)n4)c3)cc1)C2=O,,0.37590071,,,
+1531370497,*C1CC2CC1C(*)C2CCCCCCCCCC,,,,0.855255368,14.39063477
+1531901492,*C(=O)N(*)CC=C,164.8639006,,,,
+1532003406,*c1ccc(OCCCCCOc2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.35403674,,,
+1532503851,*CCCCCNC(=O)CC(=O)N*,,0.31630581,,,
+1532559190,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.38905129,,,
+1532694125,*C=CC1CC(*)C(C#N)([Si](C)(C)[Si](C)(C)C)C1,,0.44453599,,,
+1533215185,*C(=O)c1ccc(N(*)CCCCCCC)cc1,,0.39237717,,,
+1533241544,*c1ccc(C(c2ccc(N(*)c3ccc(C)cc3)cc2)c2ccc([N+](=O)[O-])cc2)cc1,,0.42700988,,,
+1533331471,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccccc5-c5ccccc5C(=O)c5ccc(*)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.38946752,,,
+1533352868,*CCN(*)C(=O)c1cc(F)c(F)cc1F,,0.34420285,,,
+1533947375,*CC1CCCC(CN2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)C1,,0.35941978,,,
+1534103449,*Nc1ccc(*)cc1CCCCCCCCCCCCCCC,,,0.314,0.876967341,16.45604164
+1534344479,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(NC(=O)CCCCCC(=O)Nc4ccc(O*)cc4)cc3)cc2)cc1,,0.33850297,,,
+1534350557,*CCCCCCOc1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)c(-c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(-c6c(-c7ccc(-c8ccccc8)cc7)cc(-c7ccc(O*)cc7)cc6-c6ccc(-c7ccccc7)cc6)cc5)cc4)cc3)c(-c3ccc(-c4ccccc4)cc3)c2)cc1,,0.36743587,,,
+1535224949,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(C(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)(C(F)(F)F)C(F)(F)F)cc2)C3=O)cc(C(C)(C)C)c1,,0.38923634,,,
+1535521231,*Nc1noc2nc3c(C(=O)OCC)cc(C(=O)c4ccccc4)c(N)c3c(N*)c12,,0.33255113,,,
+1535630026,*c1cc(C=Nc2ccc3c(c2)c2cc(N=Cc4cc(*)c(O)c(*)c4)ccc2n3CCCCCCCCCCCC)ccc1O,,0.37679256,,,
+1535916456,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2[Si](C)(C)C)c([Si](C)(C)C)c1,,0.39362302,,,
+1536018562,*Oc1ccc(OC(=O)c2cccc(Sc3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(Sc6cccc(C(*)=O)c6)cc5)cc4)cc3)c2)cc1-c1ccccc1,,0.36413304,,,
+1536084197,*CCCCCCCCCCCCCCCCCCCCCCOC(=O)CCC(=O)O*,,,0.387,0.905357803,22.64614546
+1536340511,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.37552726,,,
+1536369221,*CC(F)(F)C1(F)C(*)CC(O)(C(F)(F)F)C1(F)F,,0.30442544,,,
+1536452950,*Oc1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)c6ccc(*)c(C(C)C)c6)cc5)cc4)cc3)cc2C(C)C)cc1,,0.3952199,,,
+1536569579,*Nc1c(F)c(F)c(N*)c(F)c1F,,0.32203175,,,
+1536803717,*CC(C)(C)COC(=O)O*,,0.35059905,,,
+1537088176,*CCCCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.36937148,,,
+1537297732,*CC(*)c1ccc(Cl)c(F)c1,,,0.1275,,
+1537341767,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c5)cc3)C4=O)cc2)cc1,,0.35644883,,,
+1537875008,*Nc1ccc([C@]2(C)CC(C)(C)c3cc(N*)ccc32)cc1,,0.365803385,,,
+1538292595,*Oc1c(C(=O)Oc2ccc(OC(=O)c3ccc4ccccc4c3Oc3nc(*)nc(N4CCCCC4)n3)cc2)ccc2ccccc12,288.7077709,,,,
+1538729127,*C=C(*)c1ccccc1[Si](C)(C)C,,0.43881448,,,
+1539719616,*CCC1C(=O)N(CCCCCCCCCCCC)C(=O)C1*,,0.37602495,,,
+1540110087,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6cccc(N8C(=O)c9ccc(*)cc9C8=O)c6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.36739248,,,
+1541215652,*CC(*)(C)C(=O)OC1CCCCC1C(C)(C)C,,0.39683141,,,
+1541292142,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)CC(=O)N(*)C6=S)cc5)cc4)c4ccccc4-c4ccccc43)cc2)cc1,,0.39690658,,,
+1541529952,*c1ccc(C2(c3ccc(N4C(=O)C(c5ccccc5)=C(c5ccc(C6=C(c7ccccc7)C(=O)N(*)C6=O)cc5)C4=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.42593569,,,
+1541871733,*Nc1ccc(/C=C/c2ccc(N*)c(-c3ccc4ccccc4c3)c2-c2ccc3ccccc3c2)c(-c2ccc3ccccc3c2)c1-c1ccc2ccccc2c1,,0.37000522,,,
+1542132482,*Nc1ccccc1-c1c(N)c(N)c(-c2ccccc2N)c(-c2ccccc2N)c1-c1ccccc1N*,,0.31405732,,,
+1542525425,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(-c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.36540863,,,
+1542527766,*Nc1cccc(-c2cccc(-c3cccc(N)c3)c2-c2cccc(N*)c2)c1,,0.35421263,,,
+1542725530,*Nc1c(N*)c(-c2ccccc2)c2ccccc2c1-c1ccccc1,,0.35701914,,,
+1543008224,*CC(COC(=O)c1ccc(N2C(=O)C3C4C=CC(C4)C3C2=O)cc1)O*,,0.34283926,,,
+1543532086,*CC(CC)(CCl)CO*,,0.37740492,,,
+1543949166,*c1ccc(C=C(C#N)c2cc(OCCCCCCCC)c(C(C#N)=Cc3ccc(N(*)c4ccccc4)cc3)cc2OCCCCCCCC)cc1,,0.40051472,,,
+1543966603,*N=C1c2ccccc2C(=Nc2ccc(*)cc2)c2ccccc21,158.2579262,,,,
+1544175741,*Oc1ccc(S(=O)(=O)c2cccc3c(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6)cc5C)cc4)cccc23)cc1,,0.3844626,,,
+1544534408,*CC(*)c1ccc([Si](C)(C)O[Si](C)(C)C)cc1,,0.45403849,,,
+1544660750,*C(=O)OCC(COc1ccc(N=Nc2ccc(C#N)cc2)cc1)OC(=O)c1ccc(*)s1,,0.36682863,,,
+1544969349,*Nc1ccc(-c2c(C)c(-c3ccccc3)c(N*)c(-c3ccccc3)c2-c2ccccc2)c(C)c1-c1ccccc1,,0.38234636,,,
+1545578155,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1,,0.38323456,,,
+1545962534,*Nc1ccc2c(c1)/C(=C/c1ccccc1N*)c1ccccc1-2,,0.35465087,,,
+1546473891,*CCCCCCN1C(=O)c2ccc(C(=O)Nc3ccc(S(=O)(=O)c4cccc(NC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)c4)cc3)cc2C1=O,,0.34528361,,,
+1546892007,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)CC3CC2C2CCCC32)cc1,,0.37879602,,,
+1546988712,*c1ccc2oc(-c3ccc(Oc4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)cc3)nc2c1,,0.41418077,,,
+1547038341,*CCCCCCCCNC(=O)CCCCCCCC(=O)N*,,,0.308,0.959342877,19.77502946
+1547095755,*CC(*)c1cc(C(=O)Oc2ccc(OCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCC)cc1,,0.36628846,,,
+1547207194,*Oc1ccc(-c2ccc(OC(=O)c3ccc(C(=O)c4ccc(C(*)=O)cc4)cc3)c(-c3ccccc3)c2)cc1-c1ccccc1,,0.37083826,,,
+1547263064,*=CC#C[Si](C#CC=NN=*)(c1ccccc1)c1ccccc1,131.2577432,,,,
+1548438831,*CCCCCCCCCCCCC(=O)O*,,0.38106509,,,
+1548803977,*CC(*)(F)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.31898609,,,
+1549041363,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4cccc(Oc5c(C)cc(-c6cc(C)c(Oc7cccc8c7C(=O)N(*)C8=O)c(C)c6)cc5C)c4C3=O)c(C)c2)cc1C,,0.39657814,,,
+1549091300,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)c6)cc5)cc4)cc3)c1)C2=O,,0.35694819,,,
+1549258915,*CCC(=O)NCCCCCCNC(=O)CCS*,,0.3462525,,,
+1549335676,*c1ccc(Oc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.36487368,,,
+1549802812,*CC(*)c1cc(C(=O)OCCCCCCCCCC)ccc1-c1ccc(OCCCCCCCC)cc1,,0.39428806,,,
+1549944889,*OP(=O)(Oc1ccc(C)cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.35406248,,,
+1550023737,*Nc1ccc(NC(=O)c2ccc(P(=O)(c3ccccc3)c3ccc(C(*)=O)cc3)cc2)cc1,,0.35999445,,,
+1550446062,*CC(*)(C)C(=O)OCC(=O)c1ccccc1,,0.34187342,,,
+1550628326,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)c(Br)c4)(C(F)(F)F)C(F)(F)F)cc3Br)cc2)cc1,,0.39473159,,,
+1550853194,*c1ccc(Oc2cccc(NC(=O)c3cc(C(=O)Nc4cccc(Oc5ccc(-c6nnc(*)o6)cc5)c4)cc(N4C(=O)c5ccccc5C4=O)c3)c2)cc1,,0.35911141,,,
+1551100568,*c1ccc(Oc2ccc(P(=O)(c3ccccc3)c3ccc(Oc4ccc(-c5cc(*)n(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.39388717,,,
+1551636938,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cc(C(F)(F)F)cc(N5C(=O)c6ccc(*)cc6C5=O)c3Oc3ccc(C(F)(F)F)cc3)C4=O)cc2)cc1,,0.36716401,,,
+1551667752,*CCCCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,0.36786948,0.3463333333333334,0.92885236,22.0513884
+1551816010,*c1ccc(N2C(=O)c3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc7c(c6)C(=O)N(c6ccc8[nH]c(*)nc8c6)C7=O)cc5)cc4)cc3C2=O)cc1,,0.37965627,,,
+1551966818,*CC(C)(C)COC(=O)NC(=O)c1cccc(C(=O)NC(=O)O*)c1,,0.3112599,,,
+1552546218,*Nc1ccc(-c2ccc(N)c(-c3ccc(N)cc3)c2-c2ccc(N*)cc2)cc1,,0.35816613,,,
+1553018997,*CC(*)C(=O)NC(C)(C)CS(=O)(=O)O,,0.32186501,,,
+1553515923,*CC1CCC(COC(=O)CCC(=O)O*)CC1,,0.34271914,,,
+1553955153,*CCCCCC(*)CC,,0.40321995,,,
+1553956004,*c1cccc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37375949,,,
+1553967571,*CC(*)C(=O)Oc1ccc(C(=O)OCC)cc1,,0.34094172,,,
+1554515864,*Nc1ccc(-c2cc(N*)ccc2-c2ccccc2)cc1,,0.34986039,,,
+1554954449,*Nc1ccc2c(c1N*)[C@@H]1CC[C@H]2c2ccccc21,,0.3649256549999999,,,
+1555593891,*CS*,,,0.172,1.374050717,21.43297913
+1556257502,*CCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC,,0.35788491,,,
+1556355257,*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)c1,,0.3547336,,,
+1556370617,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.3441071,,,
+1556403536,*c1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(Oc7ccccc7-c7ccccc7Oc7ccc(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)ccc3ccccc23)cc1,,0.36530403,,,
+1557378931,*Oc1ccc(C(=O)OCCOCCOCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.33612915,,,
+1558106067,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O,,0.3701528,,,
+1558371855,*CCOCCOCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.33902352,,,
+1558525027,*CCN(*)C(=O)c1ccc(C)cc1,87.3045689,,,,
+1558867907,*CCCCCCOC(=O)OCCCCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.3458372,,,
+1558987909,*CCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,,0.359,0.932585043,19.09375808
+1559096063,*c1ccc(*)n1C(C)C(=O)OC,279.4452403,,,,
+1559539391,*C(F)C(*)(F)OC(F)(F)F,,0.31331253,,,
+1559560345,*CC(*)(C)C(=O)OCCCl,,,0.1325,1.111949892,14.37849945
+1560086845,*CC(C)(CCC)C(=O)O*,,0.36777777,,,
+1560341376,*CCN(*)C(=O)c1cccc(F)c1F,,0.34608851,,,
+1560496789,*Nc1ccc(C(c2ccc(N)cc2)(c2ccc(N*)cc2)C2CCCCC2)cc1,,0.38230718,,,
+1560564433,*Nc1ccc2c(c1)[C@@H]1c3cc(N*)ccc3[C@@H]3c4ccccc4[C@H]2[C@@]31C,,0.3710363,,,
+1560692719,*C(=O)CCCCCCCCC(=O)NC1CCCCN(*)C1,,0.34895116,,,
+1561264127,*O[Si](C=C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.42288522,,,
+1561282165,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5F)C6=O)cc4)cc3)cc2)cc1,314.5599695,,,,
+1561451639,*Nc1ccc(-c2ccc(N*)c3ccccc23)cc1,,0.34980398,,,
+1561539918,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCCCC)c(C(*)=O)cc2OCCCCCCCCCCCC)cc1C,,0.38901226,,,
+1561870991,*CC(*)(C)C(=O)OCC(O)C1CC(C)(c2ccccc2)C1,,0.35190962,,,
+1561932824,*CCCCCCS(*)(=O)=O,,,,1.13916765,20.45790421
+1561944332,*Nc1cc(C)c(C2(c3c(C)cc(N*)cc3C(C)C)c3ccccc3-c3ccccc32)c(C(C)C)c1,,0.39323,,,
+1562058837,*c1cc(CCCCCCCCCCCCCCCCCC)c(*)s1,,,0.36525,0.892099579,14.38765864
+1562112815,*CCCCOc1ccc(C=C2CCC(=Cc3ccc(O*)c(OC)c3)C2=O)cc1OC,36.19808899,,,,
+1562477562,*C=Cc1cc2c(cc1CCCCCC)c1cc(CCCCCC)c(*)cc1n2CCCCCCCC,45.45691415,,,,
+1562707517,*CCCCCCCCCCCCNC(=O)CCCCCCCCC(=O)N*,,,0.374,,
+1562741783,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(C[C@H]3CC[C@H](CCC)CC3)CC2)cc1,,0.36143785,,,
+1562941493,*CC(CCCCCCOc1ccc(N=Nc2ccc(C)cc2)cc1)COC(=O)CCCCC(=O)O*,,0.36238059,,,
+1563279330,*/C=C/c1ccc(Oc2ccc(-c3ccc(-c4ccc(-c5ccc(Oc6ccc(/C=C/c7ccc8c(c7)c7cc(*)ccc7n8CC(CC)CCCC)cc6)c(C#N)c5)cc4)cc3)cc2C#N)cc1,129.2206103,,,,
+1563303704,*Nc1ccc2c(c1)c(-c1ccc(C)cc1)c(-c1ccc(C)cc1)c1c3cccc(N*)c3c3ccc4ccccc4c3c21,,0.37838795,,,
+1563844313,*NCCSCCN*,,0.323520965,,,
+1564078436,*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3cccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.36727722,,,
+1564080444,*c1ccc(-c2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)c(-c7ccccc7)c6c5)cc4c(-c4ccccc4)c3-c3ccccc3)cc2)cc1,,0.43300062,,,
+1564406631,*c1ccc(Oc2ccc(N3C(=O)c4cccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7cccc8c7C(=O)N(*)C8=O)cc6)cc5)c4C3=O)cc2)cc1,,0.36611669,,,
+1564555962,*CCCCCCOC(=O)OCCCCCCCCOC(=O)OCCCCCCOc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.35645109,,,
+1564587390,*c1ccc(-c2cc(Oc3ccc(S(=O)(=O)O[Na])cc3)c(*)cc2Oc2ccc(S(=O)(=O)O[Na])cc2)cc1,172.5717242,,,,
+1565016562,*CCc1ccc(CCNC(=O)CCCCCCCCC(=O)N*)cc1,,0.35145021,,,
+1565300761,*N=P(*)(OCC(F)(F)C(F)(F)F)OCC(F)(F)C(F)(F)F,,0.3709865,,,
+1565790126,*CCCCCCOC(=O)OCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35226204,,,
+1566134688,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1C(C)(C)C,,0.40798893,,,
+1566591359,*/C=C/CCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCCCCCC,,,0.31,0.891002299,16.02209513
+1567869573,*CCCC(*)Cl,-30.93658282,,,,
+1567960062,*Cc1cc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.32527333,,,
+1568651103,*CC(*)c1ccc(CCCCCCCCCC)cc1,,0.40394942,0.2825,0.864032282,11.38875554
+1568726838,*CCC(=O)OC(=O)CCc1ccc(*)n1C,78.11959859,,,,
+1569031119,*O[Si](*)(c1ccccc1)c1ccccc1,,0.39090744,,,
+1569186084,*CCCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.34603241,,,
+1569573588,*CCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*,,0.33104417,,,
+1569767963,*CCCCSCCS*,,,0.226,,
+1570146193,*Nc1nc(N*)n(N)c(=O)n1,,0.30702692,,,
+1570200324,*C#Cc1cc(OCCCCCCCCCC)c(*)cc1OCCCCCCCCCC,61.85443636,,,,
+1570351096,*CC(*)(C)C(=O)OCCC1CCN(CCCCCCOc2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)CC1,,0.36030129,,,
+1570401711,*CC(*)c1ccc(C(=O)c2ccc(C)cc2)cc1,,0.38272585,,,
+1570549360,*CC(*)c1nnn[nH]1,,0.32198673,,,
+1570654852,*CC(CC(*)(C#N)C(=O)OC)c1ccccc1,127.0156605,,0.176,0.987197772,16.39022868
+1570972826,*c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.34948491,,,
+1571736697,*c1cc(CCCCCCOC(=O)COc2ccccc2)c(*)s1,,0.36035574,,,
+1571763607,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3CC=C)c2-c2ccccc2CC=C)cc1,,0.35761142,,,
+1572134392,*CC(*)c1c(F)c(F)c(OC)c(F)c1F,,0.34990398,,,
+1572433772,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(CP(=O)(OCCCl)OCCCl)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37206034,,,
+1572814745,*NC1=C(c2c(N*)ccc3c2CCCC3)[C@H]2CCCC[C@H]2C=C1,,0.36912749,,,
+1573303082,*O[Si](O[Si](O[Si](O[Si](CC[Si](C)(C)O[Si](C)(C)O[Si](C)(C)CC[Si](*)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1,,0.3950591,,,
+1573438487,*CCCCCCCCCCCCCCCCCCCCOC(=O)CCCC(=O)O*,,,0.302,0.90650602,20.12266872
+1573472944,*C1C(=O)OC(=O)C1C1C2CC(C(=O)OC(C)CC)C(C2)C1*,,0.34154769,,,
+1573776034,*CC(*)C(=O)c1ccc(Cl)cc1,,0.38087651,,,
+1573828532,*Cc1ccc(CNC(=O)CC(C)(C)CC(C)C(=O)N*)cc1,,0.33698788,,,
+1573986676,*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(C#N)cc2)cc1,,0.39092299,,,
+1574150308,*C(=O)OCCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.34482566,,,
+1574744073,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(Cl)cc5)C6=O)cc4)cc3)cc2)cc1,,0.38253331,,,
+1574797277,*CC(*)C(=O)OCc1c(Br)c(Br)c(Br)c(Br)c1Br,,0.39447334,,,
+1575153085,*Oc1ccc(C(=O)c2cccc(NC(=O)c3cccc(C(=O)Nc4cccc(C(=O)c5ccc(*)cc5)c4)c3)c2)cc1,,0.35264877,,,
+1575277153,*CC(*)C(=O)OC(C)CCCCC,,,0.2405,0.89263762,11.17976548
+1575557277,*CC(*)OCCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.36732307,,,
+1575613666,*Oc1ccc(NC(=O)c2ccc(S(=O)(=O)c3ccc(C(=O)Nc4ccc(Oc5ccc(C(c6ccc(*)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4)cc3)cc2)cc1,,0.36216369,,,
+1576291879,*c1ccc(C(=O)OCCCCCCCCCCOC(=O)c2ccc(-c3nnc(*)s3)cc2)cc1,,0.36609452,,,
+1576399281,*/C=C/C(C(=O)OC(C)C)C(*)C(=O)OC(C)C,,,,0.971892426,13.10146733
+1576797553,*CCCCCCCCOc1c(OC)cc(C=Cc2cc(CCCCCC)c(C=Cc3cc(OC)c(O*)c(OC)c3)cc2CCCCCC)cc1OC,,0.38267439,,,
+1577222221,*C(=O)Nc1cccc(C#Cc2cccc(N3C(=O)c4ccc(*)cc4C3=O)c2)c1,,0.38902251,,,
+1577413316,*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)Sc3ccc(*)cc3S4)cc2)cc1,,0.39801974,,,
+1577441045,*CC(*)(C)C(=O)OC1CCCCCCC1,,0.36870383,,,
+1577638731,*CCCCCCSS*,,0.44419731,,,
+1578441092,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6ccc(Oc7ccccc7-c7ccccc7Oc7ccc(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)n2)cc1,,0.36226249,,,
+1578457961,*c1cccc(NC(=O)c2ccc(OCCOCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)c1,,0.34384925,,,
+1578702981,*C(F)=C(F)C(F)(C(*)(F)F)C(F)(F)F,,0.32146163,,,
+1578799819,*Nc1cccc(-c2ccccc2-c2ccccc2-c2cc(N*)ccc2-c2ccccc2)c1,,0.3577192,,,
+1579112957,*CCCCCCCCCCCCNC(=O)CCc1ccc(CCC(=O)N*)cc1,,0.36073378,,,
+1579365390,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5)C6=O)cc4)cc3)cc2)cc1,,0.38301295,,,
+1579852324,*C(=O)Nc1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nc5ccccc5s4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(-c2nc3ccccc3[nH]2)c1,,0.37929546,,,
+1579941096,*Nc1c2ccccc2c(N*)c2c(-c3ccccc3)cccc12,,0.35157885,,,
+1580080124,*CC(O)CN(*)c1ccc(N=Nc2ccc(Cl)cc2)cc1,,0.37983776,,,
+1580221416,*OC(=O)c1ccc(C2(C)CC(C)(C)c3ccc(C(=O)Oc4ccc5c(c4)Oc4cc(*)ccc4C5(C)C)cc32)cc1,,0.38516135,,,
+1580258863,*CC(*)c1ccc(C[Si](C)(C)C)cc1,,0.42588217,,,
+1580474538,*c1ccc(C(=O)NNC(=O)c2ccc(C(=O)NNC(=O)c3ccc(C4(*)OC(=O)c5ccccc54)cc3)cc2)cc1,,0.35041515,,,
+1580553227,*c1ccc(-c2cc(-c3ccc(OCCCC#N)cc3)cc(-c3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)n2)cc1,,0.38498267,,,
+1580795142,*CC(*)OC(=O)C(C)(C(C)C)C(C)C,,0.38169655,,,
+1580898665,*c1ccc(-c2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)c(C)c2)cc1C,271.2577887,,,,
+1580966726,*Oc1ccc(-c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.34552239,,,
+1581168714,*C(=O)OCCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.34341892,,,
+1581284687,*Nc1ccc(C2(c3ccc(N*)cc3)CC2)cc1,,0.35420303,,,
+1581446027,*CC(*)CCCCCCCCCCCC,,0.40857787,0.339,0.805359167,11.32200697
+1581749502,*C(C)C(=O)N1C(=O)N(C(=O)C(C)n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)C(=O)C1(C)C,252.5865186,,,,
+1582367381,*CC(COc1ccc(S(=O)(=O)c2ccc(O*)cc2)cc1)OC(C)=O,,0.34921256,,,
+1582377269,*/C=C/c1cc(OCCCCCC)c(*)cc1OC,,,0.524,0.956645411,21.10266717
+1582774157,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1C,,0.36164862,,,
+1583167273,*Oc1ccc(Oc2ccc(Oc3ccc(-c4ccc(Oc5ccc(Oc6ccc(Oc7ccc(C(=O)c8ccc(*)c(S(=O)(=O)O)c8)cc7S(=O)(=O)O)cc6)cc5)cc4)cc3)cc2)cc1,151.7193386,,,,
+1583422885,*C=Cc1ccc(*)c(OCc2cc(OCc3cc(OCCC(C)CCCC(C)C)cc(OCCC(C)CCCC(C)C)c3)cc(OCc3cc(OCC(CC)CCCC)cc(OCC(CC)CCCC)c3)c2)c1,,0.38019139,,,
+1583607044,*CCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1,,0.33347035,,,
+1583883601,*Nc1ccc([C@@](C)(c2ccccc2)c2cccc(N*)c2)cc1,,0.35514854,,,
+1583954692,*C#CC#Cc1ccc2c3ccc(*)cc3n(CCCCCCCCCCCCCCCC)c2c1,68.35869686,,,,
+1584418791,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C,,0.42325418,,,
+1584609339,*O[Si](C)(C)c1ccc([Si](*)(C)C)c2ccccc12,,0.41683838,,,
+1584656772,*Oc1ccc(C(C)(C)c2ccc(OP3(Oc4ccc(C(=O)OCCC)cc4)=NP(Oc4ccc(C(=O)OCCC)cc4)(Oc4ccc(C(=O)OCCC)cc4)=NP(*)(Oc4ccc(C(=O)OCCC)cc4)=N3)cc2)cc1,,0.35923565,,,
+1584683487,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc(C3OCC4(CO3)CC3C=CC4C3)cc2)cc1,,0.3605339,,,
+1584732566,*CC(*)(C)C(=O)Oc1ccc2ccccc2c1,,,0.1639999999999999,1.079099726,12.73790543
+1586829326,*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.35733919,,,
+1587202589,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C6(c7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)CC7CC6C6CCCC76)cc5)cc3)C4=O)cc2)cc1,,0.36705724,,,
+1587391923,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)c3cc(NC(=O)c4ccc(NC(=O)C(C)N5C(=O)c6ccccc6C5=O)cc4)cc(C(*)=O)c3)cc2)cc1,,0.34562786,,,
+1587431956,*CCCS(=O)(=O)CCCS(=O)(=O)CCCOC(=O)c1cccc(C(=O)O*)c1,,0.35154023,,,
+1587454382,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(C3CCC(CC5CCC(N6C(=O)c7ccc(*)cc7C6=O)CC5)CC3)C4=O)cc2)cc1,,0.36177897,,,
+1587941896,*CCCC(C)(C)CNC(=O)CCCCCCCC(=O)N*,,0.34975469,,,
+1588269269,*Nc1c(N)c(N)c2c(c1N*)C(=O)OC2=O,,0.32340891,,,
+1588320432,*CCN(CCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1)CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.34069727,,,
+1588395205,*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,,0.3645119,,,
+1588401171,*Nc1ccc(C(CCC(c2ccc(N)cc2)c2ccc(N*)cc2)c2ccc(N)cc2)cc1,,0.35794575,,,
+1588466256,*CC(*)c1ccc(CC)cc1,,0.4044765,0.2013333333333333,,
+1588472676,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3cc(NC(=O)c4ccc(NC(=O)C(C)N5C(=O)c6ccccc6C5=O)cc4)cc(C(*)=O)c3)cc2)cc1,,0.34759081,,,
+1588623527,*Nc1ccc(C2=C(c3ccc(N*)cc3)CCCC2)cc1,,0.36462438,,,
+1588749798,*CC(=C)COCC(=C)COC(=O)O*,,0.3369446,,,
+1589240697,*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2CCCCC2)cc1,,0.37459903,,,
+1589532910,*C=CC1CC(*)C(C(=O)OC)C1C(=O)OC,,0.35233888,,,
+1589608719,*CCCCCC(*)CCCCCCCCCCCCCC,,,0.403,0.80443165,14.47745133
+1589952649,*Nc1ccc(NC(=O)CCCC(*)=O)cc1,,0.31441962,,,
+1590006077,*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C,,0.49804119,,,
+1590491705,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Oc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)c1)C2=O,,0.39872119,,,
+1591097153,*CCCCOC(=O)NCCCCNC(=O)O*,,0.32569334,,,
+1591202562,*OC(=O)OCCCCCCCCOC(=O)OC1COC2C(*)COC12,,0.33598554,,,
+1591469994,*CC(*)CC(C)C,,,0.2083333333333333,0.765836941,11.25375484
+1591544351,*C(C#N)=Cc1cc(OCCCCCCCCCCCCCCCC)c(C=C(C#N)c2cc(OCCCCCCCCCCCCCCCC)c(*)cc2OC)cc1OC,,0.39805364,,,
+1591651244,*CCC1CCC(*)C1,,,,0.880692229,25.78669657
+1591681848,*Nc1ccc(-c2c(-c3ccccc3)ccc(N*)c2-c2ccccc2)cc1,,0.36149058,,,
+1591697879,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4cc(NC(=O)c5ccncc5)cc(C(=O)Nc5ccc(*)cc5)c4)cc3)cc2)cc1,,0.35362805,,,
+1592713963,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34916517,,,
+1592724652,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)c(C(C)C)c2)cc1C(C)C,,0.39498446,,,
+1593705376,*Nc1cc(NC(=O)NC2CCC(CC3CCC(NC(*)=O)CC3)CC2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1,,0.34331689,,,
+1594377223,*c1ccc(NC(=O)CCCCCCCC(=O)Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.34520274,,,
+1594695636,*Oc1ccc(C(=O)OCCOC(=O)c2ccc(*)cc2)cc1,,0.34122405,,,
+1594797785,*Oc1ccc(Sc2ccc(Oc3ccc(-c4ccc(-c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1,,0.37806344,,,
+1594981326,*CC(=O)Nc1ccc(Oc2c(F)c(F)c(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)c(F)c2F)cc1,,0.33657991,,,
+1595107250,*Oc1ccc(C2(c3ccc(Oc4nc(*)nc(OC)n4)cc3)CCCCC2)cc1,,0.36354043,,,
+1595192960,*CCC(=O)Nc1ccc(Cc2ccc(NC(=O)CCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33616908,,,
+1595605865,*CC(*)OC(=O)c1ccccc1,,0.356021,0.1669999999999999,1.101556822,13.40000836
+1595837642,*Nc1ccc([C@@H](CC)c2ccccc2[C@H](CC)c2ccc(N*)cc2)cc1,,0.36286788,,,
+1596320590,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.35696126,,,
+1596743017,*C=C*,,0.42573737,,,
+1597060488,*Oc1ccc(Oc2ccc(OC(=O)c3ccccc3-c3ccccc3C(*)=O)cc2)cc1,,0.35136953,,,
+1597874068,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37756242,,,
+1598034786,*CC#CC(C#CCOC(=O)CCCCCCCCC(=O)O*)=Cc1ccc(C#Cc2ccccc2)cc1,,0.38414122,,,
+1598043126,*CC(*)(C)C(=O)Oc1ccc(CC#N)cc1,,0.36393077,0.1655,1.056556928,12.67707231
+1598367606,*Nc1ccc(CCc2ccc(N*)cc2)cc1,,0.3443961399999999,,,
+1598423326,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc(OC)cc2)cc1,,0.35997482,,,
+1598641133,*c1ccc(-c2ccc(Oc3ccc(C4(C)CC(C)(C)c5ccc(Oc6ccc(-c7ccc(N8C(=O)c9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9C8=O)cc7)cc6C(F)(F)F)cc54)cc3)c(C(F)(F)F)c2)cc1,,0.38259605,,,
+1599410150,*N=P(*)(Oc1ccc(Cl)cc1Cl)Oc1ccc(Cl)cc1Cl,,0.40414822,,,
+1599847368,*CC(=O)Nc1ccc(Oc2ccc(NC(=O)COc3ccc4ccccc4c3Sc3c(O*)ccc4ccccc34)cc2)cc1,,0.34293502,,,
+1599967213,*c1ccc2ccc3c(*)cc(C#CC=C)c4ccc1c2c34,,,0.429,0.757428031,34.48730282
+1600032316,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)c1)C2=O,,0.36374877,,,
+1600783315,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)c(N)c1,,0.37235553,,,
+1601240060,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(-c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O,232.2309602,,,,
+1601398939,*CCCCSC(=O)NCCCCCCNC(=O)S*,-33.13797704,,,,
+1601741460,*C(=O)c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(*)s4)cc3)cc2)cc1,,0.37738334,,,
+1601815856,*C(F)(F)C(*)(F)OC(F)C(F)F,,0.33442051,,,
+1601908846,*Oc1ccc(Sc2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)cc1,,0.36289708,,,
+1602086005,*O[Si](*)(CCC)c1ccccc1,,0.40185046,,,
+1602099447,*C=C(*)CCCOC(=O)c1cc(-c2ccc(OC)cc2)ccc1-c1ccc(OC)cc1,,0.34537263,,,
+1602838431,*CC(*)c1cc(-c2ccc(C(=O)OC3CC(C)CCC3C(C)C)cc2)ccc1-c1ccc(C(=O)OC2CC(C)CCC2C(C)C)cc1,,0.3845381,,,
+1602898253,*Nc1cccc(C(=O)c2cccc(N*)c2)c1,,0.33606974,,,
+1603135089,*CCCCNC(=O)CCCCCCCCCCC(=O)N*,,,0.332,,
+1603270914,*CCN(CCOC(=O)c1cccc(C(=O)O*)c1)c1ccccc1,,0.34654736,,,
+1603750308,*CC(*)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.34223337,,,
+1604219408,*Nc1ccc(-c2cccc(-c3cccc(N*)c3)c2)cc1,,0.34269282,,,
+1604312905,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2)cc1,,0.36139134,,,
+1605023913,*CCCCCCCCCCOc1ccc(C=Cc2ccc(OCCCCCCCCCCOP(=O)(O*)OCCCCCCCCCCOc3ccc(N=Nc4ccc(-c5ccccc5)cc4)cc3)cc2)cc1,,0.37252491,,,
+1605095414,*CC(*)C(=O)OCCCOc1ccc(C(=O)OCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.333167,,,
+1606219609,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37415859,,,
+1606350633,*CCCCCCCCCNC(=O)CCCCC(=O)N*,,0.35373615,,,
+1606377766,*CC1CCC(CNC(=O)CCCCCCCCC(=O)N*)CC1,,0.35003033,,,
+1606526460,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.42173166,,,
+1606960640,*CC=CCS(*)(=O)=O,,0.37641025,,,
+1607403554,*CCCCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.34308602,,,
+1607507482,*CCOC(=O)C(C)O*,,0.33713195,,,
+1607608174,*CCCCCCNC(=O)Cc1ccc(CC(=O)N*)cc1,,0.33740451,,,
+1607796915,*CC(*)c1ccc(CCCC)cc1,,,0.2096666666666666,,
+1607934568,*CC(*)C(=O)OCC(F)(F)C(F)(F)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.31780305,0.086,,
+1608160782,*CC(*)C(=O)NCCCCCCCC/C=C/CCCCCCCC,,,0.3065,0.860743742,12.94376545
+1608321923,*CC(*)C1CCCC(C)C1,,0.40571007,0.1806666666666666,,
+1608343989,*CC(*)C(=O)OCCCCSC,,0.39787287,0.2005,1.049744475,12.27513461
+1608850279,*Oc1ccc(C2(c3ccc(OC(=O)OC4C(C)(C)C(OC(*)=O)C4(C)C)cc3)CCC(C(C)(C)C)CC2)cc1,,0.37377889,,,
+1609267020,*CC(C)(C)COC(=O)NC(=O)c1cc(C(=O)NC(=O)O*)cc(C(C)(C)C)c1,,0.33019089,,,
+1609304413,*CCN(*)C(=O)CC(C)C,,0.36933528,,,
+1609366983,*C=CC1CC(*)C2CC=CC12,,0.39371357,,,
+1609819225,*Oc1cccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)c1,,0.36799842,,,
+1609876819,*CCCCOCO*,,0.35936946,,,
+1609935858,*Nc1cccc(C2(c3cccc(N*)c3)c3ccccc3-c3ccccc32)c1,,0.36665477,,,
+1610540990,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.37152028,,,
+1610776533,*Nc1cccc(/C(=C(\c2ccccc2)c2cccc(N*)c2)c2ccccc2)c1,,0.37286645,,,
+1611088763,*C=Cc1c(OC(=O)c2ccc(C(=O)Oc3ccc4ccccc4c3C=Cc3cccc(*)n3)cc2)ccc2ccccc12,,0.36780539,,,
+1611331043,*CCCCCCCC(=O)N*,,0.35123354,0.329,,
+1611579500,*CC(*)c1cc(Br)ccc1OCC,,0.41409342,0.124,1.223503114,13.17144668
+1611681441,*CCC(C)=C(*)C,46.49641909,,,,
+1611694557,*CCCCCCCCCCCCOC(=O)CCCCC(=O)O*,,,0.292,0.946344135,19.57740116
+1612093023,*Oc1ccc(C2(c3ccc(Oc4ccc(-c5ccc(NC(=O)c6cc(C(=O)Nc7ccc(-c8ccc(*)c(C(F)(F)F)c8)cc7)cc(C(C)(C)C)c6)cc5)cc4C(F)(F)F)cc3)c3ccccc3-c3ccccc32)cc1,,0.38174779,,,
+1612493686,*c1ccc(*)n1CC,112.6054346,,,,
+1612521768,*c1ccc(-c2ccc3c(c2)C(CCCCCCCC)(CCCCCCCC)c2cc(*)ccc2-3)cc1,,0.40991145,,,
+1612716794,*Nc1cc2c3ccccc3c3ccccc3c2cc1N*,,0.3239493,,,
+1613645225,*OC(=O)Nc1ccc(Cc2ccc(NC(=O)OC3CC4CC(*)CC(C3)O4)cc2)cc1,,0.34955799,,,
+1613665911,*Nc1ccc(NC(=O)CCCCC(*)=O)cc1,,0.32538533,,,
+1613802032,*CCN(*)C(=O)C(OC)(c1ccccc1)C(F)(F)F,,0.3539454,,,
+1613873087,*c1cccc(NC(=O)CCCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1,,0.34846813,,,
+1613930825,*Oc1ccc(C2(c3ccc(OC(=O)CCCC(*)=O)cc3)c3cc(N(C)C)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.37150042,,,
+1614031170,*c1cc(CCCCCC)c(*)s1,,0.42393246,,,
+1614212128,*Oc1c(C)cc(-c2cc(C)c(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(*)cc6)ccc5c4)cc3)c(C)c2)cc1C,,0.40029643,,,
+1614580924,*CC(*)(C)C(=O)OCCCC1CCCCC1,,0.36533383,,,
+1614865951,*OC(=O)Cc1ccc(CC(*)=O)cc1,,0.32669712,,,
+1615078461,*C(=O)Nc1cccc(NC(=O)c2ccc(-c3nc4cc(*)ccc4[nH]3)cc2)c1,,0.35660397,,,
+1615328669,*CC(*)(C)C(=O)OC(OCC)C(F)(F)F,,0.35450066,,,
+1616202781,*CC(CO)(CO)CO*,,0.29427699,,,
+1617425937,*C=CC1CCC(*)C1,,0.3888656,,,
+1617530326,*c1ccc(Cc2ccc(N3C(=O)c4c(c(-c5ccccc5)c(-c5ccc(Oc6ccc(-c7c(-c8ccccc8)c8c(c(-c9ccccc9)c7-c7ccccc7)C(=O)N(*)C8=O)cc6)cc5)c(-c5ccccc5)c4-c4ccccc4)C3=O)cc2)cc1,,0.40959126,,,
+1618024485,*CC(CO*)(CSC(C)C)CSC(C)C,,0.41655745,,,
+1618337987,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(S(=O)(=O)c4ccc(C)cc4)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.38455792,,,
+1618366828,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1,,0.41576369,,,
+1618451585,*Nc1ccc(-c2ccc(-c3cccc(N*)c3)cc2)cc1,,0.34006851,,,
+1618749599,*c1ccc(OC(=O)c2cccc(C(=O)Oc3ccc(C4(*)OC(=O)c5ccccc54)cc3C)c2)c(C)c1,,0.37167757,,,
+1618910183,*Nc1cccc(NC(=O)c2ccc(C(*)=O)cc2)c1,,0.33550053,,,
+1618911351,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(-c3cccc(OC(=O)c4ccc(OC(=O)c5ccc(OCCCCCCCCCCCC)cc5)cc4)c3)cc2)cc1,,0.36170245,,,
+1618992612,*CCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.33028108,,,
+1619024957,*CC1(*)CCCCC1,,0.36650758,0.1443333333333333,0.795413005,12.99416817
+1619167656,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.38096649,,,
+1619197321,*C(=O)OCCCCCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.34367333,,,
+1619494019,*Oc1ccc(C(=Cc2ccc(-c3ccc(C=C(c4ccccc4)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3)cc2)c2ccccc2)cc1,,0.37848459,,,
+1619516534,*CC(*)c1ccc(COCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.34583882,,,
+1619538915,*Nc1cccc(Cc2ccccc2)c1-c1cccc(C[C@H](N*)c2ccccc2)c1,,0.34966936,,,
+1619869238,*CCCCCCCCCCNC(=O)CCCCCCCCCCCC(=O)N*,,,0.3685,0.912914925,24.40781229
+1620174791,*Oc1ccc(N=C(c2ccccc2)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(C(=Nc6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1,,0.38989325,,,
+1620481410,*c1ccc(Cc2ccc(N3C(=O)C(Cl)=C(Oc4ccc(C(C)(C)c5ccc(OC6=C(Cl)C(=O)N(*)C6=O)cc5)cc4)C3=O)cc2)cc1,,0.36623451,,,
+1620933064,*/C=C(/*)CCCCCCCCCCCCCCCCCCCCC(=O)O,,,0.403,0.868463517,14.07855526
+1621708323,*OC(CCCCCCC=C)CC(*)=O,,0.37192791,,,
+1621972035,*CC(*)(C)C(=O)OCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.35758203,,,
+1622243076,*Nc1ccc2ccc3cc4c(ccc5ccccc54)cc3c2c1N*,,0.33607115,,,
+1622288666,*C=CC(C)C(*)C(=O)OCCCN(C)c1ccc(N(C)C)cc1,,0.37642636,,,
+1622533278,*Nc1cc2c3ccc(-c4ccc(C)c(C)c4)c(-c4ccc(C)c(C)c4)c3c(N*)cc2c2ccccc12,,0.36400797,,,
+1623779443,*c1ccc(Oc2ccc(-c3csc(NC(=O)Nc4ccc(Cc5ccc(NC(=O)Nc6nc(*)cs6)cc5)cc4)n3)cc2)cc1,221.6298798,,,,
+1623821086,*Nc1ccc(NC(=O)c2cc(C(*)=O)c(C(=O)O)cc2C(=O)O)cc1S(=O)(=O)O[Na],129.970858,,,,
+1623930170,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(C(c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)(C(F)(F)F)C(F)(F)F)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36693527,,,
+1624065789,*c1ccc(C(=O)NCCCCCCCCCCCCNC(=O)c2ccc(-c3nc4ccccc4nc3*)cc2)cc1,,0.36749027,,,
+1624256779,*OC(C)CCCCC(*)=O,,0.35745036,,,
+1624258093,*c1ccc(Oc2ccc(NC(=O)c3cc(C(=O)Nc4ccc(Oc5ccc(-c6nnc(*)o6)cc5)cc4)cc(N4C(=O)c5ccccc5C4=O)c3)cc2)cc1,,0.36154579,,,
+1624614703,*c1c(C)cc(C(c2cccnc2)c2cc(C)c(N3C(=O)CC(Nc4ccc(NC5CC(=O)N(*)C5=O)cc4)C3=O)c(C)c2)cc1C,,0.38844622,,,
+1624631059,*CC(*)(C)C(=O)OCCCCCCOc1ccc(OC(=O)c2ccc(OCCCC)cc2)cc1,,0.35532561,,,
+1625056718,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CC(C)CCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.3483794,,,
+1625717733,*CC(=O)N*,,,0.277,,
+1626154089,*Oc1c(-c2ccccc2)cc(*)cc1-c1ccc(C)cc1,,0.40218767,,,
+1626255936,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3cccc(C(F)(F)C(F)(F)C(F)(F)c4cccc(C(*)=O)c4)c3)cc2)cc1,,0.35753446,,,
+1626278445,*Nc1ccc([C@@]23CC4C[C@@](c5ccc(N)cc5)(C2)C[C@](c2ccc(N*)cc2)(C4)C3)cc1,,0.36179754,,,
+1626805714,*CCOCCOCCOc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1,,0.36211153,,,
+1626973373,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.35488648,,,
+1627008695,*Nc1cc(-c2ccccc2)c(-c2ccccc2)c(N*)c1-c1ccccc1,,0.36654455,,,
+1627088494,*c1ccc2c(c1)C(=O)N(c1cccc(C(O)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.37192753,,,
+1628056427,*c1ccc2c(c1)C(=O)N(c1cccc(C(O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.39990729,,,
+1628304289,*OC(*)CCC#N,,,0.1704999999999999,1.042557473,13.31406094
+1629034930,*Nc1ccc([C@]2(C)c3ccccc3C(C)(C)C2(N)N*)cc1,,0.36227751,,,
+1629642981,*CC(*)(C)C(=O)OCCCCCCc1ccc(C(C)=Cc2ccc(OC)cc2)cc1,,0.36591214,,,
+1629866625,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc4)C5=O)cc3)cc2)cc1,,0.35336088,,,
+1629965547,*CCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC,,0.35092765,,,
+1630109402,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ncccc2C)C3=O)cc(C(=O)NNC(=O)CC(*)=O)c1,,0.3203758,,,
+1630196267,*CCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,0.38015472,0.4137499999999999,0.900754814,21.54347658
+1630481907,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O,,0.37183855,,,
+1630836772,*Nc1cc2ccc3cc(N*)cc4ccc(c1)c2c34,,0.33777593,,,
+1630847021,*Nc1ccc(C(=CC=C(c2ccccc2)c2ccccc2)c2ccc(N*)cc2)cc1,,0.3719653,,,
+1631091445,*CC(S*)c1ccccc1,,0.3836093,,,
+1631230577,*Nc1cc2c(cc(N*)c3c(-c4ccccc4)c(-c4ccccc4)c(-c4ccccc4)c(-c4ccccc4)c32)c2ccccc12,,0.37324607,,,
+1631608831,*CCNC(=O)c1cccc(O*)c1,,0.32622214,,,
+1632405014,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(-c5cc(C)c(N6C(=O)c7ccc(*)cc7C6=O)c(C)c5)cc3C)C4=O)c(C)c2)cc1C,,0.37933895,,,
+1632457388,*Nc1c(N*)c2c(ccc3c4ccccc4c4ccccc4c32)c2ccccc12,,0.35171057,,,
+1632465618,*C(=O)Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(O)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.35532952,,,
+1632814958,*OS(=O)(=O)c1cc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)c(C)c4)CCCCC3)cc2C)ccc1C,,0.37582472,,,
+1632815353,*C(=O)c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc6)C7=O)cc5)cc4)OC(=O)c4ccccc43)cc2)cc1,,0.37651211,,,
+1633207070,*CC(*)c1ccccc1COCCC(C)C,,0.3832793,0.1943333333333333,0.924684677,10.79545525
+1633403102,*Nc1ccc(C(Cc2ccccc2)(Cc2ccccc2)N*)cc1,,0.35111176,,,
+1633678871,*Oc1ccc(-c2ccc(OC(=O)c3cc(OCCCc4ccccc4)c(C(*)=O)cc3OCCCc3ccccc3)cc2)cc1,,0.3524536,,,
+1634407816,*c1ccc(-c2cc(-c3ccc(OCCCCCCCCCCCC)cc3)cc(-c3ccc(-c4ccc5c(c4)C(CCCCCC)(CCCCCC)c4cc(*)ccc4-5)cc3)c2-c2ccc(OCCCCCCCCCCCC)cc2)cc1,,0.39788041,,,
+1634561399,*Nc1ccc(C(c2ccc(N*)cc2)c2ccccc2N)cc1,,0.3535621049999999,,,
+1634574344,*Oc1c(F)c(F)c(Sc2c(F)c(F)c(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(F)c2F)c(F)c1F,,0.37421023,,,
+1634828189,*CC(c1ccccc1)C1C(=O)N(c2ccc(NC(=O)OCC3CCCN3c3ccc([N+](=O)[O-])cc3)cc2)C(=O)C1*,,0.35437904,,,
+1634832271,*Oc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4cc(C(=O)Nc5ccc(*)cc5)cc(C(C)(C)C)c4)cc3)cc2)cc1,,0.36514177,,,
+1635058755,*c1ccc(-c2ccc(-c3cc(-c4ccccc4)c4cc(C5(c6ccc7nc(*)cc(-c8ccccc8)c7c6)c6ccccc6-c6ccccc65)ccc4n3)cc2)cc1,,0.43428946,,,
+1635294822,*c1ccc(Oc2ccc(Sc3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Sc8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36662186,,,
+1635582225,*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O,,0.41327874,,,
+1635861860,*c1ccc(-c2ccc(-c3ccc(-c4ccc(-c5ccc6c(c5)C(=O)N(c5cccc(N7C(=O)c8ccc(*)cc8C7=O)c5)C6=O)cc4)cc3)cc2)cc1,,0.3765538,,,
+1636249880,*Nc1nc(N*)nc(-c2ccccc2)n1,,0.33811735,,,
+1637316087,*Oc1c(C)cc(C2(c3cc(C)c(Oc4ccc(C(=O)c5cccc(C(=O)c6ccc(*)cc6)c5)cc4)c(C)c3)c3ccccc3-c3ccccc32)cc1C,,0.41055555,,,
+1637447040,*Oc1cc(Oc2ccc(C(C)(C)c3ccc(*)cc3)cc2)cc(C(=O)Oc2ccc(N=Nc3ccc(C#N)cc3)cc2)c1,,0.37807662,,,
+1637633691,*c1cccc(Oc2ccc(P(=O)(c3ccccc3)c3ccccc3)c(Oc3cccc(N4C(=O)c5ccc(Oc6ccc(-c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)c3)c2)c1,,0.37011172,,,
+1637714713,*CC(*)F,,0.35585104,,,
+1638121977,*CCOC(=O)CC(=CC(=O)O*)c1ccc(OCC)cc1,1.131191733,,,,
+1638169555,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)CC3CCC2C3)cc1,,0.36263983,,1.084486229,20.59461102
+1638182470,*Nc1ccc(NC(=O)c2cc(-c3ccc(C(=O)OC)c(C(*)=O)c3)ccc2C(=O)OC)cc1,,0.33582892,,,
+1638745393,*CC(*)(CC(=O)OCC)C(=O)OCC,,0.34796844,0.1689999999999999,0.965285515,13.08823383
+1638921879,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.3422569,,,
+1639290741,*CCCCCCOc1ccc(-c2cc(-c3ccccc3)c(-c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(-c6c(-c7ccccc7)cc(-c7ccc(O*)cc7)cc6-c6ccccc6)cc5)cc4)cc3)c(-c3ccccc3)c2)cc1,,0.37246219,,,
+1639497900,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCCCCCCCCCC)cc1,,0.35598861,,,
+1639513148,*CC(*)c1cc(C(=O)Oc2ccc(OCC)cc2)ccc1C(=O)Oc1ccc(OCC)cc1,,0.34938746,,,
+1639677802,*C([2H])([2H])C(*)(C(=O)OC([2H])([2H])[2H])C([2H])([2H])[2H],,0.33761653,,,
+1639769736,*CCCCCCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,0.3710755,,,
+1639915624,*/C=C/CC*,,,0.2486666666666666,0.8411584645000001,19.29341066
+1640063479,*CCOCCOCCOCCOC(=O)CCSCCC(=O)O*,,0.34735821,,,
+1640191566,*OCCOc1cc(*)nc2ccc(C)cc12,88.77435741,,,,
+1640510001,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.35958208,,,
+1640870840,*CCCCCCCNC(=O)CCCCCC(=O)N*,,0.34960498,0.334,,
+1640890711,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37216457,,,
+1640897195,*c1cccc(Oc2ccc(P(=O)(c3ccccc3)c3ccccc3)c(Oc3cccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.37114282,,,
+1640937510,*c1ccc(Nc2cc(CCCCCCCC)c(*)s2)cc1,49.50827798,,,,
+1641068572,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1-c1ccccc1,,0.38222364,,,
+1641679741,*Oc1cccc(OC(=O)c2ccc(Oc3ccc(C(*)=O)cc3)cc2)c1,,0.34914542,,,
+1641687238,*CCCCOC(=O)NC1CC1NC(=O)O*,47.31586772,,,,
+1642318561,*Nc1ccc(C(C)C)c(-c2cccc(-c3cc(N*)ccc3C(C)C)c2)c1,,0.3756884,,,
+1642394880,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.35213618,,,
+1642545320,*Sc1ccc(Sc2ccc(Sc3cccc4c3C(=O)N(N3C(=O)c5cccc(*)c5C3=O)C4=O)cc2)cc1,,0.38724478,,,
+1643898977,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)c3)c1)C2=O,,0.36251078,,,
+1644429324,*CCCCCCCCOc1ccc(C=Cc2cc(OCCCCCCCC)c(C=Cc3ccc(O*)cc3)cc2OCCCCCCCC)cc1,,0.38771383,,,
+1645271820,*CCCCCCCCCCCCCCOC(=O)CCCCC(=O)O*,,,0.3555,0.934642528,21.38435354
+1645416728,*CCC1C[N+](C)(C)CC1*,,0.77709707,,,
+1645801286,*Oc1ccc(N=Cc2ccc(OC(=O)CCCCC(=O)Oc3ccc(C=Nc4ccc(*)cc4)cc3OC)c(OC)c2)cc1,47.38250675,,,,
+1646057229,*Nc1ccc([C@@H](CCCCC)c2ccc(C(CCCCC)c3ccc([C@H](CCCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.37024472,,,
+1646177027,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.37532001,,,
+1646241575,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cc(OCCCCC)cc(C(=O)O*)c1,,0.34110244,,,
+1646656305,*CC(*)CCC(C)C,,,0.216,0.772296641,12.17429097
+1646893921,*CC1CCC(COC(C)OC(=O)c2cccc(C(=O)OC(C)O*)c2)CC1,,0.35679176,,,
+1646933225,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)c(-c2ccccc2)c1,,0.36556523,,,
+1646984336,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3ccccc3)c2-c2ccccc2)c(C)c1C,,0.3811955,,,
+1647029479,*Cc1cccc(CNC(=O)c2cccc(C(=O)N*)c2)c1,,0.32986558,,,
+1647212497,*Nc1ccc(C2(c3ccc(N*)cc3)CCCc3ccccc32)cc1,,0.35799942,,,
+1647532424,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCC)cc1,,0.33994048,,,
+1647821918,*CC(*)(C)C(=O)OCC1COC(CP(=O)(OCC)OCC)O1,,0.34351571,,,
+1648167915,*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OC)cc2)nc(*)c1-c1ccc(OC)cc1,,0.34869133,,,
+1648532900,*CC(*)(C)C(=O)OCCOCCOCC[N+](CC)(CC)CCCCS(=O)(=O)O,,0.40560629,,,
+1648622866,*O[Si](*)(C)CCCn1c2ccccc2c2ccccc21,,0.37981607,,,
+1649223775,*NC(C)CC(*)=O,,0.32169997,,,
+1649295248,*NC1=CCCC(c2ccc(N*)cc2)=C1c1ccccc1,,0.35914225,,,
+1649345915,*NCc1ccc(N*)cc1,,0.322561555,,,
+1649518604,*Nc1ccc(C(c2ccccc2)(c2ccccc2)c2cc(C(c3ccccc3)(c3ccccc3)c3ccc(N)cc3)cc(C(c3ccccc3)(c3ccccc3)c3ccc(N*)cc3)c2)cc1,,0.3872297,,,
+1650000609,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3c(C)cc(S(=O)(=O)c4cc(C)c(O*)c(C)c4)cc3C)cc2)cc1,,0.36906105,,,
+1650198770,*c1ccc(Cc2ccc(N3C(=O)c4ccc([Si](C)(C)c5ccc([Si](C)(C)c6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4C3=O)cc2)cc1,,0.39503017,,,
+1650263868,*CC(*)(C)C(=O)OCCOCCOCCOCCOCCOCCOCCOCCOC,,0.35087647,,,
+1650643323,*CC(*)(C)C(=O)OCCCn1c2ccccc2c2ccccc21,,0.35272474,,,
+1651023431,*CC(*)C#N,,,0.207,0.979429396,20.43708909
+1651258408,*Oc1cccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(*)cc2)c1,,0.35425809,0.319,1.057348058,21.51389258
+1651483269,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2)cc1,,0.33710135,,,
+1652170327,*c1ccc(N[Se]Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.38429931,,,
+1652404686,*CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.35651058,,,
+1652549742,*Oc1ccc(OC(=O)Oc2ccc(OC(*)=O)cc2)cc1C,120.5520321,,,,
+1652695363,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cc(-c4nc5ccccc5o4)cc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)n1,,0.36221433,,,
+1652765029,*Nc1c(C)c(N)c2c(c1O)C(=N)[C@@H](N*)[C@@H]2N,,0.32698788,,,
+1653502391,*CC(*)(CC(=O)OCCCc1ccccc1)C(=O)OCCCc1ccccc1,,,0.2055,1.080352066,10.10003467
+1653539066,*CCCCCCNC(=O)NCCCCNC(=O)N*,60.10434296,,,,
+1653668180,*CC(*)(C)C(=O)Oc1ccc(C)cc1C,,0.36833301,0.157,0.95674619,12.26970455
+1653699030,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)CCCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.3472328,,,
+1653991852,*Nc1cc(NC(=O)NC2CCC(CC3CCC(NC(*)=O)CC3)CC2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1,,0.34250678,,,
+1654010569,*CC(C)(C)COC(=O)CCC(=O)O*,,0.33568039,,,
+1654213224,*CC(*)CC1CCCCC1,,,0.2256,0.816505893,9.943107453
+1654272225,*Oc1c(Cl)cc(*)cc1Cl,,0.40066645,,,
+1654827899,*Nc1cc2ccc3cccc4ccc(c1N*)c2c34,,0.33185759,,,
+1654848941,*Oc1ccc(CCNC(=O)c2cccc(C(*)=O)c2)cc1,,0.33770384,,,
+1655220585,*c1ccc(N2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(c4ccc6[nH]c(*)nc6c4)C5=O)cc3C2=O)cc1,,0.39105508,,,
+1655318742,*OP(=O)(Oc1ccc(C)cc1)Oc1c(Br)cc(C(c2cc(Br)c(*)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,,0.39766764,,,
+1655336489,*Nc1ccc2c(c1)Cc1cc(N*)ccc1-2,,0.34702763,,,
+1655603853,*C(Cl)=C(*)CCCC,,0.40171374,,,
+1655950813,*OC1CCC(OC(=O)OCCCCCCCOC(*)=O)CC1,,0.3452967,,,
+1656073293,*O[Si](C)(C)c1cccc2c([Si](*)(C)C)cccc12,,0.41204539,,,
+1656097465,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7ccc(C(C)(C)c8ccc(Oc9cccc%10c9C(=O)N(*)C%10=O)cc8)cc7)c6C5=O)cc4)cc3)cc2)cc1,,0.36779038,,,
+1656589637,*Nc1c(C)c(C)c(NC(=O)c2ccc(C(*)=O)cc2)c(C)c1C,205.6552433,,,,
+1656633508,*CCCOC(=O)Nc1cc(NC(=O)O*)ccc1C,,0.32689509,,,
+1657554335,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2-c2ccccc2)cc1,227.700588,,,,
+1658493417,*Nc1ccc(C2=C3CC=CC=C3[C@H](c3ccc(N*)cc3)c3ccccc32)cc1,,0.36999408,,,
+1658830563,*Nc1ccc2c(c1)C1(c3ccc(C)cc3-c3cc(C)ccc31)c1cc(N*)ccc1-2,,0.3945016,,,
+1659755878,*CCCCCCCCCCCCCCNC(=O)CCCCCCCCC(=O)N*,,,0.3944999999999999,0.916585437,18.81872143
+1659835488,*CC(C)(CC)COC(=O)c1ccc(C(=O)O*)cc1,,0.35427733,,,
+1659919472,*CC(=O)Nc1ccc(Oc2cccc(NC(=O)CN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)cc1,,0.33266973,,,
+1660751847,*CCCCCCCCCCOC(=O)NCCCCCCNC(=O)O*,,0.35820456,,,
+1661316204,*c1ccc(-c2ccc(-c3cnc4cc(Oc5ccc6nc(*)cnc6c5)ccc4n3)cc2)cc1,,0.40678866,,,
+1661521840,*CCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.4001193,,,
+1661804021,*c1ccc(Sc2ccc(-c3nn(-c4ccc(S(=O)(=O)c5ccc(-n6nc(*)c7ccccc7c6=O)cc5)cc4)c(=O)c4ccccc34)cc2)cc1,,0.39793898,,,
+1662111173,*c1ccc(Oc2ccc(-c3nc4ccc(Oc5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)cc1,,0.42723558,,,
+1662427032,*Nc1ccc([C@H]2C=C[C@](N*)(c3ccccc3)C=C2)cc1,,0.34408307,,,
+1662473606,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.44568378,,,
+1662846692,*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)cc1C)C2=O,,0.39438371,,,
+1663220707,*C1C=CC(*)CC1,103.377349,,,,
+1663257488,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1,,0.36301636,,,
+1663514336,*CCCCCCCCCCCCOC(=O)CCCCCCC(=O)O*,,,0.284,0.931066388,19.58490022
+1663579742,*C1C2CCC(C2)C1S(*)(=O)=O,,0.369056,,,
+1664000422,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cc(-c5nc6ccccc6o5)cc(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)cc2)cc1,,0.36410497,,,
+1664419715,*c1cc(C(=O)Oc2cccc3cccnc23)c(*)s1,,0.37063906,,,
+1665128334,*Oc1c(Cl)cc(C2(c3cc(Cl)c(OC(*)=O)c(Cl)c3)CC3CC2C2CCCC32)cc1Cl,,0.40815455,,,
+1665519447,*CCCCCCOC(=O)CCCCC(=O)O*,,0.34866959,,,
+1665622920,*CCCCCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.39784741,,,
+1666147237,*Oc1ccc(NCC(=O)c2ccc(Sc3ccc(C(=O)CNc4ccc(*)cc4)cc3)cc2)cc1,,0.37243845,,,
+1666787967,*Nc1c(N*)c(C=C)c2c(c(N)cc3cc4ccccc4cc32)c1N,,0.33559089,,,
+1666962714,*NC(CCC(=O)OCc1ccc(C(F)(F)F)cc1)C(*)=O,,0.32727613,,,
+1667507467,*CC(=O)C(*)C,,0.3478615,,,
+1668029928,*c1ccc(Oc2ccc(-c3cccc4c3C(=O)N(Oc3ccc(C(=O)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc3)C4=O)cc2)cc1,,0.36324031,,,
+1668423016,*Cc1cc(C)c(CNC(=O)CCCCC(=O)N*)cc1C,,0.33390015,,,
+1668799995,*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cc7cc(*)ccc7oc6=O)cc5)cc4)cc3)cc2c1,,0.34606436,,,
+1669065753,*CC(*)c1c(F)c(F)c(OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c(F)c1F,,0.33681331,,,
+1669177512,*CC(*)OC(=O)C(C)(C)CCC,,0.37264329,,,
+1669233687,*Nc1ccc2c(c1)-c1cc(N*)ccc1C2(c1cccc(N)c1)c1cccc(N)c1,,0.36327094,,,
+1669324306,*CC(*)c1ccc(C(O)CCN2CCOCC2)cc1,,0.35614962,,,
+1669400778,*CC(*)(C)C(=O)OCC1CCC1,,0.36732984,,,
+1669411867,*c1ccc(Oc2cccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(c5cccc(Oc7ccc(-c8nnc(*)o8)cc7)c5)C6=O)cc4C3=O)c2)cc1,,0.38763458,,,
+1669796123,*CCOCCOC(=O)NCCCCCCNC(=O)O*,,0.33299427,,,
+1669900077,*Nc1ccc2c3ccccc3c3ccc(N*)c4ccc1c2c43,,0.32846656,,,
+1669945913,*CC(*)c1ccc([Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)cc1,,0.45056995,,,
+1670036740,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.37228943,,,
+1670250046,*CC(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2)cc1,,0.34110112,,,
+1670434070,*c1ccc(-c2ccc(-c3ccc(-c4ccc(-c5cc(-c6ccccc6)c6cc(-c7ccc8nc(*)cc(-c9ccccc9)c8c7)ccc6n5)cc4)cc3)cc2)cc1,,0.40260098,,,
+1670881683,*Nc1ccc2cc(C)ccc2c1-c1c(N*)ccc2cc(C)ccc12,,0.36235466,,,
+1671082130,*CC(O)CN(C)S(=O)(=O)c1cccc(S(=O)(=O)N(C)CC(O)COc2ccc(C3(c4ccc(O*)cc4)c4ccccc4-c4ccccc43)cc2)c1,,0.34976889,,,
+1671135336,*CC(C)(CO*)COCC(F)(F)F,,0.36946745,,,
+1671251574,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cccc(P(=O)(c4ccccc4)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c2)C3=O)n1,,0.35663308,,,
+1671488348,*Nc1ccc(-c2ccc(N*)c(-c3ccc(C=C)cc3)c2-c2ccc(C=C)cc2)cc1,,0.35874439,,,
+1671953980,*CCCCCCNC(=O)CCc1ccc(CCC(=O)N*)cc1,,0.34609374,,,
+1672793262,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4cc(C(=O)Nc5ccc(*)cc5)cc(C(C)(C)C)c4)cc3)cc2)cc1,,0.36687927,,,
+1672845324,*CC(*)c1ccc(Cl)cc1C,,,0.1366666666666667,1.05867232,13.55500415
+1672978154,*CCN(*)C(=O)c1cc(F)c(F)c(F)c1,,0.33491733,,,
+1673102109,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(C6(c7ccc8nc(*)cc(-c9ccccc9)c8c7)c7ccccc7C(=O)c7ccccc76)ccc5n4)cc3)c3ccccc3C(=O)c3ccccc32)cc1,,0.43993459,,,
+1673426681,*CC(*)(C)C(=O)OCCS(=O)CC,,0.3575264,0.199,1.117069249,12.05051013
+1673485707,*c1nc(C)nc(N(CCCCCCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.39169547,,,
+1673575394,*Nc1cccc(S(=O)(=O)c2cccc(N*)c2)c1,,0.336299775,,,
+1673823356,*NNC(=O)c1ccc(C(*)=O)cc1OCCCCCCCCCC,,0.36401661,,,
+1674464256,*Oc1ccc(C2(c3ccc(OC(*)=O)c(C)c3)CC3CCC2C3)cc1C,,0.38156407,,,
+1674887519,*CC(*)C(=O)OCCC(C)C,,,0.224,0.928263907,11.86917301
+1674948032,*Sc1c(C)cc(*)cc1C,,0.42844078,0.145,1.048021071,18.9131789
+1674954682,*c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)CC3)CC1)C2=O,,0.36673379,,,
+1675272151,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccc(C(=O)c3ccccc3)cc2)cc1,,0.34771734,,,
+1675542774,*CCC(C)(C)C(*)(C)C,,0.38272137,,,
+1675605229,*CCC1CC(*)C2C3CC(c4ccc(COC(C)CC)cc4)C(C3)C12,,0.37580265,,,
+1675845282,*CC(*)C(=O)NC(C)C,,0.36205539,0.203,0.951472262,13.50097132
+1676036975,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(-c6nc(-c7ccccn7)nnc6*)cc5)cc4)c4ccccc4-c4ccccc43)cc2)cc1,,0.40668886,,,
+1676176971,*Oc1ccc(C(CC)(c2ccccc2)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.37078236,,,
+1676315941,*Oc1ccc(C(C)(C)c2ccc(Oc3c(F)c(F)c(COC(c4cccc(C(OCc5c(F)c(F)c(*)c(F)c5F)(C(F)(F)F)C(F)(F)F)c4)(C(F)(F)F)C(F)(F)F)c(F)c3F)cc2)cc1,,0.34981945,,,
+1676729998,*CC(*)(C)C(=O)OCCN(C)c1ccc(N(C)C)cc1,,0.36781623,,,
+1676759354,*c1cccc(C(=O)Nc2ccc(Oc3cccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c5)cc4)c3)cc2)c1,,0.35354105,,,
+1676853034,*CC(*)(C)C(=O)OCc1ccc(C=CC(=O)Oc2ccc3oc(=O)ccc3c2)cc1,,0.32586124,,,
+1677073370,*C=CC1CC(*)C([Si](C)(C)C[Si](C)(C)C)C1,,0.40768894,,,
+1677533093,*Nc1ccc(-c2ccccc2)c(-c2ccc(N)c(N)c2)c1N*,,0.33277847,,,
+1677600893,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.36082508,,1.104373146,25.54111019
+1677738702,*Oc1ccc(NC(=O)c2ccc(P(=O)(c3ccccc3)c3ccc(C(=O)Nc4ccc(*)cc4)cc3)cc2)cc1,,0.36140539,,,
+1677875786,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(N5C(=O)C6CCC(C7CCC8C(=O)N(*)C(=O)C8C7)CC6C5=O)cc4)cc3)cc2)cc1,,0.37368086,,,
+1678384884,*CC(*)OCCCCCCCCCCCCCCCCCC,,,0.3868,0.834274703,12.10790061
+1678410522,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)c2)cc1,,0.36119682,,,
+1678521620,*CC(O*)c1ccco1,,0.36350816,,,
+1678768525,*C(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)c4cc(*)ccc4n5C)cc3)OC(=O)c3ccccc32)cc1,262.5942508,,,,
+1678882147,*CC(*)OC(=O)c1ccc(Br)cc1,,0.38128079,,,
+1679226425,*Nc1ccc(C2(c3ccc(N*)cc3)CCC2(c2ccc(N)cc2)c2ccc(N)cc2)cc1,,0.37813226,,,
+1679285370,*c1cccc(OCCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34918119,,,
+1680789160,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CCC(C(C)(C)CC)CC2)cc1,,0.37936354,,,
+1681110916,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccc(F)cc5)C6=O)cc4)cc3)cc2)cc1,,0.3755704,,,
+1681628667,*Nc1ccc(C2(c3ccc(N*)c(CC)c3)c3ccccc3-c3ccccc32)cc1CC,,0.38152925,,,
+1681775861,*Nc1ccc2c(-c3ccccc3)c(N*)c(-c3ccccc3)cc2c1,,0.35638928,,,
+1681905956,*Nc1ccc2cc3ccc4cccc5ccc(c2c1N*)c3c45,,0.33707635,,,
+1682827181,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.34685932,,,
+1682832675,*CCCCCCOC(=O)C(C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1)c1ccccc1,,0.35801462,,,
+1682900060,*c1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Sc5ccc(N6C(=O)c7ccc(C(=O)Nc8ccc(Oc9ccc(C%10(*)NC(=O)c%11ccccc%11%10)cc9)cc8)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.3676725,,,
+1683248477,*Nc1ccc(Cc2ccc(CCC(CC)(c3ccc(Cc4ccc(N)cc4)cc3)c3ccc(Cc4ccc(N*)cc4)cc3)cc2)cc1,,0.36374897,,,
+1683353689,*CCCCCCNC(=O)C=C(C)C(=O)N*,,0.33327284,,,
+1683593138,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O,,0.37532508,,,
+1683816678,*CC(*)CCCCCCCCCCCCC,,,0.3383333333333333,0.803265072,11.57516404
+1683834264,*CC=Cc1cc(C(C)(C)c2ccc(O)c(C=CCC3CC(=O)N(c4ccc(Cc5ccc(N6C(=O)CC(*)C6=O)cc5)cc4)C3=O)c2)ccc1O,,0.35434136,,,
+1683937350,*C(*)(F)F,,0.32274038,,,
+1684744857,*Nc1ccc(N*)cc1,,0.32369134,,,
+1684855250,*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C,,0.49693178,,,
+1685057897,*OCCCCOC1COC2C(OC(=O)CCCCC(=O)OC3COC4C(*)COC34)COC12,,0.33206648,,,
+1685441802,*Nc1ccc([C@]2(C)CC(C)(C)c3cc(N*)c(N)cc32)cc1,,0.36997618,,,
+1685639309,*Nc1ccc(N*)c2cc3ccccc3cc12,,0.33960265,,,
+1685741842,*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6)cc5C)cc4)ccc3c2)cc1,,0.39138896,,,
+1685793065,*NC1=CC[C@H](C(c2ccc(N)cc2)(c2ccc(N)cc2)c2ccc(N*)cc2)C=C1,,0.35960608,,,
+1685901918,*CCCCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.33743021,,,
+1686044145,*Nc1ccc(-c2ccc(N*)cc2-c2ccc(-c3ccccc3)cc2)cc1,,0.34993468,,,
+1686536548,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)c3cccc4ccccc34)cc1C)C2=O,,0.43039552,,,
+1686718921,*CCCCCCCCCCC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CCCCCCCCCCN4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.35369987,,,
+1686919611,*Nc1cc2c(N*)ccc3c4cc(N)c5c(O)cc(O)c6c5c4c(c(c1)c23)=CC6=N,,0.33150168,,,
+1686952032,*Oc1ccc(NC(=C(C#N)C#N)c2ccc3cc(C(Nc4ccc(*)cc4)=C(C#N)C#N)ccc3c2)cc1,,0.40578459,,,
+1687301626,*Nc1ccc(C2(c3ccc(N)c(N*)c3)c3ccccc3-c3ccccc32)cc1N,,0.374004235,,,
+1687734708,*CC(*)c1nc(C)nc(C)n1,,0.41591473,,,
+1687858276,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.36843914,,,
+1687871916,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O,,0.42868802,,,
+1687937320,*Oc1cccc(S(=O)(=O)c2ccc(*)cc2)c1,,0.36277988,,,
+1688474833,*OC(=O)c1ccc(C(F)(F)C(F)(F)C(F)(F)c2ccc(C(*)=O)cc2)cc1,,0.34059869,,,
+1688918153,*Oc1ccc(C(=O)c2cccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(=O)c4ccc(*)cc4)cc3)c2)cc1,,0.35168897,,,
+1689325307,*Nc1cc(Oc2ccccc2)c(N*)c(N)c1N,,0.32209092,,,
+1689591467,*CCCCCCCCCCCNC(=O)CCCCC(=O)N*,,,0.3295,0.9550917,21.59948372
+1689883319,*=C=C=C(CCCCOC(=O)Nc1ccccc1)C(=*)CCCCOC(=O)Nc1ccccc1,,,,1.089327475,15.55466977
+1690031324,*CC(*)c1cc(Cl)ccc1Cl,,,0.1119999999999999,1.217628771,15.81204712
+1691054850,*C(=O)Nc1ccc(Oc2c(C)cc(C(C)(C)c3cc(C)c(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C8(c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)CC9CC8C8CCCC98)cc7)cc5)C6=O)cc4)c(C)c3)cc2C)cc1,,0.382364,,,
+1691146038,*OC(COC(=O)NCCCCCCNC(*)=O)c1ccco1,,0.33835382,,,
+1691546435,*c1nc2cc(S(=O)(=O)c3ccc4oc(C5CCC(*)CC5)nc4c3)ccc2o1,,0.3932565,,,
+1692151346,*CC1CCCC(COC(=O)c2ccc(C(=O)O*)cc2)C1,,0.35298607,,,
+1692408347,*CC(*)(C)C(=O)OCCN(C)c1ccc(N=Nc2ccc(S(=O)(=O)Nc3cc(C)on3)cc2)cc1,,0.36514397,,,
+1692538741,*CC(*)(C#N)C(=O)OC,,,0.161,1.044265535,17.02409485
+1693140082,*O[Si](*)(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,-68.87734458,,,,
+1693262682,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.36953742,,,
+1693536620,*/C=C/CCC*,,,0.26655,0.834588519,18.18601938
+1694201985,*c1ccc(Oc2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(Oc5ccc(Oc6ccc(-c7nc8ccccc8nc7*)cc6)cc5)cc4)cc3)cc2)cc1,,0.38425793,,,
+1695745153,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(Cc4cc(C)c(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(C)c4)cc3C)cc1)C2=O,,0.38922074,,,
+1695808727,*N=P(*)(OCCOCCOCCOC)OCCOCCOCCOC,,0.35486325,,,
+1695838608,*O[Si](C)(C)c1cc(OCCO)c([Si](*)(C)C)cc1OCCO,,0.37188313,,,
+1695890113,*CCCCCCCCCCC(Cl)C(*)Cl,,0.39676884,0.316,1.005372382,22.70085055
+1696155039,*C([2H])([2H])C(*)([2H])Cl,,0.37387095,,,
+1696182166,*Oc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)c2ccc(C=CC3=CC(=C(C#N)C#N)CC(C)(C)C3)cc2)cc1,,0.36168331,,,
+1696331324,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(-c4cc(C)c(*)c(C)c4)cc3C)cc2)cc1,,0.39339685,,,
+1696343001,*c1ccc(OCCCOc2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.35291227,,,
+1696594426,*Nc1cc2c(cc1N*)[C@H]1CC[C@@H]2C1,,0.35993676,,,
+1696699726,*Cc1cc(COC(=O)Nc2ccc(Cc3ccc(NC(=O)O*)cc3)cc2)cc(C(C)(C)C)c1,,0.35650637,,,
+1696944079,*CC(*)(CC(=O)OC1CCCCC1)C(=O)OC1CCCCC1,,0.39639923,0.1694999999999999,0.957033344,10.04778986
+1696993963,*CC(*)c1ccc(C(=O)CC)cc1,,0.3774075,,,
+1697131748,*Nc1ccc(N*)c2c1C[C@@H](O)[C@H]2N,,0.32527801,,,
+1697480499,*c1ccc(C(=O)Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(OC(=O)c4ccc(N5C(=O)CC(Nc6ccc(NC7CC(=O)N(*)C7=O)cc6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.38079558,,,
+1697683442,*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(C(C)(C)c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34615507,,,
+1697824573,*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCCCC(*)=O)cc1C=Cc1ccc(N(C)C)cc1,,0.35455776,,,
+1697849650,*Oc1ccc(NC(=O)Nc2cc(NC(=O)Nc3ccc(*)cc3)ccc2C)cc1,,0.34206534,,,
+1697989359,*c1ccc(-c2ccc(-c3nc4cc(Oc5ccc6nc(-c7ccccc7)c(*)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.42387436,,,
+1699061144,*Nc1ccc(Cc2cccc(C(C)(C)c3ccc(N*)cc3)c2)cc1,,0.35256271,,,
+1699412272,*CCCCCCOC(=O)OCCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35593526,,,
+1699735817,*Nc1ccc(C2(c3ccc(N*)c(C(C)(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)(C)C,,0.39115319,,,
+1699979757,*C(*)C(=O)OCCC,,0.38345076,,,
+1700419938,*c1c(C)cc(C(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2cc(C)c(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.43003915,,,
+1700578264,*Oc1ccc(NC(=O)CCCCCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.34746438,,,
+1700596879,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4cccc(Oc5c(C)cc(Cc6cc(C)c(Oc7cccc8c7C(=O)N(*)C8=O)c(C)c6)cc5C)c4C3=O)c(C)c2)cc1C,,0.39281003,,,
+1700600089,*Nc1ccc(C2(c3ccc(N*)c(CC)c3C)c3ccccc3-c3ccccc32)c(C)c1CC,,0.38884265,,,
+1700861962,*CC1CCC(COC(C)OC(=O)c2ccc(C(=O)OC(C)O*)cc2)CC1,,0.36174353,,,
+1700926472,*c1cccc(-c2nc3ccc(Oc4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)c1,,0.43473817,,,
+1701051836,*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.36060547,,,
+1701314200,*Nc1ccc(C2(c3ccc(N*)cc3)C3=C(C=CCC=C3)c3ccccc32)cc1,,0.36502239,,,
+1701467144,*Oc1ccc(CC(Cc2ccc(*)cc2)(C(=O)OCC)C(=O)OCC)cc1,,0.36224127,,,
+1701555449,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)cc2)cc1,,0.39244447,,,
+1701689653,*Nc1ccc([C@]23C[C@@H]4C[C@@H]5[C@H]2C[C@@H]2C[C@H]3[C@H](C4)[C@@]5(c3ccc(N*)cc3)C2)cc1,,0.36708387,,,
+1701728451,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(Cl)c4)cc3Cl)cc2)cc1,,0.38291342,,,
+1701758700,*NC(c1ccccc1)C(C)(C)C(*)=O,154.3595069,,,,
+1701768765,*/C=C/c1cc(OCCCCCCCC)c(*)cc1OC,,,0.31,0.939190483,17.48153992
+1701867111,*C=CC1CC(*)C(C#N)(C[Si](C)(C)C)C1,,0.4252906,,,
+1702325651,*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(NC(=C(C#N)C#N)c4ccc(C(*)=C(C#N)C#N)cc4)cc3)c2)cc1,,0.40367737,,,
+1702635264,*CC(O)COc1ccc(C(=O)c2ccc(O*)cc2)cc1,,0.3378744,,,
+1702898348,*Oc1cccc(Oc2ccc(C(=O)Oc3cccc(OC(=O)c4ccc(*)cc4)c3)cc2)c1,,0.35286115,,,
+1703438177,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)Oc5ccc(C6(c7ccc(OC(=O)c8ccc(*)cc8)cc7)CCC(c7ccccc7)CC6)cc5)cc4)cc3)CCC(C(C)(C)C)CC2)cc1,,0.37255795,,,
+1703477972,*Nc1ccc2c3cccc4cccc(c5c(N*)c(N)c(N)c1c25)c43,,0.33109076,,,
+1703879338,*Nc1ccc([C@@H](CCCCCCCCC)c2ccc(C[C@H](CC)c3ccc([C@@H](CCCCCCCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36788809,,,
+1704241791,*Oc1ccc2c(c1)nc1c3cc4c(=O)n5c6cc(*)ccc6nc5c4cc3c(=O)n21,416.53,,,,
+1704546800,*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)Nc2ccccc2)c1,233.599405,,,,
+1704728176,*OC(COC(*)=O)COC(C)(C)C,,0.36210657,,,
+1704846953,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc3ccccc3c2)cc1,,0.35356045,,,
+1705151635,*CCCCCCCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1,,0.35153722,,,
+1705359198,*c1cc(-c2sc(-c3cc(CCCCCCCCCC)c(*)s3)cc2CCCCCCCCCC)c2cccccc1-2,,0.44266435,,,
+1705636874,*CCCCCC(*)C,,0.39985278,,,
+1705705134,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.35910287,,,
+1705754093,*CCNC(=O)CCCCC(=O)N*,,0.31065959,,,
+1706421625,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)c3cccc4ccccc34)cc1C)C2=O,,0.42170039,,,
+1706521131,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3cccc(C(*)=O)c3)c(C)c2)cc1C,,0.37521013,,,
+1706553936,*Nc1ccc(C(C)(CC([C@H]2CC[C@H](N)CC2)[C@H]2CC[C@H](N*)CC2)c2ccc(N)cc2)cc1,,0.35832784,,,
+1706591309,*CC(C)(C)CS*,,0.40110169,,,
+1706654876,*CCc1c(Cl)c(Cl)c(*)c(Cl)c1Cl,56.1987128,,,1.40293716,22.58883992
+1706741208,*Nc1ccc2c(-c3ccc(C)cc3)c3ccccc3c(-c3ccc(C)cc3)c2c1N*,,0.36448557,,,
+1706907888,*c1cccc(C(=O)Nc2ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.35891264,,,
+1706935597,*c1cc(Br)c(Oc2c(Br)cc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2Br)c(Br)c1,,0.4209618,,,
+1706990929,*Nc1ccc(CCC(CCc2ccc(N)cc2)CCc2ccc(N*)cc2)cc1,,0.34748423,,,
+1707333225,*CC(*)CCc1ccccc1,,0.3743389,0.1803333333333333,,
+1707392895,*CCOP(=O)(N=Nc1ccc(CCOC(=O)c2cc(C(=O)OCCc3ccc(N=NP(=O)(O*)OC)cc3)cc(C(C)(C)C)c2)cc1)OC,,0.36089138,,,
+1707460536,*Nc1ccc(-c2ccc(N*)c(-c3cccc4ccccc34)c2)cc1,,0.34848599,,,
+1708403731,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(-c4ccc(-c5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.36454739,,,
+1708543289,*OP(=O)(Oc1ccc(NC(=O)OCCCCCCOC(=O)Nc2ccc(*)cc2)cc1)c1ccccc1,,0.34447887,,,
+1709391995,*CCOCCOC(=O)C=CC(=O)NCCCCCCNC(=O)C=CC(=O)O*,,0.32070713,,,
+1710475458,*c1ccc(-c2ccc(S(*)(=O)=O)cc2)cc1,229.0539301,,,,
+1710724888,*NNC(=O)c1ccc(C(*)=O)cc1OCCCCC,,0.34962843,,,
+1710766762,*CC(*)(C)C(=O)OCCCCCCOc1ccc(N=Nc2ccc(OCCCCCCCCCC)cc2)cc1,,0.37981521,,,
+1711010706,*c1cccc(C(=O)c2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)c1,,0.36734681,,,
+1711102777,*CC(C)(C)C1C(=O)N(C(C)C)C(=O)C1*,,0.35984602,,,
+1711422948,*C(F)C(*)(F)F,,0.29955763,,,
+1711492844,*CC(*)(C)c1ccc2ccccc2c1,,0.37911502,,,
+1711893868,*Oc1ccc(N=Cc2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(C=Nc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.388023,,,
+1712077769,*c1cc(C(C)C)c(Oc2ccc(S(=O)(=O)c3ccc(Oc4cc(C)c(C5(*)OC(=O)c6ccccc65)cc4C(C)C)cc3)cc2)cc1C,,0.40334247,,,
+1712598778,*Nc1ccc(-c2cc(C)c(-c3c(C)cc(-c4ccc(N*)cc4)c4ccccc34)c3ccccc23)cc1,,0.37446309,,,
+1712933557,*CC(*)(C#N)C(=O)OCCCCCC,,0.38064914,0.217,0.966634258,12.966416
+1713382325,*CCCS(=O)(=O)CCCS(=O)(=O)CCCOC(=O)c1ccccc1C(=O)O*,,0.34598404,,,
+1713436070,*C(=C(c1ccc(*)cc1)c1ccc(Oc2ccccc2)cc1)c1ccc(Oc2ccccc2)cc1,,0.39509432,,,
+1713751559,*C(=O)c1ccc(N(*)CCCC)cc1,,0.39191799,,,
+1714607104,*C(=O)Oc1ccc(C2(c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C)c(C)c4)C5=O)cc3)C3CC4CC(C3)CC2C4)cc1,,0.37949599,,,
+1715193116,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(C4(*)OC(=O)c5ccccc54)cc3)cc2)cc1,,0.36083172,,,
+1715294926,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4cncc(-c5nnc(*)o5)c4)o3)cc2)cc1,,0.44520609,,,
+1715402105,*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8cccc9c8C(=O)N(*)C9=O)cc7C6=O)c5)cc4)cc3)cc2)c1,,0.35797628,,,
+1715831147,*c1ccc(Oc2ccc(-c3nc4cc(-c5ccc6nc(*)c(-c7ccccc7)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1,,0.41936962,,,
+1715878525,*CCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC,,0.35140206,,,
+1715886560,*Oc1ccc(C=C2CCCC(=Cc3ccc(OC(=O)c4ccc(C(*)=O)cc4)c(OC)c3)C2=O)cc1OC,105.2246285,,,,
+1715966441,*CC(*)(C)C(=O)OCCCCl,,0.35991969,,,
+1716148933,*Oc1ccc(C(=O)c2cccc(NC(=O)c3ccc(C(=O)Nc4cccc(C(=O)c5ccc(*)cc5)c4)cc3)c2)cc1,,0.35027261,,,
+1717221182,*CCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37289188,,,
+1717592504,*CCCCCC(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1,,0.33289133,,,
+1718086689,*c1ccc(C(=O)Nc2ccc(-c3ccccc3NC(=O)c3ccc(S(*)(=O)=O)cc3)cc2)cc1,,0.35178104,,,
+1718233222,*Nc1ccc2cc3c(cc2c1-c1c(N*)ccc2cc4c(cc12)CCCC4)CCCC3,,0.37579984,,,
+1718354162,*CC1CCC(COC(=O)Nc2ccc(Cc3ccc(NC(=O)O*)cc3)cc2)CC1,,0.35905174,,,
+1718419889,*NC(CO)C(*)=O,84.57547927,,,,
+1718537835,*C(=O)C1=C(*)C2C=CC1C2,,0.35458952,,,
+1718822131,*C=CC1CC(*)C2C(=O)N(c3cccc(Br)c3)C(=O)C12,,0.39327609,,,
+1719173195,*Nc1c(C)cc(C[C@H](c2cc(C)c(N*)c(C)c2)[C@@H](Cc2ccc(N)c(C(C)(C)C)c2)c2ccc(N)c(C(C)(C)C)c2)cc1C,,0.38595192,,,
+1719193785,*CCCCCCNC(=O)CCCCCCCC(=O)N*,,0.35199351,,,
+1719420059,*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(*)cc3)cc2)cc1,,0.37803494,,,
+1719450087,*OC(=O)C(CC(C)C)NC(=O)CCCCCCCCC(=O)NC(CC(C)C)C(=O)OC1COC2C(*)COC12,,0.35171013,,,
+1719719052,*Nc1ccc2cccc3c4c(N*)ccc5cccc(c1c23)c54,,0.34011693,,,
+1720612082,*CCc1ccc(*)c(Cl)c1,,0.39148425,,,
+1720647838,*CC1CC2CC1C1CCC(CN3C(=O)c4ccc(C(c5ccc6c(c5)C(=O)N(*)C6=O)(C(F)(F)F)C(F)(F)F)cc4C3=O)C21,,0.36864374,,,
+1720677048,*c1cccc(N2C(=O)C3CCC(C4CCC5C(=O)N(*)C(=O)C5C4)CC3C2=O)c1,,0.38218622,,,
+1720848416,*Nc1c(N)c(O)c(N)c(N2CCCCC2)c1N*,,0.33028423,,,
+1720993259,*Nc1ccc(Cc2ccccc2Cc2ccc(N*)cc2)cc1,,0.34535676,,,
+1721092492,*C=Nc1ccc(C(=O)OCCCCOCCCCOCCCCOC(=O)c2ccc(N=Cc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1,,0.36987967,,,
+1721498000,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c(O)c1)C2=O,,0.37996131,,,
+1721517021,*=Cc1ccc(C2=CSC(=*)S2)cc1,105.3372816,,,,
+1722126361,*CC(*)(C)C(=O)OCCCCn1c2ccccc2c2cc(N=Nc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])ccc21,,0.36195293,,,
+1722404544,*SSc1ccc(N=CNc2ccc(*)cc2)cc1,,0.41922647,,,
+1722437751,*c1ccc2c(c1)C(=O)N(c1ccc(OCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.36013194,,,
+1722752192,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4C3=O)cc2)cc1,,0.36428259,,,
+1722899407,*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Oc3ccc(C(c4ccc(OC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc1C)C2=O,,0.36997143,,,
+1723039756,*CCCCOc1ccc(C(=O)Nc2cccc(P(=O)(c3ccccc3)c3cccc(NC(=O)c4ccc(O*)cc4)c3)c2)cc1,,0.35398022,,,
+1723062866,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(-c3ccc(OC)cc3)cc2)cc1,,0.34182885,,,
+1723225912,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2C)c(C)c1-c1ccccc1,,0.37354711,,,
+1723711090,*CC(*)(C)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)F,,0.32377425,0.09,,
+1723857031,*CCCCCCOC(=O)OCCCCCCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35910685,,,
+1724528524,*CC(O)COc1ccc(C2CC(C(C)C)CCC2(C)c2ccc(O*)cc2)cc1,,0.36433734,,,
+1724863374,*Oc1ccc(CNC(=O)c2cccc(C(=O)NCc3ccc(OC4COC5C(*)COC45)cc3)c2)cc1,,0.33419104,,,
+1725180972,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5cc(*)n(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.38538397,,,
+1725259416,*c1ccc(-c2c(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(-c4cc(-c5ccc(-c6ccccc6)cc5)c(-c5ccc(-n6c(=O)c7cc8c(=O)n(*)c(=O)c8cc7c6=O)cc5)c(-c5ccc(-c6ccccc6)cc5)c4)cc3)cc2-c2ccc(-c3ccccc3)cc2)cc1,435.0,,,,
+1725823139,*CC(*)OC(=O)C(C)(C)C(C)C(C)C,,0.37195961,,,
+1726139010,*CC(*)(C)C(=O)Oc1c(C)cccc1C,,0.35811982,,,
+1726144568,*Oc1ccc(Oc2ccc(C(=Nc3ccc(Oc4ccc(N=C(c5ccccc5)c5ccc(*)cc5)cc4)cc3)c3ccccc3)cc2)cc1,,0.38246465,,,
+1726627716,*Nc1cccc(NC(=O)c2cc(NC(=O)c3ccccc3)cc(C(*)=O)c2)c1,,0.3335926,,,
+1726926869,*Nc1ccc2c(c1)-c1cc(N*)ccc1C2C#CC1c2ccc(N)cc2-c2cc(N)ccc21,,0.38806577,,,
+1727053406,*Oc1cccc(Oc2ccc(-c3ccc(*)cc3)cc2)c1C#N,,0.37561604,,,
+1727549471,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)n4)cc3)cc1)C2=O,,0.35806566,,,
+1727851724,*CC(*)C(=O)Nc1ccccc1,,0.3388664,,,
+1727933263,*Oc1ccc(NC(=O)CCCCCCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.349997,0.3745,1.073694962,27.22066116
+1727988738,*c1ccc(-c2nc3cc(-c4ccc5oc(*)nc5c4)ccc3o2)cc1,,0.43315781,,,
+1728252484,*CC(*)C(=O)OCCCCCCCCCCOc1cnc(-c2ccc(OC(=O)C3COC(C)(C)O3)cc2)nc1,,0.36105777,,,
+1728340477,*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.35936373,,,
+1729263987,*CC(*)(C)C(=O)Oc1ccc(C(=O)O)cc1,,0.31218986,,,
+1729640187,*CCCCCC(=O)Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2)cc1,,0.35751263,,,
+1729688245,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(Oc4ccc(O*)cc4)cc3)cc2)cc1,,0.34329551,,,
+1729742723,*CC(*)CCCCCCCC,,0.41042216,0.286,0.790932839,11.50351128
+1730077255,*Nc1ccc2cc3ccc(N*)cc3cc2c1,,0.34118592,,,
+1730293187,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCCCC,,0.36745632,,,
+1730621528,*CC(*)C(C)C,,,0.1868,0.75714125,12.12922155
+1731176824,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(C)(C)CCCCC(*)(C)C)cc5C4=O)cc3)cc1)C2=O,,0.37011178,,,
+1731224858,*CC(*)c1ccc(COCCCCO)cc1,,0.36446129,,,
+1731329584,*N=P(*)(OCCC)OCCC,,0.40352288,,,
+1731885131,*CC(*)c1ccc(COCCOCCCCCCCC)cc1,,0.38457352,0.282,0.931177736,11.54920484
+1732473081,*CC(CC(*)(C#N)C#N)c1ccc(OC(C)=O)cc1,,0.35920992,,,
+1732679124,*CCCCCCCCc1nnc(*)o1,-48.88416733,,0.324,,
+1733327889,*O*CCCCCCCCCC(=O)Oc1ccc(-c2ccc(C(=O)Oc3cccc(OC(=O)c4ccc(-c5ccc(OC(=O)CCCCCCCCC(*)CO*)cc5)cc4)c3[N+](=O)[O-])cc2)cc1,,0.38046147,,,
+1733763010,*Nc1ccccc1-c1ccccc1-c1cccc(-c2ccccc2-c2ccccc2)c1N*,,0.36434103,,,
+1734361691,*Cc1ccc(COC(=O)CCCCC(=O)O*)cc1,-4.158432897,,,,
+1734399308,*CCCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.39803512,,,
+1734532283,*C1(C)CC(*)(C)C(=O)N(C)C1=O,,0.35709749,,,
+1734712168,*C(c1ccc(N(C)C)cc1)C(*)c1ccc([N+](=O)[O-])cc1,,0.39669979,,,
+1734866978,*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O,,0.35680339,,,
+1735118252,*CCCCCCCCCCCCOC(=O)CCCCCCCCCCC(=O)O*,,,0.297,,
+1736400792,*CCCCCCCCCCCCCCCC(=O)O*,,,0.3954,,
+1736906548,*Nc1cccc(C2(c3cccc(N*)c3CC=C)c3ccccc3-c3ccccc32)c1CC=C,,0.37562104,,,
+1737253601,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)C(c6ccccc6)=C(c6ccc(C7=C(c8ccccc8)C(=O)N(*)C7=O)cc6)C5=O)cc4)cc3)cc2)cc1,,0.38106993,,,
+1737477599,*Nc1ccc(-c2ccc(N*)c(-c3cccc4ccccc34)c2-c2ccccc2)cc1,,0.36849354,,,
+1737643625,*Nc1cccc(Cc2cccc3ccc(N*)cc23)c1,,0.34622731,,,
+1737989724,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(O)c(N4C(=O)c5ccc(*)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1O)C2=O,,0.36411476,,,
+1738001334,*OS(=O)(=O)c1cccc(S(=O)(=O)Oc2cccc(*)c2)c1,29.19231194,,,,
+1738112244,*CC(C)(C)S*,,0.40403042,,,
+1738468386,*C(=O)Oc1ccc2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2c1,,0.35455535,,,
+1738776210,*CC(*)P(=O)(N(C)C)N(C)C,,0.37118508,,,
+1738997335,*CCN(CCCCOc1ccc(N=Nc2ccc(CCCCCC)cc2)cc1)CCOC(=O)c1ccc(C(=O)O)c(C(=O)Nc2ccc(C(C)(C)c3ccc(C(C)(C)c4ccc(NC(=O)c5ccc(C(=O)O*)cc5C(=O)O)cc4)cc3)cc2)c1,82.18427933,,,,
+1740193535,*Nc1cccc(NC(=O)CCCCC(=O)Nc2cccc(NC(=S)NC(=O)c3ccc(C(=O)NC(*)=S)cc3)c2)c1,,0.33679836,,,
+1740911960,*Nc1cccc(-c2cc(-c3ccccc3)c(N*)c(-c3ccccc3)c2)c1,,0.3551194,,,
+1740942243,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O,,0.4023531,,,
+1740967529,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccccc3)c3ccccc23)cc1,,0.35826257,,,
+1740996788,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6nc(-c7ccccc7)[nH]c6*)cc5)cc4)c3)cc2)cc1,,0.38321715,,,
+1741015152,*NCCCCCCCCN*,,0.33689515,,,
+1741494533,*Oc1ccc(Sc2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.37801304,,,
+1741924076,*CCCC(=O)Nc1ccc(NC(=O)CCCN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.32616598,,,
+1742146572,*Nc1ccc(CC(Cc2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.35372758,,,
+1742249499,*N=P(*)(Oc1ccc(F)cc1)Oc1ccc(F)cc1,,0.39101993,,,
+1742353769,*OC1CCCCC1*,,0.37752253,0.1694999999999999,0.931563064,14.6102827
+1742360510,*Nc1ccc2ccccc2c1-c1ccc(-c2c(N*)ccc3ccccc23)cc1,,0.35362502,,,
+1742360572,*CC(*)c1cccc(F)c1,,0.39194172,0.1623333333333333,,
+1743216271,*Oc1c([2H])c([2H])c(S(=O)(=O)c2c([2H])c([2H])c(Oc3c([2H])c([2H])c(C(c4c([2H])c([2H])c(*)c([2H])c4[2H])(C([2H])([2H])[2H])C([2H])([2H])[2H])c([2H])c3[2H])c([2H])c2[2H])c([2H])c1[2H],,0.37933974,,,
+1744491015,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccc(C4(c5ccc(O*)cc5)c5ccccc5-c5ccccc54)cc3)cc2)cc1,,0.35752228,,,
+1744666695,*Nc1cc(N*)c2cc3ccccc3cc2c1,,0.34003705,,,
+1744915619,*c1ccccc1C(=O)Oc1ccc2c(*)c3ccc(=O)cc-3oc2c1,,0.35798941,,,
+1744939463,*CCN(Cc1ccccc1)C(=O)S*,,0.36007699,,,
+1744997791,*O[Si](*)(CCC)CCC,,0.42983858,,,
+1745103437,*c1ccc(Sc2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)cc1,,0.43253891,,,
+1745290374,*CCOCCOCCOc1ccc(C=Cc2ccc(C=Cc3ccc(O*)c4ccccc34)cc2)c2ccccc12,,0.36046358,,,
+1745650366,*C=C(F)C(F)(F)C*,,0.35606714,,,
+1746336965,*c1cccc(S(=O)(=O)c2cccc(N3C(=O)c4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.36342185,,,
+1746379826,*Oc1ccc(Oc2ccc(C=NCCCCCCCCCCCCN=Cc3ccc(*)cc3)cc2)cc1,,0.37925907,,,
+1746456403,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O)c1ccc(C(C#N)=C(C#N)C#N)cc1,,0.37808499,,,
+1747354606,*c1ccc(O)c(N2C(=O)c3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,,0.36863924,,,
+1747846009,*CCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.34961914,,,
+1748642050,*c1ccc(Oc2ccc(-c3cc(-c4ccccc4)c4cc(Oc5ccc6nc(*)c(-c7ccccc7)c(-c7ccccc7)c6c5)ccc4n3)cc2)cc1,,0.40987104,,,
+1748671851,*C(=C(*)c1ccc(CCCC)cc1)c1ccccc1,,0.42642935,,,
+1748848667,*c1cc(CC(=O)OCC)c(*)s1,,0.394744,,,
+1748879320,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)N(C)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.37706698,,,
+1749285601,*CC(*)C(=O)n1c2ccccc2c2ccccc21,,0.37111974,,,
+1749666987,*Nc1ccc(-c2ccc(-c3ccccc3N*)cc2)cc1,,0.343504,,,
+1749688285,*c1ccc2c(c1)-c1nc3cc(Oc4ccc5c(c4)nc4n5C(=O)c5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5-4)ccc3n1C2=O,395.15,,,,
+1750306188,*CC(*)C(=O)NCCCCCCCCCCCCCCCCCCCCCC,,,0.3665,,
+1750548820,*CC1(*)OCC(c2ccccc2)O1,,0.35076731,,,
+1751325850,*CC(*)C(=O)Oc1ccc(OC)cc1,,0.33742314,0.208,1.117366408,14.44836262
+1751332443,*c1cc(C(c2ccc(OCCN(C)c3ccc(C=CC4=CC(=C(C#N)C(=O)OC)CC(C)(C)C4)cc3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCCN(c1ccccc1)c1ccc(C=CC2=CC(=C(C#N)C(=O)OC)CC(C)(C)C2)cc1,,0.3775713,,,
+1751447126,*CCCCCCCCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.38003558,,,
+1751469406,*CC(F)(F)C(F)(F)C(F)(F)CN(CC)C(=O)CCCCC(=O)N(*)CC,10.59395774,,,,
+1751782566,*CCOCCOCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.33474357,,,
+1751963173,*O[Si](C)(C)c1ccc([Si](C)(C)O[SiH](*)C)cc1,,0.43447128,,,
+1752199076,*Oc1ccc(C=Cc2cc(OCCCCCCCC)c(C=Cc3ccc(Oc4cc5c6c(ccc7c8c(*)cc9c%10c(ccc(c4c67)c%108)C(=O)N(CCCCCCCC)C9=O)C(=O)N(CCCCCCCC)C5=O)cc3)cc2OCCCCCCCC)cc1,,0.37287608,,,
+1752812344,*C(=O)c1cc(C(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)cc(-c2ccccc2)c1,,0.36476789,,,
+1753150513,*OP(=O)(Oc1ccc(NC(=O)Oc2ccc(-c3ccc(OC(=O)Nc4ccc(*)cc4)cc3)cc2)cc1)c1ccccc1,,0.34568614,,,
+1753201911,*Oc1ccc(N(CC)c2ccc(/C=C(\C#N)C(=O)NC3CCCCC3NC(=O)/C(C#N)=C/c3ccc(N(CC)c4ccc(*)cc4)cc3)cc2)cc1,,0.37635189,,,
+1753371685,*C(=Cc1cc(OC)c(C=C(c2ccc(*)cc2)c2ccc(C(F)(F)F)cc2)cc1OC)c1ccc(C(F)(F)F)cc1,,0.40216642,,,
+1753717945,*CC(*)OC(=O)c1cccc([Si](C)(C)C)c1,,0.39879763,,,
+1754228548,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(C=CC2=CC(=C(C#N)C#N)CC(C)(C)C2)cc1,,0.37248481,,,
+1754638763,*c1ccc(-c2nc3ccccc3o2)c(*)c1,,0.40450546,,,
+1754839380,*CCCCCCOC(=O)C(CCCCP(=O)(OCC)OCC)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.35900925,,,
+1754909012,*Nc1ccc(C2(c3ccc(N*)cc3)c3cc(N)ccc3-c3ccc(N)cc32)cc1,,0.3802377249999999,,,
+1754987570,*OCCCCCCCCCCCOc1ccc2nc(-c3ccc(OCCCCCCCCCCCOC(=O)c4cc(C(*)=O)cc(C(*)=O)c4)cc3)sc2c1,,0.36316614,,,
+1754996829,*CC(CO*)(CS(=O)(=O)CCC)CS(=O)(=O)CCC,,0.38361184,,,
+1755097598,*Nc1ccc(CCC)c2c(N*)cccc12,,0.34473863,,,
+1755361106,*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc3)C4=O)c2)cc1,,0.35225181,,,
+1755371243,*CCN(CCOC(=O)c1cc(OCc2cc(OCc3ccccc3)cc(OCc3ccccc3)c2)cc(C(=O)O*)c1)c1ccc(C=Cc2ccc(C=CC3=C(C#N)C(=C(C#N)C#N)OC3(c3ccccc3)C(F)(F)F)s2)cc1,,0.36152186,,,
+1755519343,*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)cc4)cc3)c1)C2=O,,0.36111327,,,
+1755789541,*CCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.33616703,,,
+1755924093,*Nc1ccc2c(c1)-c1ccc(N*)cc1C21c2ccccc2-c2ccccc21,,0.38085413,,,
+1756335423,*Nc1ccc2cc3c(-c4ccccc4)c(N*)c(-c4ccccc4)cc3cc2c1,,0.36293464,,,
+1756666691,*CCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1,,0.34276192,,,
+1756941114,*OCC(F)(F)C(F)(F)C(F)(F)COc1nc(F)c(F)c(OCC(F)(F)C(F)(F)C(F)(F)COc2c(F)c(*)nc(F)c2F)c1F,,0.33124109,,,
+1757174370,*CCNC(=O)CCCCCCCCC(=O)N*,,,0.302,1.004989804,16.962466
+1757237275,*Cc1ccc(CSSSS*)cc1,,0.42976694,,,
+1757460664,*CCCN(*)C(C)=O,,0.34005308,,,
+1757862881,*CCCCCCOC(=O)C(CCCCCP(=O)(O)O)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.3498874,,,
+1758001941,*CCCP(=O)(CCCNC(=O)CCCCC(=O)N*)c1ccccc1,,0.34157753,,,
+1758192044,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=Nc4ccc(Oc5ccc(-c6ccc(Oc7ccc(N=Cc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.3708493,,,
+1758920260,*CC(OCCCC)C1(C)C(=O)OC(=O)C1(*)C,,0.34511671,,,
+1759081904,*c1ccc(Oc2ccc(N3C(=O)C(C)C(SCCOCCSC4C(=O)N(c5ccc(Oc6ccc(N7C(=O)CC(C)(SCCOCCSC8(C)CC(=O)N(*)C8=O)C7=O)cc6)cc5)C(=O)C4C)C3=O)cc2)cc1,,0.34413754,,,
+1759526318,*CC(CCCCCCCCCC)C(CCCCCCCCCC)COC(=O)c1ccc(C(=O)O*)cc1,,0.37919414,,,
+1760524706,*c1sc(*)c(CCCCCCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)c1CCCCCC,,0.38480348,,,
+1761132878,*CC(*)CN,,0.36970834,,,
+1761703329,*C=CC1CC(=CC)C(*)C1,,0.40651799,,,
+1762139545,*Nc1cc(C)c(N*)cc1C,,0.3453129649999999,,,
+1762883597,*Oc1ccc(C=CC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(C)c4)cc3C)cc2)cc1,,0.39205535,,,
+1763751155,*CCOCCOCCOCCOCCOCCOC(=O)c1cccc(C(=O)O*)c1,,0.34081676,,,
+1763893927,*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C=CC4=O)c3)cc2)cc1,,0.33700736,,,
+1764253172,*Nc1cc(-c2ccc3ccccc3c2)cc(-c2ccc3ccccc3c2)c1N*,,0.34590052,,,
+1764580479,*C=Cc1ccc2c(c1)Sc1cc(-c3cc(CCCCCC)c(-c4sc(-c5ccc6c(c5)Sc5cc(*)ccc5N6c5ccc(OCCCCCCCCCCCC)cc5)cc4CCCCCC)s3)ccc1N2c1ccc(OCCCCCCCCCCCC)cc1,,0.39742745,,,
+1764654755,*CC(c1ccccc1)C1C(=O)N(c2ccc(C(C)(C)C)cc2)C(=O)C1*,,0.40001561,,,
+1765162399,*Nc1ccc(CCCc2ccc(N*)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)c(-c2ccc(C)cc2)c1-c1ccc(C)cc1,,0.38190375,,,
+1765350912,*CCOCCOC(=O)c1ccccc1C(=O)O*,,0.32806661,,,
+1765460904,*CC(=O)c1ccc(Oc2ccc(C(=O)COc3ccc(C=C4CCCC(=Cc5ccc(O*)c(OC)c5)C4=O)cc3OC)cc2)cc1,,0.35837048,,,
+1765837976,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Oc8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.3631463,,,
+1765927815,*CC(*)(C)C(=O)Oc1c(C(C)C)cccc1C(C)C,,0.3967853,,,
+1766008027,*Nc1c([2H])c([2H])c(*)c([2H])c1[2H],235.370823,,,,
+1766205762,*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc([N+](=O)[O-])cc3)cc(C(*)=O)c2)cc(C(=O)OC)c1,,0.33483978,,,
+1766427036,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7cc(C(C)(C)C)c(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C(C)(C)C)cc6C5=O)cc4)cc3)cc2)cc1,,0.39240961,,,
+1768246998,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)cc1,,0.3804246,,,
+1768426283,*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(C(F)(F)F)cc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.38899686,,,
+1769198640,*CC(*)(C)C(=O)OCCCCCCCC/C=C\CCCCCCCC,,0.38969165,,,
+1769217359,*N=P(*)(Oc1ccc(CC)cc1)Oc1ccc(CC)cc1,,0.40095438,,,
+1769343674,*Nc1ccc2c(c1)C(c1ccccc1)(c1ccccc1)c1cc(N*)ccc1-2,,0.37169398,,,
+1769579666,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1-c1ccccc1,,0.35976749,,,
+1769889494,*CC(C)(C)C1C(=O)N(c2c(CC)cccc2CC)C(=O)C1*,,0.36266864,,,
+1770296397,*Nc1ccc(C(C)(CC[C@](C)(c2ccc(N*)cc2)c2cccc(N)c2)c2ccc(N)cc2)cc1,,0.36112201,,,
+1770973625,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)Nc4ccc(-c5ccc(NC(=O)c6ccc(*)cc6)cc5C(F)(F)F)c(C(F)(F)F)c4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.36219263,,,
+1771399222,*CCCCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1,,0.3413806,,,
+1771648139,*C=CC(C(=O)OCc1ccccc1)C(*)C(=O)OCc1ccccc1,,0.35713258,,,
+1772221241,*c1ccc(-c2ccc(Oc3ccc(C4(C)CC(C)(C)c5ccc(Oc6ccc(-c7ccc(N8C(=O)c9ccc(Oc%10ccc(C(C)(C)c%11ccc(Oc%12ccc%13c(c%12)C(=O)N(*)C%13=O)cc%11)cc%10)cc9C8=O)cc7)cc6C(F)(F)F)cc54)cc3)c(C(F)(F)F)c2)cc1,,0.37902457,,,
+1773311444,*CCN(CCOC(=O)Nc1cccc(NC(=O)O*)c1C)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35215357,,,
+1773383580,*CCCCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12,,0.36384128,,,
+1773451274,*CC(*)c1cc(C(=O)Oc2ccc(OCCCCCCCCCCCCCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCCCCCCCCCCCCCC)cc1,,0.38467794,,,
+1773543795,*CCN(*)C(=O)c1cccc(F)c1,,0.34841899,,,
+1773625763,*O[Si](C)(C)c1ccc2ccc3c([Si](*)(C)C)ccc4ccc1c2c43,,0.39894601,,,
+1773759171,*CC(*)C(=O)c1ccc(C(C)C)cc1,,0.39294141,,,
+1773982795,*CC(*)(C)C(=O)OCCCCCn1c2ccccc2c2ccccc21,,0.35157663,,,
+1774773315,*C1C=CC(*)C1,,,0.2784999999999999,0.939550686,18.92440401
+1775591199,*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(*)c(C(C)C)c3)cc2)cc1C,,0.39987425,,,
+1775887847,*OC(=O)Nc1ccc(C)c(NC(=O)OC2COC3C(*)COC23)c1,,0.33930504,,,
+1776137292,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(-c5cnc6cc(-c7ccc8nc(*)cnc8c7)ccc6n5)cc4)cc3)cc2)cc1,,0.38383553,,,
+1776297302,*CCCCCCCCCCCCCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,,0.304,,
+1776629260,*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCCCC(=Cc5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1,,0.36733579,,,
+1777009355,*NC1CCC(CCC2CCC(NC(=O)CCCCCCCCC(*)=O)CC2)CC1,,0.36490438,,,
+1777047790,*NSC1(N*)N=C(N)NC(N)=N1,,0.31058634,,,
+1777341480,*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(O)c3)cc1O)C2=O,,0.37314442,,,
+1777517337,*Nc1cc2c(cc(N*)c3c(-c4ccc(C=C)cc4)c(-c4ccc(C=C)cc4)c(-c4ccc(C=C)cc4)c(-c4ccc(C=C)cc4)c32)c2ccccc12,,0.37091967,,,
+1777622609,*CC(*)C(=O)OC(C)CC(C)C,11.47675289,,0.207,0.905312834,11.11375504
+1777831717,*Oc1ccc(C(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1,,0.38178031,,,
+1777861386,*Nc1c(-c2ccccc2)cc(Cc2cc(-c3ccccc3)c(N*)c3ccccc23)c2ccccc12,,0.3626091,,,
+1778072997,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4C)cc3)cc2)c(C)c1,,0.38660473,,,
+1778095789,*OC(=O)c1cccc(OCCCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1,,0.34800981,,,
+1778794604,*CC#CC(C#CCOC(=O)CCCCCCCCC(=O)O*)=Cc1ccc(C#Cc2ccc(C=C(C#Cc3ccccc3)C#Cc3ccccc3)cc2)cc1,,0.397984,,,
+1778806349,*OC(CCCCCC)CC(*)=O,,0.37673739,,,
+1779122339,*c1ccc(Oc2ccc(-c3nc4ccc(S(=O)(=O)c5ccc6nc(*)[nH]c6c5)cc4[nH]3)cc2)cc1,315.1120806,,,,
+1779443972,*CC(*)(C)C(=O)OCc1ccc[se]1,,0.35173384,,,
+1779551878,*CCCCCCN(C)C(=O)CCCCCCCCCCCCCCC(=O)N(*)C,,,0.33,,
+1780220643,*CCCCCCCCCCC(=O)NCCCCCOCCCCCNC(=O)CCCCO*,,,0.3349999999999999,0.94573393,19.75537697
+1780262446,*N=P(*)(Oc1cccc(C)c1)Oc1cccc(C)c1,,0.3884914,,,
+1780443736,*Nc1ccc(-c2c(CC)ccc(N*)c2-c2ccccc2)cc1,,0.3658892,,,
+1781035787,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)n5-c5cccc(C(F)(F)F)c5)cc4)cc3)cc2)cc1,,0.38422628,,,
+1781608309,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.35216726,,,
+1781915086,*CC(C)CNC(=O)O*,,0.33053408,,,
+1781926636,*CCCCCCOc1ccc(C=Cc2ccc(O*)cc2)cc1,,0.36660231,,,
+1782813918,*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Nc4ccc(C(O)(c5ccccc5)c5ccc(C(O)(c6ccccc6)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.39392951,,,
+1782970907,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N5C(=O)C6CCC(C7CCC8C(=O)N(*)C(=O)C8C7)CC6C5=O)cc4)cc3)cc2)cc1,,0.37853273,,,
+1783140001,*CCCC=C(*)c1ccc(Cl)cc1,,0.38684815,,,
+1783164838,*Oc1c(C)cc(-c2cc(C)c(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)c(CBr)c2)cc1CBr,,0.40843427,,,
+1783173657,*CC(*)(CC(=O)OC(C)C)C(=O)OC12CC3CC(CC(C3)C1)C2,,0.39313368,,,
+1783262113,*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)c3cccc(C(*)=O)c3)c(C)c2)cc1C,,0.39532424,,,
+1783614985,*CC(*)c1cc(OC)c(OC(C)=O)c(OC)c1,,0.34893966,,,
+1784004493,*CC(CO*)(CS(=O)(=O)CCCC)CS(=O)(=O)CCCC,,0.38553918,,,
+1784137084,*CCCCCOC(=O)NCCCCCCNC(=O)O*,,0.33912475,,,
+1784177503,*Oc1cccc(OC(=O)Oc2ccc(OC(*)=O)cc2)c1,-104.3379932,,,,
+1784231547,*Oc1ccc(N=C(C)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(C(C)=Nc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37958544,,,
+1784319633,*CC(*)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2,,0.37551115,0.1635,0.949969923,9.767195327
+1785002432,*Nc1ccc(C(=O)c2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1,,0.38972953,,,
+1785099361,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(C(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)c1)C2=O,,0.36964109,,,
+1785446728,*Nc1ccc(Cc2ccc(C3(c4ccc(Cc5ccc(N*)cc5)cc4)C=C[C@@H](CCCC)CC3)cc2)cc1,,0.36309112,,,
+1785988282,*CCCCCCCCCCCCCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.40147903,,0.950245541,20.47027569
+1786011241,*CC(*)(C)C(=O)OCCCCCCCCCCCCCCC(=O)NC1OC(CO)C(OC2OC(CO)C(O)C(O)C2O)C(O)C1O,,0.32366656,,,
+1786063717,*CCC1C(=O)N(CCCCCCCCCCCCCCCC)C(=O)C1*,,0.3913188,,,
+1786072112,*Oc1ccc(C(C)(c2ccccc2)c2ccc(Oc3ccc(C(=O)C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.38132966,,,
+1786149230,*CCCCC[Si](*)(C)c1ccccc1,,0.38579865,,,
+1786272690,*Oc1ccc(NC(=O)c2ccc(C(c3ccc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)C7CC8CC(C7)CC6C8)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.36728029,,,
+1787334599,*CCOC(=O)CCCCC(=O)OCCOC(=O)c1ccc(C(=O)O*)cc1,,0.32710546,,,
+1787342270,*Oc1cc(C(=O)c2ccccc2)c(Oc2ccc(*)cc2)cc1C(=O)c1ccccc1,,0.37661047,,,
+1787772171,*CC(*)CCCCCCCCCCCCCC,,0.40318632,0.3409999999999999,0.795879987,11.75279889
+1788116403,*Oc1ccc(-c2ccc(OC(=O)c3cc(OCCCCCC)c(C(*)=O)cc3OCCCCCC)cc2)cc1,,0.3630012,,,
+1788310964,*Oc1cc(CCCCCC)cc(OC(=O)c2cccc(C(*)=O)c2)c1,,0.36594345,,,
+1788326945,*CC(*)(C)C(=O)OCc1ccc(F)cc1F,,0.3435113,,,
+1788446474,*OC(=O)c1cccc(C(=O)Oc2ccc3c(c2)Oc2cc(*)ccc2C32OC(=O)c3ccccc32)c1,,0.35989808,,,
+1788777860,*Nc1cccc(CCc2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)c1,,0.35224446,,,
+1788955118,*Oc1ccc(C(c2ccc(Oc3c(F)c(F)c(COC(c4cccc(C(OCc5c(F)c(F)c(*)c(F)c5F)(C(F)(F)F)C(F)(F)F)c4)(C(F)(F)F)C(F)(F)F)c(F)c3F)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.34613414,,,
+1789314864,*O[Si](*)(CCCOCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOCCOC,,0.36320309,,,
+1789414981,*CCC1C(=O)N(CC)C(=O)C1*,,0.33175789,,,
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+1790048084,*CCCCCCCCCCOCCCCCCOCCCCCCO*,,,0.36175,0.880404561,21.78044662
+1790316910,*Nc1ccc(Oc2ccccc2)cc1N*,,0.33227989,,,
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+1791321384,*Nc1ccc([C@]2(c3ccccc3C)C=C[C@](N*)(c3ccccc3)C=C2)cc1,,0.35212894,,,
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+1791802492,*CCN(*)C(=O)c1c(F)c(F)cc(F)c1F,,0.34317631,,,
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+1792419504,*NC1CCC(N*)CC1,,0.335675645,,,
+1792717166,*CCC1CC(*)C2C3CC(C)C(C3)C12,,0.38823486,,,
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+1793714873,*O[Si](C)(CCC(F)(F)F)CCC(F)(F)CC[Si](*)(C)CCC(F)(F)F,,0.39640664,,,
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+1795185770,*Nc1ccc(-c2c(-c3ccccc3)c(-c3ccccc3)c(N*)c(-c3cccc4ccccc34)c2-c2cccc3ccccc23)cc1,,0.3785304,,,
+1795208457,*CC/C=C(/*)C(C)C,,,0.21,,
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+1796856353,*CC(*)(C)C(=O)OC(C)C(F)(F)F,,,0.1139999999999999,1.11386163,13.75326334
+1796975944,*Nc1ccc(NC(=O)c2cc(-c3ccc(C(=O)OCC)c(C(*)=O)c3)ccc2C(=O)OCC)cc1,,0.34355176,,,
+1797111879,*Oc1c(C)cc(C2(c3cc(C)c(OC(=O)c4cccc(C(*)=O)c4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.40660507,,,
+1797127681,*CC(*)(C)C(=O)OCCCCCCN(C)c1ccc(N=Nc2ccc(S(C)(=O)=O)cc2)cc1,,0.37549174,,,
+1797412842,*CCN(*)C(=O)CC12CC3CC(CC(C3)C1)C2,,0.35920026,,,
+1797791168,*CCC(=O)NCc1cc(C)c(CNC(=O)CCO*)cc1C,,0.32886329,,,
+1797950227,*c1ccc(Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(Oc4ccc(N5C(=O)CC(Nc6ccc(Cc7ccc(NC8CC(=O)N(*)C8=O)cc7)cc6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.38103363,,,
+1798703824,*C(=O)Nc1ccc(N2C(=O)CC(N3C(=O)c4ccc(*)cc4C3=O)C2=O)cc1,,0.34885907,,,
+1798783963,*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O,,0.37947284,,,
+1798943055,*CC/C=C(/*)CCCCCCCCCC,,,,0.81139035,13.05800565
+1799071483,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1,,0.41859746,,,
+1799254140,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3C)CC3CC2C2C4CCC(C4)C32)cc1C,,0.39110607,,,
+1799502171,*C#CC(CCCCOC(=O)NCCCCCC)=C(*)CCCCOC(=O)NCCCCCC,5.219913288,,,,
+1799759378,*CCCCCCS(=O)(=O)c1ccc(N=Nc2ccc(N(C)CCCCCC(=O)O*)cc2)cc1,,0.37402313,,,
+1800029719,*Nc1ccc2c(c1)C1(c3ccccc3-c3ccccc31)c1cc(N*)ccc1-2,,0.37700036,,,
+1800644931,*Oc1c(C)cc(-c2cc(C)c(OC(=O)CCCCCC(*)=O)c(C)c2)cc1C,,0.36806189,,,
+1801218015,*CCOCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.33440308,,,
+1801224003,*CCCC(C)CNC(=O)c1ccc(C(=O)N*)cc1,,0.33451946,,,
+1801465428,*OC(=O)c1cccc(OCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1,,0.34442932,,,
+1801821900,*C=C(C#N)C(=O)OCCCCCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCC,,0.37650468,,,
+1801990211,*Nc1ccc(-c2ccc3c(c2)C(C)(C)C[C@@]3(C)c2ccc(N*)cc2)cc1,,0.36588438,,,
+1802780820,*c1ccc(C(c2ccc(N3C(=O)c4ccc(Oc5ccc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6c5)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37532506,,,
+1802973320,*C=CC1CC(*)C2C(=O)N(CCCCC)C(=O)C12,,0.36388882,,,
+1803088964,*Nc1ccc2c(c1)-c1ccc(N*)cc1C21c2cc(N)ccc2-c2ccc(N)cc21,,0.38509104,,,
+1804539920,*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc2)C(=O)N(c2ccc(CC)cc2)C3=O)cc1,,0.38009761,,,
+1804622774,*Oc1c(C)cc(C(C)(C)c2cc(C)c(ON3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)CCC(C(C)(C)C)CC6)cc5)cc4C3=O)c(C)c2)cc1C,,0.38578028,,,
+1804735174,*c1cc(OC)c(*)cc1OC,63.90036252,,,,
+1804758935,*Cc1cc2cc(C(=O)Nc3cccc(NC(=O)c4cc5cc(*)c(OC(=O)COc6ccc7ccc(=O)oc7c6)cc5oc4=O)c3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,,0.32001904,,,
+1804814245,*CC(*)CC(CC)CC,,,0.219,,
+1804819295,*CC(*)(C)C(=O)Oc1ccc(C(=O)Oc2ccccc2)cc1,,0.34588936,,,
+1804829075,*CCCCCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34124833,,,
+1804836030,*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc(N%12C(=O)c%13ccc(*)cc%13C%12=O)cc%11)cc%10)NC(=O)c%10ccccc%109)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1,,0.37607232,,,
+1805818890,*Nc1ccc(C(=O)c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.34499015,,,
+1805828893,*/C=C/c1cc(OCCCCCCCCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1OCCCCCCCCCCCCCCCC,,,0.3329999999999999,0.88205721,15.92222857
+1806252045,*COc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.33358286,,,
+1806255411,*CCCCCCCCOc1ccc(C=C2CCC(=Cc3ccc(OCCCCCCCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC,,0.36012749,,,
+1806416444,*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)c(C(C)(C)C)c2)cc1,,0.36296603,,,
+1806691588,*Nc1cc(N*)c(/N=N/c2ccc(S(=O)(=O)O)cc2)c(N)c1C,,0.34174753,,,
+1806857427,*CCc1ccc(CCNC(=O)CCCCCCCCCCCCCCC(=O)N*)cc1,65.25488595,,,,
+1806961099,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.41433043,,,
+1807042238,*CCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)CCCCCn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1,60.41501019,,,,
+1807156052,*CCC[Si](*)(C[Si](C)(C)c1ccccc1)C[Si](C)(C)c1ccccc1,,0.37926161,,,
+1807233855,*CCCCCCCCCCCOC(=O)c1cc(OCCCCCCCCCCOc2ccc(N=Nc3ccc(OC)cc3)cc2)cc(C(=O)OCCCCCCCCCCCOc2ccc(-c3ccc(O*)cc3)cc2)c1,,0.36806216,,,
+1807732967,*CCCCCC(=O)Nc1ccc(Oc2ccc(NC(=O)CCCCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.33700629,,,
+1808141855,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.35396317,,,
+1808438148,*C(*)C(=O)OC(C)C,,0.38104788,,,
+1808604040,*Oc1cccc(NC(=O)c2cccc(C(=O)Nc3ccc(*)cc3)c2)c1,,0.34287767,,,
+1808750963,*N=P(N=P(N=S(*)(=O)NCCCC)(NCCCC)NCCCC)(NCCCC)NCCCC,,0.3991644,,,
+1808991863,*CC(*)(Cl)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OCCCC)cc2)cc1,,0.35349732,,,
+1809093717,*CCP(Cl)(Cl)=Nc1ccc(N=P(*)(Cl)Cl)cc1,-27.34500252,,,,
+1809334970,*CC(*)(C)C(=O)OCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.34226697,,,
+1809372811,*c1ccc2oc(-c3ccc(C(c4ccccc4)(c4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)C(F)(F)F)cc3)nc2c1,,0.43791406,,,
+1809467126,*/C=C/c1ccc(Oc2ccc(-c3ccc(-c4ccc(-c5ccc(Oc6ccc(/C=C/c7ccc8c(c7)c7cc(*)ccc7n8CC(CC)CCCC)cc6)c(C(F)(F)F)c5)cc4)cc3)cc2C(F)(F)F)cc1,187.4626146,,,,
+1809480718,*C(C)=C(*)[Si](C)(C)CC,157.1009863,,,,
+1809618585,*/C=C/c1cc(OCCCCCCCCCCCC)c(*)cc1OC,,,0.251,0.902248035,20.35612021
+1809672801,*CCCCCCCCCNC(=O)CCCCCCCCCCCC(=O)N*,,,0.348,,
+1809695642,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(N=Nc2ccc(C3OCCO3)cc2)cc1,,0.35607146,,,
+1809858457,*Nc1cc(Cl)c(N*)cc1Cl,,0.34205564,,,
+1809876113,*NC(CCC(=O)OCCCCCC)C(*)=O,,0.3561514,,,
+1810490415,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCC(C)CC(*)=O)cc2)C(=O)OC)cc1,,0.33698176,,,
+1810774375,*Nc1ccc(-c2ccc(N*)c(-c3ccc(C(C)(C)C)cc3)c2-c2ccc(C(C)(C)C)cc2)cc1,,0.37953942,,,
+1810979250,*C=Cc1cc(OC)c(C=Cc2ccc(C=C(Oc3ccccc3)c3ccc(C(=Cc4ccc(*)cc4)c4ccc(Oc5ccccc5)cc4)cc3)cc2)cc1OC,,0.37691892,,,
+1811086141,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)NCCCCCCNC(=O)O*,,0.32095446,,,
+1811300122,*OC(C)COC(=O)c1cccc(C(*)=O)c1,,0.34359034,,,
+1811542913,*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(Oc4ccc(Cc5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.3772016,,,
+1812532838,*CCCCCOC(=O)OCCCCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O,,0.33554608,,,
+1812564153,*Nc1ccc(CC(C)(C)c2ccc(N)c(C[C@@](C)(CC)c3ccc(N*)cc3)c2)cc1,,0.35226185,,,
+1812700910,*CC(*)c1ccc(C(C)(O)CCCCC)cc1,,0.39339807,,,
+1813031373,*OC(COCC)COC(*)=O,,0.34611534,,,
+1813310957,*c1ccc(C(=O)c2ccc(N3C(=O)c4ccc(Oc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4C3=O)cc2)cc1,,0.36363712,,,
+1813912008,*OC(COC(=O)c1c(F)c(F)c(-c2c(F)c(F)c(C(*)=O)c(F)c2F)c(F)c1F)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35000272,,,
+1813959998,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)CC(=O)N(*)C5=S)cc4)cc3)cc2)cc1,,0.37421708,,,
+1814261052,*CCC(=O)N*,,0.28900346,,,
+1814313694,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.33911271,,,
+1814513311,*CC(*)(C)C(=O)OCc1ccccc1,,0.34737998,0.1755,1.051885651,12.3992761
+1814521184,*CC(*)(C)C(=O)OCCO[N+](=O)[O-],,0.49559463,,,
+1814522046,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)c(CCCC)c2)cc1CCCC,,0.39067771,,,
+1814526307,*c1ccc(Oc2ccc(N3C(=O)c4cccc(Oc5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)c4C3=O)cc2)cc1,,0.36618707,,,
+1814705967,*CCCC1(C)CCN(C(=O)CCC(=O)N2CCC(*)(C)CC2)CC1,,0.34856982,,,
+1814831585,*Nc1cc(-c2ccccc2)c(-c2ccccc2)c(-c2ccccc2)c1N*,,0.36099506,,,
+1814888987,*CCN(*)C(=O)c1cc(F)ccc1F,,0.34543558,,,
+1815199784,*Oc1ccc(C2(c3ccc(OC(=O)CCC(*)=O)cc3)c3cc(OC)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.36261779,,,
+1815220094,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)c([N-][N+]#N)c1,196.3053138,,,,
+1815368241,*NCCCNCCCCNCCCN*,,0.34373829,,,
+1815372590,*NC1CCC(CCC2CCC(NC(=O)CCCCCCCCCCC(*)=O)CC2)CC1,,0.36845122,,,
+1815391317,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(-c6ccc7nc(*)cc(-c8ccccc8)c7c6)ccc5n4)cc3)c3ccccc3C(=O)c3ccccc32)cc1,,0.46820776,,,
+1815514979,*Nc1ccc(C2(c3cc(CC)c(N*)c(CC)c3)c3ccccc3-c3ccccc32)cc1C,,0.38779866,,,
+1815907027,*OP(=O)(Oc1ccccc1)Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1,,0.35305401,,,
+1816427508,*C(C#N)=Cc1cc(OCCCCCCCCCCCC)c(C=C(C#N)c2cc(OCCCCCCCCCCCC)c(*)cc2OC)cc1OC,,0.39567001,,,
+1816536666,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C5(*)NC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.37763456,,,
+1816744100,*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)C5C6C=CC(C7C(=O)N(*)C(=O)C67)C5C4=O)cc2)C(=O)N(CCN(C)C)C3=O)cc1,,0.3795624,,,
+1817213256,*Nc1cccc(-c2cccc(-c3cccc(-c4cccc(-c5ccccc5)c4)c3)c2)c1N*,,0.34461865,,,
+1817586753,*CCCCCCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.34919746,,,
+1817646491,*CC(OO*)c1ccc(Br)cc1,,0.38242794,,,
+1817835124,*OC(F)(F)C(*)(F)F,,0.30466987,,,
+1818051082,*Nc1ccc(Cc2cc(Cc3ccc(N*)c(Cc4ccc(N)cc4)c3)ccc2N)cc1,,0.35357307,,,
+1818615314,*CC1CCC(CNC(=O)CCCCC(=O)N*)CC1,,0.33681395,,,
+1818810158,*CCCCCCCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1,,0.34707685,,,
+1819092687,*Oc1ccc(NC(=O)NCCCCCCNC(*)=O)cc1,122.4088725,,,,
+1819263382,*c1ccc(OC(=O)c2cc(C(=O)Oc3ccc(C4(*)OC(=O)c5ccccc54)cc3)cc(C(C)(C)C)c2)cc1,,0.37066817,,,
+1819602456,*O[Si](C)(C)CCCC(=O)Oc1ccc(C=Nc2ccc(N=Cc3ccc(OC(=O)CCC[Si](*)(C)C)cc3)cc2)cc1,,0.38440001,,,
+1820587217,*Oc1ccc(NC(=O)c2ccc3cc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)CC7CCC6C7)cc5)cc4)ccc3c2)cc1,,0.37134443,,,
+1820695762,*c1cccc(OCCCCCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.35443636,,,
+1820800881,*Nc1ccc(C2(c3ccc(N*)c(C(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)C,,0.39538255,,,
+1820965288,*CC(O)COc1ccc(S(=O)(=O)c2ccc(O*)cc2)cc1,,0.34218345,,,
+1821594642,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(C(C)(C)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)c2)cc1,,0.36408469,,,
+1821776890,*Oc1c(F)c(F)c(-c2c(F)c(F)c(Oc3ccc(Cc4ccc(*)c(C=O)c4)cc3C=O)c(F)c2F)c(F)c1F,,0.35996186,,,
+1822483719,*CCCCCNC(=O)CCCCCCCCCCCC(=O)N*,,,0.3545,0.943380242,21.69094086
+1822664278,*CC(*)c1ccc(O)c(OC)c1,,0.34066185,,1.031531368,14.62592281
+1823153942,*C(=O)Nc1ccc(NC(=O)Nc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.33714284,,,
+1823366229,*Oc1ccc(C(C)(CC)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,194.1629808,,,,
+1823476334,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCCCCCC)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.37139047,,,
+1823522189,*N=P(N=P(N=S(*)(=O)NCCCCCC)(NCCCCCC)NCCCCCC)(NCCCCCC)NCCCCCC,,0.40482375,,,
+1823535687,*CCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.34954591,,,
+1824329781,*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37378543,,,
+1824700293,*CC(*)C(=O)OCCCCCCCCCCCCCCCCCC,,0.40921422,0.3795,0.864259855,13.46584828
+1824932578,*Nc1ccc(C(C)(CCc2ccccc2)c2ccc(N*)cc2)cc1,,0.35015139,,,
+1825472814,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N5C(=O)c6ccc(*)cc6C5=O)cc3)C4=O)cc2)cc1,,0.36202552,,,
+1826128756,*CC(*)OC(=O)CCCCCCCCCCCCCCCCC,,,0.305,0.856119988,12.48057398
+1826423349,*c1ccc(-c2ccc(-c3ccc(C(*)(CC)C(F)(F)F)cc3)cc2)cc1,,,0.236,0.989585625,22.38019563
+1826581063,*Nc1cccc(/C=C2\c3ccccc3-c3ccc(N*)cc32)c1,,0.352832805,,,
+1826810455,*Nc1cccc2c(C)c3c(N*)cccc3c(C)c12,,0.33227337,,,
+1827057509,*CCCOCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.3420761,,,
+1827233180,*CCCCCCNC(=O)CCCCCCCC1C(CCCCCCCC(=O)N*)C=CC(CCCCCC)C1CCCCCCCC,,0.38088319,,,
+1827383703,*Nc1ccc(C(c2ccc(NC(=O)c3ccc(-c4ccc(C(*)=O)c(C)c4)cc3C)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.36225344,,,
+1827805083,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(N7C(=O)c8ccc(*)cc8C7=O)cccc56)cc3)C4=O)cc2)cc1,,0.35708671,,,
+1828252932,*Cc1ccccc1[Si](*)(c1ccccc1)c1ccccc1,,0.39224938,,,
+1828453397,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,145.3751112,,,,
+1828496288,*c1ccc(C(=O)NC(CC)COC(=O)c2ccc(N3C(=O)CC(SCCCCCSC4CC(=O)N(*)C4=O)C3=O)cc2)cc1,,0.34187681,,,
+1828551601,*OC1C(CO)OC(*)C(O)C1O,,0.29046872,,,
+1828668618,*CCC(c1ccccc1)C(*)c1ccccc1,,0.40418065,,,
+1828961126,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCCC(=Cc5ccc(Oc6ccc(S(=O)(=O)c7ccc(Oc8ccc(C(C)(C)c9ccc(*)cc9)cc8)cc7)cc6)cc5)C4=O)cc3)cc2)cc1,,0.37671926,,,
+1828973221,*c1cccc(P(=O)(c2ccccc2)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(P(=O)(c6ccccc6)c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,,0.35364817,,,
+1828977072,*C(=O)c1ccc(N(*)CCCCC)cc1,,0.38487639,,,
+1829411915,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)c3cc(OC)ccc3-c3ccc([N+](=O)[O-])cc32)cc1,,0.37589479,,,
+1829654817,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(Oc5cccc(Cc6cccc(*)c6)c5)cc4)cc3)cc2)cc1,,0.3678457,,,
+1830100599,*c1ccc(Oc2ccc(N3C(=O)C(Cl)=C(Oc4ccc(C(C)(C)c5ccc(OC6=C(Cl)C(=O)N(*)C6=O)cc5)cc4)C3=O)cc2)cc1,,0.36630147,,,
+1830469159,*CCN(*)C(=O)c1c(F)ccc(F)c1F,,0.34269166,,,
+1830893860,*CCCCCCCCCCCCCCNC(=O)CCCCCCCCCCC(=O)N*,,,0.346,0.906553684,23.16076788
+1831743927,*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(S(=O)(=O)c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc4)C5=O)cc3)cc2)cc1,,0.35854864,,,
+1832371636,*Oc1ccc(OC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2-c2ccccc2)cc1C(C)(C)c1ccccc1,,0.37013826,,,
+1832963659,*Nc1ccc(-c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.36015628,,,
+1832968600,*CC(*)(C)C(=O)OCCCCCCCCCCCCCCCC[n+]1ccc(N(C)C)cc1,,0.37909157,,,
+1833216472,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,165.0428244,,,,
+1834039585,*CCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.33870557,,,
+1834169078,*C1C(*)C2CC1C(C(=O)OCC13CC4CC(CC(C4)C1)C3)C2C(=O)OCC12CC3CC(CC(C3)C1)C2,,0.37365813,,,
+1834440826,*CCN(*)C(=O)c1ccc(F)c(F)c1,,0.34216811,,,
+1834775911,*Nc1ccc2c(c1)[C@@H]1c3ccccc3[C@H]2c2cc(N*)ccc21,,0.38083739,,,
+1835012454,*CC(*)(CCCCCCCC)C(=O)OC,,0.38929213,,,
+1835122928,*CCOCCOCCOCCOCCOCCOCCOCCOCCOCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1,,0.34589389,,,
+1835485471,*c1ccc(C(c2ccc(N3C(=O)c4cc(-c5ccc([Si](C)(C)C)cc5)c(-c5cc6c(cc5-c5ccc([Si](C)(C)C)cc5)C(=O)N(*)C6=O)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.43114374,,,
+1835490672,*OC(CC)C(*)=O,,0.35896412,,,
+1835545898,*CCc1ccc(*)c2cccnc12,70.93028064,,,,
+1835793920,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc3)C4=O)cc2)cc1,,0.35981052,,,
+1836092833,*CC(O)COc1c(C)cc(C(C)(C)c2cc(C)c(OCC(O)COc3ccc(S(=O)(=O)c4ccc(O*)cc4)cc3)c(C)c2)cc1C,,0.36142782,,,
+1836401630,*c1ccc(-c2ccc(-c3ccc4nc(Oc5cc(-c6ccccc6)c6cc(*)ccc6n5)cc(-c5ccccc5)c4c3)cc2)cc1,,0.39257783,,,
+1836411379,*c1cc(SCCCCCC)c(*)s1,,0.43728421,,,
+1836445960,*Nc1ccc(C2(c3ccc(N*)c(Cl)c3)c3ccccc3-c3ccccc32)cc1Cl,,0.37468924,,,
+1837157915,*OC(=O)c1cccc(OCCCCCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1,,0.34799982,,,
+1837160865,*OC(=O)CCCCCCCC(*)=O,-68.69432556,,,,
+1837451686,*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1,,0.35635169,,,
+1837536238,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CC,,0.3554716,,,
+1837695049,*C(=O)N1C(=S)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)=C(C(=O)OCC)C1c1ccccc1,,0.3735679,,,
+1837695884,*Nc1ccc(Cc2ccc(CC(CCCCCCC)Cc3ccc(Cc4ccc(N*)cc4)cc3)cc2)cc1,,0.35965572,,,
+1837801743,*Oc1ccc(OC(=O)c2ccc(OC(=O)c3ccc(C(=O)Oc4ccc(C(*)=O)cc4)cc3)cc2)cc1C,81.15934241,,,,
+1838148916,*NC(CCC(=O)OCCCCCCCCCCCC)C(*)=O,30.09586697,,,,
+1838224579,*CCCC(O)CN(CCCC)c1ccc(C#Cc2ccc(S(*)(=O)=O)cc2)cc1,,0.38948294,,,
+1838851947,*NC1=N[C@@H](O)NC(N*)=C1N,,0.2862332,,,
+1839284190,*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(OC4COC5C(*)COC45)cc3)c2)cc1,,0.34004989,,,
+1839465675,*Nc1ccc([C@]2(C)CC(C)(C)c3ccc(N*)cc32)cc1,,0.36097818,,,
+1839521966,*CCOC(=O)NCCCCCCNC(=O)OCCOc1ccc(C2(c3ccc(O*)cc3)c3ccccc3-c3ccccc32)cc1,,0.34888968,,,
+1840288311,*/C=C/c1ccc(*)c(-c2cc(OCCC(C)C)c(OCCC(C)C)cc2-c2ccc(OC)cc2)c1,,,0.308,0.925658948,17.48157569
+1840821848,*CC(*)c1ccc(COCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.35405781,,,
+1841717328,*c1ccc(Cc2ccc(N3C(=O)C4CCC5C(=O)N(*)C(=O)C5C4C3=O)cc2)cc1,,0.37944453,,,
+1841939316,*N[C@@H](C[C@@H](N*)[C@H](O)C[C@H](N)C(=O)O)C(=O)O,,0.28753507,,,
+1841990330,*NC1=CC[C@](c2ccc(N*)cc2)(c2cccc3ccccc23)C=C1,,0.3503374,,,
+1842093316,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3cccc(Oc4cccc(C(*)=O)c4)c3)cc2)cc1,,0.3604747,,,
+1842577004,*C(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)NC(=O)c8ccccc87)cc6)cc4)C5=O)cc3)c2)cc1,,0.36654721,,,
+1842579493,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCC(C)(C)COc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.35788134,,,
+1842592587,*O[Si](*)(CCCOCCOCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOCCOCCOC,,0.36679143,,,
+1842699132,*Nc1ccc(C(c2ccc(NC(=O)c3ccc4cc(C(*)=O)ccc4c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1,,0.36261641,,,
+1843486758,*CC(*)OC(=O)C(F)(F)C(F)(F)F,,0.31968743,,,
+1844365452,*CCNC(=O)c1cccc(C(=O)NCCOc2cccc3c(O*)cccc23)c1,,0.33080425,,,
+1844848960,*Oc1ccc(Nc2ccc(N=Nc3ccc(Nc4ccc(*)cc4)cc3)cc2)cc1,,0.39236421,,,
+1844857669,*Oc1ccc2c(c1)C(c1ccc(N(c3ccccc3)c3ccccc3)cc1)(c1ccc(N(c3ccccc3)c3ccccc3)cc1)c1cc(Oc3ccc(-c4c5ccccc5c(-c5ccc(*)cc5)c5ccc(CCC)cc45)cc3)ccc1-2,187.3941077,,,,
+1845417873,*c1cccc(P(C)(=O)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5c(Br)cc(C(C)(C)c6cc(Br)c(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)c(Br)c6)cc5Br)cc4C3=O)c2)c1,,0.37581743,,,
+1845492886,*CC(*)(C)C(=O)OCCCCCCN1CCN(c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)cc2)CC1,,0.36331592,,,
+1845526826,*OC(c1ccccc1)C(C)(C)C(*)=O,65.42132156,,,,
+1845530087,*CC(O)COc1ccc(S(=O)(=O)c2ccc(OCC(O)COc3cccc(O*)c3)cc2)cc1,,0.33700601,,,
+1846095810,*CC(*)(C)C(=O)OCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.34387832,,,
+1846410609,*OC(=O)c1ccc(SCCCCSc2ccc(C(*)=O)cc2)cc1,,0.36348377,,,
+1846941939,*Oc1cccc(Oc2ccc(S(=O)(=O)c3ccc(*)cc3)cc2)c1,,0.3644213,,,
+1847001719,*CC(OCCCC)C(C)(C(=O)OC)C(*)(C)C(=O)OC,,0.36338101,,,
+1847105802,*CC(*)(CC(=O)Oc1ccccc1)C(=O)Oc1ccccc1,,0.34963263,0.154,1.099880838,11.69552442
+1847970143,*CC(=O)NC(C)C(=O)O*,,0.30707299,,,
+1848016305,*c1ccc2oc(-c3ccc(C(c4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)(C(F)(F)F)C(F)(F)F)cc3)nc2c1,,0.42121884,,,
+1848203526,*c1cc(C(=O)NCCCCCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.36805739,,,
+1848234898,*CC(*)c1ccc(COCCCCCCOc2ccc(-c3ccc(OC)cc3)cc2)cc1,,,0.3,1.040782353,13.64379867
+1848433590,*CCC[Si](*)(C)CCC[Si](C)(C)C,,0.41861838,,,
+1848774287,*CCC=C(*)C(C)(C)C,,0.39811867,,,
+1849021129,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(P(=O)(c5ccccc5)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.38280626,,,
+1849183324,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3c(CC)cccc32)cc1,,0.37018602,,,
+1849374402,*Oc1cc(Br)cc(Br)c1*,,0.446758,,,
+1849381724,*CC(*)OCCC,-17.16506937,,,,
+1849606830,*CCCCCC(c1ccc(O)cc1)C(*)c1ccc(O)cc1C,,0.3564602,,,
+1849651127,*CC=C(C)C(C)S(*)(=O)=O,,0.37393533,,,
+1849868582,*CC(C)(CC)COC(=O)CCCCCCCCC(=O)O*,,0.35879786,,,
+1850000817,*OC(CCl)C(*)CCl,,0.38330248,,,
+1850128396,*CCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1,,0.37161152,,,
+1851184285,*OC(=O)CNC(=O)CNC(=O)CCCCCCCCC(=O)NCC(=O)NCC(=O)OC1COC2C(*)COC12,,0.31199652,,,
+1851228821,*c1ccc(-c2ccc3c(c2)C(CCCCCCCC)(CCCCCCCC)c2cc(*)ccc2-3)c(F)c1,,0.41077909,,,
+1851430426,*c1cccc(C(=O)Nc2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,,0.36210831,,,
+1851457758,*CCC(C)(C)CC(C)CNC(=O)c1ccc(C(=O)N*)cc1,,0.33983259,,,
+1851478305,*Nc1ccc(NC(=O)c2ccc(NC(=O)c3cc(NC(=O)C45CC6CC(CC(C6)C4)C5)cc(C(*)=O)c3)cc2)cc1,,0.35065916,,,
+1851842760,*CC(OCC)C(C(=O)OC)C(*)C(=O)OC,,0.35037859,,,
+1852114421,*CCN(*)C(=O)CCCCCC,,0.37545125,,,
+1852303181,*C=Cc1cc(OCCCCCCCCCCCC)c(*)cc1OC,,0.38572378,,,
+1852392195,*C[Si](*)(C)c1ccccc1,,0.3722926,,,
+1852502200,*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36663762,,,
+1852654693,*CCCCCOc1ccc(OCCCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1,,0.36383465,,,
+1852734307,*Nc1cc(N*)c(OC(=O)[C@H]2CC[C@@H](C(=O)Oc3cc(N)c(N)c(C)c3N)CC2)c(C)c1N,,0.33922468,,,
+1853131327,*Nc1ccc(Cc2ccc(Cc3ccc(Cc4ccc(N*)cc4)c(CC)c3)cc2CC)cc1,,0.35948061,,,
+1853159363,*Cc1ccc(CNC(=O)CCCCCCCCCCC(=O)N*)cc1,,0.34867284,,,
+1853589021,*Nc1ccc2c(c1)[C@@]1(C)c3ccc(N)cc3[C@]2(C)c2cc(N*)ccc21,,0.3829945,,,
+1853676235,*C(F)(F)C(*)(OC(F)(F)F)OC(F)(F)F,,0.30939912,,,
+1854475288,*Nc1ccc(C(CCc2ccccc2)c2ccc(N*)cc2)cc1,,0.3465212,,,
+1854554359,*c1cc(C(c2ccc(OCc3cc(OCCN(C)c4ccc(C=CC5=CC(=C(C#N)C#N)CC(C)(C)C5)cc4)cc(OCCN(C)c4ccc(C=CC5=CC(=C(C#N)C#N)CC(C)(C)C5)cc4)c3)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)ccc1OCc1cc(OCCN(C)c2ccc(C=CC3=CC(=C(C#N)C#N)CC(C)(C)C3)cc2)cc(OCCN(C)c2ccc(C=CC3=CC(=C(C#N)C#N)CC(C)(C)C3)cc2)c1,,0.37745339,,,
+1854822078,*CC(*)C(=O)Nc1ccc(C(=O)C=Cc2cccc(Br)c2)cc1,,0.36040488,,,
+1854968322,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5cccc(C(F)(F)F)c5)C6=O)cc4)cc3)cc2)cc1,,0.38089404,,,
+1855068918,*Nc1ccc2cc3ccccc3c(N*)c2c1,,0.33879068,,,
+1855109187,*OC(C)C#CC#CC(C)OC(=O)c1ccc(C(*)=O)cc1,,0.39611075,,,
+1855247636,*Oc1cccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4cccc(OC(=O)OCCOCCOC(*)=O)c4)cc3)cc2)c1,,0.37532958,,,
+1855385483,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)cc5C4=O)cc3)cc1)C2=O,,0.40224563,,,
+1855457163,*Nc1ccc(-c2ccc(C(=C(c3ccccc3)c3ccccc3)c3ccc(-c4ccc(N*)cc4)cc3)cc2)cc1,,0.37596721,,,
+1856131378,*NS(N)(N)(N*)(c1ccccc1)c1ccccc1,,0.3467735,,,
+1856962369,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O,,0.35645832,,,
+1857016044,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)c(C)c2)cc1C,,0.41449337,,,
+1857030958,*c1ccc(C(C)(C)c2ccc(N3C(=O)c4ccc(Oc5ccc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6c5)cc4C3=O)cc2)cc1,,0.37497844,,,
+1857184942,*Nc1cc2c(cc1N*)C1c3cc(N)c(N)cc3C2c2cc(N)c(N)cc21,,0.372622,,,
+1857351068,*c1ccc(Oc2ccc(Sc3ccc(Oc4ccc(-c5cccc(*)n5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.39151592,,,
+1857599978,*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(c6ccccc6)C(F)(F)F)ccc5o4)c3)nc2c1,,0.43815792,,,
+1858324553,*Nc1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(N*)ccc1-2,,0.366300765,,,
+1859484070,*CCCCCCOC(=O)CCCCCNC(=O)CCCCC(=O)NCCCCCC(=O)O*,,0.34745359,,,
+1860210048,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Cc4ccc(C(=O)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc4)cc3)c1)C2=O,,0.36437785,,,
+1860317687,*Nc1ccc(NC(=O)CCCCCCCCC(*)=O)cc1,,0.34362682,,,
+1860476282,*Oc1ccc(Oc2ccc(C=Nc3ccc(N=Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37280796,,,
+1860497514,*Nc1ccc(-c2c(-c3ccc(-c4ccccc4)cc3)c(-c3ccc(-c4ccccc4)cc3)c(N*)c(-c3ccc(-c4ccccc4)cc3)c2-c2ccc(-c3ccccc3)cc2)cc1,,0.36269909,,,
+1860569431,*CC(*)c1cccc(-c2ccc(-c3ccccc3)cc2)c1,,,0.1956666666666666,,
+1860871285,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(C)cc3)cc2)c(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.38583152,,,
+1861224717,*CCCCCCOC(=O)OCCCCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35889601,,,
+1861437880,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(C)(C)c4cc(C)c(*)c(C)c4)cc3C)cc2)cc1,,0.39688711,,1.014775026,13.70570836
+1861488768,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OC(C)CC)cc1,,0.3394136,,,
+1861741928,*c1c(-c2ccccc2)c(-c2ccccc2)c(*)c2cc(C3(c4ccc(C#Cc5ccccc5)c(C#Cc5ccccc5)c4)c4ccccc4-c4ccccc43)ccc12,,,,0.900037683,11.54976157
+1861909022,*C(=O)OCC(C)(C)CSCC(C)(C)COC(=O)C1(C)CNC(*)(C)CN1,,0.36496974,,,
+1862037558,*CC(*)C(=O)N(C)c1ccccc1,,,0.1775,1.036978669,11.67331343
+1862323787,*CCCCCCCCOC(=O)c1ccc(C(=O)O*)cc1,,0.35607597,,,
+1862970059,*Nc1ccc(-c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1,,0.34911211,,,
+1863175243,*CCCNC(=O)C(OC)C(OC)C(=O)N*,,0.31506853,,,
+1863577619,*Oc1cccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)c1,,0.33721798,,,
+1863578660,*CCOCCOC(=O)C(CCCCC)C(=O)O*,,0.35852428,,,
+1863583230,*CC(*)(C)C(=O)OC(F)(C(F)(F)F)C(F)(F)Cl,,,0.069,1.322806827,14.48982121
+1863818535,*CCN(CCOC(=O)c1ccc(C(=O)O*)c(OCCC)c1)c1ccc(C=C(C#N)C#N)cc1,,0.35424563,,,
+1863923314,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3ccccc3C(=O)Nc3ccc(O*)cc3)cc2)cc1,,0.34086664,,,
+1864041202,*C1C(=O)N(c2ccccc2C(=O)OCCCC)C(=O)C1*,,0.37158832,,,
+1864197716,*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)c3ccc(C(*)=O)cc3)c(C)c2)cc1C,,0.39431974,,,
+1864288375,*Nc1ccc(Br)cc1-c1ccc(NC(=O)c2cccc(C(*)=O)c2)cc1,,0.36146384,,,
+1864402555,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(-c4ccc(C(=O)Nc5ccc(*)cc5)c(C)c4)cc3C)cc2)cc1,,0.35817189,,,
+1864430039,*Cc1cc(C)c(CNC(=O)CCCCCCC(=O)N*)cc1C,,0.34359188,,,
+1864529364,*C[Si](*)(CCC)CCC,,0.40187734,,,
+1864581969,*CC(*)C(=O)N(C)C,,0.34373514,0.2125,0.999160184,14.4722603
+1864702589,*CCN(CCOC(=O)NCCCCCCNC(=O)O*)c1ccccc1,,0.34103383,,,
+1864783652,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc(-c7cc(*)n(-c8ccccc8)n7)cc6)cc5)cc4)cc3)cc2)cc1,,0.3855566,,,
+1864971169,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(-c5cc(*)[nH]n5)cc4)cc3)cc2)cc1,,0.3828364,,,
+1865123528,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)c(C(C)CC)c2)cc1C(C)CC,,0.39963061,,,
+1865362645,*CCCCCCCCCCOC(=O)CCCCS(=O)(=O)CCCCC(=O)O*,37.87494117,,0.278,,
+1865534172,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(C3CCCCC3)cc2)cc1,,0.36193603,,,
+1865789709,*COc1ccc(C(C)(C)c2ccc(Oc3ccc(*)n3-c3ccc(C#N)cc3)cc2)cc1,,0.3718604,,,
+1865790148,*c1c(C)cc(C)c(N2C(=O)c3cccc(Oc4c(C)cc(-c5cc(C)c(Oc6cccc7c6C(=O)N(*)C7=O)c(C)c5)cc4C)c3C2=O)c1C,,0.39910552,,,
+1865960070,*CC(C[Si](*)(C)C)c1ccccc1,,0.38874361,,,
+1866518598,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(*)=O)cc2)C(=O)OCCCC)cc1,,0.34652373,,,
+1866896884,*CC(*)(CC(=O)OCCOCCOCCOC)C(=O)OCCOCCOCCOC,,0.33995573,,,
+1867066449,*CCOC(=O)c1ccc2ccc(C(=O)O*)cc2c1,,0.33503651,,,
+1867844121,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(Cc3ccccc3)CC2)cc1,,0.35467906,,,
+1867867984,*c1c(C)cc(C)c(N2C(=O)c3cccc(Oc4ccc(C(C)(C)c5ccc(Oc6cccc7c6C(=O)N(*)C7=O)cc5)cc4)c3C2=O)c1C,,0.38678504,,,
+1868095680,*CC(*)(C)C(=O)OCCCCc1ccc(N=Nc2ccc(OC(F)(F)F)cc2)cc1,,0.35542759,,,
+1868650247,*Oc1ccccc1C(=O)Oc1ccc(OC(=O)c2ccccc2Oc2nc(*)nc(N(CC)c3ccccc3)n2)cc1,146.7365026,,,,
+1868729394,*c1ccc(NC(=O)CCCCCCCCC(=O)Nc2ccc(S(*)(=O)=O)cc2)cc1,,0.348619,,,
+1869058141,*CC(O)COc1c(Cl)cc(C(C)(C)c2cc(Cl)c(OCC(O)COc3ccc(C(C)(C)c4ccc(O*)cc4)cc3)c(Cl)c2)cc1Cl,,0.36154236,,,
+1869058674,*Nc1ccc(C(C)(Cc2ccccc2)c2ccc(N*)cc2)cc1,,0.35508856,,,
+1869415100,*CCCCCCOc1ccc(C(=O)Nc2ccc(-c3ccc(NC(=O)c4ccc(O*)cc4)c(Cl)c3)cc2Cl)cc1,133.9866306,,,,
+1869493071,*c1ccc(Oc2ccc(-c3c4ccccc4c(-c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(c6ccccc6)C7=O)cc5)c(C(F)(F)F)c4)c4ccccc34)cc2C(F)(F)F)cc1,,0.3887442,,,
+1869593647,*C(=O)c1ccc(N(*)C)cc1,,0.37772289,,,
+1869660806,*Nc1cc(C)c(Cc2ccc(CCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.36173822,,,
+1870206037,*Nc1c(S(=O)(=O)O)cc2cc(S(=O)(=O)O)c(N)c(O)c2c1N*,,0.32302544,,,
+1870504474,*Nc1ccc(C(C)(C)C(C)(C)c2ccc(N*)cc2)cc1,,0.35566412,,,
+1870599069,*CC(*)C(=O)OC1CC2CCC1(C)C2(C)C,,0.38692751,,,
+1870681436,*Nc1ccc2cc(C(C)(C)C)ccc2c1-c1c(N*)ccc2cc(C(C)(C)C)ccc12,,0.38263341,,,
+1870773223,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(*)cc4C3=O)c(C)cc1C)C2=O,,0.39477416,,,
+1870860682,*CC(*)OC(=O)CCCCCCCCCCCCCCC,,,0.319,0.858717726,12.0896889
+1871148344,*C(C)=C(*)SCCCCCC,,0.41450621,,,
+1871161866,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36844586,,,
+1871349402,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=NN=Cc4ccc(*)cc4)cc3)cc2)cc1,,0.37217659,,,
+1871484696,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccccc5)c(-c5cc(*)ccc5C(=O)c5ccccc5)c4)cc3)c3ccccc3-c3ccccc32)cc1,,0.39595329,,,
+1871505743,*C(=O)OCCN(CCOC(=O)c1ccc2c(c1)C(=O)N(c1ccc(N=Nc3ccc(C(C#N)=C(C#N)C#N)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O)c1ccc(C(C#N)=C(C#N)C#N)cc1,,0.36727055,,,
+1871588823,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5ccc(C(c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4C3=O)cc2)cc1,,0.37110373,,,
+1871708716,*CC(*)C(=O)Oc1ccc(C(=O)c2ccc(Cl)cc2)cc1,,0.35501942,,,
+1872303483,*C([2H])([2H])C(*)([2H])C([2H])=C([2H])[2H],,0.39062915,,,
+1872457449,*Oc1cccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)c1,,0.34349195,,,
+1872607825,*c1ccc(OC(=O)c2ccc([Si](C)(C)c3ccc(C(=O)Oc4ccc(C5(*)c6ccccc6C(=O)N5c5ccccc5)cc4)cc3)cc2)cc1,,0.39364264,,,
+1872649544,*CC(COC(=O)CCCCCN1C(=O)C2C3C=CC(C3)C2C1=O)O*,,0.33852716,,,
+1872652115,*Nc1ccc(C2(c3ccc(N*)cc3)C[C@H](C)CC(C)(C)C2)cc1,,0.36369292,,,
+1873242473,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(N=Nc3ccc(F)cc3)c3ccccc23)cc1,,0.35605621,,,
+1873390978,*CC(OCC)C1(C)C(=O)OC(=O)C1(*)C,,0.32300959,,,
+1873610912,*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(S(=O)(=O)c4ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37485249,,,
+1873714666,*Nc1ccc2ccc3cccc4cc(N*)c1c2c34,,0.33162046,,,
+1873866400,*CCN1C(=O)C(=CC=C2SC(=S)N(*)C2=O)SC1=S,164.0140939,,,,
+1874165813,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3c(C)cccc32)cc1,,0.36740099,,,
+1874299815,*Nc1ccc(-c2cc(-c3ccccc3)c(N*)c(N)c2-c2ccccc2)cc1N,,0.35451477,,,
+1874313702,*CCCC(C)(C)CNC(=O)C(=O)N*,,0.3260029,,,
+1876018160,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccccc5)cc4)cc3)cc2)cc1,,0.38502718,,,
+1876711098,*Oc1ccc(Oc2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37206623,,,
+1876833893,*CC(*)c1ccc(CCCCCCCCCCCCCC)cc1,,0.40253381,0.3399999999999999,0.848186437,12.56559059
+1877102749,*CC(F)(F)C(F)(F)C(F)(F)C(F)(F)COC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*,,0.30099588,,,
+1877161213,*Cc1cc(*)c(O)cc1C,12.40187977,,,,
+1877302307,*CC(C)(C)COC(=O)NC(=O)c1ccc(C(=O)NC(=O)O*)cc1,,0.31265064,,,
+1877327739,*CC(*)(C)C(=O)OCCCCCCCC,,,0.2415,0.891484349,11.61104884
+1877647340,*CCCCCCCCCNC(=O)N*,,0.35715169,,,
+1877844504,*Nc1ccc2c(-c3c(C)c(N)c(C)c(N*)c3C)c(N)ccc2c1,,0.36932958,,,
+1877921112,*CC(*)(CC(=O)OCCCCl)C(=O)OCCCCl,,0.3535211,,,
+1877965717,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1,,0.37375522,,,
+1878179811,*Oc1ccc(CC(*)=O)cc1,,0.3454687,,,
+1878617864,*c1cccc(-c2nc3ccc(-c4ccc5nc(*)sc5c4)cc3s2)c1,,0.46119303,,,
+1878660713,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3-c3cc(F)c(F)c(F)c3)c(-c3cc(F)c(F)c(F)c3)c1)C2=O,,0.38343995,,,
+1878904563,*NCCCOCCOCCOCCCN*,,0.3255813199999999,,,
+1879018576,*NC1=C(N)N[C@H](N)C(=O)[C@H](N*)N1,,0.29477936,,,
+1879180798,*N[C@@]12CC(=C)[C@@](C)(N*)[C@@]1(N)Cc1cccc(O)c1C2,,0.33242571,,,
+1879773381,*Oc1ccc(C=C2CCC(=Cc3ccc(OC(=O)c4ccc(N=Cc5ccc(C=Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3)C2=O)cc1,,0.36680398,,,
+1880766912,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCC(C)C)cc1,,0.33925962,,,
+1881186822,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccc(C)cc3-c3ccc(C)cc32)cc1,,0.37924418,,,
+1881349845,*Oc1ccc(C(C)(C)c2ccc(Oc3cncc(*)n3)cc2)cc1,,0.35753445,,,
+1881434698,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3c(-c4ccc(C)cc4)c(-c4ccc(C)cc4)c(-c4ccc(C)cc4)c(-c4ccc(C)cc4)c23)c2ccccc12,,0.39299822,,,
+1881628793,*CC(*)C(=O)Oc1ccccc1C(=O)OC,,0.33186429,,,
+1881927203,*CCOC(=O)c1ccc(C(=O)OCCOc2ccc(C(C)(C)c3ccc(O*)cc3)cc2)cc1,,0.34444131,,,
+1881954049,*c1ccc2c(c1)-c1cc(-c3nc4cc(-c5ccc6[nH]c(*)nc6c5)ccc4[nH]3)ccc1S2(=O)=O,355.5073659,,,,
+1882117628,*OC1C(COC(=O)Nc2ccccc2)OC(*)C(OC(=O)Nc2ccccc2)C1OC(=O)Nc1ccccc1,,0.3414528,,,
+1882780589,*CCN(*)C(=O)CCCC,,0.36217869,,,
+1882791294,*c1ccc(-c2cc(CCCCCCOC(=O)COc3ccccc3)c(*)s2)s1,,0.37333341,,,
+1882863686,*C(=C([2H])C([2H])([2H])C(*)([2H])[2H])C([2H])([2H])[2H],,0.40600876,,,
+1883003333,*CC(*)(C)c1ccc(C#N)cc1,,0.3935561,,,
+1883635741,*Oc1ccc(-c2ccc(N=Nc3ccc(C(*)=O)cc3)cc2)cc1,,0.36731588,,,
+1883833244,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5cccc(-c6cccc(C(=O)c7ccc(*)cc7)c6)c5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.38241447,,,
+1883909955,*c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(-c4ccc(-c5sc(-c6ccc([Si](C)(C)[Si](*)(C)C)s6)cc5CCCCCCCC)s4)s3)s2)s1,,0.44805866,,,
+1884578116,*NCCC[C@@H](C)CN*,,0.32730151,,,
+1885691849,*CC(*)OC(=O)c1cccc(OC)c1,,0.33959116,,,
+1885797446,*CC(*)C(=O)OCC,,,0.2305,1.001646084,14.48303439
+1885836114,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)C(C(C)CC)N4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,,0.35394467,,,
+1885883959,*Nc1ccc(-c2cc(-c3ccccc3)c(N*)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.36380664,,,
+1886025191,*c1ccc(-c2nc3ccc(S(=O)(=O)c4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1,,0.44809167,,,
+1886122212,*c1cccc(N2C(=O)c3c(c(-c4ccccc4)c(-c4ccc(-c5c(-c6ccccc6)c6c(c(-c7ccccc7)c5-c5ccccc5)C(=O)N(*)C6=O)cc4)c(-c4ccccc4)c3-c3ccccc3)C2=O)c1,,0.40285213,,,
+1886386616,*CCCCCCNC(=O)CCCCC(=O)NCC(=O)N*,,0.32496558,,,
+1887035673,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)Nc3c(C)cc(C(C)(C)c4cc(C)c(NC(=O)c5ccc(*)cc5)c(C)c4)cc3C)cc2)cc1C(C)(C)C,,0.39461572,,,
+1887357028,*C=Cc1ccc(*)c(OCc2cc(OCCC(C)CCCC(C)C)cc(OCC(CC)CCCC)c2)c1,,0.38283188,,,
+1887528434,*Nc1ccccc1C1(c2ccccc2N*)CCCCC1,,0.3550744,,,
+1887598982,*CC(*)C(=O)Nc1ccc(Cl)cc1,,0.35389835,,,
+1887729559,*OC(=O)C1CCC(C(=O)OC2COC3C(*)COC23)CC1,,0.3404303,,,
+1887902393,*Nc1cccc(NC(=O)c2ccc(C(=O)Nc3ccc(C(=O)NC(*)=S)cc3)cc2)c1,,0.33864713,,,
+1887990999,*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)c1ccccc1Cl,,0.35506882,,,
+1888278720,*CCC(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)CCN4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)cc1,,0.33989797,,,
+1888293192,*Nc1ccc(C(c2ccc(N*)cc2)c2ccc(C(C)(C)C)cc2)cc1,,0.36825917,,,
+1888415409,*c1cccc(N2C(=O)C(=O)N(c3ccc(Cc4ccc(N5C(=O)C(=O)N(*)C5=O)cc4)cc3)C2=O)n1,,0.36326196,,,
+1888603184,*Oc1ccc(C(=O)c2ccc(NC(=O)c3cccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)c3)cc2)cc1,,0.35450759,,,
+1888669156,*Nc1ccc(-c2c3ccccc3c(-c3cccc(N*)c3)c3ccccc23)cc1,,0.36094997,,,
+1888885498,*N=P(*)(OCC=Cc1ccccc1)OCC=Cc1ccccc1,,0.36094292,,,
+1889214322,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc4c(c3)C3(CC(C)(C)CC(C)(C)C3)c3cc(*)ccc3-4)ccc1-2,,0.41639141,,,
+1889222328,*Nc1cc(SCCC#N)c(NC(=O)c2ccc(C(*)=O)cc2)cc1SCCC#N,214.7483216,,,,
+1889579812,*Oc1ccc(C(=O)O)c(C(=O)Nc2ccc(NC(=O)c3cc(*)ccc3C(=O)O)cc2)c1,,0.31304057,,,
+1890198796,*CC(c1ccccc1)C1C(=O)N(CCCCNC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)C(=O)C1*,,0.32992256,,,
+1890222826,*CC(COc1ccc(C(C)(c2ccccc2)c2ccc(O*)cc2)cc1)OC(=O)c1ccccc1,,0.35693176,,,
+1890528933,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3Br)c(Br)c1)C2=O,,0.41164319,,,
+1890730007,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37306628,,,
+1891219046,*Nc1ccc(-c2cccc(-c3ccccc3N*)c2)cc1C1=CCCCC1,,0.35958468,,,
+1891267124,*CCCCCCCC(=O)OC1COC(*)OC1,,0.35442774,,,
+1891268046,*CCCCCCCCCCNC(=O)CCCCC(=O)N*,,0.35535197,,,
+1891270134,*C(=O)Nc1ccc(C(c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3c(C)cc(C)c(N5C(=O)c6ccc(*)cc6C5=O)c3C)C4=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37180888,,,
+1891991846,*C(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(-c4sc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(-c5ccccc5)c4-c4ccccc4)cc2)C3=O)cc1,,0.39311004,,,
+1892436354,*OP(=O)(Oc1ccc(Br)cc1)OC(CCl)COc1ccc(S(=O)(=O)c2ccc(OCC(*)CCl)cc2)cc1,,0.3602855,,,
+1892438799,*c1c(C)cc(-c2cc(C)c(N3C(=O)c4ccc(Oc5cc6ccccc6cc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.38934069,,,
+1892522189,*Nc1ccc([C@]2(c3ccccc3)CC[C@H](N*)CC2)cc1,,0.34589131,,,
+1892682102,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(-c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc3)C4=O)cc2)cc1,,0.35822552,,,
+1893126159,*c1ccc(Oc2ccc(C(C)(C)c3ccccc3)cc2)c(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.39256327,,,
+1893396253,*CC(*)(C)C(=O)OCCCCCCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1,,0.3618429,,,
+1893806393,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.37860625,,,
+1893896764,*CCCOC(=O)NCCCCCCNC(=O)O*,,0.33011168,,,
+1894105114,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccccc7Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.37120514,,,
+1894241731,*c1ccc(-c2ccc(-c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,357.2965065,,,,
+1894260096,*CCOC(=O)Nc1ccc(C)c(NC(=O)OCCOc2ccc(C3(c4ccc(O*)cc4)c4ccccc4-c4ccccc43)cc2)c1,,0.35870068,,,
+1894298006,*CCCCOC(=O)O*,,0.33188575,,,
+1894369854,*C(=O)c1ccc2c(c1)C(=O)N(c1ccccc1Cc1ccccc1N1C(=O)c3ccc(*)cc3C1=O)C2=O,,0.37730051,,,
+1894651804,*CC/C=C\C[Si](*)(C)c1ccccc1,,0.37572557,,,
+1894705136,*CC(*)(CC(=O)OCCCl)C(=O)OCCCl,,0.35304556,,,
+1895063835,*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.34298576,,,
+1895073862,*NN(N*)c1nc2nc(N)n(N)c2c(=O)n1N,,0.33967082,,,
+1895904608,*CC(*)(C)C(=O)OCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.34149841,,,
+1895946693,*Nc1ccc(C2(c3ccc(N*)cc3)CCCCC2)cc1,,0.352691375,,,
+1896402362,*Nc1cccc(C2(c3cccc(N*)c3)c3cccc(N)c3-c3c(N)cccc32)c1,,0.36736588,,,
+1896761463,*CC(*)(F)C(=O)OCC(Cl)(Cl)Cl,,0.36380773,,,
+1896795195,*CC(*)(C)C(=O)OCc1ccc(Cl)c(Cl)c1,,0.35729851,,,
+1896863943,*CC(*)C(=O)NCCCC,-84.72234668,,0.252,0.938352718,11.69729541
+1897620577,*CC(CCCCCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1)COC(=O)CCCCCCCCCCCCC(=O)O*,,0.37422275,,,
+1897678696,*Nc1ccc(-c2cc(-c3ccc(N)cc3C)cc(-c3ccc(N*)cc3C)c2)c(C)c1,,0.3665509,,,
+1898005321,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.35740991,,,
+1898276477,*Nc1c(C)c(C)c(N*)c(C)c1C,,0.358234115,,,
+1898882612,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(S(=O)(=O)c6ccccc6)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38778693,,,
+1899186076,*CC(*)(C)C(=O)OCC#N,,0.34496113,,,
+1899283335,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(c3ccccc3)CC2)cc1,,0.35979083,,,
+1899317427,*Nc1ccc(-c2ccc3c(c2)Cc2cc(-c4ccc(N*)cc4)ccc2-3)cc1,,0.35324332,,,
+1899542074,*Nc1ccc(Oc2ccc(Oc3ccc(N*)cc3)cc2)cc1,,0.34207479,,,
+1899805345,*c1ccc(S(=O)(=O)c2ccc(N3C(=O)c4ccc(Oc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4C3=O)cc2)cc1,,0.37206613,,,
+1899839615,*Nc1ccc(-c2ccc(N*)c(O)c2)cc1O,,0.31809801,,,
+1900067504,*CCCCOC(=O)c1ccc(NC=Nc2ccc(C(=O)O*)cc2)cc1,,0.35909112,,,
+1900387245,*Oc1ccc(-c2ccc(-c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)c(C(F)(F)F)c3)cc2)cc1C(F)(F)F,,0.37983546,,,
+1901476786,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(N7C(=O)c8ccc(*)cc8C7=O)cccc56)cc3)C4=O)cc2)cc1,,0.36485895,,,
+1901480658,*CCCCOC(=O)NCCCCCCNC(=O)OCCCCOc1ccc(N=Cc2ccc(C(=O)Oc3ccc(C=Nc4ccc(O*)cc4)cc3)cc2)cc1,-34.25655466,,,,
+1901756978,*C(=O)Nc1ccc(-c2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1,,0.36549109,,,
+1901878899,*CCCCCCOC(=O)C(CCCCCCP(=O)(O)O)C(=O)OCCCCCCOc1ccc(-n2on2-c2ccc(O*)cc2)cc1,,0.35235981,,,
+1902063591,*C(=O)OCCCCOC(=O)c1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.34037941,,,
+1902122155,*Oc1ccc(S(=O)(=O)c2ccc3ccc(S(=O)(=O)c4ccc(*)cc4)cc3c2)cc1,,0.36906396,,,
+1902160651,*Nc1ccc(/C=C/c2cccc3c(/C=C/c4ccc(N*)cc4)cccc23)cc1,,0.353423605,,,
+1903144609,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)c1)C2=O,,0.37194181,,,
+1903600886,*Nc1ccc2cccc3c2c1C(CC)=C(CC)[C@@H]3N*,,0.35278804,,,
+1903713432,*CC(C)C(C)COC(=O)c1ccc(C(=O)O*)cc1,,0.34746292,,,
+1904729380,*Nc1ccc2c(C)c3c(C)c(C)c(N*)c(C)c3cc2c1,,0.3506461,,,
+1905028152,*Nc1ccc(Cc2ccc(-c3ccccc3Cc3ccc(N*)cc3)cc2)cc1,,0.35309577,,,
+1905216047,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(C(=O)Nc4ccc(*)cc4)cc3)cc2)cc1,203.5999878,,,,
+1905277968,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.36364595,,,
+1905422493,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5ccc(*)s5)cc4C(F)(F)F)cc3)cc2)c(C(F)(F)F)c1,,0.41103531,,,
+1905713032,*Nc1ccc2c(-c3ccccc3)c(N*)c(-c3ccccc3)c(-c3ccccc3)c2c1-c1ccccc1,,0.37513122,,,
+1906030698,*c1cc(OCCCC)cc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37507763,,,
+1906044444,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(-c6nnc(*)c7c(-c8ccc(F)cc8)c(-c8ccc(F)cc8)c(-c8ccc(F)cc8)c(-c8ccc(F)cc8)c67)cc5)cc4)c4ccccc4-c4ccccc43)cc2)cc1,,0.41682374,,,
+1906060970,*Nc1c(-c2ccccc2)c(-c2ccccc2)c2ccccc2c1N*,,0.36229869,,,
+1906506911,*Nc1ccc(-c2cccc3cccc(-c4ccc(N*)cc4)c23)cc1,,0.3474253,,,
+1906633153,*Nc1ccc(NC(=O)CCCCCC(*)=O)cc1,,0.3281237,,,
+1906906209,*Nc1cc2c(-c3ccccc3)c3c(-c4ccccc4)c4cc(N*)c(N)cc4c(-c4ccccc4)c3c(-c3ccccc3)c2cc1N,,0.36326244,,,
+1907401274,*CC(*)C(=O)Nc1ccc(C(=O)C=Cc2ccc(Br)cc2)cc1,,0.36262044,,,
+1907558248,*CCC(=O)OC(=O)CCc1ccc(*)o1,,,0.216,,
+1907646642,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cn3)nc1)C2=O,,0.3708683,,,
+1907952467,*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCCCC,,0.36305702,,,
+1908228365,*CCCCCCC(=O)NCc1ccc(CNC(=O)CCCCCCS*)cc1,,0.36267887,,,
+1908364684,*Nc1ccc(-c2cccc3ccccc23)c(-c2ccccc2)c1N*,,0.35996757,,,
+1908775119,*c1ccc(Oc2ccc(Oc3ccc(-c4cnc5ccc(-c6ccc7ncc(*)nc7c6)cc5n4)cc3)cc2)cc1,,0.38500868,,,
+1908941468,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCCCCCCCC)cc1,,0.34855736,,,
+1910103715,*CCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,0.3795304,,,
+1911086845,*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(c6ccccc6)C(F)(F)F)ccc5o4)cc3)nc2c1,,0.45875481,,,
+1911532586,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)c(S(=O)(=O)O)c3)cc2S(=O)(=O)O)cc1S(=O)(=O)O,,0.3508676,,,
+1911905200,*C=CC1CC(*)C(C#N)C1,,0.39476009,,,
+1911926467,*CC(CSC)O*,,0.41981356,,,
+1911963342,*CC(*)C(=O)n1sc2ccccc2c1=O,48.67425788,,,,
+1912017685,*CC(*)[Si](C)(C)Cc1ccccc1,,0.36754872,,,
+1912083932,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCCCCCCCC,,0.37677964,,,
+1912147729,*c1c(C)cc(C(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2cc(C)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.42214994,,,
+1912251377,*CCCCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCCCC(=O)N*,,,0.3915714285714285,0.876856201,21.89554378
+1912518216,*CCCCCCCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.361536,,,
+1912601583,*OC(=O)c1ccc(C(=O)OC2CC3CC(*)CC(C2)O3)cc1,,0.34801487,,,
+1913033936,*CCC1CC(*)C2CCCC12,,0.37234991,,,
+1913604664,*C(=O)OCCOC(=O)c1cccc(N2C(=O)c3ccc(*)cc3C2=O)c1,,0.33299939,,,
+1914456185,*CC(*)(C)C(=O)NC1C(O)OC(CO)C(O)C1O,,0.28358,,,
+1914838661,*CC(*)n1c2ccccc2c2ccccc21,,,0.147,1.05806832,11.82300548
+1914886557,*Oc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(Oc5cccc(*)n5)cc4)cc3)cc2)cc1,,0.35681485,,,
+1914920451,*CC(*)(C)C(=O)OCCCCC,,0.37286152,0.1895,0.917921435,11.58525278
+1914954895,*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4cccc(Oc5ccc(NC(=O)c6ccc(*)cc6)cc5)c4)cc3)cc2c1,,0.35277036,,,
+1914982152,*C=Cc1ccc2c(c1)Sc1cc(*)ccc1N2c1ccc(OCCCCCCCCCCCC)cc1,,0.39567095,,,
+1915263871,*Nc1ccc(-c2cc(-c3ccc(N)c(CC)c3)cc(-c3ccc(N*)c(CC)c3)c2)cc1CC,,0.36495902,,,
+1915755051,*Nc1cccc(-c2cc(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c(-c3ccc(C)cc3)c2-c2ccc(C)cc2)c1N*,,0.37998545,,,
+1916198200,*OP(=O)(Oc1ccc(Br)cc1)Oc1c(C)cc(S(=O)(=O)c2cc(C)c(*)c(C)c2)cc1C,,0.37734655,,,
+1916680069,*c1ccc(C(c2ccc(N3C(=O)c4ccc(Oc5ccc6cc(Oc7ccc8c(c7)C(=O)N(*)C8=O)ccc6c5)cc4C3=O)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37815393,,,
+1916735639,*Nc1cc(N*)c2nc(-c3ccc(-c4nc5c(N)cc(N)c(N)c5c(=O)o4)cc3)oc(=O)c2c1N,,0.35583172,,,
+1916892964,*CC(O)CN(CC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)c1ccc(C#N)cc1,,0.35785256,,,
+1916979239,*c1ccc(OCCN(C)c2ccc(C=CC3=CC(=C4C(=O)c5ccccc5C4=C(C#N)C#N)C=C(C=Cc4ccc(N(C)CCOc5ccc(-c6nc7ccc(Oc8ccc9nc(*)c(-c%10ccccc%10)nc9c8)cc7nc6-c6ccccc6)cc5)cc4)O3)cc2)cc1,,0.39171687,,,
+1917029887,*OC(=O)CNC(=O)CCCCCCCCC(=O)NCC(=O)OC1COC2C(*)COC12,,0.32250215,,,
+1917189140,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)c5ccccc45)o3)cc2)cc1,,0.43988277,,,
+1917298650,*CNC(=O)CCCCC(=O)NCC1COC(*)CO1,,0.31541925,,,
+1917339697,*CC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)c1ccc(C(=O)O*)cc1,,0.3542055,,,
+1917735870,*c1ccc(-c2ccc(N(*)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)cc2)cc1,,0.44831423,,,
+1917756223,*Oc1ccc(C=NN=Cc2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1,,0.37285172,,,
+1917841461,*CCC(=O)N(CCN(C(=O)CCN1CCN(*)CC1)C(C)C)C(C)C,-43.71593166,,,,
+1917866361,*c1ccc(Nc2ccc(C(c3ccc(Nc4ccc(S(*)(=O)=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.39098411,,,
+1918165220,*ON(C(F)(F)F)C(F)(C(*)(F)F)C(F)(F)F,,0.29794304,,,
+1918242337,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C6(c7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)NC(=O)c7ccccc76)cc5)cc3)C4=O)cc2)cc1,,0.36308192,,,
+1918548284,*Nc1cc2c3ccc(-c4ccc(C(C)(C)C)cc4)c(-c4ccc(C(C)(C)C)cc4)c3c(N*)cc2c2ccccc12,,0.37420991,,,
+1919323099,*Oc1ccc(NC(=O)NC2CC(C)(C)CC(C)(CNC(=O)Nc3ccc(*)c(-c4ccc(Oc5ccccc5)cc4)c3)C2)cc1-c1ccc(Oc2ccccc2)cc1,,0.35601929,,,
+1919352729,*Nc1cccc2c1c1cccc(N)c1c1cccc(N*)c12,,0.33957884,,,
+1919555775,*CC(CCCC)O*,,0.40491209,,,
+1919636162,*CCCCCCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)cc2)cc1,,0.34461787,,,
+1919652718,*CCCCCCCCCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O,,0.34909001,,,
+1919727869,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(*)cc5)c4)cc3)cc2)cc1,,0.35823661,,,
+1919732272,*CCCOC(=O)c1ccc(NC(=O)CCCCC(=O)Nc2ccc(C(=O)O*)cc2)cc1,83.99780359,,,,
+1919768814,*Oc1ccc(C(c2ccc(OC(=O)Nc3ccc(Cc4ccc(NC(*)=O)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.3538727,,,
+1920006010,*OC1C(CO)OC(*)C(N)C1O,,0.30298041,,,
+1920455411,*c1ccc(OCCN(CC)c2ccc(-c3ccc(C(=C(C#N)C#N)c4ccc(N(CC)CC)cc4)s3)cc2)c(-c2cc(-c3ccccc3)c3cc(Oc4ccc5nc(*)cc(-c6ccccc6)c5c4)ccc3n2)c1,,0.40104761,,,
+1920671231,*C(=O)Nc1ccccc1NC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.33896532,,,
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+1925322729,*CC(CCl)(CCl)O*,,,0.1159999999999999,1.297944961,14.62361719
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+1926476264,*=c1cc2ccc3cc(=c4oc(=*)c5ccc6cccc7c8cccc9ccc4c(c98)c5c67)cc4ccc(c1)c2c34,432.43,,,,
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+1927037390,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.33804671,,,
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+1927827518,*CC(*)c1cccc(CC)c1,,,0.186,,
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+1928141683,*CCCCNC(=O)CCCCCCCCC(=O)N*,,,0.355,,
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+1928832708,*CCCCCCCCCCOc1ccc(C=Nc2ccc(-c3ccc(N=Cc4ccc(O*)cc4O)c(OC)c3)cc2OC)c(O)c1,,0.36372258,,,
+1928965066,*CCCCCCOC(=O)c1cc(OCCCCCCOc2ccc(N=Nc3ccc(C#N)cc3)cc2)cc(C(=O)OCCCCCCN2C(=O)c3ccc(-c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3C2=O)c1,,0.35328159,,,
+1929156646,*OC1CCC(OC(=O)OCCCCCOC(*)=O)CC1,,0.34122931,,,
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+1930079807,*c1cc(CC(=O)OCCCCCCCC)c(*)[nH]1,-4.076963699,,,,
+1930443303,*CCC=C(*)C,,0.40009241,,,
+1930470217,*Nc1ccc(NC(=O)c2cc(C(*)=O)c(C(=O)OCC)cc2C(=O)OCC)cc1,,0.33893458,,,
+1930894863,*CCC1C(=O)N(C)C(=O)C1*,,0.32269623,,,
+1930955815,*N=P(*)(OCCOCCOCCCC)OCCOCCOCCCC,,0.37867352,,,
+1931091921,*Oc1ccc(-c2ccc(Oc3ccc4ccccc4c3-c3c(*)ccc4ccccc34)c3ccccc23)c2ccccc12,,0.39374699,,,
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+1931637723,*Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(N*)cc4)cc3)cc2)cc1,,0.34360821,,,
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+1931909120,*Nc1ccc2c(N*)c3cc(N)ccc3c(N)c2c1,,0.34576723,,,
+1932242031,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(Oc7ccc(-c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,,0.36829952,,,
+1932390500,*C(=O)NCCCCCCCCCCNC(=O)c1ccc(*)o1,,,0.217,,
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+1932637388,*c1ccc(CC(=O)Nc2ccc(Oc3ccc(NC(=O)Cc4ccc(-c5sc(*)c(-c6ccccc6)c5-c5ccccc5)cc4)cc3)cc2)cc1,,0.37963807,,,
+1932737205,*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.3580544,,,
+1932802703,*Oc1ccc(C(c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.35070734,,,
+1933004653,*OS(=O)(=O)c1cc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCC3)cc2)ccc1C,,0.36404714,,,
+1933066795,*c1ccc(C(=O)OCCCCCCCCOc2ccc(C=C3CCCCC(=Cc4ccc(OCCCCCCCCOC(=O)c5ccc(-c6nnc(*)o6)cc5)cc4)C3=O)cc2)cc1,,0.36677031,,,
+1933529441,*C=NCCN=Cc1ccc(Sc2ccc(*)o2)o1,95.6213855,,,,
+1933712132,*c1ccc([Si](C)(C)c2ccc([SiH](*)C)s2)s1,60.04219897,,,,
+1933820661,*CCCCCCCCCNC(=O)CCCCCCCC(=O)N*,9.904824538,,0.361,0.944002177,22.95997032
+1933889140,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCCCCCCCCC)c(C(*)=O)cc2OCCCCCCCCCCCCCCCC)cc1,77.98044592,,,,
+1933958092,*CCCCCCCCCCCCCCCCCCCCC(*)Cl,,,0.468,0.869388674,22.5555845
+1934083726,*CC(*)(C)C(=O)OC(C)CC,,,0.182,0.898018787,12.66511883
+1934582540,*CC(*)(C)C(=O)OCCCCc1ccccc1,,0.36056705,,,
+1934851190,*Nc1ccccc1CCc1ccc(NC(*)=O)cc1,,0.34964607,,,
+1935091006,*CC(*)(C)C(=O)OCc1cc(Cl)cc(Cl)c1,,0.35906953,,,
+1936130559,*Nc1ccc(-c2ccc(-c3ccc(-c4ccc(N*)cc4)cc3)cc2)cc1,,0.34630699,,,
+1936588352,*Nc1ccc2c(c1)c(C)c(-c1ccccc1)c1cc(N*)ccc12,,0.35070087,,,
+1937333990,*Nc1c(CCC)c2cccc3c4cccc5cccc(c(c1N*)c23)c54,,0.35047005,,,
+1938625249,*Nc1ccc(C[C@](C)(CC)c2ccc(N)c(C[C@@](C)(CC)c3ccc(N*)cc3)c2)cc1,,0.3558969,,,
+1938664551,*Nc1cccc(-c2c(N*)cccc2-c2ccccc2)c1,,0.35005317,,,
+1939398685,*Cc1cc(C23CC4CC(CC(C4)C2)C3)cc(CN(*)c2ccccc2)c1O,,0.38557882,,,
+1939581854,*C1CCC(CC2CCC(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)CC2)CC1,,0.36158829,,,
+1939700854,*Nc1ccc([C@H](CC)c2ccc(C3(c4ccc([C@@H](CC)c5ccc(N*)cc5)cc4)CCCCC3)cc2)cc1,,0.36956425,,,
+1939922135,*Oc1ccc(C(c2ccc(OC(*)=S)cc2)c2cccc3ccccc23)cc1,,0.37286204,,,
+1940131067,*CCCCCCCCCCSCCCCS*,,,0.282,,
+1941096449,*Nc1ccc2c(N*)c3cc(N)ccc3cc2c1,,0.34432404,,,
+1941265933,*CC(*)C(C)(C)C,,,0.1363333333333333,,
+1941356734,*c1ccc(Oc2ccc(C(=O)c3ccc4cc(C(=O)c5ccc(Oc6ccc(-c7nc(-c8ccc(-c9nc(*)c(-c%10ccccc%10)[nH]9)cc8)[nH]c7-c7ccccc7)cc6)cc5)ccc4c3)cc2)cc1,,0.40364332,,,
+1941491710,*c1ccc(C(c2ccc(OCCCC)cc2)c2ccc(N(*)c3ccc(C)cc3)cc2)cc1,,0.42901003,,,
+1941500497,*NC/C=C\C(=C/CC)[C@]1(c2ccc(N*)cc2)c2ccccc2[C@@]2(C)C=CC=C[C@@H]12,,0.36729671,,,
+1941951841,*c1cc(CCCCCCCCCCCCCCCC)c(*)s1,,,0.381,,
+1942089131,*c1ccc(-c2ccc(-c3ccc(-c4ccc([Si](CCCC)(CCCC)[Si](*)(CCCC)CCCC)s4)s3)s2)s1,,0.42782111,,,
+1942247562,*c1ccc2c(c1)C(=O)N(c1c(CC)cc(Cc3cc(CC)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(CC)c3)cc1CC)C2=O,,0.41266288,,,
+1942353441,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C(F)(F)F)c(C(C)(C)C)c3)cc1)C2=O,,0.41039262,,,
+1942367812,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCCCC(*)=O)cc2)C(=O)OC(C)C)cc1,,0.35066661,,,
+1942693246,*CCC(=O)Nc1ccc(NC(=O)CCN2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.32686636,,,
+1942916315,*Nc1ccc2cc3cc(N*)ccc3cc2c1,,0.34349824,,,
+1943060109,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c1)C2=O,,0.38603279,,,
+1944234117,*C(C#N)=C(C#N)N1C(=O)c2ccc(C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.37567368,,,
+1944443208,*CCC1CC(*)C2C3CC(CC)C(C3)C12,,0.3847423,,,
+1944562220,*c1ccc(C2(c3ccc(-c4cc(-c5ccccc5)c5cc(Oc6ccc7nc(*)cc(-c8ccccc8)c7c6)ccc5n4)cc3)c3ccccc3C(=O)c3ccccc32)cc1,,0.41900126,,,
+1944776345,*CC1CC(*)C(OC(=O)CCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1OC(=O)CCCCCCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1,,0.36288651,,,
+1945309092,*OC(C)COCC(C)OC(=O)c1ccccc1C(*)=O,,0.3500213,,,
+1945809152,*CC(OC1CCCCC1)C1C(=O)OC(=O)C1*,,0.33180155,,,
+1946473006,*N=P(N=P(N=S(*)(=O)NCCC)(NCCC)NCCC)(NCCC)NCCC,,0.39552121,,,
+1947203303,*Nc1ccccc1-c1cccc(C2(c3ccc(N*)c(-c4ccccc4)c3)c3ccccc3-c3ccccc32)c1,,0.37486017,,,
+1947367944,*CC(*)(C)C(=O)OCCCCOc1ccc(OC(=O)c2ccc(OCCCCCC)cc2)cc1,,0.35714893,,,
+1947454451,*C(=O)Nc1cccc(C(=O)Nc2cccc(NC(=O)c3ccc4[nH]c(-c5ccc(-c6nc7cc(*)ccc7[nH]6)cc5)nc4c3)c2)c1,,0.36448737,,,
+1947568782,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1,,0.46091949,,,
+1947579859,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCC)cc1,,0.3461508,,,
+1947586452,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)CCCCCCCCCCC(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.34635755,,,
+1948010634,*Oc1ccc(OC(=O)c2ccc(Oc3ccc(C(*)=O)cc3)cc2)c2ccccc12,,0.35595045,,,
+1948589234,*Oc1ccc(C2(c3ccc(OC(=O)OC4C(C)(C)C(OC(*)=O)C4(C)C)cc3)CCCCC2)cc1,,0.36225678,,,
+1948698672,*OC(COC(=O)C12CC3CC(CC(C(*)=O)(C3)C1)C2)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.35736369,,,
+1949061676,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CC(C)CC(C)(C)C2)cc1,,0.38283226,,,
+1949190696,*Oc1ccc(Oc2ccc(NC(=O)c3cc(NC(=O)c4ccc(OC(C)=O)cc4)cc(C(=O)Nc4ccc(*)cc4)c3)cc2)cc1,,0.34312529,,,
+1949212177,*C(=O)Nc1ccc(-c2sc(-c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7c6)cc4)C5=O)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1,,0.38350821,,,
+1949333335,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)c(S(=O)(=O)O)c3)cc2S(=O)(=O)O)cc1,,0.35202663,,,
+1949514946,*Nc1ccc(NC(=O)C2CC(C(CC(=O)O)C(*)=O)C=C(C)C2C(=O)O)cc1,149.7687276,,,,
+1949780423,*CCSSCCO*,,0.42239943,,,
+1949823161,*Oc1ccc(C=Nc2ccc(N=Cc3ccc(OC(=O)CCCCC(*)=O)c(OC)c3)cc2)cc1OC,-41.85748469,,,,
+1949953064,*c1cccc(*)c1,,0.36874604,,0.992499014,20.5902314
+1950021660,*Oc1ccc(Oc2ccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1-c1ccccc1,,0.36433591,,,
+1950339356,*CCCOC(=O)CCC(=O)O*,,0.31978583,,,
+1950809751,*NC(O)(CC(N)(N)C(=O)O)N*,,0.2981499,,,
+1950874064,*CC(*)C(=O)N(CC)CC,56.77009786,,,,
+1951077384,*Oc1ccc(-c2ccc(Oc3ccc(-c4ccc(-c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1,,0.37694175,,,
+1951160334,*c1ccc(Oc2ccc(N3C(=O)c4c(c(-c5ccccc5)c(-c5ccc(Oc6ccc(-c7c(-c8ccccc8)c8c(c(-c9ccccc9)c7-c7ccccc7)C(=O)N(*)C8=O)cc6)cc5)c(-c5ccccc5)c4-c4ccccc4)C3=O)cc2)cc1,,0.40870638,,,
+1951638627,*Oc1ccc(CC(C#N)(Cc2ccc(*)cc2)c2ccccc2)cc1,,0.38378545,,,
+1951901460,*C=C(C#N)C(=O)OCCCCCCCCCCOC(=O)C(C#N)=Cc1ccc2c(c1)c1cc(*)ccc1n2CCCCCCCCCCCCCC,,0.38039745,,,
+1952301542,*CC(*)(CC(=O)OCCOC)C(=O)OCCOC,,0.33066892,,,
+1952404953,*C(=O)c1ccc2c(c1)C(=Nc1cccc(N=C3OC(=O)c4ccc(*)cc43)c1)OC2=O,246.6584182,,,,
+1952508895,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8c(C)cc(C(C)(C)c9cc(C)c(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)c(C)c9)cc8C)cc7C6=O)cc5)cc4)C4CC5CC(C4)CC3C5)cc2)cc1,,0.39410093,,,
+1952671139,*CC(*)C(=O)OCCCCCC,,,0.2565,0.915149553,11.92379481
+1953033868,*Nc1c(C)c2cccc3c4cccc5cccc(c(c1N*)c23)c54,,0.34334157,,,
+1953349565,*/C=C/c1sc(/C=C/c2cc(CCCCCCCCCCCC)c(*)s2)cc1CCCCCCCCCCCC,,,0.291,,
+1953482672,*c1cccc(P(=O)(c2ccccc2)c2cccc(N3C(=O)c4ccc(NC(=O)OCCOc5ccc(C(c6ccc(OCCOC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4C3=O)c2)c1,,0.35336102,,,
+1953803634,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.34861514,,,
+1954306622,*Nc1cc2c(c(N*)c1N)-c1ccccc1C21c2ccccc2-c2ccccc21,,0.37629547,,,
+1954721166,*CC(*)C(=O)OCCOC(=O)c1cc([N+](=O)[O-])cc([N+](=O)[O-])c1,,0.42097644,,,
+1954861096,*Oc1ccc(C(C)(C)c2ccc(OC(=O)CN(CC(*)=O)c3ccc(N=Nc4ccc([N+](=O)[O-])cc4)cc3)cc2)cc1,,0.36235076,,,
+1955776885,*Nc1ccc(-c2cccc3c2[C@@H](c2ccc(N*)cc2)c2ccccc2-3)cc1,,0.357501605,,,
+1956029718,*CCCCCCCCCCCCCCCCCCCCC(*)COCCOCCOCCOCCOCc1ccc2ccc3cccc4ccc1c2c34,,0.37669905,,,
+1956150521,*CC(*)(C)C(=O)OCCN(C)C,,0.36516646,0.2005,0.981910387,12.3457196
+1956367813,*C(=O)c1cc2c(cc1Cl)C(=O)N(c1cc(C(=O)O)cc(N3C(=O)c4cc(*)c(Cl)cc4C3=O)c1)C2=O,,0.37662267,,,
+1956396424,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(OC(=O)c2ccc(OCCCCCC)cc2)cc1,,0.36422964,,,
+1956587899,*CC(*)(C)C(=O)OCCN(CC)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.36616331,,,
+1956666408,*Nc1ccc([C@@]2(C)CC(C)(C)c3ccc(N*)cc32)cc1,,0.36492797,,,
+1956893558,*c1ccc(Oc2ccc(-c3nnc(*)o3)cc2)cc1,,0.40011627,,,
+1957426818,*c1c(C)cc(C(c2cccnc2)c2cc(C)c(N3C(=O)CC(Nc4ccc(Cc5ccc(NC6CC(=O)N(*)C6=O)cc5)cc4)C3=O)c(C)c2)cc1C,,0.3862315,,,
+1957470383,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(C(=O)O*)c2)c1,,0.33466722,,,
+1958192948,*c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(-c4ccc(OCCCC#N)cc4)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)n3)cc1)C2=O,,0.39133561,,,
+1958473963,*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc12,,0.35386288,,,
+1958487458,*OC1CCCCC1OC(*)=O,,0.34928658,,,
+1958554605,*CC(C[Si](*)(C)C)c1cccc2ccccc12,,0.37751563,,,
+1958618966,*CC(C)(C)O*,,0.39662664,0.1995,0.861163598,13.695157
+1958858448,*C=CC1CC(*)C([Si](C)(C)C)C1,,0.41756193,,,
+1959224488,*c1ccc(N(c2ccc(C#N)cc2)c2ccc(N3C(=O)c4ccc(Oc5ccc(C6(c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)c7ccccc7-c7ccccc76)cc5)cc4C3=O)cc2)cc1,,0.41325752,,,
+1959489258,*Nc1ccc(C(c2ccc(CC)cc2)c2ccc(N*)cc2)cc1,,0.36039896,,,
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+1960232404,*CCCCCCCCCCCCOc1ccc(OCc2ccc(COc3ccc(O*)cc3)cc2)cc1,,0.36434601,,,
+1960719660,*CCCCCCNC(=O)Cc1ccc(C(=O)N*)cc1,,0.3389375,,,
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+1960968082,*c1ccc(C(=O)OCCCCCCOc2ccc(C=CC(=O)C=Cc3ccc(OCCCCCCOC(=O)c4ccc(-c5nnc(*)o5)cc4)cc3)cc2)cc1,,0.35964696,,,
+1961009790,*Nc1ccc(C(=C(CC)c2ccccc2)c2ccc(N*)cc2)cc1,,0.36784102,,,
+1961333233,*CCN(CCOC(=O)Nc1cccc(NC(=O)O*)c1C)c1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.34593177,,,
+1961335797,*c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(-c6nnc(*)c7c(-c8ccccc8)c(-c8ccc9ccccc9c8)c(-c8ccc9ccccc9c8)c(-c8ccccc8)c67)cc5)cc4)c4ccccc4-c4ccccc43)cc2)cc1,,0.41100116,,,
+1962617217,*Nc1cc(C)c(Cc2ccc(CCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,,0.36087708,,,
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+1962833245,*c1ccc(NC2CC(=O)N(c3ccc(Cc4ccc(N5C(=O)CC(Nc6ccc(-c7nc(-c8ccc(N=Nc9ccc([N+](=O)[O-])cc9)cc8)[nH]c7*)cc6)C5=O)cc4)cc3)C2=O)cc1,,0.37673487,,,
+1963304401,*CCCCCCCCCCOC(=O)CC/C=C/CCC(=O)O*,,0.3650855,,,
+1963751846,*C=CC1CC(*)C(C2CC=CCC2)C1,,0.39319144,,,
+1964367064,*Oc1c(C)c(C)c(NC(=O)c2ccc(C(=O)Nc3ccc(*)cc3)cc2)c(C)c1C,,0.37041365,,,
+1964385683,*C(=O)c1ccc2c(c1)C(=O)N(c1ccccc1C(=O)c1ccccc1N1C(=O)c3ccc(*)cc3C1=O)C2=O,,0.38153591,,,
+1964501919,*c1ccc(Oc2ccc(C(c3ccc(Oc4ccc(-c5nnc(*)n5-c5ccc(S(C)(=O)=O)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.38547657,,,
+1964666760,*CCCCCCOC(=O)NCCCCNC(=O)O*,,0.33568423,,,
+1964792280,*c1ccc(OCCN(CCOc2ccc(-c3cc(-c4ccccc4)c4cc(Oc5ccc6nc(*)cc(-c7ccccc7)c6c5)ccc4n3)cc2)c2ccc(C(C#N)=C(C#N)C#N)cc2)cc1,,0.38278631,,,
+1964948297,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O,,0.37124661,,,
+1965550017,*CC(*)C(=O)OCCSC,,0.38690329,0.181,1.110954584,15.7280871
+1965690727,*CCCCCCCCCCOC(=O)c1cccc(NC(=O)O*)c1,45.09440244,,,,
+1965799831,*CCCCCCNC(=O)C(CCCCCCCCCCCCCCCCCC)C(=O)N*,,,0.354,,
+1965906081,*O[Si](*)(C)CCCOc1ccc(N=Nc2ccc(S(=O)(=O)O)cc2)cc1,,0.3616266,,,
+1965969714,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CC(C)CCC(*)=O)cc2)C(=O)OCCCC)cc1,,0.3483824,,,
+1966018498,*SC1CCCC(*)C1,,0.39301912,,,
+1966201897,*CC(*)c1ccc(COCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.35487318,,,
+1966629557,*CCCCCCOC(=O)OCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35116404,,,
+1966848887,*CCCCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.37735264,0.3725,0.901851851,21.13049646
+1967479741,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(c4cc(C)c(*)c(C)c4)(C(F)(F)F)C(F)(F)F)cc3C)c(Br)c2)cc1Br,,0.41274823,,,
+1967596895,*Nc1c(CCCC)cc(C2(c3cc(CCCC)c(N*)c(CCCC)c3)c3ccccc3-c3ccccc32)cc1CCCC,,0.39180486,,,
+1967679061,*CC(OCC(C)C)C1C(=O)OC(=O)C1*,,0.33696178,,,
+1967860241,*CC(*)(CC(=O)Oc1ccc(OCCC)cc1)C(=O)Oc1ccc(OCCC)cc1,,0.35583828,,,
+1968301845,*CCN(*)CCOCCOC,,0.35963141,,,
+1968819662,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C=C4CCCCC(=Cc5ccc(Oc6ccc(S(=O)(=O)c7ccc(Oc8ccc(C(C)(C)c9ccc(*)cc9)cc8)cc7)cc6)cc5)C4=O)cc3)cc2)cc1,,0.37593554,,,
+1968997516,*CC(CO*)(CS(=O)(=O)CCCCC)CS(=O)(=O)CCCCC,,0.3851805,,,
+1969004591,*c1ccc(OCCN(CCOc2ccc(-c3nc4ccc(Oc5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)c2ccc(C=Cc3ccc([N+](=O)[O-])cc3)cc2)cc1,,0.37700388,,,
+1969083594,*CCOC(=O)NCCNC(=O)O*,5.896093021,,,,
+1969287325,*Oc1ccc(OC(=O)c2ccccc2C=Cc2ccc(C=Cc3ccccc3C(*)=O)cc2)cc1,86.85824281,,,,
+1969520077,*Oc1ccc(NC(=O)c2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C6(c7ccc(*)cc7)C7CC8CC(C7)CC6C8)cc5)cc4)cc3)cc2)cc1,,0.36901705,,,
+1969822510,*CCc1ccc(*)c(CCCC)c1,,0.39722229,,,
+1969835968,*C(=O)Nc1ccc(C2(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CCC(C(C)(C)C)CC2)cc1,,0.38391661,,,
+1970056455,*Nc1cc2c3cccc(-c4ccc(C)cc4)c3c3c(-c4ccc(C)cc4)c(N*)c4ccccc4c3c2c2ccccc12,,0.38290568,,,
+1970556387,*Oc1cccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1,,0.35216883,,,
+1970744006,*Oc1ccc(C2(c3ccc(OC(*)=O)cc3)CC3CC2C2C4CCC(C4)C32)cc1,,0.37460046,,,
+1971561670,*CCCCCCNC(=O)CCCCC(=O)N*,,0.33903345,,,
+1971686624,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=O)c4ccc(-c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37362004,,,
+1971749373,*Oc1ccc(C(*)=O)cc1CCCCCC,85.05865655,,,,
+1971998855,*Cc1cccc(COC(=O)Nc2ccc(Cc3ccc(NC(=O)O*)cc3)cc2)c1,,0.34243796,,,
+1972024406,*CC(COC(=O)O*)OCCCCCCOc1ccc(C=Cc2ccc([N+](=O)[O-])cc2)cc1,,0.3517257,,,
+1972032416,*c1ccc(C(=O)OCCCCCCCCOc2ccc(C=C3CCC(=Cc4ccc(OCCCCCCCCOC(=O)c5ccc(-c6nnc(*)o6)cc5)cc4)C3=O)cc2)cc1,,0.3617754,,,
+1972109782,*c1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(N4C(=O)CC(SCCOCCSC5CC(=O)N(*)C5=O)C4=O)cc3)cc2)cc1,,0.33390562,,,
+1972780662,*CC(*)C(=O)OCCOCC,,0.35976017,,,
+1973157365,*Oc1cc(Oc2ccc(C(C)(C)c3ccc(*)cc3)cc2)cc([N+](=O)[O-])c1,,0.37041921,,,
+1973279343,*CCCCCCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1,,0.35672203,,,
+1973417069,*C=CC1CC(*)C2C(=O)N(c3ccc(F)cc3)C(=O)C12,,0.37471023,,,
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+1974222133,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(-c4cc(C)c(*)c(C)c4Br)c(Br)c3C)cc2)cc1,,0.41770931,,,
+1974242688,*c1ccc(C)c(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.39299942,,,
+1974258183,*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)c(-c4ccccc4)c3)cc2C1=O,,0.34408624,,,
+1974383215,*CCCCCCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.35929701,,,
+1974978740,*C=C(*)c1c(C)cc(C(C)(C)C)cc1C,,0.44377353,,,
+1975082239,*c1nc(-c2ccccc2)nc(N(c2ccccc2)c2ncnc(N(*)c3ccccc3)n2)n1,,0.41510699,0.185,1.133996819,15.96371163
+1975447756,*CCOC(=O)c1ccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(C(=O)O*)cc2)cc1,14.08686284,,,,
+1975514366,*c1cccc(OCCCCOc2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,,0.34563212,,,
+1975880565,*Oc1ccc(Oc2ccc(NC(=O)c3ccc(Oc4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4-c4ccccc4)cc3)cc2)cc1-c1ccccc1,,0.36793579,,,
+1976024514,*Nc1ccc(C(c2ccc(N*)cc2)C2CCCCC2)cc1,,0.36335791,,,
+1976774043,*CCCCCCOc1ccc(C(=O)N(C(=O)c2ccc(O*)cc2)c2cccc3ccccc23)cc1,,0.37512174,,,
+1977447931,*Nc1cccc(C2(c3cccc(N*)c3)c3cc(N)ccc3-c3ccc(N)cc32)c1,,0.36514594,,,
+1978316297,*Nc1ccc(-c2cc(N*)cc(-c3cccc(N)c3)c2)cc1,,0.34498231,,,
+1978540220,*C(=O)OCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.34266185,,,
+1978620404,*CC(*)CCCCCCCCCCCCCCCCCC,,0.40805705,0.381,,
+1978702733,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(C(OC)(C(F)(F)F)C(F)(F)F)c4)cc3C(OC)(C(F)(F)F)C(F)(F)F)cc2)cc1,,0.37975399,,,
+1978759933,*CCC1C(=O)N(CCCCCCCCCCCCCC)C(=O)C1*,,0.38602412,,,
+1978914025,*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(-c5c(-c6ccccc6)c(-c6ccccc6)c(-c6ccc(Oc7ccc(-c8c(-c9ccccc9)c(-c9ccccc9)c(*)c(-c9ccccc9)c8-c8ccccc8)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)cc4C3=O)cc1)C2=O,,0.40149442,,,
+1978930507,*C(=C(*)c1ccc([Si](C(C)C)(C(C)C)C(C)C)cc1)c1ccccc1,,0.45051401,,,
+1979011181,*c1ccc(Oc2ccc(-c3nc(-c4cccc(-c5nnc(-c6ccccc6)c(*)n5)n4)nnc3-c3ccccc3)cc2)cc1,,0.41817078,,,
+1979505726,*c1cccc(S(=O)(=O)NCCCCCCNS(*)(=O)=O)c1,,0.34906326,,,
+1979525193,*OC1CCC(OC(=O)C(*)=O)CC1,,0.33792482,,,
+1979934753,*c1nnc(-c2ccc(Oc3ccc(C=Cc4ccc(Oc5ccc(*)c6ccccc56)cc4)c4ccccc34)c3ccccc23)o1,,0.38836559,,,
+1979939370,*Nc1ccc(C2(c3ccc(N*)cc3)c3cc(C#CCCCC)ccc3-c3ccc(C#CCCCC)c(C#C)c32)cc1,,0.39588749,,,
+1980536603,*CCc1ccc(-n2on2-c2ccc(CCOC(=O)CCCCCCC(=O)O*)cc2)cc1,17.91289325,,,,
+1981136743,*OC(COCCOC)COC(*)=O,,0.33674194,,,
+1981457097,*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)c4)cc3)cc2)c1,,0.35415935,,,
+1981691204,*Nc1ccc(/C=C(/c2ccc(N*)cc2)c2cccc(C)c2)cc1,,0.3641488,,,
+1981721677,*NC(CCC(=O)OCc1ccc([N+](=O)[O-])cc1)C(*)=O,,0.33486234,,,
+1981870695,*Nc1nc(=S)[nH]c(N*)c1N,,0.30575132,,,
+1981886524,*c1ccc(Oc2ccc(C=Cc3ccc(Oc4ccc(-c5nnc(*)o5)cc4)c4ccccc34)cc2)cc1,,0.38751411,,,
+1982249582,*C#CC(CCCCOC(=O)NCCC)=C(*)CCCCOC(=O)NCCC,40.70123878,,,,
+1982444634,*Oc1ccc(P(C)(=O)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccc(*)cc4)C(F)(F)F)cc3)cc2)cc1,,0.38583407,,,
+1982948729,*CCOCCOCCOc1ccc(C=Cc2cc(OC)c(C=Cc3ccc(O*)c4ccccc34)cc2OC)c2ccccc12,,0.35883,,,
+1983222428,*c1ccc(NC(=O)c2ccc(OCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)cc1,,0.35057029,,,
+1984190839,*CCC=C(*)Cl,,0.39969952,,,
+1984350149,*CCN(CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C)c(C)c1,,0.35034244,,,
+1984402033,*CCCCCCCCCCOc1ccc(-c2cc(-c3ccccc3)c(-c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(-c6c(-c7ccccc7)cc(-c7ccc(O*)cc7)cc6-c6ccccc6)cc5)cc4)cc3)c(-c3ccccc3)c2)cc1,,0.37093746,,,
+1984563739,*Oc1cccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)c1,,0.3657163,,,
+1984715605,*CC(O*)C(C)(C)C,,0.38045208,,,
+1985260994,*c1cc(CCCCCCCCCCCC)c(*)cc1CCCCCCCCCCCC,,,0.356,0.832338804,11.74161872
+1985598557,*CCCCCCCCCCCCCCOC(=O)CC/C=C/CCC(=O)O*,,,0.318,,
+1985922935,*O[Si](Oc1ccc(C2(c3ccc(*)c(C)c3)c3ccccc3-c3ccccc32)cc1C)(c1ccccc1)c1ccccc1,,0.42654959,,,
+1986596083,*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1,,0.37091034,,,
+1986643136,*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(*)ccc1-2,,0.4176963,,,
+1986951321,*Oc1ccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(*)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2)cc1,,0.35696671,,,
+1987297108,*CC(*)(C)C(=O)Oc1ccc(C(=O)OC)cc1,,0.33216316,,,
+1987380350,*CCCCCCCCCCCCCNC(=O)CCCCCCCCCCCC(=O)N*,,0.37680906,0.3835,0.900220523,22.20373128
+1987420560,*Oc1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(*)cc4)c3)cc2)cc1S(=O)(=O)O,,0.36037958,,,
+1987532740,*Nc1cc2c3ccccc3c(N*)cc2c2ccccc12,,0.3388047,,,
+1987791441,*C=CC1CC(*)C2C(=O)N(CCCCCC)C(=O)C12,,0.36643423,,,
+1987932881,*Oc1c(OC)cc(-c2cc(OC)c(OC(=O)CCCCCCCCC(*)=O)c(OC)c2)cc1OC,,0.35167826,,,
+1988065987,*CCCCCCOP(=O)(N=Nc1ccc(-c2ccc(N=NP(=O)(O*)OC)cc2)cc1)OC,,0.36135767,,,
+1988157490,*C(=O)c1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(Cc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc6)C7=O)cc5)cc4)OC(=O)c4ccccc43)cc2)cc1,,0.38151091,,,
+1988158741,*NC1=C(c2c(N*)ccc3ccccc23)[C@@H]2C=CC=CC2=CC1,,0.35485105,,,
+1988515420,*C(=O)Nc1ccc(Cc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1,,0.3587615,,,
+1988563349,*CCCCCCOP(=O)(N=Nc1ccc(CCOC(=O)c2cc(C(=O)OCCc3ccc(N=NP(=O)(O*)OC)cc3)cc(C(C)(C)C)c2)cc1)OC,,0.36086299,,,
+1988613729,*c1ccc(Nc2cccc(Nc3ccc(S(*)(=O)=O)cc3)c2)cc1,,0.38606656,,,
+1988695621,*C=Cc1cc(OCCCC)c(*)cc1OC,,0.38141349,,,
+1989009104,*Nc1ccc(Cc2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.35067325,,,
+1989599237,*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OC(=O)C45CC6CC(CC(C6)C4)C5)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OC(=O)C13CC4CC(CC(C4)C1)C3)C2=O,,0.39735492,,,
+1991040777,*C(OC(F)(F)F)C(*)(F)F,,0.31744193,,,
+1991510657,*Oc1ccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)cc1C(C)(C)C,,0.38863246,,,
+1991701752,*C1C(*)C2CC1C(C(=O)OCCCc1ccccc1)C2C(=O)OCCCc1ccccc1,,0.35924036,,,
+1991992647,*CC(CC)(CO)CO*,,0.3383421,,,
+1992818207,*Oc1ccc(C(C)(C)c2ccc(OC(=O)OCC3CCC(COC(*)=O)CC3)cc2)cc1,,0.35273302,,,
+1992987283,*CC(=O)NNC(=O)c1ccc(C(=O)NNC(=O)CN2C(=O)c3ccc(C(c4ccc5c(c4)C(=O)N(*)C5=O)(C(F)(F)F)C(F)(F)F)cc3C2=O)cc1,226.7602549,,,,
+1993503133,*Oc1ccc(C=C2CCCC(=Cc3ccc(OC(=O)c4cccc(C(*)=O)c4)c(OC)c3)C2=O)cc1OC,,0.35590759,,,
+1994005610,*c1cccc(C(F)(F)C(F)(F)C(*)(F)F)c1,,0.35942273,,,
+1995309810,*NNC(=O)c1ccccc1C(=O)Nc1cccc(C=C2CCC(=Cc3cccc(NC(=O)c4ccccc4C(*)=O)c3)C2=O)c1,,0.3556635,,,
+1995487445,*CC(*)(C)C(=O)OCCO[Si](C)(C)C,,0.40711704,,,
+1995567642,*CC(*)c1ccc(C(=O)CCN2CCOCC2)cc1,,0.35976341,,,
+1995858685,*NC(C)CNC(=O)NCCCCCCNC(*)=O,55.35434565,,,,
+1995899423,*Oc1ccc(CC(NC(=O)Cc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCC)cc1,,0.33327297,,,
+1997329694,*CCCCCCCCCCCCCCCCOC(=O)C/C=C/CC(=O)O*,,0.37864549,0.315,,
+1997475828,*CC(*)CCCCCC,,0.40777595,0.238,,
+1997665708,*Nc1ccc2c(c1)[C@]1(c3ccccc3-2)c2ccccc2-c2ccc(N*)c(-c3ccccc3)c21,,0.377512415,,,
+1997781379,*OP(=O)(Oc1ccccc1)Oc1c(C)cc(S(=O)(=O)c2cc(C)c(*)c(C)c2)cc1C,,0.3661802,,,
+1998038943,*c1ccc(N2C(=O)CC(C3C=C(C)C4C(=O)N(*)C(=O)C4C3)C2=O)cc1,,0.38067827,,,
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+1998608102,*CC(*)(C)C(=O)OCCCCCCCCCCOc1ccc(C#Cc2cc(C)c(C=Cc3ccncc3)cc2C)cc1,,0.38135827,,,
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+2000269306,*C/C=C\C[Si](*)(C)CCCOc1ccccc1,,0.37468563,,,
+2000354912,*Nc1ccc([C@@]2(C)CC(C)(C)c3ccc(N*)c(C)c32)cc1C,,0.37566233,,,
+2000390968,*CC(*)(C)C(=O)OC1CCC1,,0.36717327,,,
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+2001829595,*Nc1ccc([C@@H](C)CC(C)(C)c2ccc(N*)cc2)cc1,,0.35096863,,,
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+2002874744,*CCCCCCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.37454114,,,
+2002898366,*Nc1ccc2c(-c3ccccc3)c(N*)ccc2c1-c1ccccc1,,0.35542966,,,
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+2003251624,*Oc1ccc(OC(=O)c2ccc(OC(=O)c3ccc(C(=O)Oc4ccc(C(=O)Oc5ccc(OC(=O)c6cccc(C(*)=O)c6)cc5)cc4)cc3)cc2)cc1,119.4238893,,,,
+2003333074,*CCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.37842036,0.381,,
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+2004174775,*C=Cc1ccc2c(c1)C(CCCCCCOc1ccc3ccc(=O)oc3c1)(CCCCCCOc1ccc3ccc(=O)oc3c1)c1cc(C=Cc3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(*)ccc3-4)ccc1-2,,0.3721787,,,
+2004281729,*CC(*)c1cc(C)ccc1C,,,0.173,0.907377009,13.45177375
+2004522316,*C(=O)Oc1ccc(OC(=O)c2ccc3c(c2)C(=O)N(c2ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c(C(C)(C)C)c1,,0.37267294,,,
+2004781454,*Nc1ccc(C2CCCCC2)c(C2CCCCC2)c1N*,,0.37629915,,,
+2004817978,*CCCCCCCCCCCCNC(=O)CCP(C)(=O)CCC(=O)N*,,0.35766138,,,
+2004822166,*CC1CCCC(CN2C(=O)c3ccc(C(c4ccc5c(c4)C(=O)N(*)C5=O)(C(F)(F)F)C(F)(F)F)cc3C2=O)C1,,0.36224817,,,
+2005210923,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(Oc8ccc(Oc9ccc(Oc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)cc8)cc7C6=O)cc5)cc4)c3)cc2)cc1,,0.36312657,,,
+2005402308,*CC(C)(C)COC(=O)CCCCCCCC(=O)O*,17.01342593,,,,
+2005457521,*CCCC(=O)O*,,0.32557378,,,
+2005551662,*C1C2CC(CCCCc3ccccc3)C(C2)C1*,,0.36435732,,,
+2005844572,*NC1=Cc2c(c3ccc(N*)cc3c3ccccc23)C[C@@H]1c1ccccc1,,0.34384971,,,
+2006295259,*Sc1cc(C)c(*)cc1C,,0.42409378,,,
+2006448599,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C3(c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C)c4)c4ccccc4-c4ccccc43)cc1C)C2=O,,0.44643789,,,
+2006513781,*Nc1ccc(C(c2ccc(N*)cc2)[C@@H](CCC)[C@H]2CC[C@H](/C=C/[C@H]3CC[C@H](C(C)C)CC3)CC2)cc1,,0.37803401,,,
+2006683718,*CC(*)C(=O)c1ccc(C)cc1,,0.38303868,0.1975,1.017121715,11.41539018
+2006821254,*Oc1ccc(OC(*)=O)cc1,105.0499992,,,,
+2006996474,*CCCCCCCCCCOC(=O)NCc1ccc(CNC(=O)O*)cc1,,0.34727776,,,
+2007281733,*c1cc(CCCCCCCCCC)c(*)s1,,0.41339329,,,
+2007312638,*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1,,0.37573653,,,
+2007560490,*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cc3)nc2c1,,0.45097253,,,
+2007665966,*OCC(F)(F)C(F)(F)C(F)(F)COc1nc(F)nc(*)c1F,,0.34008549,,,
+2007994317,*CC(C)(C)COC(=O)Cc1ccc(C(=O)O*)cc1,,0.35161343,,,
+2008537603,*C=C(*)c1ccccc1,66.12991425,,,,
+2008605500,*CC(C)(CO*)COCCC#N,,0.38263098,,,
+2008709540,*Nc1cc(-c2c(N*)ccc3ccccc23)c(N)cc1[C@@H]1c2ccccc2C=C[C@H]1N,,0.34308661,,,
+2009603005,*Nc1cccc(-c2cccc3ccccc23)c1N*,,0.34900624,,,
+2009816260,*Nc1ccc2ccc3c(N*)c(-c4ccccc4)c(-c4ccccc4)c4ccc1c2c34,,0.36314793,,,
+2010311547,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccc(C(=O)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.36073846,,,
+2010970125,*NCC(C)(C)N*,,0.32677864,,,
+2011509747,*c1ccc(C=C2CCCC(=Cc3ccc(-c4csc(NC(=O)NCCCCCCNC(=O)Nc5nc(*)cs5)n4)cc3)C2=O)cc1,47.02441211,,,,
+2011999310,*OC(COCCCC)COC(*)=O,,0.35933677,,,
+2012503945,*CC(*)OCCOc1ccc(C=C(C#N)C(=O)OC)cc1,,0.34910915,,,
+2012816779,*OP(=O)(Oc1ccccc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1,,0.34868252,,,
+2013254124,*CC(O)COc1ccc(C(C)(c2ccccc2)c2ccc(O*)cc2)cc1,,0.35423156,,,
+2013412684,*Nc1ccc(NC(=O)c2cc(SCCCCCCCC)c(C(*)=O)cc2SCCCCCCCC)cc1,69.67482713,,,,
+2014114432,*c1ccc(Oc2ccc(C(=O)c3cccc(C(=O)c4ccc(Oc5ccc(-c6c7c(c(*)c8ccccc68)C(=O)N(C)C7=O)cc5)cc4)c3)cc2)cc1,,0.37310295,,,
+2014263245,*OC(F)(F)C(F)(F)C(F)(F)C(*)(F)F,,0.3276198,,,
+2014301760,*CCOCCOCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1,,0.33785763,,,
+2014418725,*c1cc(CCCCCCCCCCCCCCCC)c(*)[nH]1,,,0.4075,0.866052711,14.71408344
+2014622631,*CC(*)c1cccc(Cl)c1,,,0.14175,1.095935741,14.9638119
+2014653068,*CC1CCC(COC(=O)NCCSCCCCCCSCCNC(=O)O*)CC1,,,0.24,,
+2014740524,*CC(*)OC(=O)c1ccc(CC)cc1,,0.37318074,,,
+2015016927,*Oc1ccc(S(=O)(=O)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,,0.3572516,,,
+2015176684,*CCCCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccc(-c3ccccc3)cc2)c1,,0.35011454,,,
+2015484909,*CCCCCCOC(=O)OCCCCCCCCCOC(=O)OCCCCCCOc1ccc(Oc2ccc(O*)cc2)cc1,,0.35651223,,,
+2015550348,*C(=O)c1ccc(C(=O)N(CC)c2ccc(-c3ccc(N(*)CC)cc3)cc2)cc1,62.56896406,,,,
+2015619350,*C(=O)OCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.33665398,,,
+2015647676,*CCCCCCCCCCCCOC(=O)c1ccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(C(=O)O*)cc2)cc1,-26.12929226,,,,
+2015742836,*C(=O)NCCCCCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.33363359,,,
+2016514618,*c1cccc(CCCOc2cccc(N3C(=O)c4cccc(-c5ccc6c(c5)C(=O)N(*)C6=O)c4C3=O)c2)c1,,0.35569719,,,
+2016694996,*N=P(*)(OCCCCCCCCCC)OCCCCCCCCCC,,0.40515813,,,
+2016828093,*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCCCCC)c(OCCCCC)c(OCCCCC)c4)cc3)cc2)cc1,,0.36863152,,,
+2017537150,*CC(F)(F)C(F)(F)COC(=O)NCCCCCCNC(=O)O*,,0.32427748,,,
+2017623024,*CCCCCCCCCCc1nc2ccc(-c3ccc4nc(*)oc4c3)cc2o1,,0.37701196,,,
+2018770254,*c1ccc(OC(=O)c2ccc([Si](C)(C)c3ccc(C(=O)Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.37413124,,,
+2019089439,*Oc1ccc(Oc2ccc(Oc3cccc(C(=O)Oc4ccc(-c5ccc(OC(=O)c6cccc(*)c6)cc5)cc4)c3)cc2)cc1,,0.35465002,,,
+2019160480,*c1cccc(C(c2cccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)(C(F)(F)F)C(F)(F)F)c1,,0.37386438,,,
+2019662589,*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(-c5ccc(CCCCCC)s5)s4)c4nsnc34)cc2)cc1,,0.38938672,,,
+2019749596,*Nc1ccc2cccc3c2c1C=C[C@@H]3N*,,0.32848339,,,
+2020299176,*Oc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)c6ccc(S(=O)(=O)c7ccc(C(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.36431498,,,
+2020359190,*SC1CCCCC1*,,0.39542833,,,
+2020708371,*CCCCCCCCCCOC(=O)CCCCCCCCCCC(=O)O*,,,0.319,,
+2020818351,*OP(=O)(Oc1ccc(Cl)cc1)Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1,,0.36942228,,,
+2020857359,*C1C(*)C2CC1C(C(=O)OCC1CC3CCC1C3)C2C(=O)OCC1CC2CCC1C2,,0.3915968,,,
+2020969412,*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)CCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,,0.33946762,,,
+2021130143,*Nc1ccc([C@]2(C)CC(C)(C)c3cc(N*)c(N)c(N)c32)c(N)c1N,,0.35556128,,,
+2021290286,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)ccc5c4)cc3)cc1)C2=O,,0.3742742,,,
+2021383868,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(-c4cc(-c5ccccc5)cc(-c5cccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)c5)n4)c3)cc1)C2=O,,0.3772372,,,
+2021473660,*CCOC(=O)CCCCCCCCCCC(=O)OCCOC(=O)c1ccc(C(=O)O*)cc1,,0.34409739,,,
+2021478343,*CCC1C(=O)N(CCCCNC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)C(=O)C1*,,0.31483442,,,
+2022011396,*Oc1ccc(Oc2ccc(Nc3ccc(S(=O)(=O)c4ccc(Nc5ccc(*)cc5)cc4)cc3)cc2)cc1,,0.39625118,,,
+2022159696,*CCNC(=O)c1ccc(C(=O)NCCOc2ccc3c(O*)cccc3c2)cc1,,0.33252314,,,
+2022505233,*CC(*)C(=O)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F,,0.32766063,,,
+2023609610,*Nc1ccc2ccc(N*)c3c4cccc5cccc(c1c23)c54,,0.33884923,,,
+2023841912,*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.3587456,,,
+2024518785,*Nc1ccc2c3ccc(N*)c4cccc(c5cccc1c52)c43,,0.33265387,,,
+2024524302,*CC(*)c1cc(OC)c(O)c(OC)c1,,0.32962822,,,
+2024808471,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cccc(-c2cccc(C(=O)O*)c2)c1,,0.33493908,,,
+2024926859,*Nc1ccc(C2(c3ccc(N*)cc3)Cc3ccc(cc3)C2)cc1,,0.34766302,,,
+2025206311,*NC1=C2C=CC=C[C@@H]2[C@@](N*)(c2c(C)cc(N)c3ccccc23)C(C)=C1,,0.36137025,,,
+2025276311,*C/C=C\C[Si](*)(C)CCCOc1ccc(-c2ccccc2)cc1,,0.36376356,,,
+2025508516,*Oc1cccc(C(*)=O)c1,,0.34146211,,,
+2025809998,*OC(Cc1ccccc1)C(*)=O,,0.35974569,,,
+2025970688,*CCCCCCCCCCCCNC(=O)C(OC)C(OC)C(=O)N*,,0.36064464,,,
+2026398041,*c1cccc(N2C(=O)c3cccc(Oc4ccc(Oc5cccc6c5C(=O)N(*)C6=O)cc4)c3C2=O)c1,,0.35986411,,,
+2026422493,*c1ccc(Oc2cc3ccccc3cc2Oc2ccc(N3C(=O)c4ccc(S(=O)(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37064659,,,
+2026602715,*Oc1ccc(CC2(Cc3ccc(*)cc3)c3ccccc3-c3ccccc32)cc1,,0.38673574,,,
+2026869258,*c1ccc(Oc2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.37709986,,,
+2026975698,*CC(*)(C)C(=O)OCCCN(CCO)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1,,0.35100241,,,
+2027806037,*C[Si](*)(C)CCCn1c2ccccc2c2ccccc21,,0.36146235,,,
+2028307602,*Nc1cc2c(cc1N*)C1(CC1)CC2(C)C,,0.36330601,,,
+2028500149,*CC(*)CCC1CCCCC1,,,0.2176666666666666,,
+2028527037,*Cc1ccc(CSS*)cc1,,0.40158924,,,
+2028683291,*CC(=O)Nc1ccc(C2(c3ccc(NC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)c3ccccc3-c3ccccc32)cc1,,0.36284889,,,
+2029399840,*CCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.3346262,,,
+2030056583,*CC(*)C(=O)N(C(C)C)C(C)C,,,0.1865,0.844059103,11.58798505
+2030717926,*CCCCCCC(=O)NCCCCCCNC(=O)CCCO*,1.467345964,,,,
+2030734021,*Oc1ccc(NC(=O)c2c(F)c(F)c(C(=O)Nc3ccc(*)cc3)c(F)c2F)cc1,168.6168885,,,,
+2031010727,*CC(*)c1ccc(C(=O)OCCCCCC)cc1,,0.37921152,,,
+2031068763,*Oc1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1-c1ccccc1,,0.38107447,,,
+2031096142,*Oc1ccc(Oc2ccc(OC(=O)c3cccc(Oc4ccc(Oc5cccc(C(*)=O)c5)cc4)c3)cc2)cc1,,0.35436946,,,
+2031152163,*c1ccc(N2C(=O)c3ccc(Oc4ccc(C(C)(C)c5cccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)c5)cc4)cc3C2=O)cc1,,0.37049123,,,
+2031378457,*CC(*)CCC(C)CC,,,0.229,0.791153015,11.37248496
+2031413397,*CCN(CCOC(=O)c1cc(OC2CCN(c3ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc3)CC2)cc(C(=O)O*)c1)c1ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1,,0.34244923,,,
+2031789072,*CC(*)CNc1ccc([N+](=O)[O-])cn1,,0.37295814,0.192,1.22830711,12.89532483
+2032432464,*CCC(C(=O)OC)C(*)C(=O)OC,,0.33473342,,,
+2032438783,*Nc1ccc2c(c1)[C@@]1(c3ccccc3-2)c2ccccc2-c2cccc(-c3ccccc3N*)c21,,0.3737942,,,
+2033020840,*CC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)NCCCCCCNC(=O)O*,,0.3457558,,,
+2033581658,*C1C(*)C2CC1C(C(=O)OCCC1CC3CCC1C3)C2C(=O)OCCC1CC2CCC1C2,,0.38027867,,,
+2033646210,*Nc1cccc(-c2cc(-c3ccccc3C)c(-c3ccccc3C)c(-c3ccccc3)c2-c2ccccc2)c1N*,,0.3749928,,,
+2033898259,*Cc1ccc(CNC(=O)CCCCCCCCCCCCCCCCC(=O)N*)cc1,,0.36245212,0.3299999999999999,0.962041851,19.70952259
+2034204725,*CCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCCCC(=O)N*,,,0.3873333333333333,0.892460081,24.81577292
+2034484019,*Oc1ccc(OC(=O)c2ccc(OCCCCCCCCCCCCOc3ccc(C(*)=O)cc3)cc2)cc1S(=O)(=O)c1ccccc1,,0.36220577,,,
+2035254874,*CCCCCCCCCCNC(=O)C=C(C)C(=O)N*,,0.35574403,,,
+2035543497,*CCCCNC(=O)c1cccc(C(=O)N*)c1,,0.32480195,,,
+2035696631,*Nc1ccccc1C1(c2cc(C(C)(C)C)ccc2N*)CCCCC1,,0.37402396,,,
+2035735306,*Oc1ccc(P(C)(=O)c2ccc(*)cc2)cc1,,0.38176997,,,
+2035749307,*CC(*)OC(=O)c1ccc(OC(=O)CC)cc1,,0.34337593,,,
+2035906358,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(-c5nnc(*)c6c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c(-c7ccc(F)cc7)c56)cc4)cc3)cc2)cc1,,0.4058732,,,
+2036221295,*Nc1ccc(C(c2ccc(N)cc2)c2ccc(N*)cc2)cc1,,0.35715159,,,
+2036690814,*C(CC)=C(*)c1ccccc1,,0.40534286,,,
+2036937559,*Nc1ccc(-c2ccc(N*)cc2-c2ccc(C(C)(C)C)cc2)cc1,,0.3670132,,,
+2036966102,*C(*)O,,0.24565388,,,
+2037050348,*NCc1ccc(CN*)cc1,,0.320806375,,,
+2037235529,*Nc1nc(N*)nc(N(C)C)n1,,0.33908484,,,
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+2037755126,*Oc1ccc(S(=O)(=O)c2ccc(Oc3c(C)cc(C(c4cc(C)c(*)c(C)c4)(C(F)(F)F)C(F)(F)F)cc3C)cc2[Si](C)(C)C)c([Si](C)(C)C)c1,,0.41286543,,,
+2038032102,*Cc1ccc(COC(=O)Cc2ccc(C(=O)O*)cc2)cc1,,0.34236476,,,
+2038593828,*CCCCCCCCCCOC(=O)NCCCCCCCCCCNC(=O)O*,,,0.316,,
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+2038692066,*C=C(*)c1ccccc1[Ge](C)(C)C,,0.44377353,,,
+2038784698,*Nc1ccc(*)cc1CC,162.1855704,,,,
+2038813510,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(C(C)(C)C)c3)cc1C(C)(C)C)C2=O,,0.4037542,,,
+2039137441,*C(*)CC,,,0.206,,
+2039185015,*C1OCOC(C(*)(F)F)C1(F)F,,0.31802359,,,
+2039265517,*NCC1=Cc2c(N*)ccc3cccc(c23)C1,,0.33495644,,,
+2039296323,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(-c5nnc(*)c6c(-c7ccccc7)c(-c7ccc8ccccc8c7)c(-c7ccc8ccccc8c7)c(-c7ccccc7)c56)cc4)cc3)cc2)cc1,,0.40570846,,,
+2039689406,*CC(*)(C)c1nc(N(CCOCCOCCOC)CCOCCOCCOC)nc(N(CCOCCOCCOC)c2ccccc2)n1,,0.35963207,,,
+2040418137,*c1c(C)cc(Cc2cc(C)c(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c(C)c2)cc1C,,0.39213223,,,
+2040580299,*Oc1ccc(Oc2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1,,0.37740838,,,
+2040837596,*CCCCCCCCCCCCCCCCCCCCCCOC(=O)CCCCC(=O)O*,,,0.376,0.893790404,19.43446517
+2040930933,*Nc1ccc2ccc3c(N*)c(-c4cccc(C)c4)cc4ccc1c2c43,,0.34832984,,,
+2041026757,*c1cc(CCCC)c(*)s1,,0.4491673,,,
+2041864294,*c1ccc(OCCCCCCCCCCCOC(=O)c2ccc(C(=O)OCCCCCCCCCCCOc3cccc(-c4nnc(*)s4)c3)cc2)cc1,,0.37179476,,,
+2041930704,*c1ccc(Oc2ccc(C(=O)c3ccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)cc4)cc3)cc2)cc1,,0.37294801,,,
+2042324873,*C=C(*)c1nc2ccccc2n1C,-6.104199835,,,,
+2042618640,*C=Cc1ccc2c3ccc(*)cc3n(-c3ccc(OCCCCCCCCCC)c(OCCCCCCCCCC)c3)c2c1,,0.38669468,,,
+2042805767,*c1ccc(Oc2ccc(-c3ccc(-c4ccc(-c5ccc(Oc6ccc(-c7c8c(c(*)c9ccccc79)C(=O)N(c7ccccc7)C8=O)cc6)c(C(F)(F)F)c5)cc4)cc3)cc2C(F)(F)F)cc1,,0.38592112,,,
+2042962957,*CC(*)c1cc(Br)ccc1OCCC,,0.4104551,0.136,,
+2042985265,*NCCc1c(N*)ccc2ccccc12,,0.33294897,,,
+2043343589,*c1ccc(-c2ccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c(C)c2)cc1C,,0.38576498,,,
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+2044319304,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(-c5c6c(c(*)c7ccccc57)C(=O)N(c5ccccc5-c5ccccc5)C6=O)cc4)cc3)cc2)cc1,,0.38414881,,,
+2044554925,*Nc1ccc2c(c1)[C@H]1CC[C@@H]2c2ccc(N*)cc21,,0.3658294,,,
+2044694871,*CC(CCCCCC)C(CCCCCC)COC(=O)c1ccc(C(=O)O*)cc1,,0.37457172,,,
+2044966386,*Nc1ccc([C@H](CC)c2ccc([C@@](CCC)(c3ccc([C@H](CC)c4ccc(N*)cc4C)cc3)C3CCCCC3)cc2)c(C)c1,,0.38839084,,,
+2045712230,*Nc1ccc2cccc3c2c1C(c1ccc(CCCC)cc1)=C(c1ccc(CCCC)cc1)[C@H]3N*,,0.3682026,,,
+2046153733,*Nc1ccc(C[C@@H](c2ccc(N*)cc2)C(c2cccc(N)c2)c2cccc(N)c2)cc1,,0.35616115,,,
+2046167476,*CCCCCCSCCCCCS*,,,0.241,,
+2046736007,*NC1=C(CC=C)[C@H](CC=C)c2cccc3ccc(N*)c1c23,,0.33818589,,,
+2046892348,*CCCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1,,0.33643184,,,
+2046956333,*Nc1ccc(-c2ccc(N*)c(C(c3cc(-c4ccc(N)cc4)ccc3N)(c3cc(-c4ccc(N)cc4)ccc3N)c3cc(-c4ccc(N)cc4)ccc3N)c2)cc1,,0.34585373,,,
+2047082921,*Nc1cccc(NC(=O)c2cc(NC(=O)C34CC5CC(CC(C5)C3)C4)cc(C(*)=O)c2)c1,,0.3518154,,,
+2047303918,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7c(Oc8cccc9c8C(=O)N(*)C9=O)cc(C(C)(C)C)cc7C(C)(C)C)c6C5=O)cc4)cc3)cc2)cc1,,0.3876841,,,
+2047470696,*Nc1ccc2c(c1)C1c3cc(N)ccc3C2c2ccc(N*)cc21,,0.38546708,,,
+2047526312,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3cccc(NC(=O)CCCC(=O)Nc4cccc(O*)c4)c3)cc2)cc1,,0.33823192,,,
+2047662238,*C=CC(COCc1ccccc1)C(*)COCc1ccccc1,,0.36790087,,,
+2047820714,*CC(*)(C)C(=O)OCCCCCCCCOc1ccc(N=Nc2ccc(C#N)cc2)cc1,,0.37032483,,,
+2047965728,*CCCCCOC(=O)O*,,0.3428874,,,
+2048401412,*CC(*)c1ccc([Si](C)(O[Si](C)(C)C)O[Si](C)(C)C)cc1,,0.46187463,,,
+2048601766,*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(S(*)=O)cc4)cc3)cc2)cc1,,0.38833103,,,
+2049016668,*CCCCCCCCC(Cl)C(*)Cl,,0.39594162,,,
+2049074412,*O[Si](C)(C)CC[Si](*)(C)O[Si](C)(C)C,,0.47403099,,,
+2049185141,*NC(CC(*)=O)c1ccccc1,-30.79261317,,,,
+2049372451,*CCC([2H])C(*)([2H])C,,0.40087335,,0.8045306,19.33954208
+2049646481,*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(C)cn2)C3=O)cc(C(=O)NNC(=O)CC(*)=O)c1,,0.33031505,,,
+2049675087,*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1,,0.35340123,,,
+2049722638,*c1ccc(-c2ccc(-c3nc4cc5sc(*)nc5cc4s3)cc2)cc1,343.9030515,,,,
+2049724174,*Nc1cc2ccc3cc(N*)c(-c4ccccc4N)c4ccc(c1-c1ccccc1N)c2c34,,0.3574156,,,
+2050460955,*CC(*)(C)C(=O)OCCCCCCCCCCCOc1ccc(N=Nc2ccc(OC(=O)c3ccc4c(c3)OCCOCCOCCOCCO4)cc2)cc1,,0.35856938,,,
+2050550943,*C#CC(CCCCOC(=O)NCC(=O)OCCCC)=C(*)c1cncnc1,73.99227071,,,,
+2051195531,*c1ccc2nc(*)cc(-c3ccccc3)c2c1,,0.44585296,,,
+2051477971,*CCCCCCCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1,,0.36034305,,,
+2052142146,*NC(N*)(C(=O)O)[C@@](N)(CC)[C@H](C)CC,,0.32721584,,,
+2052261609,*Nc1c(CC)cc2ccccc2c1-c1c(N*)c(CC)cc2ccccc12,,0.36951998,,,
+2052739724,*CC(*)(C)C(=O)OCCn1c2ccccc2c2cc(N=Nc3ccc([N+](=O)[O-])cc3)ccc21,,0.36133223,,,
+2053050930,*CC(=O)OC(=O)COc1ccc(O*)cc1,,0.31004011,,,
+2053127764,*C(=O)NCCC[Si](C)(C)O[Si](C)(C)CCCNC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,143.0502256,,,,
+2053139068,*CCC1C(=O)N(CCCCCC)C(=O)C1*,,0.35708568,,,
+2053145171,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OC(c2ccccc2)c2ccccc2)c1,,0.35378729,,,
+2053208810,*c1ccc(N2C(=O)c3ccc(C(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6nc7cc(Oc8ccc9nc(-c%10ccccc%10)c(*)nc9c8)ccc7nc6-c6ccccc6)cc4)C5=O)cc3C2=O)cc1,,0.41165056,,,
+2053534445,*CC(CC(C)C)S(*)(=O)=O,,0.37757511,,,
+2053664112,*NCCN*,,0.30366476,,,
+2053842039,*C=Nc1ccc(N=Cc2cccc(*)n2)cc1,139.1234551,,,,
+2053942851,*CCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.37997569,,,
+2054944349,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)CCCCCCC(*)=O)cc2)C(=O)OCCOCCOCC)cc1,,0.34633358,,,
+2055026518,*Oc1ccc(S(=O)(=O)c2ccc(-c3ccc(S(=O)(=O)c4ccc(Oc5ccc(-c6ccc(-c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1,,0.37441268,,,
+2055219346,*Oc1ccc(-c2ccc(OC(=O)c3ccc(C(*)=O)cc3Oc3ccc(C(C)(C)c4ccccc4)cc3)cc2)cc1,135.0490958,,,,
+2055251139,*CC(*)(C)C(=O)OCCCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.33838791,,,
+2055325406,*C#CC(OS(=O)(=O)c1ccc(C)cc1)=C(*)OS(=O)(=O)c1ccc(C)cc1,164.1018919,,,,
+2056311741,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccccc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O,,0.38469129,,,
+2057063340,*c1ccc(Oc2cccc(Oc3ccc(N4C(=O)c5cccc(Oc6ccc(-c7ccc(Oc8cccc9c8C(=O)N(*)C9=O)cc7)cc6)c5C4=O)cc3)c2)cc1,,0.361259,,,
+2057209972,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C4(c5ccc(*)cc5)C5CC6CC(C5)CC4C6)cc3)cc2)cc1,,0.38601547,,,
+2057275568,*CC(*)(C#N)C(=O)OCC,,0.35400751,0.1805,1.034280592,15.74241811
+2057370661,*c1ccc(OCCCCCCN(CC)c2ccc(C(C#N)=C(C#N)C#N)cc2)c(-c2cc(-c3ccccc3)c3cc(Oc4ccc5nc(*)cc(-c6ccccc6)c5c4)ccc3n2)c1,,0.38886127,,,
+2057806483,*CC(=O)O*,,0.29139366,,,
+2058153586,*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2C(C)(C)C)cc1,,0.35895204,,,
+2058169887,*c1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(-c4nc5ccccc5nc4*)cc3)cc2)cc1,,0.36797942,,,
+2058537463,*c1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(C5(*)OC(=O)c6ccccc65)cc4)cc3)cc2)cc1,,0.38030093,,,
+2058722180,*Nc1ccc2c(c1)C(/C(C)=C/C(=C)C(=C)c1ccccc1)(C(/C=C(\C)C(=C)c1ccccc1C)=C/C)c1cc(N*)ccc1-2,,0.359086085,,,
+2058912273,*c1cccc(C(=O)c2c(C)cc(C)c(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2C)c1,,0.3935505,,,
+2059069732,*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)c1,,0.35539719,,,
+2059156449,*NC[C@@H](CC(O)(COCCO)C[C@@H](N)CN)N*,,0.29619414,,,
+2059422357,*CCCCCCNC(=O)C(CCCCCCCCCCCCCCCC)C(=O)N*,,,0.3235,0.89124948,13.96037752
+2059649155,*CCN(*)C(=O)CCCCCCCCCCCCCCCCC,,0.39935287,0.4005,0.856888696,12.96507874
+2059802205,*c1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,,0.37114536,,,
+2060308564,*CCCCCCN1C(=O)C(=O)N(c2ccc(Cc3ccc(N4C(=O)C(=O)N(*)C4=O)cc3)cc2)C1=O,,0.33818344,,,
+2061363511,*O[Si](C)(C)c1ccc(-c2ccc([Si](*)(C)C)cc2)cc1,,0.40778557,,,
+2061931988,*Nc1cccc(-c2ccc(N*)c(-c3ccccc3)c2-c2ccccc2)c1-c1ccccc1,,0.36810011,,,
+2062389305,*C(=O)c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cccc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O,,0.42825804,,,
+2062460228,*OC(C)CCOC(=O)C(CCCCCCOc1ccc(N=Cc2ccc([N+](=O)[O-])cc2)cc1)C(*)=O,,0.3535486,,,
+2062588877,*C/C=C\C[Si](*)(c1ccccc1)c1ccccc1,,0.36746772,,,
+2062611807,*CC(*)OC(=O)CCCCCCCCCCC,,,0.261,0.878322012,11.53444859
+2062868312,*Oc1ccc(NC(=O)Nc2cc(NC(=O)Nc3ccc(*)c(-c4ccc(Oc5ccccc5)cc4)c3)ccc2C)cc1-c1ccc(Oc2ccccc2)cc1,,0.35573638,,,
+2062933383,*Nc1ccc(-c2ccc(N*)c(-c3cccc4ccccc34)c2-c2cccc3ccccc23)cc1,,0.36652016,,,
+2063026043,*c1ccc(-c2ccc(C(*)(C)C)cc2)cc1,210.9469969,,0.247,0.952850626,21.2582938
+2063396498,*Nc1cc2ccc3cc4ccccc4cc3c2cc1N*,,0.33601961,,,
+2063473981,*CC(*)C(=O)NCCCCCCCCCCCCCCCCCC,,,0.383,0.856385762,12.90114502
+2063616676,*C(=O)c1ccc2c(c1)C(=O)N(c1c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(N5C(=O)c6ccc(*)cc6C5=O)c(-c5ccccc5)c4)cc3)cc1-c1ccccc1)C2=O,,0.41151687,,,
+2063798200,*Nc1ccc(-c2cc(-c3ccccc3)cc(-c3ccc(N*)cc3)c2)cc1,,0.34830668,,,
+2064196181,*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1,,0.35498107,,,
+2064758203,*CCCCCCNC(=O)c1cccc(C(=O)N*)c1,,0.33764857,,,
+2065005983,*Nc1cc(NC(=O)C(*)=O)ccc1C,,0.3160157,,,
+2065007908,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(-c4sc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(-c5ccccc5)c4-c4ccccc4)cc2)C3=O)c1,,0.38895508,,,
+2065090283,*CCOCCOC(=O)C(CCCCCCCCC)C(=O)O*,,0.37130804,0.224,,
+2065153152,*Nc1cc(OC)c(*)cc1OC,168.2573371,,,,
+2065252291,*CCCCCCC(=O)NCCCCCCNC(=O)CCCCCCO*,,,0.3215,0.964171286,21.61972619
+2065279499,*c1cc(C(=O)NCCCCCC)cc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1,,0.37249776,,,
+2065383771,*Oc1ccc(C2(c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)c3ccccc3-c3ccccc32)cc1,,0.37279646,,,
+2065621883,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cccc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O,,0.42381981,,,
+2066065208,*CC(O)COc1ccc(C(C)(C)c2ccc(OCC(O)COc3c(C)cc(C(C)(C)c4cc(C)c(O*)c(C)c4)cc3C)cc2)cc1,,0.36071143,,,
+2066192425,*CC(*)(CC(=O)OC(C)C)C(=O)OC(C)C,,0.37215507,,,
+2066262373,*CC1CCCC(CNC(=O)CCCCCCCCCCC(=O)N*)C1,26.18556129,,,,
+2066820207,*c1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5C4=O)cc3)cc2)cc1,191.2304459,,,,
+2066955666,*c1ccc(Oc2ccc(N3C(=O)c4ccc(Oc5c(C)cc(Cc6cc(C)c(Oc7ccc8c(c7)C(=O)N(*)C8=O)c(C)c6)cc5C)cc4C3=O)cc2)cc1,,0.38651077,,,
+2067168064,*COC(=O)c1ccc(C(C#N)=C(c2ccc(OC)cc2)N2CCCC(*)C2)cc1,,0.37236843,,,
+2067238428,*CC(C)(C)COC(=O)c1ccc(C(=O)O*)cc1,,0.351598,,,
+2067309904,*c1ccc2c(c1)C(=O)N(c1ccc(OC(CCC)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,,0.36517902,,,
+2067484299,*CC(*)C(=O)NCCCCCCCCCCCC,21.73577755,,0.3435,0.870895681,11.87004823
+2068061676,*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4ccccc4C)c3-c3ccccc3C)cc2)cc1,,0.37174868,,,
+2068076576,*Nc1ccc(C2c3ccc(N)cc3-c3cc(N*)ccc32)cc1,,0.36276266,,,
+2068084714,*C(=O)NCC(C)(C)CNC(=O)c1ncn(C)c1*,,0.33746625,,,
+2068351519,*Oc1ccc(C(=O)CCCCCCCCC(=O)c2ccc(OC(*)=O)cc2)cc1,6.568396994,,,,
+2068477355,*Cc1ccc(CNC(=O)CCCCCCCCCCCC(=O)N*)cc1,,0.35427449,,,
+2068736249,*Oc1ccc(OC(=O)c2cc(OCCCCCCCCOCC3CCCN3c3ccc([N+](=O)[O-])cc3)c(C(*)=O)cc2OCCCCCCCCOCC2CCCN2c2ccc([N+](=O)[O-])cc2)cc1,,0.35365329,,,
+2069332669,*Cc1ccccc1[Si](*)(C)c1ccccc1,,0.38506793,,,
+2069899879,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(C=NC(=S)Nc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)ccc1Cl)C2=O,,0.36685025,,,
+2070158798,*Oc1ccc(OC(=O)c2ccc(C(*)=O)cc2Oc2ccc(Br)cc2)cc1,,0.36519909,,,
+2070291544,*CC(*)C(C)CC,,,0.1897499999999999,0.768938442,10.88423334
+2070641529,*c1ccc(C(=O)Oc2c(C)cc(C(c3cccnc3)c3cc(C)c(OC(=O)c4ccc(N5C(=O)CC(Nc6ccc(Cc7ccc(NC8CC(=O)N(*)C8=O)cc7)cc6)C5=O)cc4)c(C)c3)cc2C)cc1,,0.3762598,,,
+2070649419,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)cc3)CCC(C(C)(C)C)CC2)cc1,,0.37035787,,,
+2070663964,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cc(C(=O)O*)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c1,,0.32423467,,,
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+2071844706,*N1CCOC1(*)CC,,0.35941583,,,
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+2072168111,*Nc1ccc(-c2ccc(N*)c([C@H]3C[C@H](C)CC[C@H]3C(C)C)c2[C@@H]2C[C@@H](C)CC[C@@H]2C(C)C)cc1,,0.3809251,,,
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+2072866385,*CCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.37538316,0.3955,0.910662796,20.51606091
+2072955704,*CC(*)(C)OC,,0.34572991,,,
+2073349658,*c1ccc(OCCCCCCCCCCCOC(=O)c2cccc(C(=O)OCCCCCCCCCCCOc3ccc(-c4nnc(*)s4)cc3)c2)cc1,,0.36776086,,,
+2073433279,*OP(=O)(Oc1ccc(OC)cc1)Oc1c(Br)cc(C(c2cc(Br)c(*)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,,0.38658695,,,
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+2074006647,*Nc1ccc(NC(=O)c2ccc(C(c3ccc(C(*)=O)c(C(=O)OCC)c3)(C(F)(F)F)C(F)(F)F)cc2C(=O)OCC)cc1,,0.34977204,,,
+2074158001,*CC(F)(F)C(F)(F)C(F)(F)C(F)(F)COC(=O)O*,,0.31456225,,,
+2074267479,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCCCCCC)cc1,,0.34629979,,,
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+2074469442,*CC(*)(C)C(=O)OCCCCCCCCCCCn1c2ccccc2c2ccccc21,,0.36342293,,,
+2074481050,*C=CC(C)C(*)C,78.17880502,,,,
+2074575987,*CCCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.34397494,,,
+2075083572,*/C=C/c1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(F)c(C(F)(F)F)c1,,,0.185,1.037733826,22.21132757
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+2075610197,*CC(OO*)c1ccc(C)cc1,,0.3659097,,,
+2076091344,*CC(*)CC[Si](C)(C)C,,0.42654899,,,
+2076186789,*Nc1ccc(*)cc1OC,,0.38337049,,,
+2076683779,*CC(C)(C)COC(=O)C1CCC(C(=O)O*)CC1,,0.35306926,,,
+2076949860,*CC(*)(CC(=O)O)C(=O)O,,0.2269924,,,
+2077226118,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,126.2551791,,,,
+2077350791,*N=P(N=P(N=S(*)(=O)NCC)(NCC)NCC)(NCC)NCC,,0.37475847,,,
+2078434455,*c1c(C)cc(C)c(N2C(=O)c3ccc(Sc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1C,,0.41533211,,,
+2078494815,*CC(*)(Cl)C(=O)OCCOc1ccc(C(=O)Oc2ccc(OCCCC)cc2)cc1,,0.34720014,,,
+2078520197,*CCN(*)C(=O)c1cc(F)cc(F)c1,,0.34415412,,,
+2078647419,*CCCCCCC1CC(CCC2CC(*)OC2=O)C(=O)O1,,0.33989367,,,
+2079303447,*CCCCCCCCCCCCCCC(*)Cl,,,0.3735,0.886561889,21.91642918
+2079401317,*CC(*)C(=O)Oc1ccc(Cl)cc1,,0.35839079,0.158,1.211730555,14.07962276
+2079617222,*Nc1ccc2c(c1)CCC21CCc2cc(N*)ccc21,,0.359460575,,,
+2079708609,*CC(*)OC(=O)CC(C)(C)c1ccccc1,,0.35480602,,,
+2079820575,*CCC1CC(*)C2C3CC(C12)C(C)(C(=O)OC)C3,,0.3681131,,,
+2080041266,*=Nc1cccc(Oc2cccc(Oc3cccc(N=C4OC(=O)c5ccc(-c6ccc7c(c6)C(=O)OC7=*)cc54)c3)c2)c1,193.7356518,,,,
+2080614685,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccc(N)cc3-c3cc(N)ccc32)cc1,,0.37760376,,,
+2080644160,*Nc1cc2c3ccc(-c4ccc(C(C)C)cc4)c(-c4ccc(C(C)C)cc4)c3c(N*)cc2c2ccccc12,,0.37311633,,,
+2081117789,*Nc1ccc([C@H](C)Cc2ccc(C[C@@H](C)c3ccc(N*)cc3)cc2)cc1,,0.36158847,,,
+2081485306,*CC(*)C(=O)OCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1,,0.33434555,,,
+2081569182,*CCCNC(=O)c1ccc(C(=O)N*)cc1,,0.31568914,,,
+2081754132,*Nc1ccc([C@]2(c3ccccc3)c3ccccc3-c3cccc(N*)c32)cc1,,0.36468241,,,
+2081766771,*CC(*)OC(=O)CC1CCCCC1,,0.3629501,,,
+2081786405,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.39990412,,,
+2082251214,*CCCCCCCCCCCCCCCCCCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*,,0.38491292,0.4533333333333333,0.880970816,22.40951609
+2082516558,*Nc1ccc([C@H]2C[C@H]3CC[C@@H]2CC[C@@H]2CC[C@H](CC3)[C@H](c3ccc(N*)c(N)c3)C2)cc1N,,0.35134124,,,
+2082878937,*ON(C(F)(F)F)C(F)(F)C(*)(F)SC(F)(F)F,,0.3185822,,,
+2083351176,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23,,0.40558784,,,
+2083399085,*C(=O)c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCC)cc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O,,0.36928432,,,
+2083473001,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(-c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1,,0.37583933,,,
+2084095989,*C(=O)Nc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8c7)cc5)C6=O)cc4)cc3)cc2)cc1,,0.35762799,,,
+2084277972,*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)c3c2[C@@H]2CC[C@H]3C2)cc1,,0.36155112,,,
+2085027374,*Nc1ccc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(-c4ccc(N*)cc4)ccc2-3)cc1,,0.37055299,,,
+2085033079,*C(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5C)c(C)c3)C4=O)cc2C)c(C)c1,,0.37394713,,,
+2085100639,*Cc1ccccc1CN(CC)c1ccc(/C=C(\C#N)C(=O)Nc2cccc(NC(=O)/C(C#N)=C/c3ccc(N(*)CC)cc3)c2)cc1,,0.36570128,,,
+2085613609,*C=Cc1ccc(-c2ccc(C(C#N)=Cc3ccc4c(c3)C(CC)(CC)c3cc(C=C(C#N)c5ccc(-c6ccc(C=CC7=CC(=C(C#N)C#N)C=C(*)O7)s6)cc5)ccc3-4)cc2)s1,,0.40560476,,,
+2086500241,*CCC(C)O*,,0.3787093,,,
+2086671305,*CC(*)c1ccccc1C,,,0.1903333333333333,0.876818696,11.81194142
+2087529784,*C(=O)c1ccc2c(c1)C(=Nc1cccc(Oc3cccc(Oc4cccc(N=C5OC(=O)c6ccc(*)cc65)c4)c3)c1)OC2=O,,0.35278937,,,
+2087902310,*c1ccc(-c2ccc(-c3c(-c4ccccc4)nc4ccc(-c5ccc6nc(-c7ccccc7)c(*)c(-c7ccccc7)c6c5)cc4c3-c3ccccc3)cc2)cc1,,0.41084325,,,
+2087962619,*Oc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(Cc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)c2)cc1,,0.36716736,,,
+2088540616,*Oc1ccc(C2(c3ccc(OC(=O)c4ccc(C(*)=O)cc4)c(C)c3)CCC3CCCCC3C2)cc1C,,0.38501147,,,
+2088836138,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OCCOc2ccc(C=CC(=O)c3ccccc3)cc2)c1,,0.33943135,,,
+2088992800,*CCCCCCOC(=O)c1cc(N=Nc2ccc(OCC)cc2)ccc1-c1ccc(N=Nc2ccc(OCC)cc2)cc1C(=O)O*,,0.36903817,,,
+2089130160,*/C=C/C(C)CCCCC*,,,0.2486666666666666,,
+2089319482,*CC(*)C(=O)OCCOCCOC(F)(F)C(F)F,,0.33433583,,,
+2089653204,*OC(=O)c1ccc(CCCCCc2ccc(C(*)=O)cc2)cc1,,0.35615105,,,
+2089703645,*NC(CCC(=O)OCc1ccccc1)C(*)=O,,0.32872163,,,
+2089849247,*Oc1cc(C(C)(C)C)c(Oc2ccc(C(=O)Nc3ccc(C(C)(C)c4ccc(NC(=O)c5ccc(*)cc5)c(C)c4)cc3C)cc2)cc1C(C)(C)C,,0.38910548,,,
+2089875346,*CCC/C=C(/*)c1ccc(Cl)cc1,,,0.184,1.085646157,16.28575806
+2090259325,*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(Nc4ccc(C(=O)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)c2)cc1,,0.38954498,,,
+2090445254,*CCCCCCCCCCCCCCCCCCOCO*,,,0.406,0.865128678,20.46039767
+2090672547,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(Oc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c3)c1)C2=O,,0.35973284,,,
+2090987332,*C(F)(F)C1(*)OC(F)(F)C(F)(C(F)(F)F)O1,,0.35247357,,,
+2091289727,*c1nc(-c2ccccc2)nc(N(CCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.38631278,,,
+2091496127,*CC1CC(*)C(OC(=O)CCCCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1,,0.36711221,,,
+2091819408,*C(C)=C(*)CCC,,0.40232091,,,
+2092024077,*Oc1ccc(C2(c3ccc(Oc4ccc(C(=O)C(=O)c5ccc(*)cc5)cc4)cc3)c3ccccc3-c3ccccc32)cc1,,0.3923538,,,
+2092061977,*CC(*)(CCCCC)C(=O)OC,,0.38074638,,,
+2092067263,*Nc1ccc(C[C@@H](c2ccc(N*)cc2)[C@@H](C)c2ccc(N)cc2)cc1,,0.35733643,,,
+2092210478,*c1ccc(-c2sc(-c3cc(CCCCCCCCCCCC)c(*)s3)cc2CCCCCCCCCCCC)cc1,,0.41997349,0.413,0.941065148,19.30135388
+2092278480,*Oc1ccc(C(=O)Nc2ccccc2-c2ccc(NC(=O)c3ccc(*)cc3)cc2)cc1,,0.34957751,,,
+2092674324,*CC(F)(F)C(F)(F)C(F)(F)COC(=O)c1cccc(C(=O)O*)c1OCCCCC,,0.34076472,,,
+2092935211,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(Oc4ccc(C5(C)CC(C)(C)c6ccc(Oc7ccc(-c8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7C(F)(F)F)cc65)cc4)c(C(F)(F)F)c3)cc1)C2=O,,0.38328824,,,
+2093130856,*CCCCCCCCCCNC(=O)CCCCCCCCC(=O)N*,,0.36935846,0.408,0.92195685,22.1235356
+2093549092,*c1ccc(Oc2ccc(C(c3ccccc3)(c3ccccc3)c3ccc(Oc4ccc(-c5nc(*)nc(-c6ccccc6)n5)cc4)cc3)cc2)cc1,,0.42256432,,,
+2093679226,*CCCCC(=O)NCc1ccc(CNC(=O)CCCCS*)cc1,,0.35168032,,,
+2094132157,*CC(*)c1ccccc1OC(C)=O,,0.35684736,,,
+2094564559,*Oc1c(Br)cc(C2(c3cc(Br)c(OC(*)=O)c(Br)c3)CC3CCC2C3)cc1Br,,0.43211216,,,
+2094965474,*Oc1ccc(C(C)(C)c2ccc(OC(=O)c3ccccc3-c3ccccc3C(*)=O)cc2)cc1,,0.35835285,,,
+2095077077,*CCCCC(=O)NCCCCCCNC(=O)CCCCO*,-56.49395983,,,,
+2095656240,*c1ccc(Nc2ccc(Nc3ccc(S(*)(=O)=O)cc3)cc2)cc1,,0.38954826,,,
+2096099449,*Oc1ccc(C2(c3ccc(OC(=O)OC4C(C)(C)C(OC(*)=O)C4(C)C)cc3)c3ccccc3-c3ccccc32)cc1,,0.37809726,,,
+2097065173,*NC1CCC(CC2CCC(N*)C(C)C2)CC1C,,0.36266838,,,
+2097105908,*Nc1ccc(C2(c3ccc(N*)cc3)C(C(C)C)=CC=C[C@H]2C(C)C)cc1,,0.36819096,,,
+2097907422,*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Oc4ccc(Sc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,,0.37984664,,,
+2098139319,*CCCCNC(=O)CCCCCCCCCCCCCCC(=O)N*,,,0.3735,0.924969057,22.41096002
+2098169121,*Oc1ccc(-c2ccc(Oc3ccc(C(=Nc4ccc(N=C(c5ccccc5)c5ccc(*)cc5)cc4)c4ccccc4)cc3)cc2)cc1,,0.37996197,,,
+2098235605,*c1ccc(OCCCCCCCCCCCCCCCC)c(N2C(=O)CC(C3C=C(C)C4C(=O)N(*)C(=O)C4C3)C2=O)c1,,0.36937049,,,
+2098276804,*CCCCCCCCOc1c(OC)cc(C=Cc2cc(C(C)C)c(C=Cc3cc(OC)c(O*)c(OC)c3)cc2C(C)C)cc1OC,,0.3769967,,,
+2098860245,*Nc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(N)c(N*)c2)cc1N,,0.36426991,,,
+2098949434,*CCCCCCCCCNC(=O)CCCCCCCCC(=O)NCCCCCCCCCNC(=O)C(=O)N*,,,0.312,0.96452094,18.37068887
+2098983815,*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c(O)cc1O)C2=O,,0.37827714,,,
+2099106036,*CCCCCCCCCCCN1C(=O)c2ccc(-c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O,,0.35197017,,,
+2099208394,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(C)c6cccc(C(C)(C)c7ccc(Oc8ccc(C(=O)Nc9ccc(*)cc9)cc8)cc7)c6)cc5)cc4)cc3)cc2)cc1,,0.36584283,,,
+2099643847,*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3cccc4c(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cccc34)ccc1C)C2=O,,0.36752651,,,
+2099648126,*Nc1c(C)cc(C2(c3cc(C)c(N*)c(C(C)C)c3)c3ccccc3-c3ccccc32)cc1C(C)C,,0.40331022,,,
+2100810597,*c1ccc(-c2nc3cc(C)ccc3o2)c(*)c1,,0.42938572,,,
+2101703893,*N=P(*)(OCCCCC)OCCCCC,,0.4064028,,,
+2101723306,*O[Si](C)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)c1ccccc1,,0.39715022,,,
+2102034218,*c1ccc2c(c1)C(=O)N(c1ccc(NC(=S)N=Cc3ccc(Cl)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)cc1)C2=O,,0.37564087,,,
+2102039309,*Oc1ccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)c4)cc3)cc2)cc1,,0.36086621,,,
+2102048266,*CC(C)(C)C1C(=O)OC(=O)C1*,,0.29898887,,,
+2102097019,*Nc1ccc([C@H](CCCCCC)c2ccc(CCc3ccc([C@@H](CCCCCC)c4ccc(N*)cc4)cc3)cc2)cc1,,0.36359418,,,
+2102135850,*CCC(*)=O,,0.32553925,,1.073855453,22.19638383
+2102234224,*CC(*)(C#N)C(=O)OCCCCCCC,,0.38098493,,,
+2102406766,*CC(*)c1ccccc1Br,,0.40945525,,,
+2102601525,*/C=C/CCCCCCCC*,,,0.3688333333333332,0.822061556,19.34707652
+2102738323,*Oc1ccc(C(=O)c2ccc(OC(=O)c3ccc(C(C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1,,0.35881201,,,
+2102908349,*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCC)C1=O,,0.39850334,,,
+2103171558,*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc([N+](=O)[O-])cc3)cc(C(*)=O)c2)cc(C(=O)OC2CC(C)CCC2C(C)C)c1,,0.35015329,,,
+2103300246,*c1cc(C(=O)OCC(F)(F)C(F)(F)C(F)(F)F)c(*)s1,,0.3741336,,,
+2103389743,*CC(*)(C)C(=O)OCCCCCCCCCCCCOc1ccc2cc(C(=O)Oc3ccccc3)ccc2c1,,0.35950718,,,
+2103408562,*SC(*)(F)F,,0.37718963,0.114,1.840998909,24.17920498
+2103439506,*CCC[Si](*)(C)C[Si](C)(C)C,,0.40507893,,,
+2103456476,*CC(*)c1ccc(CCCCCCCCC)cc1,,0.39955076,0.277,0.87039516,11.44773012
+2103595659,*CCOCCOCCOC(=O)CCCCC(=O)O*,,0.33678464,,,
+2103648490,*Nc1ccc2c(-c3ccccc3)c3ccccc3c(-c3ccccc3)c2c1N*,,0.35655362,,,
+2104121723,*Nc1ccc(-c2ccc(C[C@@H](C)c3ccc(N*)cc3)cc2)cc1,,0.35318823,,,
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+2104280105,*OC(F)(F)COC(=O)c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(C(=O)OCC(*)(F)F)c2)c1,,0.33058531,,,
+2104394110,*Nc1ccc(-c2ccc(N*)c(C/C=C/c3ccccc3)c2C/C=C/c2ccccc2)cc1,,0.35337188,,,
+2104615575,*Nc1ccc(CC(CC)(CC)c2ccc(N)c(CC(CC)(CC)c3ccc(N*)cc3)c2)cc1,,0.35803867,,,
+2104808505,*NC1CC(C)(C)CC(C)(CNC(=O)CCCCC(*)=O)C1,,0.3442808,,,
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+2105181780,*c1ccc2c(c1)C(=O)N(c1c(C)cc(C3(c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C(C)C)c4)c4ccccc4-c4ccccc43)cc1C(C)C)C2=O,,0.44640545,,,
+2105271867,*Nc1ccc(C(c2ccc(N)c(C)c2C)c2ccc(N*)c(C)c2C)c(C)c1C,,0.38011149,,,
+2105289986,*CC(*)c1ccc(OC(C)(C)C)cc1,,0.38657315,,,
+2105342564,*Nc1cccc(C2=C(c3ccccc3)C=C[C@@H](c3ccccc3)[C@]2(c2ccccc2)c2cccc(N*)c2)c1,,0.37740484,,,
+2105808752,*Nc1ccc(/C=C/[C@H]2[C@@H]3C[C@H]4C[C@@H](C3)C[C@@H]2C4)cc1N*,,0.35587947,,,
+2106014739,*CC(*)(CC(=O)OC1CCC1)C(=O)OC1CCC1,,0.38532463,,,
+2106275080,*CCCCCCCCCCC(=O)Nc1ccc(Oc2ccc(NC(=O)CCCCCCCCCCN3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,,0.35025785,,,
+2107302149,*CC(*)c1cc(Br)ccc1OC,,0.39846668,0.132,,
+2107491529,*CC(*)(C)C(=O)OCC(F)(F)F,,0.33930857,,,
+2107706475,*CCCCCCCCCOCO*,,,0.277,,
+2107751440,*Nc1ccc2c(c1)C1(c3cc(C)ccc3-2)c2cc(N)ccc2-c2ccc(N*)cc21,,0.38616703,,,
+2108285261,*CCCCCNC(=O)Cc1ccc(C(=O)N*)cc1,,0.33164611,,,
+2108373019,*CC(*)C(=O)OCCCCCCCCCCOC(=O)c1ccc(OC(=O)c2ccc(C#N)cc2)cc1,,0.35628363,,,
+2109042008,*CC(*)c1ccc(S(=O)(=O)O)cc1,,,,1.219503167,14.50652497
+2109143268,*CC(*)(CC(=O)OCCC)C(=O)OCCC,,0.36758128,0.1845,0.95089128,11.16357076
+2109376187,*c1ccc(C(=O)OCCCCCCCCOc2ccc(C=CC(=O)C=Cc3ccc(OCCCCCCCCOC(=O)c4ccc(-c5nnc(*)o5)cc4)cc3)cc2)cc1,,0.36096728,,,
+2110077418,*CCCC1(CCCNC(=O)CCCCCCCCC(=O)N*)c2ccccc2-c2ccccc21,,0.35257757,,,
+2111187299,*C(=Cc1ccc(C=C(c2ccccc2)c2ccc(-c3ccc(*)cc3)cc2)cc1)c1ccccc1,,0.39715192,,,
+2111235464,*CC(*)c1ccccc1COCC,,0.37386236,0.1846666666666666,,
+2111449660,*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4cc(C)ccc4C)c3-c3cc(C)ccc3C)cc2)cc1,,0.37917526,,,
+2111641058,*CC(*)C(=O)OCCCCC,,0.37816739,,,
+2111688362,*NN(N*)[C@](CC)(CCC)C[C@@](C)(CC)N(N)N,,0.33398342,,,
+2112106908,*Oc1c(C)cc(*)cc1C(C)CCCCC,,0.41074836,,,
+2112160292,*CC(*)(OC(C)=O)c1ccc(OC(C)=O)cc1,74.85696518,,,,
+2112420491,*Nc1ccc(*)cc1,,0.39788166,,,
+2112498322,*c1nnc(-c2sc(-c3nnc(-c4cc(OCCCCCCCC)c(*)cc4OCCCCCCCC)o3)cc2CCCCCCCC)o1,,0.41777055,,,
+2112731378,*CCOCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1,,0.3366937,,,
+2112734421,*CC(c1ccccc1)C1C(=O)N(CCCN(CC)c2ccc(N=Nc3ccc([N+](=O)[O-])cc3)c(C)c2)C(=O)C1*,,0.37385822,,,
+2112770434,*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCCCCCC)C1=O,,0.39556672,,,
+2112796549,*c1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(N4C(=O)c5ccc(NC(=O)Nc6cccc(NC(=O)Nc7ccc8c(c7)C(=O)N(*)C8=O)n6)cc5C4=O)cc3)n2)cc1,,0.35158971,,,
+2112814629,*CC(CO*)C(C)(C)C,,0.37336489,,,
+2113232814,*CC(O)CN(*)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2C)cc1,,0.36561272,,,
+2113271620,*CC(*)(CC(=O)OC)C(=O)OC,,0.32193525,0.152,,
+2113327142,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)(C(F)(F)F)C(F)(F)F)cc1)C2=O,,0.39124786,,,
+2113413468,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(c4ccc(*)c([Si](C)(C)C)c4)(C(F)(F)F)C(F)(F)F)cc3[Si](C)(C)C)cc2)cc1,,0.40476699,,,
+2113953037,*Oc1ccc2cc(OC(=O)c3cccc(Oc4ccc(Oc5cccc(C(*)=O)c5)cc4)c3)ccc2c1,,0.34970341,,,
+2113988141,*C(=O)Nc1ccc(NC(=O)c2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1,,0.34372152,,,
+2114154599,*O[Si](C)(C)c1cccc([Si](*)(C)C)c1,,0.42523073,,,
+2114560231,*c1ccc(CCc2ccccc2N2C(=O)C(=O)N(c3ccc(Oc4ccc(N5C(=O)C(=O)N(*)C5=O)cc4)cc3)C2=O)cc1,,0.35389023,,,
+2114893562,*c1ccc(C(C)(C)c2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1,,,,1.146465554,25.11101976
+2115270537,*CC(*)C(=O)OCC(F)(F)C(F)(F)OC(F)(F)C(F)(F)F,,0.31915236,,,
+2115445958,*C(=O)Nc1cccc(NC(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)n1,,0.34115453,,,
+2115845960,*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nc3ccccc3oc2=O)c1,,0.33876246,,,
+2116023272,*O[Si](C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1,,0.42501353,,,
+2116043723,*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5ccc(-c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1,,0.35854556,,,
+2116336179,*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2)cc1,,0.3506411,,,
+2116365788,*Nc1cc(SCCC#N)c(NC(=O)c2cccc(C(*)=O)c2)cc1SCCC#N,38.16065966,,,,
+2116457345,*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nc5ccc(Cc6ccc7nc(*)oc(=O)c7c6)cc5c(=O)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O,,0.36193236,,,
+2116593356,*c1cc(CCCCCC)c(-c2ccc3c4ccc(-c5sc(-c6sc(*)c7nc(CC)c(CC)nc67)cc5CCCCCC)cc4n(CCCCCCCCCCCC)c3c2)s1,,0.43100315,,,
+2117478348,*/C=C/c1ccc(*)c(-c2cc(OCC(CC)CCCC)c(-c3ccccc3)cc2OCC(CC)CCCC)c1,,,0.237,0.947690837,13.74155321
+2117609308,*Oc1ccc(P(C)(=O)c2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1,,0.38699983,,,
+2117950580,*c1ccc(C2C(C(=O)OCC)C(*)C2C(=O)OCC)cc1,164.3224631,,,1.011107838,16.56532099
+2118544879,*c1ccc(Oc2ccc(-c3ccc(Oc4ccc(N5C(=O)c6cccc(Oc7ccc(C(c8ccc(Oc9cccc%10c9C(=O)N(*)C%10=O)cc8)(C(F)(F)F)C(F)(F)F)cc7)c6C5=O)cc4)cc3)cc2)cc1,,0.36630102,,,
+2118719720,*O[Si](C)(C)CC(C)(C)COC(=O)c1cccc(C(=O)OCC(C)(C)C[Si](*)(C)C)c1,,0.3817269,,,
+2118723389,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCCOCC(*)=O)cc2)C(=O)OCc2ccccc2)cc1,,0.33524164,,,
+2118767527,*c1cccc(N2C(=O)c3ccc(Oc4ccc5cc(Oc6ccc7c(c6)C(=O)N(*)C7=O)ccc5c4)cc3C2=O)c1,,0.3713948,,,
+2118906166,*CC(*)CNc1ccc([N+](=O)[O-])cc1,,0.3745043,,,
+2119541663,*C=CC1CC(*)C2C(=O)N(c3ccccc3F)C(=O)C12,,0.35885634,,,
+2119800195,*C=C(*)CCCOc1ccc(OC(=O)c2ccc(OCCCCCCCC)cc2)cc1,,0.36337472,,,
+2119910769,*CC(*)(Cl)C(=O)OCC,,,0.1985,1.162819805,16.552184
+2120362276,*Nc1ccc2c(c1)[C@]1(CCC)c3ccc(N*)cc3[C@]3(CCC)c4ccc(N)cc4[C@@]2(CCC)C31C,,0.39344519,,,
+2120569877,*c1cccc(OCCCCCCCCCCCOC(=O)c2cccc(C(=O)OCCCCCCCCCCCOc3cccc(-c4nnc(*)s4)c3)c2)c1,,0.36865264,,,
+2120715373,*CCCCCCCCCCCCOc1c(OC)cc(/C=C/c2ccc(/C=C/c3cc(OC)c(O*)c(OC)c3)cc2)cc1OC,,0.36749274,,,
+2120990962,*Oc1ccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(*)cc2)cc1,,0.35479832,,,
+2121376176,*Nc1ccc(-c2c3cc(N*)c(N)cc3c(-c3ccc(N)cc3)c3c(-c4ccc(N)cc4)c4cc(N)c(N)cc4c(-c4ccc(N)cc4)c23)cc1,,0.35674675,,,
+2121841286,*CC(*)c1ccc([Si](C)(C)O)cc1,,0.41238443,,,
+2122013892,*CC(OC(=O)C(CC(C)C)NC(=O)OCc1ccccc1)C(COC(=O)O*)OC(=O)C(CC(C)C)NC(=O)OCc1ccccc1,,0.34504989,,,
+2122531737,*c1ccc(N2C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)cc1,,0.40605491,,,
+2123056342,*Nc1ccc(C2(c3ccc(N*)cc3)CCC(C(C)(C)C)CC2)cc1,,0.36342823,,,
+2123073246,*CC(*)OC(=O)OC([2H])([2H])[2H],,0.32119753,,,
+2123232751,*c1nc(-c2ccccc2)nc(N(CCCCN(*)c2ccccc2)c2ccccc2)n1,,0.38631994,,,
+2124031511,*c1ncnc(N(CCCCCCN(*)c2ccccc2)c2ccccc2)n1,,0.38019427,,,
+2124040823,*Oc1ccc(C=Cc2ccc(C=Cc3ccc(OC(=O)CCCCCCCCC(*)=O)c(C)c3)cc2)cc1C,35.47523522,,,,
+2124483443,*CC(*)(C)C(=O)OCc1ccc2c(c1)c1ccccc1n2CCCCCC,,0.36971613,,,
+2124605218,*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1,,0.36722758,,,
+2124727719,*CCN(CCOC(=O)CCC#CC#CCCC(=O)O*)c1ccc([N+](=O)[O-])cc1,,0.36177609,,,
+2125797590,*Nc1ccc(NC(=O)c2cccc(C(=O)c3ccc(C(*)=O)cc3)c2)cc1,,0.34473646,,,
+2125841447,*Nc1ccc(C2(c3ccc(N*)cc3[C@]34C[C@H]5C[C@H](C[C@H](C5)C3)C4)c3ccccc3-c3ccccc32)c([C@]23C[C@H]4C[C@H](C[C@H](C4)C2)C3)c1,,0.39009071,,,
+2126001551,*Nc1c(F)c(F)c(F)c(N*)c1F,,0.32142208,,,
+2126142504,*CCCCCCN(CC)C(=O)CCCCCCCCCCCCCCCCC(=O)N(*)CC,,,0.333,,
+2127024895,*OC(=CCC)C(CC)C(*)=O,,0.37690028,,,
+2127124600,*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)C6CCC(C7CCC8C(=O)N(*)C(=O)C8C7)CC6C5=O)cc4)cc3)cc2)cc1,,0.37305974,,,
+2127233037,*CCN(*)C(=O)CCCCCCCCCCCCCCC,,,0.3359999999999999,0.870810763,12.67666102
+2127356325,*c1cc(CCCCCCCCCCCC)c(*)s1,,0.41022463,,,
+2127384896,*C(=O)OCCCCCCCOC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1,,0.34264115,,,
+2127609495,*CCCCCCc1nc2cc(NC(=O)CCCCCCC(=O)Nc3ccc4oc(*)nc4c3)ccc2o1,,0.35267308,,,
+2127644258,*c1ccc2c(c1)C(=O)N(c1cccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O,,0.34082447,,,
+2127806896,*CCCCCCCCCCCCCCNC(=O)NCc1ccc(CNC(=O)N*)cc1,,0.35745185,,,
+2128237869,*CC(*)c1ccccc1F,,,0.1746666666666666,,
+2128329222,*CCN(*)C(=O)CSC,,0.36204137,,,
+2128364241,*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O,,0.39846881,,,
+2128965450,*CCCCCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.34767798,,,
+2129332781,*CCCCCCNC(=O)CCCCCCCCC(=O)N*,,0.35371267,,,
+2129403327,*Nc1ccc(C(Cc2ccccc2)c2ccc(N*)c(C)c2C)c(C)c1C,,0.36251454,,,
+2130807414,*CC(*)C(=O)OCC1(C)COC(C)(C)OC1,95.74104893,,,,
+2130816588,*OC(=O)c1ccc(C(=O)OC2COC3C(*)COC23)o1,,0.35695181,,,
+2130846532,*Nc1cc([C@]2(C)CC(C)(C)c3cc(N*)c(N)cc32)ccc1N,,0.36873621,,,
+2131450394,*Nc1ccccc1-c1ccc2cc3ccccc3cc2c1-c1ccccc1N*,,0.35945408,,,
+2131565375,*Nc1cc2ccccc2c(Cc2c(N*)c(N)cc3ccccc23)c1N,,0.34344352,,,
+2132236820,*OC1CCC(OC(=O)CCCCC(*)=O)CC1,,0.34360259,,,
+2132602127,*Nc1ccc(Cc2cc(C)cc(Cc3ccc(N*)cc3)c2)cc1,,0.35392035,,,
+2132630814,*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4cccc(-c5nnc(*)o5)n4)o3)cc2)cc1,,0.49159892,,,
+2132767837,*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3-c3ccccc32)cc1,,0.36811921,,,
+2132825345,*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C,,0.43542054,,,
+2133999177,*Nc1ccccc1-c1c(N*)c(-c2ccccc2)c(-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,,0.3746419,,,
+2134272821,*CC(C)(C)COC(=O)Nc1ccc(C)c(NC(=O)O*)c1,,0.3446265,,,
+2134740083,*O[Si](C)(C)O[Si](C)(C)c1c(F)c(F)c(F)c([Si](*)(C)C)c1F,,0.41170032,,,
+2135550018,*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)cc3)C4=O)cc2)cc1,,0.3631707,,,
+2135749372,*CCCCCCCCCCCCNC(=O)c1ccc(C(=O)N*)cc1,,0.35898892,,,
+2135907142,*C(=O)c1ccc2c(c1)C(=O)N(CC1(C)CC(N3C(=O)c4ccc(*)cc4C3=O)CC(C)(C)C1)C2=O,,0.37151766,,,
+2136380644,*Oc1ccc(CC(NC(=O)CCc2ccc(OC(=O)COCC(*)=O)cc2)C(=O)OCC)cc1,,0.33546601,,,
+2136556254,*Oc1ccc(C(c2ccccc2)(c2ccccc2)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc2)cc1,,0.37842734,,,
+2136774388,*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(Cc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O,,0.36986106,,,
+2136777400,*Oc1ccc(C(C)(C)c2ccc(Oc3ccc(C(=O)c4ccc(*)c(S(=O)(=O)O)c4)cc3S(=O)(=O)O)cc2)cc1,,0.36520818,,,
+2136833669,*Nc1ccc([C@H](C)c2ccc(C3(c4ccc([C@@H](C)c5ccc(N*)cc5)cc4)CCC(C)CC3)cc2)cc1,,0.36603759,,,
+2137189788,*CCCCCCOC(=O)Nc1ccc(NC(=O)OCCCCCCOc2ccc(-c3ccc(O*)cc3)cc2)c(C)c1,,0.34994782,,,
+2137510659,*c1cccc(N/C=C\C(=O)c2cccc(C(=O)/C=C\Nc3cccc(S(*)(=O)=O)c3)c2)c1,,0.37814125,,,
+2137901854,*O[Si](C)(C)c1c(F)c(F)c(F)c([Si](*)(C)C)c1F,,0.39677257,,,
+2138950642,*Nc1c2ccccc2c(-c2c3ccccc3c(N*)c3c(-c4ccccc4)c(-c4ccccc4)c(-c4ccccc4)c(-c4ccccc4)c23)c2ccccc12,,0.38138687,,,
+2139033638,*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c(OCCN(CC)c3ccc(N=Nc4ccc([N+](=O)[O-])cc4)cc3)c1)C2=O,,0.37475004,,,
+2139220347,*Oc1ccc(Oc2ccc(Oc3cccc(C(=O)Oc4ccc(OC(=O)c5cccc(*)c5)cc4)c3)cc2)cc1,,0.35258751,,,
+2139752461,*Nc1ccc([C@](C)(CC)c2cccc([C@](C)(CC)c3ccc(N*)cc3)c2)cc1,,0.36284184,,,
+2139764431,*c1ccc(Oc2ccc(Oc3ccc(Oc4ccc(-c5cnc6ccc(-c7ccc8nc(*)cnc8c7)cc6n5)cc4)cc3)cc2)cc1,,0.38627985,,,
+2140175803,*CC(CO*)(CS(=O)(=O)C(C)C)CS(=O)(=O)C(C)C,,0.38031514,,,
+2140329869,*Nc1ccc2c(c1)C1(C3=C(C=C[C@@H](N*)C3)C2)c2ccccc2-c2ccccc21,,0.36640776,,,
+2140546586,*C(=O)Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5C)c(C)c3)C4=O)c(C)c2)cc1C,,0.39302078,,,
+2141222333,*CC(*)C1CC=CCC1,,,0.18075,0.84208322,12.70353444
+2141227349,*CC(*)Cl,,,0.139,1.263489368,21.12197193
+2141673799,*CCCCCC(=O)O*,,0.34897959,,1.002695727,18.83367597
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diff --git a/simson_modeling/kaggle_comp/train_supplement/dataset1.csv b/simson_modeling/kaggle_comp/train_supplement/dataset1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..26da2b52bba2efcd6cd52efb0f5715f30171a290
--- /dev/null
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diff --git a/simson_modeling/kaggle_comp/train_supplement/dataset2.csv b/simson_modeling/kaggle_comp/train_supplement/dataset2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..339abd5d246a828824ae9967fd141ce13978721c
--- /dev/null
+++ b/simson_modeling/kaggle_comp/train_supplement/dataset2.csv
@@ -0,0 +1,7209 @@
+SMILES
+*/C(=C(/*)c1ccc(C(C)(C)C)cc1)c1ccccc1
+*/C(=C(/*)c1ccc(CCCC)cc1)c1ccccc1
+*/C(=C(/*)c1ccc(Oc2ccccc2)cc1)c1ccccc1
+*/C(=C(/*)c1ccc([Si](C(C)C)(C(C)C)C(C)C)cc1)c1ccccc1
+*/C(=C(/*)c1ccc([Si](C)(C)C)cc1)c1ccccc1
+*/C(=C(/*)c1cccc([Ge](C)(C)C)c1)c1ccccc1
+*/C(=C(/*)c1cccc([Si](C)(C)C)c1)c1ccccc1
+*/C(=C(/c1ccc(*)cc1)c1ccc(Oc2ccccc2)cc1)c1ccc(Oc2ccccc2)cc1
+*/C(=C(\C#N)c1ccc(C(=O)OC2CCN(*)CC2)cc1)c1ccc(OC)cc1
+*/C(=C(\[2H])C([2H])([2H])C(*)([2H])[2H])C([2H])([2H])[2H]
+*/C(=C/c1cc(OC)c(/C=C(/c2ccc(*)cc2)c2ccc(C(F)(F)F)cc2)cc1OC)c1ccc(C(F)(F)F)cc1
+*/C(=C/c1cc(OC)c(/C=C(/c2ccc(*)cc2)c2ccc(F)cc2)cc1OC)c1ccc(F)cc1
+*/C(=C/c1cc(OC)c(/C=C(/c2ccc(*)cc2)c2ccc(OC)cc2)cc1OC)c1ccc(OC)cc1
+*/C(=C/c1cc(OC)c(/C=C(\c2ccc(F)cc2)c2ccc(-c3ccc(*)cc3)cc2)cc1OC)c1ccc(F)cc1
+*/C(=C/c1cc(OC)c(/C=C(\c2ccc(F)cc2)c2ccc3cc(*)ccc3c2)cc1OC)c1ccc(F)cc1
+*/C(=C/c1cc(OC)c(/C=C(\c2ccccc2)c2ccc(-c3ccc(*)cc3)cc2)cc1OC)c1ccccc1
+*/C(=C/c1cc(OC)c(/C=C(\c2ccccc2)c2ccc3cc(*)ccc3c2)cc1OC)c1ccccc1
+*/C(=C/c1cc(OCCCCCCCC)c(/C=C(\c2ccccc2)c2ccc3c(c2)Sc2ccc(*)cc2S3)cc1OCCCCCCCC)c1ccccc1
+*/C(=C/c1ccc(/C=C(\c2ccccc2)c2ccc(-c3ccc(*)cc3)cc2)cc1)c1ccccc1
+*/C(=N\c1ccccc1)c1ccc(-c2ccc(*)cc2)cc1
+*/C(C#N)=C(\C#N)N1C(=O)c2ccc(C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O
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+*/C(C#N)=C/c1cc(OCC(CC)CCCC)c(/C=C(\C#N)c2ccc3c4ccc(*)cc4n(CC(CC)CCCC)c3c2)cc1OCC(CC)CCCC
+*/C(C#N)=C/c1cc(OCCCCCCCC)c(/C=C(\C#N)c2cc(OCCCCCCCC)c(*)cc2OC)cc1OC
+*/C(C#N)=C/c1cc(OCCCCCCCCCC)c(/C=C(\C#N)c2cc(OCCCCCCCCCC)c(*)cc2OC)cc1OC
+*/C(C#N)=C/c1cc(OCCCCCCCCCCCC)c(/C=C(\C#N)c2cc(OCCCCCCCCCCCC)c(*)cc2OC)cc1OC
+*/C(C#N)=C/c1cc(OCCCCCCCCCCCCCCCC)c(/C=C(\C#N)c2cc(OCCCCCCCCCCCCCCCC)c(*)cc2OC)cc1OC
+*/C(C#N)=C/c1ccc(N(CC)Cc2ccccc2CN(CC)c2ccc(/C=C(\C#N)S(*)(=O)=O)cc2)cc1
+*/C(C)=C(/*)CCC
+*/C(C)=C(/*)CCCCC
+*/C(C)=C(/*)CCCCCCC
+*/C(C)=C(/*)SCC
+*/C(C)=C(/*)SCCCCCC
+*/C(C)=C(/*)SCCCCCCCCCC
+*/C(C)=C(/*)[Ge](C)(C)C
+*/C(C)=C(/*)[Si](C)(C)C
+*/C(C)=C(/*)[Si](C)(C)CCCCCC
+*/C(C)=C(/*)[Si](C)(C)CC[Si](C)(C)C
+*/C(C)=C(/*)[Si](C)(C)C[Si](C)(C)C
+*/C(C)=C(/*)c1ccc([Si](C)(C)C)cc1
+*/C(CC)=C(/*)c1ccccc1
+*/C(CCCCCC)=C(/*)c1ccccc1
+*/C(Cl)=C(/*)CCCC
+*/C(Cl)=C(/*)CCCCCC
+*/C(Cl)=C(/*)CCCCCCCC
+*/C(Cl)=C(/*)c1ccccc1
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+*/C(OC)=C(\OC)c1ccc(*)s1
+*/C=C(/*)C(C)(C)C
+*/C=C(/*)C(CCC)[Si](C)(C)CCCCCC
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+*/C=C(\F)C(F)(F)C*
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+*/C=C/C1CC(*)C([Si](C)(C)C)C1
+*/C=C/C1CC(*)C([Si](C)(C)C[Si](C)(C)C)C1
+*/C=C/C1CC(*)C2C(=O)N(CCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(CCCCCCCCCCCC)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(c3cc(C(F)(F)F)cc(C(F)(F)F)c3)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(c3ccc(C)cc3)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(c3cccc(C)c3)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(c3ccccc3)C(=O)C12
+*/C=C/C1CC(*)C2C(=O)N(c3ccccc3C)C(=O)C12
+*/C=C/C1CC(*)C2C3CCC(C3)C12
+*/C=C/C1CC(*)C2CC=CC12
+*/C=C/C1CC(*)C2CC=CC12
+*/C=C/C1CCC(*)C1
+*/C=C/CC(C*)(C(=O)OC12CC3CC(CC(C3)C1)C2)C(=O)OC12CC3CC(CC(C3)C1)C2
+*/C=C/CC(C*)c1ccccc1
+*/C=C/CC*
+*/C=C/CC*
+*/C=C/CC*
+*/C=C/CCC*
+*/C=C/CCC*
+*/C=C/CCC*
+*/C=C/CCCC(Cl)CCC*
+*/C=C/CCCCCC*
+*/C=C/CCCCCCC(Cl)CCCCCC*
+*/C=C/CCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCCCCCC
+*/C=C/CCCCCCCCCC(CCCCCCCCC*)COCCOCCOCCOCCOCc1ccc2ccc3cccc4ccc1c2c34
+*/C=C/CCCCCCCCCC(Cl)CCCCCCCCC*
+*/C=C/CCCCCCCCCC*
+*/C=C/[Ge](*)(c1ccccc1)c1ccccc1
+*/C=C/[Ge](/C=C/[Si](*)(c1ccccc1)c1ccccc1)(c1ccccc1)c1ccccc1
+*/C=C/[Si](*)(c1ccccc1)c1ccccc1
+*/C=C/c1c(OC(=O)c2ccc(C(=O)Oc3ccc4ccccc4c3/C=C/c3cccc(*)n3)cc2)ccc2ccccc12
+*/C=C/c1c(OC(=O)c2ccc(C(=O)Oc3cccc4ccc(*)nc34)cc2)ccc2ccccc12
+*/C=C/c1cc(CCCCCCCCCC)c(/C=C/c2ccc(-c3ccc(*)nc3)cn2)cc1CCCCCCCCCC
+*/C=C/c1cc(CCCCCCCCCCCCCCCC)c(/C=C/c2nc(*)c(C#N)c(/C=C/c3ccc(N(c4ccccc4)c4ccccc4)cc3)c2C#N)cc1CCCCCCCCCCCCCCCC
+*/C=C/c1cc(OC)c(/C=C/c2ccc(/C=C(\Oc3ccccc3)c3ccc(/C(=C/c4ccc(*)cc4)c4ccc(Oc5ccccc5)cc4)cc3)cc2)cc1OC
+*/C=C/c1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(F)c(C(F)(F)F)c1
+*/C=C/c1cc(OCC(CC)CCCC)c(*)cc1-c1ccc(N(c2ccc(OC)cc2)c2ccc(OC)cc2)cc1
+*/C=C/c1cc(OCC(CC)CCCC)c(*)cc1OC
+*/C=C/c1cc(OCC(CC)CCCC)c(/C=C/c2cc(OC)c(*)cc2OC)cc1OC
+*/C=C/c1cc(OCC(CC)CCCC)c(/C=C/c2ccc3c4ccc(*)cc4n(CC(CC)CCCC)c3c2)cc1OCC(CC)CCCC
+*/C=C/c1cc(OCC2CC3CC2C2CCCC32)c(*)cc1OC
+*/C=C/c1cc(OCCC(C)CCCC(C)C)c(*)cc1OC
+*/C=C/c1cc(OCCCC)c(*)cc1OC
+*/C=C/c1cc(OCCCCCC)c(*)cc1OC
+*/C=C/c1cc(OCCCCCC)c(*)cc1OC
+*/C=C/c1cc(OCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1OCCCCCCC
+*/C=C/c1cc(OCCCCCCCC)c(*)cc1OC
+*/C=C/c1cc(OCCCCCCCC)c(/C=C/c2ccc(*)c3nsnc23)cc1OCCCCCCCC
+*/C=C/c1cc(OCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1OCCCCCCCCC
+*/C=C/c1cc(OCCCCCCCCCCCC)c(*)cc1OC
+*/C=C/c1cc(OCCCCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1OCCCCCCCCCCCC
+*/C=C/c1cc(OCCCCCCCCCCCCCCCC)c(/C=C/c2ccc(*)cc2)cc1OCCCCCCCCCCCCCCCC
+*/C=C/c1cc(OCCc2ccccc2)c(*)cc1OC
+*/C=C/c1ccc(*)c(-c2c(OCC(CC)CCCC)ccc3cc(-c4ccccc4)ccc23)c1
+*/C=C/c1ccc(*)c(-c2c(OCC(CC)CCCC)ccc3ccccc23)c1
+*/C=C/c1ccc(*)c(-c2cc(OCCC(C)C)c(OCCC(C)C)cc2-c2ccccc2)c1
+*/C=C/c1ccc(*)c(-c2ccc(OCC(CC)CCCC)c3ccccc23)c1
+*/C=C/c1ccc(*)c(OCc2cc(OCCC(C)CCCC(C)C)cc(OCC(CC)CCCC)c2)c1
+*/C=C/c1ccc(*)c(OCc2cc(OCc3cc(OCCC(C)CCCC(C)C)cc(OCCC(C)CCCC(C)C)c3)cc(OCc3cc(OCC(CC)CCCC)cc(OCC(CC)CCCC)c3)c2)c1
+*/C=C/c1ccc(*)cc1
+*/C=C/c1ccc(-c2nnc(-c3ccc(/C=C/c4ccc5c(c4)c4cc(*)ccc4n5CCCCCCCC)cc3)n2-c2ccccc2)cc1
+*/C=C/c1ccc(-c2nnc(-c3ccc(/C=C/c4ccc5c(c4)c4cc(-c6ccc7c(c6)c6cc(*)ccc6n7CCCCCCCC)ccc4n5CCCCCCCC)cc3)n2-c2ccccc2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(C)c2ccc([Si](*)(C)C)cc2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(C)c2ccc([Si](*)(C)C)cc2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(C)c2cccc([Si](*)(C)C)c2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(C)c2cccc([Si](*)(C)C)c2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(CCC(F)(F)F)c2ccc([Si](*)(C)CCC(F)(F)F)cc2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(CCC(F)(F)F)c2ccc([Si](*)(C)CCC(F)(F)F)cc2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(CCC(F)(F)F)c2cccc([Si](*)(C)CCC(F)(F)F)c2)cc1
+*/C=C/c1ccc(/C=C/[Si](C)(CCC(F)(F)F)c2cccc([Si](*)(C)CCC(F)(F)F)c2)cc1
+*/C=C/c1ccc(/C=C/c2ccc3c(c2)C(CCCCCCOc2ccc4ccc(=O)oc4c2)(CCCCCCOc2ccc4ccc(=O)oc4c2)c2cc(*)ccc2-3)cc1
+*/C=C/c1ccc(/C=C/c2ccc3c(c2)Sc2cc(*)ccc2N3c2ccc(OCCCCCCCCCCCC)cc2)cc1
+*/C=C/c1ccc(/C=C/c2ccc3c(c2)Sc2cc(*)ccc2N3c2ccc(OCCCCCCCCCCCC)cc2)s1
+*/C=C/c1ccc(/N=N/c2ccc(N(C)CCOC(=O)Nc3ccc(C)c(NC(=O)OCCN(C)c4ccc(/N=N/c5ccc(/C=C/C6=CC(=C(C#N)C#N)C=C(*)O6)cc5)cc4)c3)cc2)cc1
+*/C=C/c1ccc(N(C)CCOC(=O)Nc2ccc(C)c(NC(=O)OCCN(C)c3ccc(/C=C/C4=CC(=C(C#N)C#N)C=C(*)O4)s3)c2)s1
+*/C=C/c1ccc(N(CC)Cc2ccccc2CN(CC)c2ccc(/C=C/c3ccc(/C=C(\C#N)S(=O)(=O)/C(C#N)=C/c4ccc(*)s4)s3)cc2)cc1
+*/C=C/c1ccc(N(c2ccc(/C=C/c3ccc4c5ccc(*)cc5n(CC(CC)CCCC)c4c3)cc2)c2ccc(OCC(CC)CCCC)cc2)cc1
+*/C=C/c1ccc(OC(=O)CCCCCCCCC(=O)Oc2ccc(-c3nnc(*)s3)cc2)cc1
+*/C=C/c1ccc(OC(=O)CCCCCCCCC(=O)Oc2cccc(-c3nnc(*)s3)c2)cc1
+*/C=C/c1ccc(OC(=O)c2ccc(C(=O)Oc3cccc4ccc(*)nc34)cc2)cc1
+*/C=C/c1ccc2c(c1)C(CC(CC)CCCC)(CC(CC)CCCC)c1cc(*)ccc1-2
+*/C=C/c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(/C=C/c3nc(*)c(C#N)c(/C=C/c4ccc(N(c5ccccc5)c5ccccc5)cc4)c3C#N)ccc1-2
+*/C=C/c1ccc2c(c1)C(CCCCCCOc1ccc3ccc(=O)oc3c1)(CCCCCCOc1ccc3ccc(=O)oc3c1)c1cc(/C=C/c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(*)ccc3-4)ccc1-2
+*/C=C/c1ccc2c(c1)Sc1cc(*)ccc1N2c1ccc(OCCCCCCCCCCCC)cc1
+*/C=C/c1ccc2c3ccc(*)cc3n(-c3ccc(OCCCCCCCCCC)c(OCCCCCCCCCC)c3)c2c1
+*/C=C/c1cccc(/C=C/[Si](C)(C)c2ccc([Si](*)(C)C)cc2)c1
+*/C=C/c1cccc(/C=C/[Si](C)(C)c2ccc([Si](*)(C)C)cc2)c1
+*/C=C/c1cccc(/C=C/[Si](C)(C)c2cccc([Si](*)(C)C)c2)c1
+*/C=C/c1cccc(/C=C/[Si](C)(CCC(F)(F)F)c2ccc([Si](*)(C)CCC(F)(F)F)cc2)c1
+*/C=C/c1cccc(/C=C/[Si](C)(CCC(F)(F)F)c2ccc([Si](*)(C)CCC(F)(F)F)cc2)c1
+*/C=C/c1cccc(/C=C/[Si](C)(CCC(F)(F)F)c2cccc([Si](*)(C)CCC(F)(F)F)c2)c1
+*/C=C/c1cccc(OC(=O)CCCCCCCCC(=O)Oc2ccc(-c3nnc(*)s3)cc2)c1
+*/C=C/c1cccc(OC(=O)CCCCCCCCC(=O)Oc2cccc(-c3nnc(*)s3)c2)c1
+*/C=C/c1nc(/C=C/c2ccc3c(c2)c2cc(*)ccc2n3CCCCCC)c(C#N)c(/C=C/c2ccc(N(c3ccccc3)c3ccccc3)cc2)c1C#N
+*/C=C/c1sc(-c2ccc(-c3ccc(-c4ccc(-c5sc(/C=C/C6=CC(=C(C#N)C#N)C=C(*)O6)c(CCCCCC)c5CCCCCC)s4)s3)s2)c(CCCCCC)c1CCCCCC
+*/C=C/c1sc(-c2ccc(-c3ccc(-c4sc(/C=C/C5=CC(=C(C#N)C#N)C=C(*)O5)c(CCCCCC)c4CCCCCC)s3)s2)c(CCCCCC)c1CCCCCC
+*/C=C/c1sc(-c2ccc(-c3sc(/C=C/C4=CC(=C(C#N)C#N)C=C(*)O4)c(CCCCCC)c3CCCCCC)s2)c(CCCCCC)c1CCCCCC
+*/C=C/c1sc(-c2ccc3c(c2)C(CCCCCC)(CCCCCC)c2cc(-c4sc(/C=C/C5=CC(=C(C#N)C#N)C=C(*)O5)c(CCCCCC)c4CCCCCC)ccc2-3)c(CCCCCC)c1CCCCCC
+*/C=C/c1sc(/C=C/c2ccc3c4ccc(*)cc4n(CC(CC)CCCC)c3c2)c(CC(CC)CCCC)c1CC(CC)CCCC
+*/C=N/c1ccc(C(=O)OCCCCOCCCCOCCCCOC(=O)c2ccc(/N=C/c3ccc(*)s3)cc2)cc1
+*/C=N/c1ccc(C(=O)OCCCCOCCCCOCCCCOC(=O)c2ccc(/N=C/c3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1
+*/N=N/c1ccc(C(=O)NCCCCNC(=O)c2ccc(*)cc2)cc1
+*/N=N/c1ccc(NC(=O)CCC(=O)Nc2ccc(*)cc2)cc1
+*/N=N/c1ccc(Nc2ccc(C(=O)c3ccc(C(=O)c4ccc(Nc5ccc(*)cc5)cc4)cc3)cc2)cc1
+*/N=N/c1ccc(Nc2ccc(C(=O)c3ccc(Nc4ccc(*)cc4)cc3)cc2)cc1
+*/N=N/c1ccc([Te]c2ccc(*)cc2)cc1
+*=CNc1ccc(CCc2ccc(N=*)cc2)cc1
+*=CNc1ccc(Cc2ccc(N=*)cc2)cc1
+*=Cc1cc(OCCCCCCCC)c(/C=N/c2ccc(N=*)cc2)cc1OCCCCCCCC
+*=Cc1ccc(/C=N/c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(N=*)c(-c5ccccc5)c4)cc3)cc2-c2ccccc2)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(Br)cc1)(Oc1ccc(Br)cc1)Oc1ccc(Br)cc1)(Oc1ccc(Br)cc1)Oc1ccc(Br)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(C(=O)OC)cc1)(Oc1ccc(C(=O)OC)cc1)Oc1ccc(C(=O)OC)cc1)(Oc1ccc(C(=O)OC)cc1)Oc1ccc(C(=O)OC)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(C(C)(C)C)cc1)(Oc1ccc(C(C)(C)C)cc1)Oc1ccc(C(C)(C)C)cc1)(Oc1ccc(C(C)(C)C)cc1)Oc1ccc(C(C)(C)C)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(C(C)(C)c2ccccc2)cc1)(Oc1ccc(C(C)(C)c2ccccc2)cc1)Oc1ccc(C(C)(C)c2ccccc2)cc1)(Oc1ccc(C(C)(C)c2ccccc2)cc1)Oc1ccc(C(C)(C)c2ccccc2)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(C(F)(F)F)cc1)(Oc1ccc(C(F)(F)F)cc1)Oc1ccc(C(F)(F)F)cc1)(Oc1ccc(C(F)(F)F)cc1)Oc1ccc(C(F)(F)F)cc1
+*=NP(=NP(=NC(=*)Oc1ccc(OC)cc1)(Oc1ccc(OC)cc1)Oc1ccc(OC)cc1)(Oc1ccc(OC)cc1)Oc1ccc(OC)cc1
+*=NP(=NP(=NC(=*)Oc1ccccc1)(Oc1ccccc1)Oc1ccccc1)(Oc1ccccc1)Oc1ccccc1
+*=NP(=NP(=NC(=*)Oc1ccccc1C(F)(F)F)(Oc1ccccc1C(F)(F)F)Oc1ccccc1C(F)(F)F)(Oc1ccccc1C(F)(F)F)Oc1ccccc1C(F)(F)F
+*=NP(Cl)(Cl)=NP(Cl)(Cl)=NC(=*)Cl
+*=Nc1ccc(/N=C(/C=C/C(=*)c2ccccc2)c2ccccc2)cc1
+*=Nc1ccc(/N=C(\c2ccccc2)c2ccc(C(=*)c3ccccc3)cc2)cc1
+*=Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(/N=C4/OC(=O)c5ccc(Oc6ccc7c(c6)C(=O)OC7=*)cc54)cc3)cc2)cc1
+*=Nc1ccc(Cc2ccc(NC(=*)C)cc2)cc1
+*=Nc1ccc(Cc2ccc(NC(=*)CC)cc2)cc1
+*=Nc1ccc(Cc2ccc(NC(=*)CCC)cc2)cc1
+*=Nc1ccc(Cc2ccc(NC(=*)CCCC)cc2)cc1
+*=Nc1ccc(Cc2ccc(NC(=*)c3ccccc3)cc2)cc1
+*=Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(/N=C5/OC(=O)c6cc7c(cc65)C(=*)OC7=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*=Nc1cccc(/N=C(/C=C/C(=*)c2ccccc2)c2ccccc2)c1
+*=Nc1cccc(/N=C(\c2ccccc2)c2ccc(C(=*)c3ccccc3)cc2)c1
+*=Nc1cccc(S(=O)(=O)c2cccc(/N=C3/OC(=O)c4ccc(Oc5ccc6c(c5)C(=O)OC6=*)cc43)c2)c1
+*C#C/C(COS(=O)(=O)c1ccc(C)cc1)=C(/*)COS(=O)(=O)c1ccc(C)cc1
+*C#CC#Cc1cc(C(C)(C)C)cc2c3cc(C(C)(C)C)cc(*)c3n(CCCCCCCCCCCCCCCC)c12
+*C#Cc1cc(C(C)(C)C)cc2c3cc(C(C)(C)C)cc(*)c3n(CCCCCCCCCCCCCCCC)c12
+*C#Cc1cccc(C#C[SiH2]*)c1
+*C#Cc1cccc(C#C[SiH](*)C)c1
+*C#Cc1cccc(C#C[SiH](*)c2ccccc2)c1
+*C#Cc1cccc(C#C[Si](*)(C)C)c1
+*C#Cc1ccccc1C#C[SiH](*)c1ccccc1
+*C(*)(F)Cl
+*C(*)(F)F
+*C(*)C(=O)OC
+*C(*)C(=O)OC(C)C
+*C(*)C(=O)OC1CCCCC1
+*C(*)C(=O)OCC
+*C(*)C(=O)OCCC
+*C(*)F
+*C(*)O
+*C(=O)CCC1(CCC(=O)N2CCN(*)CC2)c2ccccc2-c2ccccc21
+*C(=O)CCCCCCCCC(=O)N1CC(C)N(*)CC1C
+*C(=O)CCCCCCCCC(=O)N1CCN(*)CC1
+*C(=O)CCCCCCCCC(=O)NC1CCCCN(*)C1
+*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)(C)C1=O
+*C(=O)N1C(=O)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(CC)(CC)C1=O
+*C(=O)N1C(=S)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)=C(C(=O)OCC)C1c1ccc(OC)cc1OC
+*C(=O)N1C(=S)N(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)C(C)=C(C(=O)OCC)C1c1ccccc1
+*C(=O)NC1CCC(CC2CCC(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)CC2)CC1
+*C(=O)NC1CCCC(*)O1
+*C(=O)NCC(C)(C)CNC(=O)c1ncn(C)c1*
+*C(=O)NCCCCCCNC(=O)c1ccc(*)nc1
+*C(=O)NCCCCCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1
+*C(=O)NCCCCCCNC(=O)c1ncn(C)c1*
+*C(=O)NCCN(CCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1)c1ccc(/C=C/c2ccc([N+](=O)[O-])cc2)cc1
+*C(=O)NCCN(CCNC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O)c1ccc(/C=C/c2ccc([N+](=O)[O-])cc2)cc1
+*C(=O)NCCN(CCNC(=O)c1cccc(N2C(=O)c3ccc(*)cc3C2=O)c1)c1ccc(/C=C/c2ccc([N+](=O)[O-])cc2)cc1
+*C(=O)NCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1
+*C(=O)NCCNC(=O)c1ncn(C)c1*
+*C(=O)NNC(=O)c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(C(=O)NNC(=O)c3ccc(*)nc3)cc2)cc1
+*C(=O)NNC(=O)c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(C(=O)NNC(=O)c3ccc(*)o3)cc2)cc1
+*C(=O)NNC(=O)c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(C(=O)NNC(=O)c3cncc(*)c3)cc2)cc1
+*C(=O)Nc1c(C)cc(C)c(NC(=O)c2ccc3c(c2)C(=O)N(c2c(C)cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c2C)C3=O)c1C
+*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(/N=N/c5ccc(F)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C
+*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(/N=N/c5ccc(OC(F)(F)F)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C
+*C(=O)Nc1c(C)cc(Cc2cc(C)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(/N=N/c5ccc([N+](=O)[O-])cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(C)c2)cc1C
+*C(=O)Nc1c(CC)cc(Cc2cc(CC)c(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(/N=N/c5ccc(C)cc5)c(N5C(=O)c6ccc(*)cc6C5=O)c3)C4=O)c(CC)c2)cc1CC
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+*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccccc7-c7ccccc7Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(-c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5cccc(N7C(=O)c8ccc(*)cc8C7=O)c5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(=O)c3ccc(Oc4ccc(NC(=O)c5ccc(*)nc5)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(-c7sc(-c8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)c(-c8ccccc8)c7-c7ccccc7)cc5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8c7)cc5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc(N8C(=O)c9ccc(*)cc9C8=O)c7)cc5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(C)(C)c3cccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)c3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc(C8(c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)CC9CC8C8CCCC98)cc7)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7ccc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8c7)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C(c3ccc(Oc4ccc(NC(=O)c5ccc6c(c5)C(=O)N(c5ccc(Oc7cccc8c(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cccc78)cc5)C6=O)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(C)c(N8C(=O)c9ccc(*)cc9C8=O)c6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(N8C(=O)c9ccc(*)cc9C8=O)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(C9(c%10ccc(Oc%11ccc(N%12C(=O)c%13ccc(*)cc%13C%12=O)cc%11)cc%10)NC(=O)c%10ccccc%109)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)c(C)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8ccc(S(=O)(=O)c9ccc(Oc%10ccc(N%11C(=O)c%12ccc(*)cc%12C%11=O)cc%10)cc9)cc8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8cccc(N9C(=O)c%10ccc(*)cc%10C9=O)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6ccc(Oc8cccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)c8)cc6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(C3(c4ccc(Oc5ccc(NC(=O)c6ccc7c(c6)C(=O)N(c6cccc(N8C(=O)c9ccc(*)cc9C8=O)c6)C7=O)cc5)cc4)NC(=O)c4ccccc43)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(N3C(=O)CC(N4C(=O)c5ccc(*)cc5C4=O)C3=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(N3C(=O)c4ccc(*)cc4C3=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(C3CCC(CC5CCC(N6C(=O)c7ccc(*)cc7C6=O)CC5)CC3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(-c5sc(-c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c(-c6ccccc6)c5-c5ccccc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(NC(=O)Nc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C6(c7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)CC7CC6C6CCCC76)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(C6(c7ccc(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cc7)NC(=O)c7ccccc76)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5-c5ccccc5)c(-c5ccccc5)c3)C4=O)cc2-c2ccccc2)c(-c2ccccc2)c1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc6c(N7C(=O)c8ccc(*)cc8C7=O)cccc56)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3cccc(P(=O)(c5ccccc5)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)c3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(-c6sc(-c7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)c(-c7ccccc7)c6-c6ccccc6)cc4)C5=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)CC8CC7C7CCCC87)cc6)cc4)C5=O)cc3)c(C(C)(C)C)c2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)CC8CC7C7CCCC87)cc6)cc4)C5=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(C7(c8ccc(Oc9ccc(N%10C(=O)c%11ccc(*)cc%11C%10=O)cc9)cc8)NC(=O)c8ccccc87)cc6)cc4)C5=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc4)C5=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4ccc(Oc6cccc7c(Oc8ccc(N9C(=O)c%10ccc(*)cc%10C9=O)cc8)cccc67)cc4)C5=O)cc3)cc2)cc1
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+*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)c2)cc1
+*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6c5)cc3)C4=O)c2)cc1
+*C(=O)Nc1ccc(Oc2cccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc3)C4=O)c2)cc1
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+*C(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)c4ccc5c(c4)C(=O)N(c4cccc(Oc6cccc(Oc7cccc(N8C(=O)c9ccc(*)cc9C8=O)c7)c6C#N)c4)C5=O)cc3)c2)cc1
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+*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(N5C(=O)c6ccc(*)cc6C5=O)cc3)C4=O)cc2)cc1
+*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(-c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc3)C4=O)cc2)cc1
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+*C(=O)Nc1ccc(Oc2ccccc2-c2ccccc2Oc2ccc(NC(=O)c3ccc4c(c3)C(=O)N(c3ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc3)C4=O)cc2)cc1
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+*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc2)cc1
+*C(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(=O)c3ccc4[nH]c(-c5cc(-c6nc7cc(*)ccc7[nH]6)cc(N6C(=O)c7ccccc7C6=O)c5)nc4c3)cc2)cc1
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+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(=O)c3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(C(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3cccc(C(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)(C(F)(F)F)C(F)(F)F)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)(c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)C(F)(F)F)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(C)CC(C)(C)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc43)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c4ccccc4-c4ccccc43)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(CP(=O)(OCC)OCC)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(CP(=O)(OCCCl)OCCCl)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(C(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4ccc(Cc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(Cc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(C(C)(C)C)c3)cc1C(C)(C)C)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)c(C)c3)cc1C)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cl)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(C#N)cc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccccc3)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(*)cc4C3=O)c(S(=O)(=O)O[Na])c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(NC(=O)c3cccc(C(=O)Nc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCC(C)(C)COc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(OCCOc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(C(c4cc(C)c(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(C)c4)c4cccc5ccccc45)cc3C)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(Cc4cc(C)c(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)c(C)c4)cc3C)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc4ccccc4cc3Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(Oc7ccc(N8C(=O)c9ccc(*)cc9C8=O)cc7)cc6)ccc5c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)n4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6C(F)(F)F)cc5)n4)cc3)c(C(F)(F)F)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccccc4)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(*)cc7C6=O)cc5C(F)(F)F)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(C(F)(F)F)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3Br)c(Br)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C(F)(F)F)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(S(=O)(=O)c4ccc(C)cc4)cc3)c(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(-c4cc(-c5ccccc5)cc(-c5cccc(Oc6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)c5)n4)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)c3C#N)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3Oc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(S(=O)(=O)c6ccc(N7C(=O)c8ccc(*)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cn3)nc1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc3c(c1)C(=O)c1cc(N4C(=O)c5ccc(*)cc5C4=O)ccc1-3)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccc3c(c1)Cc1cc(N4C(=O)c5ccc(*)cc5C4=O)ccc1-3)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C#Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)N(C)c3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(N4C(=O)c5ccc(*)cc5C4=O)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3c(C)cc(C)c(N4C(=O)c5ccc(*)cc5C4=O)c3C)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Cc4ccc(C(=O)c5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4C(F)(F)F)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(=O)c3ccccc3N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(C(c3ccccc3)(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)C(F)(F)F)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(C(=O)c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(Cc4ccc(Cc5cccc(N6C(=O)c7ccc(*)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccc(Cc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)cc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Cc3ccccc3N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(*)cc4C3=O)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(NC(=O)c3cccc(C(=O)Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(-c4ccccc4)n3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(N(C)C)n3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3nc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)nc(N(c4ccccc4)c4ccccc4)n3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Nc3ncnc(Nc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)n3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OCCOCCOc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OCCOc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(OP(=O)(Oc3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c3ccccc3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(Oc4cccc(N5C(=O)c6ccc(*)cc6C5=O)c4)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(C(F)(F)F)cc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(Oc4ccc(C56CC7CC(CC(C7)C5)C6)cc4)cc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccccc3)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1cccc(S(=O)(=O)c3cccc(N4C(=O)c5ccc(*)cc5C4=O)c3)c1)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccccc1C(=O)c1ccccc1N1C(=O)c3ccc(*)cc3C1=O)C2=O
+*C(=O)c1ccc2c(c1)C(=O)N(c1ccccc1Cc1ccccc1N1C(=O)c3ccc(*)cc3C1=O)C2=O
+*C(=O)c1ccc2c(c1OCc1ccccc1[N+](=O)[O-])C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(*)c(OCc6ccccc6[N+](=O)[O-])c5C4=O)cc3)cc1)C2=O
+*C(=O)c1ccc2nc(-c3ccc(-c4cnc5cc(*)ccc5n4)cc3)cnc2c1
+*C(=O)c1ccc2nc(-c3ccc(-c4nc5ccc(*)cc5nc4-c4ccccc4)cc3)c(-c3ccccc3)nc2c1
+*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(-c4nc5cc(*)ccc5nc4-c4ccccc4)cc3)nc2c1
+*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(-c4nc5ccc(*)cc5nc4-c4ccccc4)cc3)nc2c1
+*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(N4C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(-c8nc9cc(*)ccc9nc8-c8ccccc8)cc6)C7=O)cc5C4=O)cc3)nc2c1
+*C(=O)c1ccc2nc(-c3ccccc3)c(-c3ccc(Oc4ccc(-c5nc6cc(*)ccc6nc5-c5ccccc5)cc4)cc3)nc2c1
+*C(=O)c1ccc2ncc(-c3ccc(-c4cnc5ccc(*)cc5n4)cc3)nc2c1
+*C(=O)c1cccc(C(=O)N(*)c2ccccc2)c1
+*C(=O)c1cccc(C(=O)N2CCN(*)CC2)c1
+*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(C(c4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)(C(F)(F)F)C(F)(F)F)cc2)C3=O)c1
+*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc(Oc4ccc(N5C(=O)c6ccc(*)cc6C5=O)cc4)cc2)C3=O)c1
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+*C(=O)c1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2cccc(N4C(=O)c5ccc(*)cc5C4=O)c2)C3=O)c1
+*C(=O)c1ccccc1-c1ccccc1C(=O)N1CCN(*)CC1
+*C(=S)Nc1ccc(Oc2ccc(NC(=S)N3C(=O)c4ccc(C(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1
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+*C(=S)Nc1ccc(Oc2ccc(NC(=S)n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1
+*C(C(=O)OC)C(*)C(=O)OC(C)(C)C
+*C(C)C(*)C(=O)OC(C)C
+*C(C)C(*)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2
+*C(C)C(*)C(=O)OC12CC3CC(CC(C3)C1)C2
+*C(C)C(*)C(=O)OCC
+*C(C)C(*)C(=O)OCCC
+*C(C)C(*)C(=O)OCCCC
+*C(CC(C)C)C(=O)Nc1ccc(Oc2ccc(NC(=O)NCC3(C)CC(NC(=O)Nc4ccc(Oc5ccc(NC(=O)C(CC(C)C)n6c(=O)c7cc8c(=O)n(*)c(=O)c8cc7c6=O)cc5)cc4)CC(C)(C)C3)cc2)cc1
+*C(CC(C)C)C(=O)Nc1ccc(Oc2ccc(NC(=O)Nc3ccc(Cc4ccc(NC(=O)Nc5ccc(Oc6ccc(NC(=O)C(CC(C)C)n7c(=O)c8cc9c(=O)n(*)c(=O)c9cc8c7=O)cc6)cc5)cc4)cc3)cc2)cc1
+*C(CC)COC(=O)NCCCCCCNC(=O)OCC(CC)n1c(=O)c2cc3c(=O)n(*)c(=O)c3cc2c1=O
+*C(F)(F)C(*)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*C(F)(F)C(*)(F)C(F)(F)F
+*C(F)(F)C(*)(F)Cl
+*C(F)(F)C(*)(F)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*C(F)(F)C(*)(F)OC(F)(F)C(F)(F)F
+*C(F)(F)C(*)(F)OC(F)(F)F
+*C(F)(F)C(*)(F)OC(F)C(F)F
+*C(F)(F)C(*)(F)c1c(F)c(F)c(F)c(F)c1F
+*C(F)(F)C(*)(F)c1ccccc1
+*C(F)(F)C(*)(OC(F)(F)F)OC(F)(F)F
+*C(F)(F)C(=O)NCCCCCCNS(*)(=O)=O
+*C(F)(F)C1(*)OC(F)(C(F)(F)F)C(F)(C(F)(F)F)O1
+*C(F)(F)C1(*)OC(F)(F)C(F)(C(F)(F)F)O1
+*C(F)(F)C1(*)OC(F)(F)C(F)(C(F)(F)F)O1
+*C(F)(F)C1(*)OC(F)(F)C(F)(F)O1
+*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(Cl)C1(F)F
+*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(F)C(F)(F)C1(F)F
+*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C(F)(F)C1(F)F
+*C(F)(F)C1(F)C(F)(F)C(*)(F)C(F)(F)C1(F)F
+*C(F)(F)C1(F)C(F)(F)C(*)(F)C1(F)F
+*C(F)C(*)(F)F
+*C(F)C(*)(F)OC(F)(F)F
+*C(F)C(*)C(F)(F)F
+*C(OC(F)(F)F)C(*)(F)F
+*C([2H])([2H])C(*)(C(=O)OC([2H])([2H])C([2H])([2H])[2H])C([2H])([2H])[2H]
+*C([2H])([2H])C(*)(C(=O)OC([2H])([2H])[2H])C([2H])([2H])[2H]
+*C([2H])([2H])C(*)([2H])C([2H])([2H])[2H]
+*C([2H])([2H])C(*)([2H])C([2H])=C([2H])[2H]
+*C([2H])([2H])C(*)([2H])Cl
+*C([2H])([2H])C(*)([2H])c1c([2H])c([2H])c([2H])c([2H])c1[2H]
+*C(c1ccc(N(C)C)cc1)C(*)c1ccc([N+](=O)[O-])cc1
+*C*
+*C/C(C)=C/C(C)S(*)(=O)=O
+*C/C=C(\C)C(C)S(*)(=O)=O
+*C/C=C(\C)CS(*)(=O)=O
+*C/C=C/C(C)S(*)(=O)=O
+*C/C=C/C(CC)S(*)(=O)=O
+*C/C=C/COC(=O)C(Cc1ccccc1)NC(=O)/C=C/C(=O)NC(Cc1ccccc1)C(=O)O*
+*C/C=C/COC(=O)C(Cc1ccccc1)NC(=O)CCCCC(=O)NC(Cc1ccccc1)C(=O)O*
+*C/C=C/COC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)O*
+*C/C=C/COC(=O)CCCCCCCCC(=O)O*
+*C/C=C/COC(=O)CCCCCCCCC(=O)O*
+*C/C=C/COC(=O)NCCCCCCNC(=O)O*
+*C/C=C/COC(=O)NCCCCCCNC(=O)O*
+*C/C=C/CS(*)(=O)=O
+*C/C=C/C[Si](*)(C)C
+*C/C=C/C[Si](*)(C)CCCOc1ccc(-c2ccccc2)cc1
+*C/C=C/C[Si](*)(C)CCCOc1ccc2ccccc2c1
+*C/C=C/C[Si](*)(C)CCCOc1ccccc1
+*C/C=C/C[Si](*)(C)c1ccccc1
+*C/C=C/C[Si](*)(C=C)C=C
+*C/C=C/C[Si](*)(CCCOc1ccccc1)c1ccccc1
+*C/C=C/C[Si](*)(c1ccccc1)c1ccccc1
+*C/C=C/N(/C=C/CC1CC(=O)N(c2ccc(Cc3ccc(N4C(=O)CC(*)C4=O)cc3)cc2)C1=O)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)cc1
+*C/C=C/c1cc(C(C)(C)c2ccc(O)c(/C=C/CC3CC(=O)N(c4ccc(Cc5ccc(N6C(=O)CC(*)C6=O)cc5)cc4)C3=O)c2)ccc1O
+*C1(C)CC(*)(C)C(=O)N(C)C1=O
+*C1(F)OC(C(F)(F)F)(C(F)(F)Cl)OC1(*)F
+*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCC)C1=O
+*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCCCCCC)C1=O
+*C1=C(c2ccc(-c3ccc4c(c3)C(CCCCCC)(CCCCCC)c3cc(-c5ccc(*)cc5)ccc3-4)cc2)C(=O)N(CCCCCCCCCCCC)C1=O
+*C1C(*)C2CC1C(C(=O)OCC13CC4CC(CC(C4)C1)C3)C2C(=O)OCC12CC3CC(CC(C3)C1)C2
+*C1C(*)C2CC1C(C(=O)OCC1CC3CCC1C3)C2C(=O)OCC1CC2CCC1C2
+*C1C(*)C2CC1C(C(=O)OCCC13CC4CC(CC(C4)C1)C3)C2C(=O)OCCC12CC3CC(CC(C3)C1)C2
+*C1C(*)C2CC1C(C(=O)OCCC1CC3CCC1C3)C2C(=O)OCCC1CC2CCC1C2
+*C1C(*)C2CC1C(C(=O)OCCCc1ccccc1)C2C(=O)OCCCc1ccccc1
+*C1C(*)C2CC1C(C(=O)OCCc1ccccc1)C2C(=O)OCCc1ccccc1
+*C1C(*)C2CC1C(C(=O)OCc1ccccc1)C2C(=O)OCc1ccccc1
+*C1C(*)C2CC1C(COCc1ccccc1)C2COCc1ccccc1
+*C1C(*)C2CC1C1C=CCC12
+*C1C(=O)N(C(C)(C)C)C(=O)C1*
+*C1C(=O)N(C(C)CC)C(=O)C1*
+*C1C(=O)N(C2CCCCC2)C(=O)C1*
+*C1C(=O)N(CCCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(CCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*C1C(=O)N(c2c(CC)cccc2CC)C(=O)C1*
+*C1C(=O)N(c2cc(Br)c(O[Si](C)(C)C(C)(C)C)c(Br)c2)C(=O)C1*
+*C1C(=O)N(c2cc(Br)c(O[Si](C)(C)C)c(Br)c2)C(=O)C1*
+*C1C(=O)N(c2cc(Br)c(O[Si](CC)(CC)CC)c(Br)c2)C(=O)C1*
+*C1C(=O)N(c2cc(Br)c(O[Si](c3ccccc3)(c3ccccc3)c3ccccc3)c(Br)c2)C(=O)C1*
+*C1C(=O)N(c2ccc(O[Si](C)(C)C(C)(C)C)cc2)C(=O)C1*
+*C1C(=O)N(c2ccc(O[Si](C)(C)C)cc2)C(=O)C1*
+*C1C(=O)N(c2ccc(O[Si](CC)(CC)CC)cc2)C(=O)C1*
+*C1C(=O)N(c2ccc(O[Si](c3ccccc3)(c3ccccc3)c3ccccc3)cc2)C(=O)C1*
+*C1C(=O)N(c2ccccc2C(=O)OC)C(=O)C1*
+*C1C(=O)N(c2ccccc2C(=O)OCC)C(=O)C1*
+*C1C(=O)N(c2ccccc2C(=O)OCCCC)C(=O)C1*
+*C1C(=O)N(c2ccccc2C)C(=O)C1*
+*C1C(=O)OC(=O)C1C1C2CC(C(=O)OC(C)CC)C(C2)C1*
+*C1C(=O)OC(=O)C1C1C2CC(C(=O)OC3CCCCC3)C(C2)C1*
+*C1C(=O)OC(=O)C1C1C2CC(C(=O)OCCOCC)C(C2)C1*
+*C1C(=O)OC(=O)C1C1C2CC(COCCC)C(C2)C1*
+*C1C2C/C(=C\C)C(C2)C1*
+*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4cccc(C)c4)cc3)C(C2)C1*
+*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4cccc5ccccc45)cc3)C(C2)C1*
+*C1C2CC(C(=O)OCCc3ccc(N(c4ccccc4)c4ccccc4)cc3)C(C2)C1*
+*C1C2CC(C)C(C2)C1*
+*C1C2CC(C1*)C([Si](C)(C)C)C2
+*C1C2CC(CCCCCCCCC(=O)OCC)C(C2)C1*
+*C1C2CC(CCCCc3ccccc3)C(C2)C1*
+*C1C2CCC(C2)C1*
+*C1C2CCC(C2)C1S(*)(=O)=O
+*C1C=C(CCCCCC)C(*)S1
+*C1C=CC(*)CC1
+*C1CC(*)(C#N)C1
+*C1CC2CC1C(*)C2CCCC
+*C1CC2CC1C(*)C2CCCCCC
+*C1CCC(*)C1
+*C1CCC(CC2CCC(N3C(=O)C4C5C=CC(C6C(=O)N(*)C(=O)C56)C4C3=O)C(C)C2)CC1C
+*C1CCC(CC2CCC(N3C(=O)C4C5C=CC(C6C(=O)N(*)C(=O)C56)C4C3=O)CC2)CC1
+*C1CCC(CC2CCC(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C(C)C2)CC1C
+*C1CCC(CC2CCC(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)CC2)CC1
+*C1CCC(CC2CCC(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)C(C)C2)CC1C
+*C1CCC(CC2CCC(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)CC2)CC1
+*C1CCC(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC1
+*C1CCC(N2C(=O)c3ccc(S(=O)(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC1
+*C1CCCC(S(*)(=O)=O)C1
+*C1CCCCC1S(*)(=O)=O
+*C1CCOC1*
+*C1Cc2ccccc2C1*
+*C1OC(C(F)(F)F)(C(F)(F)F)OC1(*)F
+*C1OCCCC1C1C(=O)OC(=O)C1*
+*C1OCOC(C(*)(F)F)C1(F)F
+*C1Oc2ccccc2C1*
+*C1c2cccc3cccc(c23)C1*
+*CC#C/C(C#CCOC(=O)CCCCCCCCC(=O)O*)=C\c1ccc(C#Cc2ccc(C=C(C#Cc3ccccc3)C#Cc3ccccc3)cc2)cc1
+*CC#C/C(C#CCOC(=O)CCCCCCCCC(=O)O*)=C\c1ccc(C#Cc2ccc([N+](=O)[O-])cc2)cc1
+*CC#C/C(C#CCOC(=O)CCCCCCCCC(=O)O*)=C\c1ccc(C#Cc2ccccc2)cc1
+*CC#CC#CCOC(=O)c1ccc(C(=O)O*)cc1
+*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(/N=N/c3ccc(/N=N/c4cc(C)c(C#N)c(C)c4)cc3)cc2)cc1
+*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(/N=N/c3ccc(/N=N/c4cc(C)c([N+](=O)[O-])c(C)c4)cc3)cc2)cc1
+*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(/N=N/c3ccc(C#N)cc3)cc2)cc1
+*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc(/N=N/c3ccc([N+](=O)[O-])cc3)cc2)cc1
+*CC#CC#CCOc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(O*)cc2)c2ccc([N+](=O)[O-])cc2)cc1
+*CC#CC#CCOc1cccc(C(=O)OCCN(CCOC(=O)c2cccc(O*)c2)c2ccc(/N=N/c3ccc(C#N)cc3)cc2)c1
+*CC#CCOC(=O)CCCCCCC(=O)O*
+*CC#CCOC(=O)CCCCCCCCC(=O)O*
+*CC#CCOC(=O)CCCCCCCCCCC(=O)O*
+*CC#CCOC(=O)NCCCCCCNC(=O)O*
+*CC(*)(C#N)C(=O)OC
+*CC(*)(C#N)C(=O)OC(C)C
+*CC(*)(C#N)C(=O)OCC
+*CC(*)(C#N)C(=O)OCC(C)C
+*CC(*)(C#N)C(=O)OCCCC
+*CC(*)(C#N)C(=O)OCCCCCC
+*CC(*)(C#N)C(=O)OCCCCCCC
+*CC(*)(C(=O)OC)c1ccccc1
+*CC(*)(C)C
+*CC(*)(C)C#N
+*CC(*)(C)C(=O)NC(=O)OC(C)COc1c(Br)cc(S(=O)(=O)c2cc(Br)c(OCC(C)O)c(Br)c2)cc1Br
+*CC(*)(C)C(=O)NC(=O)OC(C)COc1ccc(S(=O)(=O)c2ccc(OCC(C)O)cc2)cc1
+*CC(*)(C)C(=O)NC(=O)OCCOc1c(Br)cc(S(=O)(=O)c2cc(Br)c(OCCO)c(Br)c2)cc1Br
+*CC(*)(C)C(=O)NC(=O)OCCOc1ccc(S(=O)(=O)c2ccc(OCCO)cc2)cc1
+*CC(*)(C)C(=O)NC(=O)Oc1c(Br)cc(S(=O)(=O)c2cc(Br)c(O)c(Br)c2)cc1Br
+*CC(*)(C)C(=O)NC(=O)Oc1ccc(S(=O)(=O)c2ccc(O)cc2)cc1
+*CC(*)(C)C(=O)NC(C)c1cccc2ccccc12
+*CC(*)(C)C(=O)NC(C)c1ccccc1
+*CC(*)(C)C(=O)NC1C(O)OC(CO)C(O)C1O
+*CC(*)(C)C(=O)Nc1ccc(C(=O)/C=C/c2ccc(OC)cc2)cc1
+*CC(*)(C)C(=O)Nc1cccc(-c2nnc(-c3ccccc3)o2)c1
+*CC(*)(C)C(=O)O
+*CC(*)(C)C(=O)OC
+*CC(*)(C)C(=O)OC(C(F)(F)F)C(F)(F)F
+*CC(*)(C)C(=O)OC(C)(C)C
+*CC(*)(C)C(=O)OC(C)C
+*CC(*)(C)C(=O)OC(C)C(C)(C)C
+*CC(*)(C)C(=O)OC(C)CC
+*CC(*)(C)C(=O)OC(COc1cc2ccccc2c2ccccc12)COc1cc2ccccc2c2ccccc12
+*CC(*)(C)C(=O)OC(COc1ccc(-c2ccccc2)cc1)COc1ccc(-c2ccccc2)cc1
+*CC(*)(C)C(=O)OC(COc1cccc2ccccc12)COc1cccc2ccccc12
+*CC(*)(C)C(=O)OC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*CC(*)(C)C(=O)OC(OCC)C(F)(F)F
+*CC(*)(C)C(=O)OC1(C)C2CC3CC(C2)CC1C3
+*CC(*)(C)C(=O)OC1(C)CCCN(c2ccc(/N=N/c3ccc(/C=C(\C#N)S(C)(=O)=O)cc3)cc2)C1
+*CC(*)(C)C(=O)OC1(C)CCOC(=O)C1
+*CC(*)(C)C(=O)OC12CC3CC(C)(CC(C)(C3)C1)C2
+*CC(*)(C)C(=O)OC12CC3CC(CC(C3)C1)C2
+*CC(*)(C)C(=O)OC1CC(C)CC(C)(C)C1
+*CC(*)(C)C(=O)OC1CC(C)CCC1C(C)C
+*CC(*)(C)C(=O)OC1CC2CC1C1C3CCC(C3)C21
+*CC(*)(C)C(=O)OC1CC2CCC1(C)C2(C)C
+*CC(*)(C)C(=O)OC1CCC(C(C)(C)C)CC1
+*CC(*)(C)C(=O)OC1CCC(C)CC1
+*CC(*)(C)C(=O)OC1CCC1
+*CC(*)(C)C(=O)OC1CCC2CCCCC2C1
+*CC(*)(C)C(=O)OC1CCCC(C)C1
+*CC(*)(C)C(=O)OC1CCCC1
+*CC(*)(C)C(=O)OC1CCCCC1
+*CC(*)(C)C(=O)OC1CCCCC1C
+*CC(*)(C)C(=O)OC1CCCCC1C(C)(C)C
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+*CC(*)c1ccc(COCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc1
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+*CC(*)c1ccc(COCCOCCCC)cc1
+*CC(*)c1ccc(COCCOCCCCCCCC)cc1
+*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(-c5ccc(CCCCCC)s5)s4)c4nsnc34)cc2)cc1
+*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(C)cc4)c4nsnc34)cc2)cc1
+*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(C)s4)c4nsnc34)cc2)cc1
+*CC(*)c1ccc(COc2ccc(-c3ccc(C#N)cc3)cc2)cc1
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+*CC(*)c1ccc(Cl)c(C)c1
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+*CC(*)c1ccc([Si](C)(O[Si](C)(C)C)O[Si](C)(C)C)cc1
+*CC(*)c1ccc([Si](O[Si](C)(C)C)(O[Si](C)(C)C)O[Si](C)(C)C)cc1
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+*CC(*)c1ccccc1COCCCCCCCC
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+*CC(=O)Nc1ccc(NC(=O)COc2ccc3ccccc3c2Sc2c(O*)ccc3ccccc23)cc1
+*CC(=O)Nc1ccc(NC(=O)c2ccc3c(c2)C(=O)N(*)C3=O)cc1
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+*CC(=O)OC(=O)COc1ccc(O*)cc1
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+*CC(C)(C)C1C(=O)OC(=O)C1*
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+*CC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1
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+*CC(CC)(CC)COC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1
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+*CC(CC)N(*)C(=O)CCCC
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+*CC(COc1c(Cl)cc(C(C)(C)c2cc(Cl)c(O*)c(Cl)c2)cc1Cl)OC(C)=O
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+*CC(COc1ccc(-c2ccc(OC)cc2)cc1)O*
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+*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)c1ccccc1Cl
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+*CC(O)COC(=O)CCCCC(=O)OCC(O)COc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1
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+*CC(O)COC(=O)CCCCCCCCC(=O)OCC(O)COc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1
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+*CC(O)COC(=O)CCCCCCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1
+*CC(O)COC(=O)CCCCCCCCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1
+*CC(O)COC(=O)CCCCCCCCCCC(=O)OCC(O)COc1ccc(O*)cc1
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+*CC(c1ccccc1)C1C(=O)N(CCCCNC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)C(=O)C1*
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+*CC(c1ccccc1)C1C(=O)N(CCCN(CC)c2ccc(/N=N/c3ccc([N+](=O)[O-])cc3Cl)c(C)c2)C(=O)C1*
+*CC(c1ccccc1)C1C(=O)N(CCCOc2ccc(-c3ccc(C#N)cc3)cc2)C(=O)C1*
+*CC(c1ccccc1)C1C(=O)N(CCN(CC)c2ccc(/N=N/c3ccc([N+](=O)[O-])cc3)cc2)C(=O)C1*
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+*CC/C=C(/*)Cl
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+*CC/C=C(/*)[Sn](CCCC)(CCCC)CCCC
+*CC/C=C(/*)c1ccccc1
+*CC/C=C/C[Si](*)(C)c1ccccc1
+*CC1(*)CC(=O)N(c2ccccc2)C1=O
+*CC1(*)CCCCC1
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+*CC1(C)CC(*)(C)C(=O)OC1=O
+*CC1(C)CC(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC(C)(C)C1
+*CC1(C)CC(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)CC(C)(C)C1
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+*CCCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1
+*CCCCCOC(=O)c1ccc(/C=C/c2ccc(C(=O)O*)cc2)cc1
+*CCCCCOC(=O)c1ccc(C(=O)O*)cc1
+*CCCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1
+*CCCCCOC(=O)c1cccc(C(=O)O*)c1
+*CCCCCOC(=O)c1ccccc1C(=O)O[Cd]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)O*
+*CCCCCOC(=O)c1ccccc1C(=O)O[Cd]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1
+*CCCCCOC(=O)c1ccccc1C(=O)O[Pb]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)O*
+*CCCCCOC(=O)c1ccccc1C(=O)O[Pb]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1
+*CCCCCOCO*
+*CCCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12
+*CCCCCOc1ccc(/C=C/C(=O)OCCN(CCOC(=O)/C=C/c2ccc(O*)cc2)c2ccc(/N=N/c3ccc([N+](=O)[O-])cc3)cc2)cc1
+*CCCCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*CCCCCOc1ccc(CCc2ccc(O*)cc2C)cc1
+*CCCCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1
+*CCCCCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)cc2)cc1
+*CCCCCOc1ccc(OCCCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1
+*CCCCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1
+*CCCCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1
+*CCCCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1
+*CCCCCSS*
+*CCCCC[Si](*)(C)c1ccccc1
+*CCCCN1C(=O)C2C3C=CC(C4C(=O)N(*)C(=O)C34)C2C1=O
+*CCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O
+*CCCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CCCCNC(=O)C(=O)N*
+*CCCCNC(=O)C(OC)C(OC)C(=O)N*
+*CCCCNC(=O)CC(=O)N*
+*CCCCNC(=O)CCCCC(=O)N*
+*CCCCNC(=O)Cc1ccc(C(=O)N*)cc1
+*CCCCNC(=O)c1ccc(C(=O)N*)c(Oc2ccccc2)c1
+*CCCCNC(=O)c1ccc(C(=O)N*)c(Sc2ccccc2)c1
+*CCCCNC(=O)c1ccc(C(C)(CC)c2ccc(C(=O)N*)cc2)cc1
+*CCCCNC(=O)c1cccc(C(=O)N*)c1
+*CCCCO*
+*CCCCOC(=O)/C=C/C(=O)O*
+*CCCCOC(=O)/C=C/C(=O)O*
+*CCCCOC(=O)C(Cc1ccccc1)NC(=O)/C=C/C(=O)NC(Cc1ccccc1)C(=O)O*
+*CCCCOC(=O)C1CCC(C(=O)O*)CC1
+*CCCCOC(=O)CCC(=O)O*
+*CCCCOC(=O)CCCC(=O)O*
+*CCCCOC(=O)CCCCC(=O)O*
+*CCCCOC(=O)CCCCC(=O)OCCCCOC(=O)c1ccc(C(=O)O*)cc1
+*CCCCOC(=O)CCCCCCCC(=O)O*
+*CCCCOC(=O)CCCCCCCCC(=O)O*
+*CCCCOC(=O)CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC(=O)O*
+*CCCCOC(=O)CCCCCNC(=O)CCCCC(=O)NCCCCCC(=O)O*
+*CCCCOC(=O)CCSCCCCCCSCCC(=O)O*
+*CCCCOC(=O)COCC(=O)O*
+*CCCCOC(=O)NCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CNC(=O)O*
+*CCCCOC(=O)NCCCCCCNC(=O)O*
+*CCCCOC(=O)NCCCCNC(=O)O*
+*CCCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*
+*CCCCOC(=O)Nc1ccc(-c2ccc(NC(=O)OCCCCn3c4ccccc4c4cc(/C=C(\C#N)C(=O)OCCCCCCOC(=O)/C(C#N)=C/c5ccc6c(c5)c5ccccc5n6*)ccc43)c(C)c2)cc1C
+*CCCCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1
+*CCCCOC(=O)Nc1ccc(C)c(NC(=O)OCCCCn2c3ccccc3c3cc(/C=C(\C#N)C(=O)OCCCCCCOC(=O)/C(C#N)=C/c4ccc5c(c4)c4ccccc4n5*)ccc32)c1
+*CCCCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1
+*CCCCOC(=O)Nc1ccc(Cc2ccc(NC(=O)OCCCCn3c4ccccc4c4cc(/C=C(\C#N)C(=O)OCCCCCCOC(=O)/C(C#N)=C/c5ccc6c(c5)c5ccccc5n6*)ccc43)cc2)cc1
+*CCCCOC(=O)O*
+*CCCCOC(=O)OCC1CCC(COC(=O)O*)CC1
+*CCCCOC(=O)OCCCCCCOC(=O)OCCCCn1c(=O)c2cc3c(=O)n(*)c(=O)c3cc2c1=O
+*CCCCOC(=O)OCCCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O
+*CCCCOC(=O)c1ccc(-c2ccc(-c3ccc(C(=O)O*)cc3)cc2)cc1
+*CCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1
+*CCCCOC(=O)c1ccc(C(=O)O*)cc1
+*CCCCOC(=O)c1ccc(C(C)(C)c2ccc(C(=O)O*)cc2)cc1
+*CCCCOC(=O)c1ccc(N/C=N/c2ccc(C(=O)O*)cc2)cc1
+*CCCCOC(=O)c1ccc([Si](C)(C)c2ccc(C(=O)O*)cc2)cc1
+*CCCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1
+*CCCCOC(=O)c1cccc(C(=O)O*)c1
+*CCCCOCC(O)CN(CC(O)CO*)c1ccc(N(C)C)cc1
+*CCCCOCCCCOCCCCOC(=O)c1ccc(/N=C/c2cc(OCCC(C)CCCC(C)C)c(/C=N/c3ccc(C(=O)O*)cc3)cc2OCCC(C)CCCC(C)C)cc1
+*CCCCOCCCCOCCCCOC(=O)c1ccc(/N=C/c2cc(OCCCCCC)c(/C=C/c3cc(OCCCCCC)c(/C=C/c4cc(OCCCCCC)c(/C=N/c5ccc(C(=O)O*)cc5)cc4OCCCCCC)cc3OCCCCCC)cc2OCCCCCC)cc1
+*CCCCOCCCCOCCCCOC(=O)c1ccc(/N=C/c2ccc(/C=N/c3ccc(C(=O)O*)cc3)cc2)cc1
+*CCCCOCO*
+*CCCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12
+*CCCCOc1ccc(/C=C2\CC/C(=C\c3ccc(OCCCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC
+*CCCCOc1ccc(/C=C2\CC/C(=C\c3ccc(OCCCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC
+*CCCCOc1ccc(C(=O)Nc2cccc(P(=O)(c3ccccc3)c3cccc(NC(=O)c4ccc(O*)cc4)c3)c2)cc1
+*CCCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*CCCCOc1ccc(C(=O)OCCOC(=O)c2ccc(O*)cc2)cc1
+*CCCCOc1ccc(CCc2ccc(O*)cc2C)cc1
+*CCCCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)c(C(=O)OCC=C)c2)cc1
+*CCCCOc1ccc(OCCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1
+*CCCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1
+*CCCCP(C)(=O)CCCNC(=O)CCCCC(=O)N*
+*CCCCSS*
+*CCCCSSCCCCO*
+*CCCCSSCCCCOCO*
+*CCCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1
+*CCCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1
+*CCCN(*)C
+*CCCN(*)C(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*CCCN(*)C(=O)CC
+*CCCN(*)C(=O)CCCCC
+*CCCN(*)C(=O)c1ccccc1
+*CCCN(*)C(C)=O
+*CCCN(C)c1ccc(C(=O)c2ccc(N(*)C)cc2)cc1
+*CCCN(Cc1ccccc1)C(=O)S*
+*CCCN1C(=O)C2C3C=CC(C4C(=O)N(*)C(=O)C34)C2C1=O
+*CCCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CCCNC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NCCCOCCOCCO*
+*CCCNC(=O)C(NC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NC(C(=O)NCCCOCCOCCO*)C(C)C)C(C)C
+*CCCNC(=O)C(OC)C(OC)C(=O)N*
+*CCCNC(=O)CCCCC(=O)NCCCN(*)C
+*CCCNC(=O)CCCCC(=O)NCCCO*
+*CCCNC(=O)CCCCCCCCC(=O)NCCCOCCOCCO*
+*CCCNC(=O)CNC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NCC(=O)NCCCOCCOCCO*
+*CCCNC(=O)CNC(=O)C(NC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)NC(C(=O)NCC(=O)NCCCOCCOCCO*)C(C)C)C(C)C
+*CCCNC(=O)Cc1ccc(C(=O)N*)cc1
+*CCCNC(=O)O*
+*CCCNC(=O)c1ccc(C(=O)N*)cc1
+*CCCNC(=O)c1ccc(C(=O)NCCCO*)cc1
+*CCCO*
+*CCCOC(=O)C(C)O*
+*CCCOC(=O)C1CCC(C(=O)O*)CC1
+*CCCOC(=O)CCC(=O)O*
+*CCCOC(=O)CCCC(=O)O*
+*CCCOC(=O)CCCCC(=O)O*
+*CCCOC(=O)CCCCCCC(=O)O*
+*CCCOC(=O)NCCCCCCNC(=O)O*
+*CCCOC(=O)NNC(=O)CCCCCCCCC(=O)NNC(=O)O*
+*CCCOC(=O)Nc1cc(NC(=O)O*)ccc1C
+*CCCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1
+*CCCOC(=O)O*
+*CCCOC(=O)OCCCCCCOC(=O)OCCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O
+*CCCOC(=O)OCCCCCCOC(=O)OCCCn1c(=O)c2cc3c(=O)n(*)c(=O)c3cc2c1=O
+*CCCOC(=O)c1cc(/N=N/c2ccc(OCC)cc2)ccc1-c1ccc(/N=N/c2ccc(OCC)cc2)cc1C(=O)O*
+*CCCOC(=O)c1ccc(C(=O)O*)cc1
+*CCCOC(=O)c1ccc2cc(C(=O)O*)ccc2c1
+*CCCOC(=O)c1cccc(C(=O)O*)c1
+*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(-c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O
+*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(C(C)(C)c4ccc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3)cc2C1=O
+*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)c(-c4ccccc4)c3)cc2C1=O
+*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc(OC(=O)c4ccc5c(c4)C(=O)N(*)C5=O)cc3)cc2C1=O
+*CCCOCCCCOCCCN1C(=O)c2ccc(C(=O)Oc3ccc4cc(OC(=O)c5ccc6c(c5)C(=O)N(*)C6=O)ccc4c3)cc2C1=O
+*CCCOCCCOC(=O)c1ccc(-c2ccc(C(=O)O*)cc2)cc1
+*CCCOc1ccc(-c2ccc(O*)c3ccccc23)c2ccccc12
+*CCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*CCCOc1ccc(OCCCc2nnc(-c3ccc(-c4nnc(*)o4)cc3)o2)c(C)c1
+*CCCOc1cccc(C(=O)OC(=O)c2cccc(O*)c2)c1
+*CCCP(=O)(CCCNC(=O)CCCCC(=O)N*)c1ccccc1
+*CCCP(C)(=O)CCCNC(=O)CCCCC(=O)N*
+*CCCP(CCCCCCCC)CCCNC(=O)CCCCC(=O)N*
+*CCCP(CCCNC(=O)CCCCC(=O)N*)CC(C)C
+*CCCP(CCCNC(=O)CCCCC(=O)N*)c1ccccc1
+*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)O*
+*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)c1cccc(C(=O)O*)c1
+*CCCS(=O)(=O)CCCCS(=O)(=O)CCCOC(=O)c1ccccc1C(=O)O*
+*CCCS(=O)(=O)CCCS(=O)(=O)CCCOC(=O)c1cccc(C(=O)O*)c1
+*CCCS(=O)(=O)CCCS(=O)(=O)CCCOC(=O)c1ccccc1C(=O)O*
+*CCCS(=O)(=O)c1ccc(/N=N/c2ccc(N(CCCC)CC(=O)O*)cc2)cc1
+*CCCS*
+*CCC[Si](*)(C)C
+*CCC[Si](*)(C)CCC[Si](C)(C)C
+*CCC[Si](*)(C)C[Si](C)(C)C
+*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(-c2ccc(C#N)cc2)cc1
+*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C#N)cc1
+*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(C#N)cc3)cc2)cc1
+*CCC[Si](*)(C)O[Si](C)(C)CCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1
+*CCC[Si](*)(C)c1ccc(N(C)C)cc1
+*CCC[Si](*)(C)c1cccc(C)c1
+*CCC[Si](*)(C)c1ccccc1
+*CCC[Si](*)(C[Si](C)(C)c1ccccc1)C[Si](C)(C)c1ccccc1
+*CCC[Si](*)(c1ccc(C)cc1)c1ccc(C)cc1
+*CCC[Si](*)(c1cccc2ccccc12)C1CCCCC1
+*CCC[Si](*)(c1ccccc1)c1ccc(N(C)C)cc1
+*CCC[Si](C)(C)O[Si](C)(C)CCCN1C(=O)c2ccc(-c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O
+*CCC[Si](C)(C)O[Si](C)(C)CCCN1C(=O)c2ccc(C(=O)c3ccc4c(c3)C(=O)N(*)C4=O)cc2C1=O
+*CCC[Sn](C)(C)CCCOC(=O)C(CCCCCCCCOc1ccc(-c2ccc(OCc3ccc(C#N)cc3)cc2)cc1)C(=O)O*
+*CCC[Sn](C)(C)CCCOC(=O)C(CCCCCCOc1ccc(-c2ccc(OCc3ccc([N+](=O)[O-])cc3)cc2)cc1)C(=O)O*
+*CCCc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)cc3)c(-c3ccccc3)c2-c2ccccc2)cc1
+*CCCc1ccc(-c2c(-c3ccccc3)cc(-c3cccc(-c4cc(-c5ccccc5)c(-c5ccc(*)cc5)c(-c5ccccc5)c4-c4ccccc4)c3)c(-c3ccccc3)c2-c2ccccc2)cc1
+*CCCc1nc2cc(-c3ccc4[nH]c(*)nc4c3)ccc2[nH]1
+*CCN(*)C
+*CCN(*)C(=O)C(C)C
+*CCN(*)C(=O)C(OC)(c1ccccc1)C(F)(F)F
+*CCN(*)C(=O)C1(c2ccc(Cl)cc2)CCC1
+*CCN(*)C(=O)C12CC3CC(CC(C3)C1)C2
+*CCN(*)C(=O)C1CC1
+*CCN(*)C(=O)CC
+*CCN(*)C(=O)CC(C)C
+*CCN(*)C(=O)CC12CC3CC(CC(C3)C1)C2
+*CCN(*)C(=O)CCC
+*CCN(*)C(=O)CCC(=O)OC
+*CCN(*)C(=O)CCCC
+*CCN(*)C(=O)CCCCC
+*CCN(*)C(=O)CCCCCC
+*CCN(*)C(=O)CCCCCCC
+*CCN(*)C(=O)CCCCCCCCCCC
+*CCN(*)C(=O)CCCCCCCCCCCCCCCCC
+*CCN(*)C(=O)CSC
+*CCN(*)C(=O)Cc1ccc(C(F)(F)F)cc1
+*CCN(*)C(=O)c1c(F)c(F)c(F)c(F)c1F
+*CCN(*)C(=O)c1c(F)c(F)cc(F)c1F
+*CCN(*)C(=O)c1c(F)cc(F)c(F)c1F
+*CCN(*)C(=O)c1c(F)cc(F)cc1F
+*CCN(*)C(=O)c1c(F)ccc(F)c1F
+*CCN(*)C(=O)c1c(F)cccc1F
+*CCN(*)C(=O)c1cc(F)c(F)c(F)c1
+*CCN(*)C(=O)c1cc(F)c(F)c(F)c1F
+*CCN(*)C(=O)c1cc(F)c(F)cc1F
+*CCN(*)C(=O)c1cc(F)cc(F)c1
+*CCN(*)C(=O)c1cc(F)cc(F)c1F
+*CCN(*)C(=O)c1cc(F)ccc1F
+*CCN(*)C(=O)c1ccc(F)c(F)c1
+*CCN(*)C(=O)c1ccc(F)c(F)c1F
+*CCN(*)C(=O)c1ccc(F)cc1
+*CCN(*)C(=O)c1ccc(F)cc1F
+*CCN(*)C(=O)c1ccc(SC(F)(F)F)cc1
+*CCN(*)C(=O)c1ccc2ccccc2c1
+*CCN(*)C(=O)c1cccc(F)c1
+*CCN(*)C(=O)c1cccc(F)c1F
+*CCN(*)C(=O)c1ccccc1
+*CCN(*)C(=O)c1ccccc1F
+*CCN(*)C(C)=O
+*CCN(*)CC
+*CCN(*)CCOCCOC
+*CCN(*)CC[Si](C)(C)C
+*CCN(*)Cc1ccccc1
+*CCN(C)C(=O)CCCCCCCCC(=O)N(*)C
+*CCN(CCCCCCOc1ccc(/C=C/c2ccc([N+](=O)[O-])cc2)cc1)CCOC(=O)Nc1ccc(C)c(NC(=O)O*)c1
+*CCN(CCCCCCOc1ccc(/N=N/c2ccccc2)cc1)CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1
+*CCN(CCCCCOc1ccc(/N=N/c2ccccc2)cc1)CCOC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/C=C/C2=CC(=C(C#N)C#N)CC(C)(C)C2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/N=N/c2ccc(C3OCCO3)cc2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/N=N/c2ccc(C=O)cc2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)cc1
+*CCN(CCN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(*)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2C)cc1
+*CCN(CCNC(=O)c1ccc2c(c1)C(=O)N(*)C2=O)c1ccc(/C=C/c2ccc([N+](=O)[O-])cc2)cc1
+*CCN(CCOC(=O)/C=C/c1ccc(/C=C/C(=O)O*)cc1)c1ccc(/N=N/c2ccc([N+](=O)[O-])cc2)cc1
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+*CCN(CCOC(=O)CCC#CC#CCCC(=O)O*)c1ccc([N+](=O)[O-])cc1
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+*CCOCCOCCOCCOc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(O*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1
+*CCOCCOCCOc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1
+*CCOCCOCCOc1ccc(/C=C/c2cc(OC)c(/C=C/c3ccc(O*)c4ccccc34)cc2OC)c2ccccc12
+*CCOCCOCCOc1ccc(/C=C/c2ccc(/C=C/c3ccc(O*)c4ccccc34)cc2)c2ccccc12
+*CCOCCOCCOc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(O*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1
+*CCOCCOCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1
+*CCOCCOCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1
+*CCOCCOCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1
+*CCOCCOP(=O)(/N=N/c1ccc(-c2ccc(/N=N/P(=O)(O*)OC)cc2)cc1)OC
+*CCOCCOP(=O)(/N=N/c1ccc(C(=O)c2ccc(/N=N/P(=O)(O*)OC)cc2)cc1)OC
+*CCOCCOP(=O)(/N=N/c1ccc(Oc2ccc(/N=N/P(=O)(O*)OC)cc2)cc1)OC
+*CCOCCOc1ccc(/C=C(\C)c2ccc(O*)cc2)cc1
+*CCOCCOc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(O*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1
+*CCOCCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*CCOCCOc1ccc(N/C=N/c2ccc(O*)cc2)cc1
+*CCOCCOc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(O*)cc3)c2)cc1
+*CCOCCOc1cccc(NC(=O)c2ccc(C(=O)Nc3cccc(O*)c3)cc2)c1
+*CCOCCOc1cccc(NC(=O)c2cccc(C(=O)Nc3cccc(O*)c3)c2)c1
+*CCOCO*
+*CCOP(=O)(/N=N/c1ccc(-c2ccc(/N=N/P(=O)(O*)OCC)cc2)cc1)OCC
+*CCOP(=O)(/N=N/c1ccc(C(=O)c2ccc(/N=N/P(=O)(O*)OCC)cc2)cc1)OCC
+*CCOP(=O)(/N=N/c1ccc(CCOC(=O)c2cc(C(=O)OCCc3ccc(/N=N/P(=O)(O*)OC)cc3)cc(C(C)(C)C)c2)cc1)OC
+*CCOP(=O)(/N=N/c1ccc(COC(=O)c2cc(C(=O)OCc3ccc(/N=N/P(=O)(O*)OC)cc3)cc(C(C)(C)C)c2)cc1)OC
+*CCOP(=O)(/N=N/c1ccc(Oc2ccc(/N=N/P(=O)(O*)OCC)cc2)cc1)OCC
+*CCOc1ccc(/C=C2\CC/C(=C\c3ccc(OCCOP(=O)(O*)OC)c(OC)c3)C2=O)cc1OC
+*CCOc1ccc(/C=C2\CC/C(=C\c3ccc(OCCOP(=O)(O*)OCC)c(OC)c3)C2=O)cc1OC
+*CCOc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(O*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1
+*CCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*CCOc1ccc(C2(c3ccc(OCCO[Si](O*)(c4ccccc4)c4ccccc4)cc3)c3ccccc3-c3ccccc32)cc1
+*CCOc1ccc(OC(=O)c2ccc(C(=O)Oc3ccc(O*)cc3)c(OCCCCC)c2)cc1
+*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(Br)cc3)cc2)cc1
+*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(C)cc3)cc2)cc1
+*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc(OC)cc3)cc2)cc1
+*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccc([N+](=O)[O-])cc3)cc2)cc1
+*CCOc1ccc(S(=O)(=O)c2ccc(OCCOP(=O)(O*)Oc3ccccc3)cc2)cc1
+*CCS*
+*CCSCc1ccc(Cc2ccc(CSCCOC(=O)CCSCc3ccc(Cc4ccc(CSCCC(=O)O*)cc4)cc3)cc2)cc1
+*CCSCc1ccc(Cc2ccc(CSCCOC(=O)CSCc3ccc(Cc4ccc(CSCC(=O)O*)cc4)cc3)cc2)cc1
+*CCSS*
+*CCSSCCO*
+*CCSSCCOCO*
+*CCSSSS*
+*CCSSSSCCO*
+*CC[Si](C)(C)O[Si]1(*)O[Si](C)(C)O[Si](C)(C)O1
+*CCc1cc(C)c(*)cc1C
+*CCc1cc(Cl)c(*)c(Cl)c1
+*CCc1ccc(*)c(Br)c1
+*CCc1ccc(*)c(C#N)c1
+*CCc1ccc(*)c(C)c1
+*CCc1ccc(*)c(CC)c1
+*CCc1ccc(*)c(CCCC)c1
+*CCc1ccc(*)c(Cl)c1
+*CCc1ccc(*)c2ccccc12
+*CCc1ccc(*)cc1
+*CCc1ccc(*)o1
+*CCc1ccc(*)s1
+*CCc1ccc(CCNC(=O)CCCCCCCCC(=O)N*)cc1
+*CCc1ccc(CCNC(=O)CCCCCCCCCC(=O)N*)cc1
+*CCc1ccc(CCNC(=O)CCCCCCCCCCCCC(=O)N*)cc1
+*CCc1ccc(CCNC(=O)CCCCCCCCCCCCCCCCC(=O)N*)cc1
+*CCc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(CCOC(=O)c4cccc(C(=O)O*)c4)cc3)c2)cc1
+*CCc1cccc(*)c1
+*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nc5cc(-c6ccc7oc(*)nc7c6)ccc5o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nc5ccc(Cc6ccc7nc(*)oc(=O)c7c6)cc5c(=O)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nnc(-c5ccc(-c6nnc(*)o6)c(Oc6ccccc6)c5)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nnc(-c5ccc(-c6nnc(*)o6)cc5)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CN1C(=O)c2ccc(C(c3ccc4c(c3)C(=O)N(Cc3nnc(-c5ccc(Oc6ccc(-c7nnc(*)o7)cc6)cc5)o3)C4=O)(C(F)(F)F)C(F)(F)F)cc2C1=O
+*CNC(=O)CCCCC(=O)NCC1COC(*)CO1
+*CNC(=O)CCCCCCCCC(=O)NCC1COC(*)CO1
+*CNC(=O)OC(c1ccco1)C(OC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1)c1ccco1
+*CNC(=O)OCC(OC(=O)NCc1ccc(*)o1)c1ccco1
+*CNC(=O)OCC(OC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1)c1ccco1
+*CNC(=O)OCC(OC(=O)NCc1ccc(C(CCC)c2ccc(*)o2)o1)c1ccco1
+*CNC(=O)OCCOC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1
+*CNC(=O)OCc1ccc(COC(=O)NCc2ccc(C(C)(C)c3ccc(*)o3)o2)o1
+*CNC(=O)OCc1ccc(COCc2ccc(COC(=O)NCc3ccc(C(C)(C)c4ccc(*)o4)o3)o2)o1
+*CNC(=O)OCc1cocc1COC(=O)NCc1ccc(C(C)(C)c2ccc(*)o2)o1
+*CO*
+*COC(=O)NCCCCCCNC(=O)OCc1ccc(*)o1
+*COC(=O)NCc1ccc(CNC(=O)OCc2ccc(*)o2)o1
+*COC(=O)Nc1ccc(Cc2ccc(NC(=O)OCc3ccc(*)o3)cc2)cc1
+*COC(=O)Nc1ccc(Cc2ccc(NC(=O)OCc3cocc3*)cc2)cc1
+*COC(=O)OC1C(*)OC2OC(C)(C)OC21
+*COC(=O)OCC1OC(C)(C)OC1*
+*COC(=O)OCC1OC(OCC)(OCC)OC1*
+*COC(=O)OCC1OC(OCC)OC1*
+*COC(=O)c1ccc(/C(C#N)=C(\c2ccc(OC)cc2)N2CCCC(*)C2)cc1
+*COC1OC(*)C(O)C(O)C1O
+*COCOc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*COc1ccc(C(=C(Cl)Cl)c2ccc(O*)cc2)cc1
+*COc1ccc(C(=O)OC(=O)c2ccc(O*)cc2)cc1
+*COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1
+*COc1ccc(C(C)(C)c2ccc(Oc3ccc(*)n3-c3ccc(C#N)cc3)cc2)cc1
+*COc1ccc(C(C)(C)c2ccc(Oc3ccc(*)n3-c3ccc([N+](=O)[O-])cc3)cc2)cc1
+*COc1ccc(C(c2ccc(O*)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*COc1ccc(C(c2ccc(OCC3(*)COC3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*C[Ge](C)(C)Cc1cocc1*
+*C[Si](*)(C)C
+*C[Si](*)(C)CCCCCC
+*C[Si](*)(C)CCCN(CC)CC
+*C[Si](*)(C)CCCOCCOCCOC
+*C[Si](*)(C)CCCn1c2ccccc2c2ccccc21
+*C[Si](*)(C)c1ccccc1
+*C[Si](*)(CCC)CCC
+*C[Si](*)(CCCC)CCCC
+*C[Si](*)(CCCCC)CCCCC
+*C[Si](*)(CCCCCC)CCCCCC
+*C[Si](C)(C)[Si](C)(C)[Si](C)(C)[Si](*)(C)C
+*Cc1c(C)c(C)c(CNC(=O)CCCC(=O)N*)c(C)c1C
+*Cc1c(C)c(C)c(CNC(=O)CCCCCC(C)CC(=O)N*)c(C)c1C
+*Cc1cc(C(C)(C)C)cc(*)c1O
+*Cc1cc(C)c(CNC(=O)CCCCC(=O)N*)cc1C
+*Cc1cc(C)c(CNC(=O)c2ccc(C(=O)N*)cc2)cc1C
+*Cc1cc(C23CC4CC(CC(C4)C2)C3)cc(CN(*)C)c1O
+*Cc1cc(C23CC4CC(CC(C4)C2)C3)cc(CN(*)c2ccccc2)c1O
+*Cc1cc(CNC(=O)CCCCC(=O)N*)cc(C(C)(C)C)c1
+*Cc1cc(CNC(=O)c2cccc(C(=O)N*)c2)cc(C(C)(C)C)c1
+*Cc1cc(COC(=O)Nc2ccc(Cc3ccc(NC(=O)O*)cc3)cc2)cc(C(C)(C)C)c1
+*Cc1cc2cc(C(=O)Nc3ccc(-c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3ccc(Cc4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3ccc(NC(=O)c4cc5cc(*)c(OC(=O)COc6ccc7ccc(=O)oc7c6)cc5oc4=O)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1cc2cc(C(=O)Nc3cccc(NC(=O)c4cc5cc(*)c(OC(=O)COc6ccc7ccc(=O)oc7c6)cc5oc4=O)c3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1
+*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1
+*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(C(c5ccc6c(c5)C(=O)N(*)C6=O)(C(F)(F)F)C(F)(F)F)cc4C3=O)o2)o1
+*Cc1ccc(C(C)(C)c2ccc(CN3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1
+*Cc1ccc(C(C)(C)c2ccc(Cn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)o2)o1
+*Cc1ccc(C(C)(CC)c2ccc(Cn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)o2)o1
+*Cc1ccc(C(C)(CCC)c2ccc(Cn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)o2)o1
+*Cc1ccc(C(C)c2ccc(CN3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)o2)o1
+*Cc1ccc(C(C)c2ccc(Cn3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)o2)o1
+*Cc1ccc(CNC(=O)CC(C)(C)CC(C)C(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)CCCCCCCCCCCCCCCCC(=O)N*)cc1
+*Cc1ccc(CNC(=O)c2cc(C(=O)N*)cc(C(C)(C)C)c2)cc1
+*Cc1ccc(CO*)cc1
+*Cc1ccc(COC(=O)Cc2ccc(C(=O)O*)cc2)cc1
+*Cc1ccc(COC(=O)c2cccc(C(=O)O*)c2)cc1
+*Cc1ccc(COP(=O)(/N=N/c2ccc(-c3ccc(/N=N/P(=O)(O*)OC)cc3)cc2)OC)cc1
+*Cc1ccc(COP(=O)(/N=N/c2ccc(C(=O)c3ccc(/N=N/P(=O)(O*)OC)cc3)cc2)OC)cc1
+*Cc1ccc(COP(=O)(/N=N/c2ccc(Oc3ccc(/N=N/P(=O)(O*)OC)cc3)cc2)OC)cc1
+*Cc1ccc(CSS*)cc1
+*Cc1ccc(CSSS*)cc1
+*Cc1ccc(CSSSS*)cc1
+*Cc1ccc(C[n+]2ccc(-c3cc[n+](*)cc3)cc2)cc1
+*Cc1ccc2nc(-c3cc(-c4nc5ccc(*)cc5c(=O)o4)cc(N4C(=O)c5ccccc5C4=O)c3)oc(=O)c2c1
+*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cc6cc(*)ccc6oc5=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2c1
+*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7cc8cc(*)ccc8oc7=O)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2c1
+*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cc6cc(*)ccc6oc5=O)cc4)cc3)cc2c1
+*Cc1ccc2oc(=O)c(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cc7cc(*)ccc7oc6=O)cc5)cc4)cc3)cc2c1
+*Cc1cccc(CNC(=O)/C=C(\C)C(=O)N*)c1
+*Cc1cccc(CNC(=O)C2=C(C(=O)N*)C3C=CC2C3)c1
+*Cc1cccc(CNC(=O)CCC2(CCC(=O)N*)c3ccccc3-c3ccccc32)c1
+*Cc1cccc(CNC(=O)CCCCC(=O)N*)c1
+*Cc1cccc(CNC(=O)c2cc(C(=O)N*)cc(C(C)(C)C)c2)c1
+*Cc1cccc(CNC(=O)c2cccc(C(=O)N*)c2)c1
+*Cc1cccc(CNC(=O)c2ccccc2-c2ccccc2C(=O)N*)c1
+*Cc1cccc(COC(=O)Nc2ccc(Cc3ccc(NC(=O)O*)cc3)cc2)c1
+*Cc1cccc(C[n+]2ccc(-c3cc[n+](*)cc3)cc2)c1
+*Cc1ccccc1CN(CC)c1ccc(/C=C(\C#N)C(=O)Nc2cccc(NC(=O)/C(C#N)=C/c3ccc(N(*)CC)cc3)c2)cc1
+*Cc1ccccc1CN(CC)c1ccc(/C=C/c2ccc(/C=C(\C#N)C(=O)NC3CCCCC3NC(=O)/C(C#N)=C/c3ccc(/C=C/c4ccc(N(*)CC)cc4)cc3)cc2)cc1
+*Cc1ccccc1CSS*
+*Cc1ccccc1C[Si](*)(C)C
+*Cc1ccccc1C[n+]1ccc(-c2cc[n+](*)cc2)cc1
+*Cc1ccccc1[Si](*)(C)C
+*Cc1ccccc1[Si](*)(C)c1ccccc1
+*Cc1ccccc1[Si](*)(c1ccccc1)c1ccccc1
+*N(C)[Si](*)(C)C
+*N(C)[Si](*)(C)C=C
+*N(C)[Si](C)(C)N(C)[Si](*)(C)CC
+*N(C)[Si](C)(C=C)N(C)[Si](*)(C)C
+*N(CCC)[Si](C)(C)C[Si](*)(C)C
+*N/C(C#N)=C(\C#N)NC(=O)c1ccc(C(*)=O)cc1
+*N1CCOC1(*)CC
+*N=P(*)(C)C
+*N=P(*)(C)CCCC
+*N=P(*)(C)CCCCCC
+*N=P(*)(C)c1ccccc1
+*N=P(*)(CC)c1ccccc1
+*N=P(*)(CC1COCCOCCOCCO1)OCC1COCCOCCOCCO1
+*N=P(*)(CCC)CCC
+*N=P(*)(Cl)Cl
+*N=P(*)(Cl)Oc1ccc(C)cc1
+*N=P(*)(N(C)C)N(C)C
+*N=P(*)(N1CCCCC1)N1CCCCC1
+*N=P(*)(NC(C)C(=O)OCC)NC(C)C(=O)OCC
+*N=P(*)(NCC)NCC
+*N=P(*)(NCCC)NCCC
+*N=P(*)(NCCCC)NCCCC
+*N=P(*)(Nc1ccccc1)Nc1ccccc1
+*N=P(*)(OC)OC
+*N=P(*)(OC/C=C/c1ccccc1)OC/C=C/c1ccccc1
+*N=P(*)(OC1COCCOCCOCCOCCOC1)OC1COCCOCCOCCOCCOC1
+*N=P(*)(OCC(COC)OC)OCC(COC)OC
+*N=P(*)(OCC(COCCOC(C)C)OCCOC(C)C)OCC(COCCOC(C)C)OCCOC(C)C
+*N=P(*)(OCC(COCCOC)OCCOC)OCC(COCCOC)OCCOC
+*N=P(*)(OCC(COCCOCCCC)OCCOCCCC)OCC(COCCOCCCC)OCCOCCCC
+*N=P(*)(OCC(COCCOCCOC)OCCOCCOC)OCC(COCCOCCOC)OCCOCCOC
+*N=P(*)(OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)OCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*N=P(*)(OCC(F)(F)C(F)(F)F)OCC(F)(F)C(F)(F)F
+*N=P(*)(OCC(F)(F)F)OCC(F)(F)F
+*N=P(*)(OCC)OCC
+*N=P(*)(OCC1COCCOCCOCCOCCO1)OCC1COCCOCCOCCOCCO1
+*N=P(*)(OCC1COCCOCCOCCOCCOCCO1)OCC1COCCOCCOCCOCCOCCO1
+*N=P(*)(OCCC)OCCC
+*N=P(*)(OCCC)OCCCCC
+*N=P(*)(OCCCC)OCCCC
+*N=P(*)(OCCCCC)OCCCCC
+*N=P(*)(OCCCCCC)OCCCCCC
+*N=P(*)(OCCCCCCC)OCCCCCCC
+*N=P(*)(OCCCCCCCC)OCCCCCCCC
+*N=P(*)(OCCCCCCCCC)OCCCCCCCCC
+*N=P(*)(OCCCCCCCCCC)OCCCCCCCCCC
+*N=P(*)(OCCCCCCOC1COCCOCCOCCOCCOC1)OCCCCCCOC1COCCOCCOCCOCCOC1
+*N=P(*)(OCCCOC1COCCOCCOCCOCCOC1)OCCCOC1COCCOCCOCCOCCOC1
+*N=P(*)(OCCCc1ccccc1)OCCCc1ccccc1
+*N=P(*)(OCCOC)OCCOC
+*N=P(*)(OCCOCCOC)OCCOCCOC
+*N=P(*)(OCCOCCOCC)OCCOCCOCC
+*N=P(*)(OCCOCCOCCCC)OCCOCCOCCCC
+*N=P(*)(OCCOCCOCCOC)OCCOCCOCCOC
+*N=P(*)(OCCOCCOCCOCCOC)OCCOCCOCCOCCOC
+*N=P(*)(OCCOCCOCCOCCOCCCCCCCCCCCC)OCCOCCOCCOCCOCCCCCCCCCCCC
+*N=P(*)(OCCOCCOCCOCCOCCOC)OCCOCCOCCOCCOCCOC
+*N=P(*)(OCCOCCOCCOCCOCCOCCOC)OCCOCCOCCOCCOCCOCCOC
+*N=P(*)(OCCOCCOCCOCCOCCOCCOCCOCCOC)OCCOCCOCCOCCOCCOCCOCCOCCOC
+*N=P(*)(OCCOCCOCCOP1(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=N1)OCCOCCOCCOP1(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=NP(OCCOCCOC)(OCCOCCOC)=N1
+*N=P(*)(OCCOCCOc1ccc(CCCCCCCC)cc1)OCCOCCOc1ccc(CCCCCCCC)cc1
+*N=P(*)(OCCOc1ccc(-c2ccc(Br)cc2)cc1)OCCOc1ccc(-c2ccc(Br)cc2)cc1
+*N=P(*)(OCCOc1ccc(-c2ccc(I)cc2)cc1)OCCOc1ccc(-c2ccc(I)cc2)cc1
+*N=P(*)(OCCOc1ccc(-c2ccccc2)cc1)OCCOc1ccc(-c2ccccc2)cc1
+*N=P(*)(OCCOc1ccc(-c2ccccc2)cc1I)OCCOc1ccc(-c2ccccc2)cc1I
+*N=P(*)(OCCOc1ccc(/N=N/c2ccc(CCCC)cc2)cc1)OCCOc1ccc(/N=N/c2ccc(CCCC)cc2)cc1
+*N=P(*)(OCCOc1ccc2cc(Br)ccc2c1)OCCOc1ccc2cc(Br)ccc2c1
+*N=P(*)(OCCOc1ccc2cc(I)ccc2c1)OCCOc1ccc2cc(I)ccc2c1
+*N=P(*)(OCCOc1ccc2ccccc2c1)OCCOc1ccc2ccccc2c1
+*N=P(*)(OCCc1ccccc1)OCCc1ccccc1
+*N=P(*)(OCc1ccc(-c2ccccc2)cc1)OCc1ccc(-c2ccccc2)cc1
+*N=P(*)(OCc1ccc(Br)cc1)OCc1ccc(Br)cc1
+*N=P(*)(OCc1ccccc1)OCc1ccccc1
+*N=P(*)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1
+*N=P(*)(Oc1ccc(Br)cc1)Oc1ccc(Br)cc1
+*N=P(*)(Oc1ccc(C(=O)OC)cc1)Oc1ccc(C(=O)OC)cc1
+*N=P(*)(Oc1ccc(C(C)(C)C)cc1)Oc1ccc(C(C)(C)C)cc1
+*N=P(*)(Oc1ccc(C(C)C)cc1)Oc1ccc(C(C)C)cc1
+*N=P(*)(Oc1ccc(C(C)CC)cc1)Oc1ccc(C(C)CC)cc1
+*N=P(*)(Oc1ccc(C)c(C)c1)Oc1ccc(C)c(C)c1
+*N=P(*)(Oc1ccc(C)cc1)Oc1ccc(C)cc1
+*N=P(*)(Oc1ccc(C)cc1)n1cncn1
+*N=P(*)(Oc1ccc(CC)cc1)Oc1ccc(CC)cc1
+*N=P(*)(Oc1ccc(Cc2ccccc2)cc1)Oc1ccc(Cc2ccccc2)cc1
+*N=P(*)(Oc1ccc(Cl)cc1)Oc1ccc(Cl)cc1
+*N=P(*)(Oc1ccc(Cl)cc1Cl)Oc1ccc(Cl)cc1Cl
+*N=P(*)(Oc1ccc(F)cc1)Oc1ccc(F)cc1
+*N=P(*)(Oc1ccc(I)cc1)Oc1ccc(I)cc1
+*N=P(*)(Oc1ccc(OC)cc1)Oc1ccc(OC)cc1
+*N=P(*)(Oc1ccc2cc(I)ccc2c1)Oc1ccc2cc(I)ccc2c1
+*N=P(*)(Oc1ccc2ccccc2c1)Oc1ccc2ccccc2c1
+*N=P(*)(Oc1cccc(Br)c1)Oc1cccc(Br)c1
+*N=P(*)(Oc1cccc(C(F)(F)F)c1)Oc1cccc(C(F)(F)F)c1
+*N=P(*)(Oc1cccc(C)c1)Oc1cccc(C)c1
+*N=P(*)(Oc1cccc(Cl)c1)Oc1cccc(Cl)c1
+*N=P(*)(Oc1cccc(F)c1)Oc1cccc(F)c1
+*N=P(*)(Oc1ccccc1)Oc1ccccc1
+*N=P(Cl)(Cl)N=P(Cl)(Cl)/N=S(/*)Cl
+*N=P(Cl)(Cl)N=P(Cl)(Cl)N=S(*)(=O)F
+*N=P(Cl)(N=P(/N=S(/*)Oc1cccc(-c2ccccc2)c1)(Oc1cccc(-c2ccccc2)c1)Oc1cccc(-c2ccccc2)c1)Oc1cccc(-c2ccccc2)c1
+*N=P(Cl)(N=P(Cl)(/N=S(/*)Oc1ccccc1-c1ccccc1)Oc1ccccc1-c1ccccc1)Oc1ccccc1-c1ccccc1
+*N=P(N=P(/N=S(/*)Oc1ccc(-c2ccccc2)cc1)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1
+*N=P(N=P(/N=S(/*)Oc1ccc(C(C)(C)C)cc1)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1)(Oc1ccc(-c2ccccc2)cc1)Oc1ccc(-c2ccccc2)cc1
+*N=P(N=P(N=S(*)(=O)NC)(NC)NC)(NC)NC
+*N=P(N=P(N=S(*)(=O)NCC)(NCC)NCC)(NCC)NCC
+*N=P(N=P(N=S(*)(=O)NCC=C)(NCC=C)NCC=C)(NCC=C)NCC=C
+*N=P(N=P(N=S(*)(=O)NCCC)(NCCC)NCCC)(NCCC)NCCC
+*N=P(N=P(N=S(*)(=O)NCCCC)(NCCCC)NCCCC)(NCCCC)NCCCC
+*N=P(N=P(N=S(*)(=O)NCCCCCC)(NCCCCCC)NCCCCCC)(NCCCCCC)NCCCCCC
+*N=P(N=P(N=S(*)(=O)Nc1ccccc1)(Nc1ccccc1)Nc1ccccc1)(Nc1ccccc1)Nc1ccccc1
+*N=P1(*)Oc2ccc3ccccc3c2-c2c(ccc3ccccc23)O1
+*N=P1(*)Oc2ccccc2-c2ccccc2O1
+*NC(=O)NCCCCCCNC(=O)Nc1cccc(*)n1
+*NC(=O)Nc1ccc(Cc2ccc(NC(=O)Nc3cccc(*)n3)cc2)cc1
+*NC(C)(CC)C(*)=O
+*NC(C)CC(*)=O
+*NC(C)CCC(C)(C)CCCNC(=O)c1ccc(C(*)=O)cc1
+*NC(CCC(=O)Nc1ccc(N(c2ccccc2)c2ccccc2)cc1)C(*)=O
+*NC(CCC(=O)OC)C(*)=O
+*NC(CCC(=O)OCC)C(*)=O
+*NC(CCC(=O)OCCC)C(*)=O
+*NC(CCC(=O)OCCCC)C(*)=O
+*NC(CCC(=O)OCCCCC)C(*)=O
+*NC(CCC(=O)OCCCCCC)C(*)=O
+*NC(CCC(=O)OCCCCCCCC)C(*)=O
+*NC(CCC(=O)OCCCCCCCCCC)C(*)=O
+*NC(CCC(=O)OCc1ccc(C(F)(F)F)cc1)C(*)=O
+*NC(CCC(=O)OCc1ccc(F)cc1)C(*)=O
+*NC(CCC(=O)OCc1ccc([N+](=O)[O-])cc1)C(*)=O
+*NC(CCC(=O)OCc1ccccc1)C(*)=O
+*NC1CC(C)(C)CC(C)(CNC(=O)CCCCC(*)=O)C1
+*NC1CC(C)(C)CC(C)(CNC(=O)c2cc(NC(=O)C(C(C)CC)N3C(=O)c4ccccc4C3=O)cc(C(*)=O)c2)C1
+*NC1CCC(CC(C)(C)NC(=O)CCCCC(*)=O)CC1
+*NC1CCC(CC2CCC(NC(=O)CCCCC(*)=O)CC2)CC1
+*NC1CCC(CC2CCC(NC(=O)CCCCCCCCC(*)=O)CC2)CC1
+*NC1CCC(CC2CCC(NC(=O)CCCCCCCCCCC(*)=O)CC2)CC1
+*NC1CCC(CC2CCC(NC(=O)CCCCCCCCCCCCC(*)=O)CC2)CC1
+*NC1CCC(CCC2CCC(NC(=O)CCCCCCCCC(*)=O)CC2)CC1
+*NC1CCC(CCC2CCC(NC(=O)CCCCCCCCCCC(*)=O)CC2)CC1
+*NCCCCCCNc1nc(*)nc(-c2ccccc2)n1
+*NNC(=O)/C=C/C(=O)NCCCCCCNC(=O)/C=C/C(*)=O
+*NNC(=O)/C=C/C(=O)Nc1cccc(/C=C2\CC/C(=C\c3cccc(NC(=O)/C=C/C(*)=O)c3)C2=O)c1
+*NNC(=O)/C=C/C(=O)Nc1cccc(/C=C2\CCC/C(=C\c3cccc(NC(=O)/C=C/C(*)=O)c3)C2=O)c1
+*NNC(=O)c1ccc(C(*)=O)cc1OCC
+*NNC(=O)c1ccc(C(*)=O)cc1OCCCCC
+*NNC(=O)c1ccc(C(*)=O)cc1OCCCCCCCC
+*NNC(=O)c1ccc(C(*)=O)cc1OCCCCCCCCCC
+*NNC(=O)c1ccc(C(=O)NNC(=O)c2ccc([Si](c3ccccc3)(c3ccccc3)c3ccc(C(*)=O)cc3)cc2)c2ccccc12
+*NNC(=O)c1ccccc1C(=O)NCCCCCCNC(=O)c1ccccc1C(*)=O
+*NNC(=O)c1ccccc1C(=O)Nc1cccc(/C=C2\CC/C(=C\c3cccc(NC(=O)c4ccccc4C(*)=O)c3)C2=O)c1
+*NNC(=O)c1ccccc1C(=O)Nc1cccc(/C=C2\CCC/C(=C\c3cccc(NC(=O)c4ccccc4C(*)=O)c3)C2=O)c1
+*Nc1c(-c2ccccc2)cc(-c2ccc(-c3cc(-c4ccccc4)c(NC(=O)c4ccc(C(*)=O)cc4)c(-c4ccccc4)c3)cc2)cc1-c1ccccc1
+*Nc1c(C)c(C)c(C(=O)c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)c(C)c1C
+*Nc1cc(NC(=O)C(*)=O)ccc1C
+*Nc1cc(NC(=O)CCCCC(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc(NC(=O)CCCCCCCCC(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc(NC(=O)NC2CCC(CC3CCC(NC(*)=O)CC3)CC2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)NC2CCC(CC3CCC(NC(*)=O)CC3)CC2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)Nc2ccc(CCc3ccccc3NC(*)=O)cc2)ccc1C
+*Nc1cc(NC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)Nc2ccc(NC(*)=O)cc2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)Nc2ccc(NC(*)=O)cc2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1
+*Nc1cc(NC(=O)c2cc(NC(=O)C(C(C)CC)N3C(=O)c4ccccc4C3=O)cc(C(*)=O)c2)ccc1C
+*Nc1cc(NC(=O)c2cc(OCCCN(C)c3ccc(/N=N/c4ccc(S(C)(=O)=O)cc4)cc3)cc(C(*)=O)c2)cc(C(=O)OCCCN(C)c2ccc(/N=N/c3ccc(S(C)(=O)=O)cc3)cc2)c1
+*Nc1cc(NC(=O)c2cc(OCCCS(=O)(=O)c3ccc(/N=N/c4ccc(N(C)C)cc4)cc3)cc(C(*)=O)c2)cc(C(=O)OCCCN(C)c2ccc(/N=N/c3ccc(S(C)(=O)=O)cc3)cc2)c1
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(C#N)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(C#N)cc2)c1
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(C#N)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc2)c1
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(C#N)cc2)c1
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc([N+](=O)[O-])cc3)cc(C(*)=O)c2)cc(C(=O)OC)c1
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc([N+](=O)[O-])cc3)cc(C(*)=O)c2)cc(C(=O)OC2CC(C)CCC2C(C)C)c1
+*Nc1cc(NC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc(NC(=O)c2ccc(-c3ccc(C(*)=O)cc3)cc2)cc(C(=O)OCCOc2ccc(/C=C/C(=O)c3ccccc3)cc2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nc3ccccc3[nH]2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nc3ccccc3oc2=O)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OC(c2ccccc2)c2ccccc2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OCCOC(=O)/C=C/c2ccc(N(C)C)cc2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)OCCOc2ccc(/C=C/C(=O)c3ccccc3)cc2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2ccc(-c3ccccc3)cc2)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2cccc3ccccc23)c1
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2ccccc2)c1
+*Nc1cc(NC(=O)c2ccc3cc(C(*)=O)ccc3c2)cc(C(=O)OCCOc2ccc(/C=C/C(=O)c3ccccc3)cc2)c1
+*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)OCCOC(=O)/C=C/c2ccc(N(C)C)cc2)c1
+*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)OCCOc2ccc(/C=C/C(=O)c3ccccc3)cc2)c1
+*Nc1cc(NC(=O)c2cccc(C(*)=O)c2)cc(C(=O)Oc2ccc(Oc3ccc(C#N)c(C#N)c3)cc2)c1
+*Nc1cc(NC(=O)c2ccccc2C(*)=O)cc(-c2nnc(-c3ccccn3)o2)c1
+*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCC(*)=O)cc1/C=C/c1ccc(N(C)C)cc1
+*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCC(*)=O)cc1/C=C/c1ccc(N(C)C)cc1
+*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCC(*)=O)cc1/C=C/c1ccc(N(C)C)cc1
+*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCCCC(*)=O)cc1/C=C/c1ccc(N(C)C)cc1
+*Nc1cc([N+](=O)[O-])c(NC(=O)CCCCCCCCCCCCC(*)=O)cc1/C=C/c1ccc(N(C)C)cc1
+*Nc1ccc(*)cc1
+*Nc1ccc(*)cc1C
+*Nc1ccc(*)cc1OC
+*Nc1ccc(*)cc1OCCCCCCCCCC
+*Nc1ccc(*)cc1OCCCCCCCCCCCC
+*Nc1ccc(*)cc1OCCCCCCCCCCCCCCCC
+*Nc1ccc(*)cc1OCCCCCCCCCCOc1ccc(C2CCC(CCCCC)CC2)cc1
+*Nc1ccc(-c2c(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(-c4cc(-c5ccc(-c6ccccc6)cc5)c(-c5ccc(NC(=O)c6ccc(C(*)=O)cc6)cc5)c(-c5ccc(-c6ccccc6)cc5)c4)cc3)cc2-c2ccc(-c3ccccc3)cc2)cc1
+*Nc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(NC(=O)c6ccc(C(*)=O)cc6)cc5)c(-c5ccccc5)c4)cc3)cc2-c2ccccc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)Cc3cc(C)c(CC(*)=O)cc3C)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)Cc3cc(CC(*)=O)c(C)cc3C)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3cc(NC(=O)c4ccc(NC(=O)C(C)N5C(=O)c6ccccc6C5=O)cc4)cc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3cc(NC(=O)c4ccncc4)cc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3ccc(NC(=O)c4ccc([Si](C)(C)c5ccc(C(=O)Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3)cc2)cc1
+*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(O)c2)cc1O
+*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(OC)c2)cc1OC
+*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)c(S)c2)cc1S
+*Nc1ccc(-c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(-c2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1
+*Nc1ccc(/C=C/c2ccc(NC(=C(C#N)C#N)c3ccc(C(*)=C(C#N)C#N)cc3)cc2)cc1
+*Nc1ccc(Br)cc1-c1ccc(NC(=O)c2ccc(C(*)=O)cc2)cc1
+*Nc1ccc(Br)cc1-c1ccc(NC(=O)c2cccc(C(*)=O)c2)cc1
+*Nc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(NC(=O)c5ccc([Si](C)(C)c6ccc(C(*)=O)cc6)cc5)cc4)cc3)cc2)cc1
+*Nc1ccc(C(=O)c2c(C)c(C)c(C(=O)c3ccc(NC(=O)c4ccc(C(*)=O)cc4)cc3)c(C)c2C)cc1
+*Nc1ccc(C(=O)c2ccc(C(=O)c3ccc(Nc4ccc5c(c4)c4cc(*)ccc4n5CC(CC)CCCC)cc3)cc2)cc1
+*Nc1ccc(C(=O)c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)cc1
+*Nc1ccc(C(=O)c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Nc1ccc(C(=O)c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(C(=O)c2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Nc4ccc(C(=O)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Nc4ccc(C(O)(c5ccccc5)c5ccc(C(O)(c6ccccc6)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6CCC(C6)C5C4=O)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5CC=CCC5C4=O)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C=CC4=O)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cc(NC(=O)c4ccccc4)cc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(NC(=C(C#N)C#N)c4ccc(C(*)=C(C#N)C#N)cc4)cc3)c2)cc1
+*Nc1ccc(C(C)(C)c2cccc(C(C)(C)c3ccc(Nc4ccc(C(=O)c5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)c2)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(C(*)=O)cc(C(C)(C)C)c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)C45CC6CC(CC(C6)C4)C5)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)CCCCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)CCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc(-c4ccc(C(*)=O)c(C)c4)cc3C)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc(-c4ccc(C(*)=O)cc4)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(*)=O)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc(C(c4ccc(C(*)=O)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3ccc4cc(C(*)=O)ccc4c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Nc1ccc(C(c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)(P2(=O)Oc3ccccc3-c3ccccc32)P2(=O)Oc3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C2(c3ccc(NC(=O)c4cc(C(*)=O)cc(C(C)(C)C)c4)cc3)c3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C2(c3ccc(NC(=O)c4cc(NC(=O)C56CC7CC(CC(C7)C5)C6)cc(C(*)=O)c4)cc3)c3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C2(c3ccc(NC(=O)c4ccc(-c5ccc(C(*)=O)c(C)c5)cc4C)cc3)c3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C2(c3ccc(NC(=O)c4ccc(C(*)=O)cc4)cc3)c3ccccc3-c3ccccc32)cc1
+*Nc1ccc(C2(c3ccc(NC(=O)c4cccc(C(*)=O)c4)cc3)c3ccccc3-c3ccccc32)cc1
+*Nc1ccc(CC(C)(C)NC(=O)CCC(*)=O)cc1
+*Nc1ccc(CC(C)(C)NC(=O)CCCCC(*)=O)cc1
+*Nc1ccc(CC(C)(C)NC(=O)CCCCCCCCC(*)=O)cc1
+*Nc1ccc(CC(C)(C)NC(=O)c2ccc(C(*)=O)cc2)cc1
+*Nc1ccc(CCNC(=O)CCCCC(*)=O)cc1
+*Nc1ccc(Cc2ccc(NC(=O)/C=C/c3ccc(/C=C/C(*)=O)cc3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)CCCCCCCC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)CCCCCCCCC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCCCCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCNC(*)=O)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)Nc3ccccc3CCc3ccc(NC(*)=O)cc3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6C=CC(C6)C5C4=O)c3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6CCC(C6)C5C4=O)c3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5CC=CCC5C4=O)c3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C=CC4=O)c3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(S(=O)(=O)c4ccccc4)c3)cc2)cc1
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+*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)CCCCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1
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+*Nc1ccc(Cc2ccc(NC(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(*)=O)cc4)cc3)cc2)cc1
+*Nc1ccc(Cc2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)cc1
+*Nc1ccc(NC(=O)CCCC(*)=O)cc1
+*Nc1ccc(NC(=O)CCCCC(*)=O)cc1
+*Nc1ccc(NC(=O)CCCCCC(*)=O)cc1
+*Nc1ccc(NC(=O)CCCCCCC(*)=O)cc1
+*Nc1ccc(NC(=O)CCCCCCCC(*)=O)cc1
+*Nc1ccc(NC(=O)CCCCCCCCC(*)=O)cc1
+*Nc1ccc(NC(=O)Cc2cc(C)c(CC(*)=O)cc2C)cc1
+*Nc1ccc(NC(=O)Cc2cc(CC(*)=O)c(C)cc2C)cc1
+*Nc1ccc(NC(=O)c2cc(-c3ccc(C(=O)O)c(C(*)=O)c3)ccc2C(=O)O)cc1
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+*Nc1ccc(NC(=O)c2cc(-c3ccc(C(=O)OCC)c(C(*)=O)c3)ccc2C(=O)OCC)cc1
+*Nc1ccc(NC(=O)c2cc(C(*)=O)c(C(=O)O)cc2C(=O)O)cc1
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+*Nc1ccc(NC(=O)c2ccc(C(=O)c3ccc(C(*)=O)c(C(=O)O)c3)cc2C(=O)O)cc1
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+*Nc1ccc(NC(=O)c2ccc(P(=O)(c3ccccc3)c3ccc(C(*)=O)cc3)cc2)cc1
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+*Nc1ccc(NC(=O)c2cccc(C(=O)c3ccc(C(*)=O)cc3)c2)cc1
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+*Nc1ccc(Nc2ccc(C(=O)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Nc1ccc(Nc2ccc(C(O)(c3ccccc3)c3ccc(C(O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1
+*Nc1ccc(Oc2ccc(Nc3ccc4c(c3)c3cc(*)ccc3n4CC(CC)CCCC)cc2)cc1
+*Nc1cccc(/C=C/c2ccc(NC(=O)c3cccc(C(*)=O)c3)cc2)c1
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+*OC(=O)c1ccc(C(=O)Oc2ccc3c(c2)Oc2cc(*)ccc2C32c3ccccc3-c3ccccc32)cc1
+*OC(=O)c1ccc(C(C)(C)c2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(C(F)(F)C(F)(F)C(F)(F)c2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(C2(C)CC(C)(C)c3ccc(C(=O)Oc4ccc5c(c4)Oc4cc(*)ccc4C5(C)C)cc32)cc1
+*OC(=O)c1ccc(CCCCCCc2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(CCCCCc2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(CCCCc2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(Cc2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1ccc(SCCCCSc2ccc(C(*)=O)cc2)cc1
+*OC(=O)c1cccc(C(*)=O)c1
+*OC(=O)c1cccc(C(=O)OC2CC3CC(*)CC(C2)O3)c1
+*OC(=O)c1cccc(C(=O)Oc2ccc3c(c2)Oc2cc(*)ccc2C32OC(=O)c3ccccc32)c1
+*OC(=O)c1cccc(OCCCCCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1
+*OC(=O)c1cccc(OCCCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1
+*OC(=O)c1cccc(OCCCCCCOc2cccc(C(=O)OC3COC4C(*)COC34)c2)c1
+*OC(C)(CC(*)=O)C(F)(F)F
+*OC(C)C#CC#CC(C)OC(=O)c1ccc(C(*)=O)cc1
+*OC(C)C(*)=O
+*OC(C)C(=O)NC(C)C(*)=O
+*OC(C)C(=O)OCC(*)=O
+*OC(C)CC(*)=O
+*OC(C)CCC(C)OC(=O)CCCCCCCCC(*)=O
+*OC(C)CCCC(C)C(*)=O
+*OC(C)CCCCC(*)=O
+*OC(C)CCOC(=O)C(CCCCCCOc1ccc(/N=C/c2ccc([N+](=O)[O-])cc2)cc1)C(*)=O
+*OC(C)CCOC(=O)c1ccc(-c2ccc(C(*)=O)cc2)cc1
+*OC(C)COC(*)=O
+*OC(C)COC(=O)/C=C/C(*)=O
+*OC(C)COC(=O)c1ccc(C(*)=O)cc1
+*OC(C)COC(=O)c1cccc(C(*)=O)c1
+*OC(C)COC(=O)c1ccccc1C(*)=O
+*OC(C)COC(C)COC(=O)c1ccc(C(*)=O)cc1
+*OC(C)COCC(C)OC(=O)c1ccccc1C(*)=O
+*OC(CC(*)=O)C(C)(Cl)Cl
+*OC(CC(*)=O)C(Cl)(Cl)Cl
+*OC(CC)C(*)=O
+*OC(CC)CC(*)=O
+*OC(CCCCC)CC(*)=O
+*OC(CCCCCC)CC(*)=O
+*OC(CCCCCCC=C)CC(*)=O
+*OC(CCOc1ccccc1)CC(*)=O
+*OC(CCc1ccccc1)CC(*)=O
+*OC(CCl)C(*)CCl
+*OC(CCl)COC(=O)C1=C(C(=O)OCC(CCl)OC(=O)c2ccc(C(*)=O)cc2)C2C=CC1C2
+*OC(COC(*)=O)COC(C)(C)C
+*OC(COC(*)=O)COc1ccccc1
+*OC(COC(=O)C12CC3CC(CC(C(*)=O)(C3)C1)C2)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)C1=C(C(=O)OCC(COC(=O)C2=C(c3ccccc3)C3C=CC2C3)OC(=O)c2ccc(C(*)=O)cc2)C2C=CC1C2)COC(=O)C1=C(c2ccccc2)C2C=CC1C2
+*OC(COC(=O)C1CCC(C(*)=O)CC1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)NCCCCCCNC(*)=O)c1ccco1
+*OC(COC(=O)Nc1ccc(Cc2ccc(NC(*)=O)cc2)cc1)c1ccco1
+*OC(COC(=O)c1c(F)c(F)c(-c2c(F)c(F)c(C(*)=O)c(F)c2F)c(F)c1F)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1cc(C(*)=O)cc(C(C)(C)C)c1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(-c2cc(CCCCCCCC)c(-c3ccc(C(*)=O)c(F)c3F)cc2CCCCCCCC)c(F)c1F)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(-c2ccc(C(*)=O)cc2)cc1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(C(*)=O)c(OC)c1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(C(*)=O)c2ccccc12)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(C(*)=O)cc1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc(C)cc1C(*)=O)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccc2cc(C(*)=O)ccc2c1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1cccc(-c2ccc(C(*)=O)cc2)c1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1cccc(C(*)=O)c1)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccccc1-c1ccccc1C(*)=O)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COC(=O)c1ccccc1C(*)=O)COc1ccc(/N=N/c2ccc(C#N)cc2)cc1
+*OC(COCCCC)COC(*)=O
+*OC(COCCOC)COC(*)=O
+*OC(COc1ccccc1)CC(*)=O
+*OC(F)(C(*)(F)F)C(F)(F)F
+*OC(F)(C(=O)N([Li])S(=O)(=O)C(F)(F)C(*)(F)F)C(F)(F)F
+*OC(F)(F)C(*)(F)F
+*OC(F)(F)C(F)(F)C(*)(F)F
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+*OC(F)(F)COC(=O)c1cc(OCCCCC)cc(C(=O)OCC(*)(F)F)c1
+*OC(F)(F)COC(=O)c1cccc(C(=O)OCC(*)(F)F)c1
+*OC(F)(F)COC(=O)c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(C(=O)OCC(*)(F)F)c2)c1
+*OC(c1ccco1)C(OC(=O)Nc1ccc(Cc2ccc(NC(*)=O)cc2)cc1)c1ccco1
+*OC1C(C)(C)C(OC(*)=O)C1(C)C
+*OC1C(C)(C)C(OC(=O)C2CCC(C(*)=O)CC2)C1(C)C
+*OC1C(C)(C)C(OC(=O)c2ccc(C(*)=O)cc2)C1(C)C
+*OC1C(CO)OC(*)C(N)C1O
+*OC1C(CO)OC(*)C(NC(C)=O)C1O
+*OC1C(CO)OC(*)C(O)C1O
+*OC1C(COC(=O)Nc2ccccc2)OC(*)C(OC(=O)Nc2ccccc2)C1OC(=O)Nc1ccccc1
+*OC1C(COC(C)=O)OC(*)C(OC(C)=O)C1O
+*OC1C(COC(C)=O)OC(*)C(OC(C)=O)C1OC(C)=O
+*OC1C(COCC)OC(*)C(OCC)C1OCC
+*OC1C(COCO)OC(*)C(O)C1O
+*OC1CCC(OC(*)=O)CC1
+*OC1CCC(OC(=O)C(*)=O)CC1
+*OC1CCC(OC(=O)CCCCC(*)=O)CC1
+*OC1CCC(OC(=O)CCCCCCCCC(*)=O)CC1
+*OC1CCC(OC(=O)OCCCCCCCOC(*)=O)CC1
+*OC1CCC(OC(=O)OCCCCCCOC(*)=O)CC1
+*OC1CCC(OC(=O)OCCCCCOC(*)=O)CC1
+*OC1CCC(OC(=O)OCCCCOC(*)=O)CC1
+*OC1CCC(OC(=O)c2ccc(C(*)=O)cc2)CC1
+*OC1CCCCC1*
+*OC1CCCCC1OC(*)=O
+*OCC(F)(F)C(F)(F)C(F)(F)COc1nc(F)c(F)c(OCC(F)(F)C(F)(F)C(F)(F)COc2c(F)c(*)nc(F)c2F)c1F
+*OCC(F)(F)C(F)(F)C(F)(F)COc1nc(F)nc(*)c1F
+*OCCCCOC1COC2C(OC(=O)CCCCC(=O)OC3COC4C(*)COC34)COC12
+*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OC)cc2)nc(*)c1-c1ccc(OC)cc1
+*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1
+*OCCOC(=O)CCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OCCCC)cc1
+*OCCOC(=O)CCCCC(=O)OCCOc1nc(-c2ccc(OC)cc2)nc(*)c1-c1ccc(OC)cc1
+*OCCOC(=O)CCCCC(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1
+*OCCOC(=O)c1cc(/N=N/c2ccc(OCC)cc2)ccc1-c1ccc(/N=N/c2ccc(OCC)cc2)cc1C(=O)OCCOc1nc(-c2ccc(OCCCC)cc2)nc(*)c1-c1ccc(OC)cc1
+*OCCOC(=O)c1ccc(C(=O)OCCOc2nc(-c3ccc(OCCCC)cc3)nc(*)c2-c2ccc(OC)cc2)cc1
+*OCCOC(=O)c1cccc(C(=O)OCCOc2nc(-c3ccc(OCCCC)cc3)nc(*)c2-c2ccc(OC)cc2)c1
+*ON(C(F)(F)C(*)(F)F)C(F)(F)C(F)(F)Br
+*ON(C(F)(F)F)C(F)(Br)C(*)(F)F
+*ON(C(F)(F)F)C(F)(C(*)(F)F)C(F)(F)F
+*ON(C(F)(F)F)C(F)(Cl)C(*)(F)F
+*ON(C(F)(F)F)C(F)(F)/C(F)=C(\F)C(*)(F)F
+*ON(C(F)(F)F)C(F)(F)C(*)(F)F
+*ON(C(F)(F)F)C(F)(F)C(*)(F)SC(F)(F)F
+*OP(=O)(Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl
+*OP(=O)(Oc1c(Cl)cc(Cl)cc1Cl)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl
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+*OP(=O)(Oc1ccc(Br)cc1)OC(CCl)COc1ccc(S(=O)(=O)c2ccc(OCC(*)CCl)cc2)cc1
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+*OP(=O)(Oc1ccc(Br)cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1
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+*OP(=O)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)Oc1c(Cl)cccc1Cl
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+*OP(=O)(Oc1ccc(C)cc1)Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1
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+*OP(=O)(Oc1ccccc1)Oc1c(Cl)c(Cl)c(*)c(Cl)c1Cl
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+*OS(=O)(=O)c1cc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCC3)cc2)ccc1C
+*OS(=O)(=O)c1cc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCCC3)cc2)ccc1C
+*OS(=O)(=O)c1ccc(*)cc1
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+*OS(=O)(=O)c1cccc(C(=O)c2cccc(S(=O)(=O)Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)c2)c1
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+*OS(=O)(=O)c1cccc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCCC3)cc2)c1
+*O[Ca]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)OCCCCCOC(=O)c1ccccc1C(*)=O
+*O[Ca]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)Nc1cc(NC(=O)OCCCCCOC(=O)c2ccccc2C(*)=O)ccc1C
+*O[SiH](*)C
+*O[SiH](*)Nc1cccc(N[Si](*)(C)O*)c1
+*O[Si](*)(C([2H])([2H])[2H])C([2H])([2H])[2H]
+*O[Si](*)(C)C
+*O[Si](*)(C)C
+*O[Si](*)(C)C1CC2CC1C1C3CC(CC3C(=O)OC(C)(C)C)C21
+*O[Si](*)(C)C1CC2CC1CC2OC(=O)OC(C)(C)C
+*O[Si](*)(C)C=C
+*O[Si](*)(C)C=C
+*O[Si](*)(C)CC
+*O[Si](*)(C)CCC
+*O[Si](*)(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*O[Si](*)(C)CCC(F)(F)F
+*O[Si](*)(C)CCCC
+*O[Si](*)(C)CCCC(=O)O
+*O[Si](*)(C)CCCC(=O)OC
+*O[Si](*)(C)CCCC(=O)OCC
+*O[Si](*)(C)CCCC(=O)OCCCC
+*O[Si](*)(C)CCCC(=O)OCCO
+*O[Si](*)(C)CCCCC
+*O[Si](*)(C)CCCCC(=O)OCCCCCCCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*O[Si](*)(C)CCCCC1COC(=O)O1
+*O[Si](*)(C)CCCCCC
+*O[Si](*)(C)CCCCCCCC
+*O[Si](*)(C)CCCCCCCCCCC(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*O[Si](*)(C)CCCCCCCCCCC(=O)OCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)CCCCC(=O)OC3CC(C)CCC3C(C)C)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc(OCC(C)CC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc(OCC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(-c2ccc(OC(=O)c3ccc(OCCCCCCC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)OCCOc2ccc(C(=O)OC3CCC4(C)C(=CCC5C4CCC4(C)C(C(C)CCCC(C)C)CCC54)C3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)CCCCC(=O)OC4CC(C)CCC4C(C)C)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4ccc(OCC(C)CC)cc4)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)Oc2ccc(-c3ccc(OCC(C)CC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)Oc2ccc(OC(=O)CCCCC(=O)OC3CC(C)CCC3C(C)C)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(OCC(C)CC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc(OC(=O)c2ccc(OCC(C)CC)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCC(=O)Oc1ccc2cc(C(=O)OC3CCC4(C)C(=CCC5C4CCC4(C)C(C(C)CCCC(C)C)CCC54)C3)ccc2c1
+*O[Si](*)(C)CCCCCCCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F
+*O[Si](*)(C)CCCCCCCCCCCOC(=O)c1cc(OC(=O)c2ccc(OCCOCCOCCOCCOCCOCCOCCOCCOC)cc2)ccc1OC(=O)c1ccc(OCCOCCOCCOCCOCCOCCOCCOCCOC)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(-c2ccc(C(=O)Oc3ccc(C(=O)OC(C)C(=O)OC(C)C(=O)OC(C)C(=O)OCC(C)CC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(-c2ccc(C(=O)Oc3ccc(C(=O)OC(C)C(=O)OC(C)C(=O)OCC(C)CC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(OC)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(/C=C/c2ccc(OCCCC)cc2)cc1
+*O[Si](*)(C)CCCCCCCCCCCOc1ccc(C(=O)OC/C=C(\C)CCC=C(C)C)cc1
+*O[Si](*)(C)CCCCCCCCCCCn1c2ccccc2c2ccccc21
+*O[Si](*)(C)CCCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2C)cc1
+*O[Si](*)(C)CCCCCCOC(=O)c1cc(OC(=O)c2ccc(OCCOCCOCCOCCOCCOCCOC)cc2)ccc1OC(=O)c1ccc(OCCOCCOCCOCCOCCOCCOC)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(-c2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2C)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)OCC(C)CC)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)Oc3ccc(C=C(C(=O)OCCCCCCCCC)C(=O)OCCCCCCCCC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)Oc3ccc(C=C(C(=O)OCCCCCCCCC)C(=O)OCCCCCCCCC)cc3)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1
+*O[Si](*)(C)CCCCCCOc1ccc(C(=O)Oc2ccc(OCCCCCC)cc2)cc1
+*O[Si](*)(C)CCCCCCn1c2ccccc2c2ccccc21
+*O[Si](*)(C)CCCCCOc1ccc(/C=C/c2ccc(OC)cc2)cc1
+*O[Si](*)(C)CCCCCn1c2ccccc2c2ccccc21
+*O[Si](*)(C)CCCCOc1ccc(-c2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCOC(=O)c1cc(OC(=O)c2ccc(OCCOCCOCCOCCOC)cc2)ccc1OC(=O)c1ccc(OCCOCCOCCOCCOC)cc1
+*O[Si](*)(C)CCCOC(=O)c1cc(OC(=O)c2ccc(OCCOCCOCCOCCOCCOCCOC)cc2)ccc1OC(=O)c1ccc(OCCOCCOCCOCCOCCOCCOC)cc1
+*O[Si](*)(C)CCCOCC1COC(=O)O1
+*O[Si](*)(C)CCCOCCOCCOC
+*O[Si](*)(C)CCCOCCOCCOCCOC
+*O[Si](*)(C)CCCOCCOCCOc1ccc(COc2ccc3cc(C#N)ccc3c2)cc1
+*O[Si](*)(C)CCCOc1ccc(-c2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(-c2ccc(OC(=O)CCCCC(=O)OC3CC(C)CCC3C(C)C)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(/N=N/c2ccc(S(=O)(=O)O)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)OC2CCC3(C)C(=CCC4C3CCC3(C)C(C(C)CCCC(C)C)CCC43)C2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)OC2CCC3(C)C(=CCC4C3CCC3(C)C(C(C)CCCC(C)C)CCC43)C2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)OC2CCC3(C)C(=CCC4C3CCC3(C)C(C(C)CCCC(C)C)CCC43)C2)cc1S(=O)(=O)O[K]
+*O[Si](*)(C)CCCOc1ccc(C(=O)OCCOc2ccc(C(=O)OC3CCC4(C)C(=CCC5C4CCC4(C)C(C(C)CCCC(C)C)CCC54)C3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)CCCCC(=O)OC4CC(C)CCC4C(C)C)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCC(C)CC)c(OCC(C)CC)c(OCC(C)CC)c4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCC)c(OCC)c(OCC)c4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCC)cc(OCC)c4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCCCCC)c(OCCCCC)c(OCCCCC)c4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(-c3ccc(OC(=O)c4ccc(OCC)cc4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(C#N)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(C(=O)Oc3ccc(OC)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(OC(=O)CCCCC(=O)OC3CC(C)CCC3C(C)C)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(OC)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(OCCCCCC)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc(N(c2ccc(C)cc2)c2ccc(-c3ccc(N(c4ccccc4)c4ccc(C)cc4)cc3)cc2)cc1
+*O[Si](*)(C)CCCOc1ccc2c(c1)c1cc(-c3ccc4c(c3)c3ccccc3n4CC(CC)CCCC)ccc1n2CC(CC)CCCC
+*O[Si](*)(C)CCCn1c2ccccc2c2ccccc21
+*O[Si](*)(C)CC[Si](C)(C)O[Si](C)(C)CCCOc1cc(OC(=O)c2ccc(OCCCC)cc2)ccc1/N=N/c1ccc(OCCCC)cc1
+*O[Si](*)(C)CC[Si](OCCOC)(OCCOC)OCCOC
+*O[Si](*)(C)CCc1cc(-c2nnc(-c3ccc(OCCCCCCCCCCCC)cc3)o2)ccc1-c1nnc(-c2ccc(OCCCCCCCCCCCC)cc2)o1
+*O[Si](*)(C)CCc1cc(C(=O)Oc2ccc(OC)cc2)ccc1C(=O)Oc1ccc(OC)cc1
+*O[Si](*)(C)CCc1cc(C(=O)Oc2ccc(OCCCC)cc2)ccc1-c1ccc(C(=O)Oc2ccc(OCCCC)cc2)cc1
+*O[Si](*)(C)CCc1cc(C(=O)Oc2ccc(OCCCC)cc2)ccc1C(=O)Oc1ccc(OCCCC)cc1
+*O[Si](*)(C)CCc1cc(C(=O)Oc2ccc(OCCCCCCCCCCCC)cc2)ccc1-c1ccc(C(=O)Oc2ccc(OCCCCCCCCCCCC)cc2)cc1
+*O[Si](*)(C)CCc1ccc(O)cc1
+*O[Si](*)(C)OCCCCCCCCCCCOc1ccc(C2COC(c3ccc(OC)cc3)OC2)cc1
+*O[Si](*)(C)OCCCCCCCCCCCOc1ccc(C2COC(c3ccc(OCC(C)CC)cc3)OC2)cc1
+*O[Si](*)(C)OCCOCCOCCOC
+*O[Si](*)(C)OCCOCCOCCOCCOCCOCCOCCOCCOC
+*O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F
+*O[Si](*)(C)c1ccc(C)cc1
+*O[Si](*)(C)c1ccccc1
+*O[Si](*)(CC)CC
+*O[Si](*)(CCC)CCC
+*O[Si](*)(CCC)c1ccccc1
+*O[Si](*)(CCCOCCOC)CCCOCCOC
+*O[Si](*)(CCCOCCOCCOC)CCCOCCOCCOC
+*O[Si](*)(CCCOCCOCCOCCOC)CCCOCCOCCOCCOC
+*O[Si](*)(CCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOC
+*O[Si](*)(CCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOC
+*O[Si](*)(CCCOCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOCCOC
+*O[Si](*)(CCCOCCOCCOCCOCCOCCOCCOCCOC)CCCOCCOCCOCCOCCOCCOCCOCCOC
+*O[Si](*)(c1ccccc1)c1ccc(OC(C)(C)C)cc1
+*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C
+*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C
+*O[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](C)(C)C#C[Si](*)(C)C
+*O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1
+*O[Si](C)(C)CC(C)(C)COC(=O)c1cccc(C(=O)OCC(C)(C)C[Si](*)(C)C)c1
+*O[Si](C)(C)CCC/N=C/c1cc(Cc2ccc(O)c(/C=N/CCC[Si](*)(C)C)c2)ccc1O
+*O[Si](C)(C)CCC/N=C1\C=C(O)/C(=N\CCC[Si](*)(C)C)C=C1O
+*O[Si](C)(C)CCC/N=C1\C=C/C(=N/CCC[Si](*)(C)C)c2c(O)ccc(O)c21
+*O[Si](C)(C)CCC/N=C1\c2ccccc2/C(=N\CCC[Si](*)(C)C)c2c(O)ccc(O)c21
+*O[Si](C)(C)CCCC(=O)Oc1ccc(/C=N/c2ccc(/N=C/c3ccc(OC(=O)CCC[Si](*)(C)C)cc3)cc2)cc1
+*O[Si](C)(C)CCCC(=O)Oc1ccc(/C=N/c2ccc(Cc3ccc(/N=C/c4ccc(OC(=O)CCC[Si](*)(C)C)cc4)cc3)cc2)cc1
+*O[Si](C)(C)CCCC(=O)Oc1ccc(/C=N/c2ccc(Oc3ccc(/N=C/c4ccc(OC(=O)CCC[Si](*)(C)C)cc4)cc3)cc2)cc1
+*O[Si](C)(C)CCCCCCCC[Si](*)(C)C
+*O[Si](C)(C)CCCCCCOC(=O)C(C)Oc1ccc(OC(=O)c2ccc(-c3ccc(OCCCCCCCCCC[Si](*)(C)C)cc3)cc2)cc1
+*O[Si](C)(C)CCCCCCOC(=O)C(C)Oc1ccc(OC(=O)c2ccc(-c3ccc(OCCCCCCCCCC[Si](*)(C)C)cc3)cc2)cc1[N+](=O)[O-]
+*O[Si](C)(C)CCCCCC[Si](*)(C)C
+*O[Si](C)(C)CCCNC(=O)C(=O)NCCC[Si](*)(C)C
+*O[Si](C)(C)CC[Si](*)(C)C
+*O[Si](C)(C)CC[Si](*)(C)O[Si](C)(C)C
+*O[Si](C)(C)CC[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C
+*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(/C=C4\CC/C(=C\c5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1
+*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(/C=C4\CCC/C(=C\c5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1
+*O[Si](C)(C)COC(=O)CCCCCCCCC(=O)Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(/C=C4\CCCC/C(=C\c5ccc(Oc6ccc(S(=O)(=O)c7ccc(OC(=O)CCCCCCCCC(=O)OC[Si](*)(C)C)cc7)cc6)cc5)C4=O)cc3)cc2)cc1
+*O[Si](C)(C)OC(CCl)COc1ccc(C(C)(C)c2ccc(OCC(*)CCl)cc2)cc1
+*O[Si](C)(C)O[Si](*)(C)CCCCCCCCOc1ccc(C(=O)Oc2ccc(C#N)cc2C)cc1
+*O[Si](C)(C)O[Si](*)(C)CCCCOc1ccc(-c2ccc(C#N)cc2)cc1
+*O[Si](C)(C)O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F
+*O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C
+*O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[SiH](*)O[Si](C)(C)C=C
+*O[Si](C)(C)O[Si](C)(C)O[Si](*)(C)c1c(F)c(F)c(F)c(F)c1F
+*O[Si](C)(C)O[Si](C)(C)O[Si](*)(c1c(F)c(F)c(F)c(F)c1F)c1c(F)c(F)c(F)c(F)c1F
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C#Cc1ccc(C#C[Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC(C)CCCCC(*)C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)OC1=C(*)c2cccc3cccc1c23
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1cccc([Si](*)(C)C)c1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(c1ccccc1)c1cccc([Si](*)(C)c2ccccc2)c1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc(Oc2ccc([Si](*)(C)C)cc2)cc1
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C)O[Si](C)(C)Oc1c(C)cc(-c2cc(C)c(*)c(C)c2)cc1C
+*O[Si](C)(C)O[Si](C)(C)Oc1ccc(-c2ccc(*)cc2)cc1
+*O[Si](C)(C)O[Si](C)(C)c1c(F)c(F)c(F)c([Si](*)(C)C)c1F
+*O[Si](C)(C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C)Oc1ccc(*)cc1
+*O[Si](C)(C)Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1
+*O[Si](C)(C)[Si](*)(C)C
+*O[Si](C)(C)c1c(F)c(F)c(F)c([Si](*)(C)C)c1F
+*O[Si](C)(C)c1cc(OCCO)c([Si](*)(C)C)cc1OCCO
+*O[Si](C)(C)c1cc(OCCOCc2ccccc2)c([Si](*)(C)C)cc1OCCOCc1ccccc1
+*O[Si](C)(C)c1ccc(-c2ccc([Si](*)(C)C)cc2)cc1
+*O[Si](C)(C)c1ccc([Si](*)(C)C)c2ccccc12
+*O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C)c1ccc([Si](C)(C)O[SiH](*)C)cc1
+*O[Si](C)(C)c1ccc([Si](C)(C)O[SiH](*)CCC(F)(F)F)cc1
+*O[Si](C)(C)c1ccc([Si](C)(C)Oc2c(C)cc(-c3cc(C)c(*)c(C)c3)cc2C)cc1
+*O[Si](C)(C)c1ccc2cc([Si](*)(C)C)ccc2c1
+*O[Si](C)(C)c1ccc2cc3cc([Si](*)(C)C)ccc3cc2c1
+*O[Si](C)(C)c1ccc2ccc([Si](*)(C)C)cc2c1
+*O[Si](C)(C)c1ccc2ccc3c([Si](*)(C)C)ccc4ccc1c2c43
+*O[Si](C)(C)c1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc([Si](*)(C)C)c2)c1
+*O[Si](C)(C)c1cccc(C(F)(F)C(F)(F)c2cccc([Si](*)(C)C)c2)c1
+*O[Si](C)(C)c1cccc([Si](*)(C)C)c1
+*O[Si](C)(C)c1cccc2c([Si](*)(C)C)cccc12
+*O[Si](C)(C)c1cccc2c1ccc1c([Si](*)(C)C)cccc12
+*O[Si](C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(C=C)O[Si](C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)CCC(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(CCC(F)(F)F)O[Si](C)(CCC(F)(F)F)O[Si](C)(CCC(F)(F)F)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)CC[Si](*)(C)CCC(F)(F)F
+*O[Si](C)(Oc1ccc(*)cc1)c1ccccc1
+*O[Si](C)(Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1)c1ccccc1
+*O[Si](C=C)(C=C)O[Si](C)(C)c1ccc([Si](*)(C)C)cc1
+*O[Si](O[Si](C)(C)c1ccc([Si](*)(C)C)cc1)(c1ccccc1)c1ccccc1
+*O[Si](Oc1ccc(-c2ccc(*)cc2)cc1)(c1ccccc1)c1ccccc1
+*O[Si](Oc1ccc(C2(c3ccc(*)c(C)c3)c3ccccc3-c3ccccc32)cc1C)(c1ccccc1)c1ccccc1
+*O[Si](Oc1ccc(C2(c3ccc(*)cc3)c3ccccc3-c3ccccc32)cc1)(c1ccccc1)c1ccccc1
+*O[Si](c1ccccc1)(c1ccccc1)c1ccc(-c2ccc([Si](*)(c3ccccc3)c3ccccc3)cc2)cc1
+*O[Si](c1ccccc1)(c1ccccc1)c1ccc([Si](*)(c2ccccc2)c2ccccc2)cc1
+*O[Si](c1ccccc1)(c1ccccc1)c1cccc(*)c1
+*O[Sn](CCCC)(CCCC)OC(=O)/C=C/C(*)=O
+*O[Sn](CCCC)(CCCC)OC(=O)CCCCC(*)=O
+*O[Sn](CCCC)(CCCC)OC(=O)c1ccc(C(*)=O)cc1
+*O[Zn]OC(=O)c1ccccc1C(=O)OCCCCCOC(=O)NCCCCCCNC(=O)OCCCCCOC(=O)c1ccccc1C(*)=O
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+*Oc1ccc(-c2ccc(Oc3ccc(-c4ccc(-c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(/C(=N/c4ccc(/N=C(\c5ccccc5)c5ccc(*)cc5)cc4)c4ccccc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccccc5)c(-c5ccc6ccccc6c5)c(-c5ccc6ccccc6c5)c4-c4ccccc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(*)c(S(=O)(=O)O)c4)cc3S(=O)(=O)O)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccc5cc(C(=O)c6ccc(*)cc6)ccc5c4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4cccc5cccc(C(=O)c6ccc(*)cc6)c45)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(=O)c4ccccc4-c4ccccc4C(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(-c2ccc(Oc3ccc(Oc4cc(Oc5ccc(*)cc5)ccc4CCCCCC)cc3)c3ccccc23)c2ccccc12
+*Oc1ccc(-c2ccc(Oc3ccc(Oc4ccc(Oc5ccc(*)cc5)cc4)cc3)c3ccccc23)c2ccccc12
+*Oc1ccc(-c2ccc(Oc3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc(*)cc6)cc5)cc4)cc3)c3ccccc23)c2ccccc12
+*Oc1ccc(-c2ccc(Oc3ccc(Oc4cccc(Oc5ccc(*)cc5)c4C)cc3)c3ccccc23)c2ccccc12
+*Oc1ccc(-c2ccc(Oc3ccc4ccccc4c3-c3c(*)ccc4ccccc34)c3ccccc23)c2ccccc12
+*Oc1ccc(-c2ccc(Oc3cccc(*)n3)cc2)cc1
+*Oc1ccc(/C(=C/c2cc(OC)c(/C=C(/c3ccc(*)cc3)c3ccc(C(F)(F)F)cc3)cc2OC)c2ccc(C(F)(F)F)cc2)cc1
+*Oc1ccc(/C(=C/c2cc(OC)c(/C=C(/c3ccc(*)cc3)c3ccc(F)cc3)cc2OC)c2ccc(F)cc2)cc1
+*Oc1ccc(/C(=C/c2ccc(-c3ccc(/C=C(\c4ccccc4)c4ccc(OC(=O)CCCCC(*)=O)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(=C/c2ccc(-c3ccc(/C=C(\c4ccccc4)c4ccc(OC(=O)CCCCCCCCC(*)=O)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(=C/c2ccc(-c3ccc(/C=C(\c4ccccc4)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(=C/c2ccc(-c3ccc(/C=C(\c4ccccc4)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(=N/c2ccc(/N=C(\c3ccccc3)c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(=N/c2ccc(/N=C(\c3ccccc3)c3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)c2ccccc2)cc1
+*Oc1ccc(/C(C)=N/c2ccc(/N=C(\C)c3ccc(Oc4ccc(C(C)(C)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/C(C)=N/c2ccc(/N=C(\C)c3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/C=C/C(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c(C)c4)cc3C)cc2)cc1
+*Oc1ccc(/C=C/C(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/C=C/C(=O)c2ccc(Oc3ccc(C(C)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/C=C/c2cc(OCCCCCCCC)c(/C=C/c3ccc(Oc4cc5c(=O)n(CCCCCCCC)c(=O)c6ccc7c8c(*)cc9c(=O)n(CCCCCCCC)c(=O)c%10ccc(c4c7c65)c8c%109)cc3)cc2OCCCCCCCC)cc1
+*Oc1ccc(/C=C2\CC/C(=C\c3ccc(OC(=O)c4ccc(/N=C/c5ccc(/C=N/c6ccc(C(*)=O)cc6)cc5)cc4)c(OC)c3)C2=O)cc1OC
+*Oc1ccc(/C=C2\CC/C(=C\c3ccc(OC(=O)c4ccc(/N=C/c5ccc(/C=N/c6ccc(C(*)=O)cc6)cc5)cc4)cc3)C2=O)cc1
+*Oc1ccc(/C=C2\CCC/C(=C\c3ccc(OC(=O)CCCCCCCCC(*)=O)c(OC)c3)C2=O)cc1OC
+*Oc1ccc(/C=C2\CCC/C(=C\c3ccc(OC(=O)c4cccc(C(*)=O)c4)c(OC)c3)C2=O)cc1OC
+*Oc1ccc(/C=N/N=C/c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/C=N/N=C/c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/C=N/N=C/c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1
+*Oc1ccc(/C=N/c2ccc(/N=C/c3ccc(Oc4ccc(C(c5ccccc5)(c5ccccc5)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C(\C)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(/C(C)=N/c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C(\C)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(/C(C)=N/c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C(\c2ccccc2)c2ccc(Oc3ccc(-c4ccc(Oc5ccc(/C(=N/c6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C(\c2ccccc2)c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(/C(=N/c6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C(\c2ccccc2)c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(/C(=N/c6ccc(*)cc6)c6ccccc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C/C=N/c2ccc(OC(=O)NC3CC(C)(C)CC(C)(CNC(*)=O)C3)cc2)cc1
+*Oc1ccc(/N=C/C=N/c2ccc(OC(=O)NCCCCCCNC(*)=O)cc2)cc1
+*Oc1ccc(/N=C/C=N/c2ccc(OC(=O)Nc3cc(NC(*)=O)ccc3C)cc2)cc1
+*Oc1ccc(/N=C/C=N/c2ccc(OC(=O)Nc3ccc(Cc4ccc(NC(*)=O)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C/CCC/C=N/c2ccc(OC(=O)NC3CC(C)(C)CC(C)(CNC(*)=O)C3)cc2)cc1
+*Oc1ccc(/N=C/CCC/C=N/c2ccc(OC(=O)NCCCCCCNC(*)=O)cc2)cc1
+*Oc1ccc(/N=C/CCC/C=N/c2ccc(OC(=O)Nc3cc(NC(*)=O)ccc3C)cc2)cc1
+*Oc1ccc(/N=C/CCC/C=N/c2ccc(OC(=O)Nc3ccc(Cc4ccc(NC(*)=O)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C/c2ccc(N(c3ccccc3)c3ccc(/C=N/c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C/c2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(/C=N/c6ccc(Oc7ccc(-c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=C/c2ccc(Oc3ccc(C(c4ccc(Oc5ccc(/C=N/c6ccc(Oc7ccc(-c8ccc(*)cc8)cc7)cc6)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)cc1
+*Oc1ccc(/N=C/c2ccc(Oc3ccc(C(c4ccccc4)(c4ccccc4)c4ccc(Oc5ccc(/C=N/c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=N/c2c(C)cc(Cc3cc(C)c(/N=N/c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)c(C)c3[N+](=O)[O-])c([N+](=O)[O-])c2C)cc1
+*Oc1ccc(/N=N/c2ccc(Cc3ccc(/N=N/c4ccc(OC(=O)c5ccc(/N=N/c6ccc(C(*)=O)cc6)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1
+*Oc1ccc(/N=N/c2ccc(Cc3ccc(/N=N/c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1
+*Oc1ccc(/N=N/c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(/N=N/c2ccc(S(=O)(=O)c3ccc(/N=N/c4ccc(OC(=O)c5ccc(/N=N/c6ccc(C(*)=O)cc6)cc5)cc4)cc3[N+](=O)[O-])c([N+](=O)[O-])c2)cc1
+*Oc1ccc(C(*)=O)cc1
+*Oc1ccc(C(*)=O)cc1CCCCC
+*Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=O)cc2)cc1
+*Oc1ccc(C(=O)CNc2ccc(S(=O)(=O)c3ccc(NCC(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)NCC(C)(C)CCCNC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)NNC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)NNC(=O)c2ccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)cc2)cc1
+*Oc1ccc(C(=O)NNC(=O)c2ccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)cc2Oc2ccccc2)cc1
+*Oc1ccc(C(=O)NNC(=O)c2cccc(C(=O)NNC(=O)c3ccc(OC(=O)c4cccc(C(*)=O)c4)cc3)c2)cc1
+*Oc1ccc(C(=O)Nc2cc(C(c3ccc(C)c(NC(=O)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)c3)(C(F)(F)F)C(F)(F)F)ccc2C)cc1
+*Oc1ccc(C(=O)Nc2cc(C(c3ccc(C)c(NC(=O)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)c3)(C(F)(F)F)C(F)(F)F)ccc2C)cc1
+*Oc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1
+*Oc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(*)cc3)cc(C(=O)OCCOC(=O)/C=C/c3ccc(N(C)C)cc3)c2)cc1
+*Oc1ccc(C(=O)Nc2cc(SCCC#N)c(NC(=O)c3ccc(*)cc3)cc2SCCC#N)cc1
+*Oc1ccc(C(=O)Nc2ccc(Br)cc2-c2ccc(NC(=O)c3ccc(*)cc3)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(*)cc4)cc3)(P3(=O)Oc4ccccc4-c4ccccc43)P3(=O)Oc4ccccc4-c4ccccc43)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(OC(=O)c5ccc(C(*)=O)cc5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(C(c3ccc(NC(=O)c4ccc(OC(=O)c5cccc(C(*)=O)c5)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)c6ccc(S(=O)(=O)c7ccc(C(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(Cc3ccc(NC(=O)c4ccc(Oc5nc(*)nc(Sc6ccccc6)n5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)c6ccc(S(=O)(=O)c7ccc(C(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4ccc(Oc5nc(*)nc(Sc6ccccc6)n5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)Nc2ccccc2-c2ccc(NC(=O)c3ccc(*)cc3)cc2)cc1
+*Oc1ccc(C(=O)O)c(C(=O)Nc2ccc(NC(=O)c3cc(*)ccc3C(=O)O)cc2)c1
+*Oc1ccc(C(=O)OC(C)COC(C)COC(C)COC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Oc1ccc(C(=O)OCC(C)(C)COC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCCCCCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Oc1ccc(C(=O)OCCCCCCOC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCCCCOC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCCCOC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCCOC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCN(CCOC(=O)c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)c2ccc(/C=C/C3=CC(=C(C#N)C#N)CC(C)(C)C3)cc2)cc1
+*Oc1ccc(C(=O)OCCOC(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)OCCOCCOC(=O)c2ccc(OC(*)=O)cc2)cc1
+*Oc1ccc(C(=O)OCCOCCOCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Oc1ccc(C(=O)OCCOCCOCCOCCOC(=O)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(*)c(C(C)C)c3)cc2)cc1C
+*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(*)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(Oc4ccc(Cc5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(Oc4cccc(Cc5cccc(*)c5)c4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(NC(=O)c3ccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(NC(=O)c3cccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)c3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(OC(=O)c3ccc(C(C)(C)c4ccc(C(*)=O)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c(-c5ccc(F)cc5)c4-c4ccc(F)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4c(C(=O)c5ccc(*)cc5)c(-c5ccccc5)c(-c5ccc6ccccc6c5)c(-c5ccc6ccccc6c5)c4-c4ccccc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(Cc6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(Oc5cccc(Cc6cccc(*)c6)c5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(*)cc5)c4)cc3)cc2)cc1
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc2)c2ccccc12
+*Oc1ccc(C(=O)c2ccc(Oc3ccc(C4(c5ccc(*)cc5)c5ccccc5-c5ccccc54)cc3)cc2)c2ccccc12
+*Oc1ccc(C(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1
+*Oc1ccc(C(=O)c2cccc(-c3cccc(C(=O)c4ccc(*)cc4)c3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(C(C)C)c3)c2)cc1C
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(C(C)C)c3)c2)cc1CC
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(CC)c3)c2)cc1CC
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)cc3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(Oc4ccc(Cc5ccc(*)cc5)cc4)cc3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(Oc4cccc(Cc5cccc(*)c5)c4)cc3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(NC(=O)CCCCCCCCC(=O)Nc3ccc(C(=O)c4ccc(*)cc4)cc3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(NC(=O)CCCCCCCCC(=O)Nc3cccc(C(=O)c4ccc(*)cc4)c3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(NC(=O)c3ccc(C(=O)Nc4cccc(C(=O)c5ccc(*)cc5)c4)cc3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(NC(=O)c3cccc(C(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)c3)c2)cc1
+*Oc1ccc(C(=O)c2cccc(NC(=O)c3cccc(C(=O)Nc4cccc(C(=O)c5ccc(*)cc5)c4)c3)c2)cc1
+*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(-c4ccc(*)cc4)cc3)ccc2C(=O)c2ccccc2)c1
+*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)ccc2C(=O)c2ccccc2)c1
+*Oc1ccc(C(=O)c2ccccc2)c(-c2cc(Oc3ccc(C(c4ccc(*)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc2C(=O)c2ccccc2)c1
+*Oc1ccc(C(C#N)(c2ccccc2)c2ccc(OC(*)=O)cc2)cc1
+*Oc1ccc(C(C)(C)C)cc1Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(C)(C)C)cc1Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1
+*Oc1ccc(C(C)(C)c2cc(Cl)c(OC(*)=O)c(Cl)c2)cc1
+*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(OC(*)=O)cc3)cc2)cc1
+*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(Cc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5ccc(NC(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(Oc4ccc(C(=O)Nc5cccc(NC(=O)c6ccc(*)cc6)c5)cc4)cc3)cc2)cc1
+*Oc1ccc(C(C)(C)c2ccc(OC(*)(Oc3ccccc3)Oc3ccccc3)cc2)cc1
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+*Oc1ccc(C(C)(CC)c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1
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+*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2C3CCC(C3)C2C)cc1
+*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2C3CCC(C3)C2c2ccccc2)cc1
+*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2CC3CC2C2C4CCC(C4)C32)cc1
+*Oc1ccc(C(c2ccc(OC(*)=O)cc2)C2CCCCC2)cc1
+*Oc1ccc(C(c2ccc(OC(*)=S)cc2)C(C)C)cc1
+*Oc1ccc(C(c2ccc(OC(*)=S)cc2)C(CC)CC)cc1
+*Oc1ccc(C(c2ccc(OC(*)=S)cc2)c2cccc3ccccc23)cc1
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+*Oc1ccc(C(c2ccc(OC(=O)c3ccc(OCCCCCCCCCCOc4ccc(C(*)=O)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Oc1ccc(C(c2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Oc1ccc(C(c2ccc(OC(=O)c3ccccc3-c3ccccc3C(*)=O)cc2)(C(F)(F)F)C(F)(F)F)cc1
+*Oc1ccc(C(c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)(C(F)(F)F)C(F)(F)F)cc1
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+*Oc1ccc(NC(=O)c2ccc([Si](C)(C)c3ccc(C(*)=O)cc3)cc2)cc1
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+*Oc1ccc(NC(=O)c2cccc(C(=O)Nc3ccc(*)cc3)c2)cc1
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+*Oc1ccc(Nc2ccc(/N=N/c3ccc(Nc4ccc(*)cc4)cc3)cc2)cc1
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+*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(S(=O)(=O)c4ccc(-c5ccc(S(=O)(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(Sc4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc4c(=O)n5c6cc(Oc7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc(Oc3cccc(NC(=O)c4ccc(-c5ccc(C(=O)Nc6cccc(*)c6)c(C)c5)cc4C)c3)cc2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6)cc5C)cc4)ccc3c2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6C)c(C)c5C)cc4)ccc3c2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc3cc(S(=O)(=O)c4ccc(Oc5ccc(-c6ccc(*)cc6)cc5)cc4)ccc3c2)cc1
+*Oc1ccc(S(=O)(=O)c2ccc3ccc(S(=O)(=O)c4ccc(*)cc4)cc3c2)cc1
+*Oc1ccc(S(=O)(=O)c2cccc3c(S(=O)(=O)c4ccc(Oc5c(C)cc(-c6cc(C)c(*)c(C)c6)cc5C)cc4)cccc23)cc1
+*Oc1ccc(S(=O)(=O)c2cccc3c(S(=O)(=O)c4ccc(Oc5ccc(-c6ccc(*)cc6)cc5)cc4)cccc23)cc1
+*Oc1ccc(S(=O)c2ccc(Oc3ccc(/C=N/N=C/c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(S(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(SC(=O)Oc2ccc(C(C)(C)c3ccc(OC(=O)Sc4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc(SC(=O)Sc2ccc(*)cc2)cc1
+*Oc1ccc(SSc2ccc(*)cc2)cc1
+*Oc1ccc(Sc2ccc(OC(*)=O)cc2)cc1
+*Oc1ccc(Sc2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1
+*Oc1ccc(Sc2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)cc1
+*Oc1ccc(Sc2ccc(Oc3ccc(-c4ccc(-c5ccc(*)c(C(F)(F)F)c5)cc4)cc3C(F)(F)F)cc2)cc1
+*Oc1ccc(Sc2ccc(Oc3ccc(-c4ccc(-c5ccc(-c6ccc(*)c(C(F)(F)F)c6)cc5)cc4)cc3C(F)(F)F)cc2)cc1
+*Oc1ccc(Sc2ccc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1
+*Oc1ccc(Sc2ccc(Oc3ccc4c(=O)n5c6cc(Oc7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)cc2)cc1
+*Oc1ccc(Sc2ccc(Sc3ccc(Sc4ccc(*)cc4)cc3)cc2)cc1
+*Oc1ccc2c(c1)C(C)(c1ccc(OC(*)=O)cc1)CC2(C)C
+*Oc1ccc2c(c1)C(C)(c1ccc(OC(=O)c3ccc(C(*)=O)cc3)cc1)CC2(C)C
+*Oc1ccc2c(c1)C(C)(c1ccc(OC(=O)c3cccc(C(*)=O)c3)cc1)CC2(C)C
+*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(*)=O)cc21
+*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(=O)c3ccc(C(*)=O)cc3)cc21
+*Oc1ccc2c(c1)C1(CC2(C)C)CC(C)(C)c2ccc(OC(=O)c3cccc(C(*)=O)c3)cc21
+*Oc1ccc2cc(C(*)=O)ccc2c1
+*Oc1ccc2cc(OC(=O)c3ccc(-c4ccc(C(*)=O)cc4)cc3-c3ccccc3)ccc2c1
+*Oc1ccc2cc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)ccc2c1
+*Oc1ccc2cc(OC(=O)c3cccc(Oc4ccc(Oc5cccc(C(*)=O)c5)cc4)c3)ccc2c1
+*Oc1ccc2cc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)ccc2c1
+*Oc1ccc2cc(Oc3ccc(S(=O)(=O)c4ccc(*)cc4)cc3)ccc2c1
+*Oc1ccc2cc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)ccc2c1
+*Oc1ccc2cc(Oc3ccc4c(=O)n5c6cc(-c7ccc8c(c7)nc7c9ccc(*)c%10cccc(c(=O)n87)c%109)ccc6nc5c5cccc3c45)ccc2c1
+*Oc1ccc2ccc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(/C(=N/c4ccc(/N=C(\c5ccccc5)c5ccc(*)cc5)cc4)c4ccccc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(/C(=N/c4ccc(Oc5ccc(/N=C(\c6ccccc6)c6ccc(*)cc6)cc5)cc4)c4ccccc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(C(C)(C)c5ccc(C(C)(C)c6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(C)c6ccc(C(C)(C)c7ccc(Oc8ccc(NC(=O)c9ccc(*)cc9)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(C)(c6ccccc6)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(C(c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)(C(F)(F)F)C(F)(F)F)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(Oc6ccc(NC(=O)c7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc(NC(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4cccc(NC(=O)c5ccc(*)cc5)c4)cc3)cc2c1
+*Oc1ccc2ccc(Oc3ccc(C(=O)Nc4cccc(Oc5ccc(NC(=O)c6ccc(*)cc6)cc5)c4)cc3)cc2c1
+*Oc1cccc(*)c1
+*Oc1cccc(C(*)=O)c1
+*Oc1cccc(C(=O)Nc2ccc(-c3ccc(NC(=O)c4cccc(Oc5nc(*)nc(Sc6ccccc6)n5)c4)cc3)cc2)c1
+*Oc1cccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4cccc(Oc5nc(*)nc(Sc6ccccc6)n5)c4)cc3)cc2)c1
+*Oc1cccc(C(=O)OCC(F)(F)C(F)(F)C(F)(F)COC(=O)c2cccc(*)c2)c1
+*Oc1cccc(C(=O)c2ccc(*)cc2)c1
+*Oc1cccc(C(=O)c2ccc(Oc3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)c1
+*Oc1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(OC(*)=O)c2)c1
+*Oc1cccc(C(F)(F)C(F)(F)C(F)(F)c2cccc(OC(=O)c3cccc(C(F)(F)C(F)(F)C(F)(F)c4cccc(C(*)=O)c4)c3)c2)c1
+*Oc1cccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccc(/N=N/c5ccc([N+](=O)[O-])cc5)cc4)c4cccc(OC(=O)OCCOCCOC(*)=O)c4)cc3)cc2)c1
+*Oc1cccc(N(c2ccccc2)c2ccc(-c3ccc(N(c4ccccc4)c4cccc(OC(=O)OCCOCCOC(*)=O)c4)cc3)cc2)c1
+*Oc1cccc(NC(=O)CCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)CCCCCCCCCCC(=O)Nc2ccc(*)cc2)c1
+*Oc1cccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)c1
+*Oc1cccc(NC(=O)c2cc(C(=O)Nc3ccc(*)cc3)cc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)c2)c1
+*Oc1cccc(NC(=O)c2cc(NC(=O)c3ccc(OC(C)=O)cc3)cc(C(=O)Nc3ccc(*)cc3)c2)c1
+*Oc1cccc(NC(=O)c2ccc(C(=O)Nc3ccc(*)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc(C(=O)c3cccc(C(=O)Nc4ccc(*)cc4)c3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc(Oc3ccc(C(C)(C)c4cccc(C(C)(C)c5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)c4)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc(P(=O)(c3ccccc3)c3ccc(C(=O)Nc4cccc(Oc5ccc(P(=O)(c6ccccc6)c6ccc(*)cc6)cc5)c4)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2ccc([Si](C)(C)c3ccc(C(*)=O)cc3)cc2)c1
+*Oc1cccc(NC(=O)c2cccc(C(=O)Nc3ccc(*)cc3)c2)c1
+*Oc1cccc(NC(=O)c2cccc(C(=O)Nc3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)c2)c1
+*Oc1cccc(OC(=O)CCC(*)=O)c1
+*Oc1cccc(OC(=O)Oc2ccc(C(C)(C)c3ccc(OC(*)=O)cc3)cc2)c1
+*Oc1cccc(OC(=O)c2cc(OCCCCC)cc(C(*)=O)c2)c1
+*Oc1cccc(OC(=O)c2ccc(C(C)(C)c3ccc(C(*)=O)cc3)cc2)c1
+*Oc1cccc(OC(=O)c2ccc(OC(=O)c3cccc(C(=O)Oc4ccc(C(*)=O)cc4)c3)cc2)c1
+*Oc1cccc(OC(=O)c2ccc(Oc3ccc(C(*)=O)cc3)cc2)c1
+*Oc1cccc(OC(=O)c2ccc([Si](C)(C)c3ccc(C(*)=O)cc3)cc2)c1
+*Oc1cccc(OC(=O)c2cccc(C(*)=O)c2)c1
+*Oc1cccc(OC2(F)C(*)(F)C(F)(F)C2(F)F)c1
+*Oc1cccc(Oc2ccc(-c3ccc(*)cc3)cc2)c1C#N
+*Oc1cccc(Oc2ccc(/N=C/c3ccc(N(c4ccccc4)c4ccc(/C=N/c5ccc(*)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)O)c(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7cc(*)ccc7C(=O)O)cc6)cc5)cc4)cc3)c2)c1
+*Oc1cccc(Oc2ccc(C(=O)Oc3cccc(OC(=O)c4ccc(*)cc4)c3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)c3ccc(*)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)CCCCCCCCC(=O)Nc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4ccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)c3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(C(=O)c6ccc(*)cc6)cc5)c4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(NC(=O)c3cc(NC(=O)c4ccc(OC(C)=O)cc4)cc(C(=O)Nc4ccc(*)cc4)c3)cc2)c1
+*Oc1cccc(Oc2ccc(NC(=O)c3ccc(-c4ccc(C(=O)Nc5ccc(*)cc5)c(C)c4)cc3C)cc2)c1
+*Oc1cccc(Oc2ccc(NC(=O)c3ccc(Oc4cccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)c4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(Oc3cccc(OC(=O)c4ccc(C(*)=O)cc4)c3)cc2)c1
+*Oc1cccc(Oc2ccc(S(=O)(=O)c3ccc(*)cc3)cc2)c1
+*Oc1cccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(C(=O)c5ccc(*)cc5)cc4)cc3)cc2)c1
+*Oc1cccc(Oc2ccc3c(=O)n4c5cc(-c6ccc7c(c6)nc6c8ccc(*)c9cccc(c(=O)n76)c98)ccc5nc4c4cccc2c34)c1
+*Oc1cccc(Oc2ccc3c(=O)n4c5cc(Oc6ccc7c(c6)nc6c8ccc(*)c9cccc(c(=O)n76)c98)ccc5nc4c4cccc2c34)c1
+*Oc1cccc(Oc2cccc(*)c2)c1C#N
+*Oc1cccc(Oc2cccc(*)n2)c1
+*Oc1cccc(Oc2cccc(NC(=O)c3cc(Oc4ccc(C(=O)O)c(C(=O)Nc5cccc(*)c5)c4)ccc3C(=O)O)c2)c1
+*Oc1cccc(Oc2cccc(NC(=O)c3cc(Oc4ccc(C(=O)O)c(C(=O)Nc5cccc(*)c5)c4)ccc3C(=O)O)c2)c1C#N
+*Oc1cccc(Oc2cccc(NC(=O)c3cccc(Oc4cccc(Oc5cccc(C(=O)Nc6cccc(*)c6)c5)c4)c3)c2)c1
+*Oc1cccc(Oc2cccc(OC(*)=O)c2)c1
+*Oc1cccc(S(=O)(=O)c2ccc(*)cc2)c1
+*Oc1cccc2c(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)cccc12
+*Oc1cccc2c(OC3(F)C(*)(F)C(F)(F)C3(F)F)cccc12
+*Oc1cccc2cc(OC(=O)c3ccc(Oc4ccc(C(*)=O)cc4)cc3)ccc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4cc(C(=O)Nc5ccc(*)cc5)cc(C(C)(C)C)c4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(C(c5ccc(C(=O)Nc6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(C)c6cccc(C(C)(C)c7ccc(Oc8ccc(C(=O)Nc9ccc(*)cc9)cc8)cc7)c6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(C(C)(c6ccccc6)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccc(S(=O)(=O)c6ccc(Oc7ccc(C(=O)Nc8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5cccc(Oc6ccc(C(=O)Nc7ccc(*)cc7)cc6)c5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(Oc5ccccc5Oc5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4ccc(S(=O)(=O)c5ccc(C(=O)Nc6ccc(*)cc6)cc5)cc4)cc3)cc12
+*Oc1cccc2ccc(Oc3ccc(NC(=O)c4cccc(C(=O)Nc5ccc(*)cc5)c4)cc3)cc12
+*Oc1ccccc1-c1ccccc1Oc1ccc2c(=O)n3c4cc(-c5ccc6c(c5)nc5c7ccc(*)c8cccc(c(=O)n65)c87)ccc4nc3c3cccc1c23
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)C(C)C(C)C(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)C(CC)C(CC)C(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)CCCCCCCCC(*)=O
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)c1ccc(C(*)=O)cc1
+*Oc1ccccc1C(=O)OC(=O)c1ccccc1OC(=O)c1cccc(C(*)=O)c1
+*Oc1ccccc1Oc1ccc(S(=O)(=O)c2ccc(*)cc2)cc1
+*Oc1ccccc1S(=O)(=O)c1ccc(*)cc1
+*SC(*)(F)F
+*SC(C)C(*)=O
+*SC1CCCC(*)C1
+*SC1CCCCC1*
+*SCC(=O)N/N=C/c1ccc(OCCCCCCCCCCOc2ccc(/C=N/NC(=O)CSc3nnc(*)s3)cc2)cc1
+*SCC(=O)N/N=C/c1ccc(OCCCCCCCCCCOc2ccc(/C=N/NC(=O)CSc3nnc(*)s3)cc2OC)c(OC)c1
+*SSc1ccc(/N=C/Nc2ccc(*)cc2)cc1
+*Sc1c(C)cc(*)cc1C
+*Sc1cc(C)c(*)cc1C
+*Sc1ccc(*)cc1
+*Sc1ccc(C(=O)CNCCCCCCCCNCC(=O)c2ccc(*)cc2)cc1
+*Sc1ccc(C(=O)c2ccc(C(C)(C)c3ccc(C(=O)c4ccc(*)cc4)cc3)cc2)cc1
+*Sc1ccc(C(=O)c2ccc(C(c3ccc(C(=O)c4ccc(*)cc4)cc3)(C(F)(F)F)C(F)(F)F)cc2)cc1
+*Sc1ccc(Cc2ccc(SC(*)=O)cc2)cc1
+*Sc1ccc(Nc2ccc(*)cc2)cc1
+*Sc1ccc(Nc2ccc(Nc3ccc(*)cc3)cc2)cc1
+*Sc1ccc(P(=O)(c2ccccc2)c2ccc(Sc3ccc(-c4ccc(*)cc4)cc3)cc2)cc1
+*Sc1ccc(Sc2ccc(Sc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)cc1
+*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)C(=O)N(N3C(=O)c5ccc(*)cc5C3=O)C4=O)cc2)cc1
+*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)C(=O)N(N3C(=O)c5cccc(*)c5C3=O)C4=O)cc2)cc1
+*Sc1ccc(Sc2ccc(Sc3ccc4c(c3)Sc3ccc(*)cc3S4)cc2)cc1
+*Sc1ccc(Sc2ccc(Sc3cccc4c3C(=O)N(N3C(=O)c5cccc(*)c5C3=O)C4=O)cc2)cc1
+*Sc1ccc2c(c1)Sc1ccc(*)cc1S2
+*Sc1cccc(Sc2ccc(Sc3ccc(*)cc3)cc2)c1
+*Sc1cccc(Sc2ccc(Sc3ccc(*)cc3)cc2)c1
+*[Se]c1c(Cl)c(Cl)c(*)c(Cl)c1Cl
+*[Si](*)(C)CCC(F)(F)F
+*[Si](*)(C)CCCOCC1COC(C)(C)O1
+*[Si](*)(C)c1ccc(COCCN(CC)c2ccc(/N=N/c3ccc([N+](=O)[O-])cc3)cc2)cc1
+*[Si](*)(C)c1ccccc1
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+*c1ccc(-c2nc3cc(Oc4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)cc1
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+*c1ccc(-c2nc3cc4nc(-c5ccccc5)c(*)nc4cc3nc2-c2ccccc2)cc1
+*c1ccc(-c2nc3cc4sc(*)nc4cc3s2)cc1
+*c1ccc(-c2nc3ccc(-c4ccc5nc(*)c(-c6ccccc6)c(-c6ccccc6)c5c4)cc3c(-c3ccccc3)c2-c2ccccc2)cc1
+*c1ccc(-c2nc3ccc(-c4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1
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+*c1ccc(-c2nc3ccc(Oc4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1
+*c1ccc(-c2nc3ccc(S(=O)(=O)c4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)cc1
+*c1ccc(-c2nc3ccccc3o2)c(*)c1
+*c1ccc(-c2nnc(*)o2)c(OCC)c1
+*c1ccc(-c2nnc(*)o2)c(OCCCCC)c1
+*c1ccc(-c2nnc(*)o2)c(OCCCCCCCC)c1
+*c1ccc(-c2nnc(*)o2)c(OCCCCCCCCCC)c1
+*c1ccc(-c2nnc(-c3cccc(-c4nnc(*)n4-c4ccccc4)c3)n2-c2ccccc2)cc1
+*c1ccc(-c2nnc(-c3cccc(-c4nnc(*)o4)c3)o2)cc1
+*c1ccc(-c2sc(-c3cc(CCCC)c(*)s3)cc2CCCC)cc1
+*c1ccc(-c2sc(-c3cc(CCCCCCCC)c(*)s3)cc2CCCCCCCC)cc1
+*c1ccc(-c2sc(-c3cc(CCCCCCCCCCCC)c(*)s3)cc2CCCCCCCCCCCC)cc1
+*c1ccc(-c2sc(-c3cc(SCCCC)c(*)s3)cc2SCCCC)cc1
+*c1ccc(-c2sc(-c3cc(SCCCCCCCC)c(*)s3)cc2SCCCCCCCC)cc1
+*c1ccc(-c2sc(-c3cc(SCCCCCCCCCCCC)c(*)s3)cc2SCCCCCCCCCCCC)cc1
+*c1ccc(-c2sc(-c3ccc(-c4cc(CCCC)c(*)s4)cc3)cc2CCCC)cc1
+*c1ccc(-c2sc(-c3ccc(-c4cc(CCCCCCCC)c(*)s4)cc3)cc2CCCCCCCC)cc1
+*c1ccc(-c2sc(-c3ccc(-c4cc(CCCCCCCCCCCC)c(*)s4)cc3)cc2CCCCCCCCCCCC)cc1
+*c1ccc(-c2sc(-c3ccc(-c4cc(SCCCC)c(*)s4)cc3)cc2SCCCC)cc1
+*c1ccc(-c2sc(-c3ccc(-c4cc(SCCCCCCCC)c(*)s4)cc3)cc2SCCCCCCCC)cc1
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+*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)cc1
+*c1ccc(-n2ccc(-c3ccc(-c4ccn(*)n4)cc3)n2)cc1
+*c1ccc(-n2ccc(-c3cccc(-c4ccn(*)n4)c3)n2)cc1
+*c1ccc(/C=C(\C#N)C(=O)NC2CCCCC2NC(=O)/C(C#N)=C/c2ccc(N(c3ccccc3)c3ccc(N(*)c4ccccc4)cc3)cc2)cc1
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+*c1ccc(/C=C/c2cc(OCC(CC)CCCC)c(/C=C/c3ccc(N(*)c4ccccc4)cc3)cc2OC)cc1
+*c1ccc(/C=C/c2cc(OCCCCCCCC)c(/C=C/c3ccc(N(*)c4ccccc4)cc3)cc2OCCCCCCCC)cc1
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+*c1ccc(/C=N/c2cccc(/N=C/c3ccc(-c4ccc(*)s4)cc3)c2)cc1
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+*c1ccc(S(=O)(=O)c2ccc(NC(=O)c3cccc(C(=O)Nc4ccc(S(=O)(=O)c5ccc(-n6c(=O)c7cc8c(=O)n(*)c(=O)c8cc7c6=O)cc5)cc4)c3)cc2)cc1
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+*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)C5C6C=CC(C7C(=O)N(*)C(=O)C67)C5C4=O)cc2)C(=O)N(CCN(C)C)C3=O)cc1
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+*c1ccc(Sc2cc3c(cc2Sc2ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc2)C(=O)N(c2ccc(CC)cc2)C3=O)cc1
+*c1ccc(Sc2ccc(-c3nc4cc(-c5ccc6nc(-c7ccccc7)c(*)nc6c5)ccc4nc3-c3ccccc3)cc2)cc1
+*c1ccc(Sc2ccc(-c3nc4ccc(-c5ccc6nc(*)c(-c7ccccc7)nc6c5)cc4nc3-c3ccccc3)cc2)cc1
+*c1ccc(Sc2ccc(-c3nn(-c4ccc(S(=O)(=O)c5ccc(-n6nc(*)c7ccccc7c6=O)cc5)cc4)c(=O)c4ccccc34)cc2)cc1
+*c1ccc(Sc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)cc1
+*c1ccc(Sc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cn2)nc1
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+*c1ccc(Sc2ccc(S(*)(=O)=O)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)s2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Sc6ccc(Sc7ccc(Sc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Sc6ccc(Sc7ccc(Sc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)s2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(-n5c(=O)c6cc7c(=O)n(*)c(=O)c7cc6c5=O)cc4)cc3)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1
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+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(Sc7ccc(Sc8ccc(Sc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(S(*)(=O)=O)cc4S(=O)(=O)O)cc3)cc2)c(S(=O)(=O)O)c1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(S(*)=O)cc4)cc3)cc2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)cc5)s4)s3)s2)cc1
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(Sc8ccc(Sc9ccc(Sc%10ccc%11c(c%10)C(=O)N(*)C%11=O)cc9)cc8)cc7C6=O)cc5)s4)s3)s2)cc1
+*c1ccc(Sc2ccc3c(c2)Sc2ccc(Sc4ccc(-n5c(=O)c6cc7c(=O)n(*)c(=O)c7cc6c5=O)cc4)cc2S3)cc1
+*c1ccc(Sc2cccc(Sc3ccc(S(*)=O)cc3)c2)cc1
+*c1ccc([Si](*)(C)C)s1
+*c1ccc([Si](C)(C)[Si](*)(C)C)s1
+*c1ccc([Si](CCCC)(CCCC)[Si](*)(CCCC)CCCC)s1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3ccc4c(c3)c3cc(*)ccc3n4CCCCCC#N)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(*)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)c5ccccc45)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)cc4)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)nc4)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)o4)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4cccc(-c5nnc(*)o5)n4)o3)cc2)cc1
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4cncc(-c5nnc(*)o5)c4)o3)cc2)cc1
+*c1ccc2[nH]c(*)nc2c1
+*c1ccc2c(c1)C(*)(C)CC2(C)C
+*c1ccc2c(c1)C(=O)N(C1CC(C)(C)CC(C)(CN3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)C1)C2=O
+*c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)C(C)C3)CC1C)C2=O
+*c1ccc2c(c1)C(=O)N(C1CCC(CC3CCC(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)CC3)CC1)C2=O
+*c1ccc2c(c1)C(=O)N(C1CCC(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)CC1)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(C(c3ccccc3)(c3cc(C(C)C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)C(F)(F)F)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(Cc3cc(C(C)C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)c(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c(C)c1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(-c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C)c4)cc3C)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c4ccccc34)c3ccccc13)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)c3cccc4ccccc34)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3cccc(C(F)(F)F)c3)c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C(c3ccccc3)(c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)C(F)(F)F)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C)c(C(=O)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C3(c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C(C)C)c4)c4ccccc4-c4ccccc43)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(C3(c4cc(C)c(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C)c4)c4ccccc4-c4ccccc43)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)C)c3)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(Cc3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1c(CC)cc(Cc3cc(CC)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(CC)c3)cc1CC)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(-c3ccc(O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)ccc1O)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(Br)c(Oc3c(Br)cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3Br)c(Br)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3c(C)cc(-c4cc(C)c(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)c(C)c4)cc3C)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3cccc4c(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cccc34)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)O)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Oc3ccc(C(c4ccc(OC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1O)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OC(=O)C45CC6CC(CC(C6)C4)C5)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OC(=O)C13CC4CC(CC(C4)C1)C3)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OC(C)=O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OC(C)=O)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C(c3ccc(OCCN(CC)c4ccc(/N=N/c5ccc([N+](=O)[O-])cc5)cc4)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)ccc1OCCN(CC)c1ccc(/N=N/c3ccc([N+](=O)[O-])cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C)c(-c3c(C)cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C)c(C)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c(O)cc1O)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1OCCCCCCOc1ccc(-c3ccc(OCC)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)ccc1OCCCCCCOc1ccc(/N=N/c3ccc(OCC)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(NC(=O)OCCN(CCOC(=O)Nc3cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)ccc3C)c3ccc(/N=N/c4ccc([N+](=O)[O-])cc4)cc3)ccc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(OCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(OCCCCCCCCCCCCCCCC)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(OCCN(CC)c3ccc(/N=N/c4ccc([N+](=O)[O-])cc4)cc3)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cc(Oc3c(C)cc(-c4cc(C)c(Oc5cc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc(C(F)(F)F)c5)c(C)c4)cc3C)cc(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3cc(-c4ccc(OCCCC#N)cc4)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)n3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C)c3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(O)c3)cc1O)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OC(=O)/C=C/c4ccccc4)c3)cc1OC(=O)/C=C/c1ccccc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCCCCCOc4ccc(/C=C/c5ccc(F)cc5)cc4)c3)cc1OCCCCCCOc1ccc(/C=C/c3ccc(F)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCN(CC)c4ccc(C(C#N)=C(C#N)C#N)cc4)c3)cc1OCCN(CC)c1ccc(C(C#N)=C(C#N)C#N)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCOc4ccc5c(C)cc(=O)oc5c4)c3)cc1OCCOc1ccc3c(C)cc(=O)oc3c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(c6ccccc6)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(Oc4ccc5c(c4)C(C)(c4ccc(Oc6ccc(-c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6C(F)(F)F)cc4)CC5(C)C)c(C(F)(F)F)c3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c(C)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7ccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)cc7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4cccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4ccccc4Oc4ccc(NC(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(C(C)(C)c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(C)(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)cc1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)(C(F)(F)F)C(F)(F)F)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccccc3)(c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)C(F)(F)F)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(C(C)C)c4)c4ccccc4-c4ccccc43)cc1C(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c(F)c4)c4ccccc4-c4ccccc43)cc1F)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c4ccccc4-c4ccccc43)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(C(C)(C)C)c3)cc1C(C)(C)C)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(C)(C)CCCCC(*)(C)C)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Cc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N(C)CCN(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(/C=C/c4ccc([N+](=O)[O-])cc4)cc3)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N(c3ccc(C#N)cc3)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(-c5c(-c6ccccc6)c(-c6ccccc6)c(-c6ccc(Oc7ccc(-c8c(-c9ccccc9)c(-c9ccccc9)c(*)c(-c9ccccc9)c8-c8ccccc8)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)cc4C3=O)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(-c5c(-c6ccccc6)c(-c6ccccc6)c(-c6ccc(Sc7ccc(-c8c(-c9ccccc9)c(-c9ccccc9)c(*)c(-c9ccccc9)c8-c8ccccc8)cc7)cc6)c(-c6ccccc6)c5-c5ccccc5)cc4C3=O)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(NC(=S)/N=C/c3ccc(Cl)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OC(C)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OC(CCC)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OC(CCCCCC)COc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCCCCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCN(CCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c3ccc(C(C#N)=C(C#N)C#N)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCN(CCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c3ccccc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOCCOCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C(C)(C)C)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C(C)(C)C)cc(-c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(C(c4cc(C)c(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)c(C)c4)c4cccc5ccccc45)cc3C)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3c(C)cc(Cc4cc(C)c(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)c(C)c4)cc3C)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cc(C(C)(C)C)c(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3C(C)(C)C)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(-c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)n4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(C(=O)c5ccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6C(F)(F)F)cc5)n4)cc3)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)(C(F)(F)F)C(F)(F)F)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5C(F)(F)F)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Oc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Sc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c(O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c(OCCN(CC)c3ccc(/N=N/c4ccc([N+](=O)[O-])cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3Br)c(Br)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O[Si](C)(C)C)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(C)(C)CCCCCCCCC(*)(C)C)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4c3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(-c4cc(-c5ccccc5)cc(-c5cccc(Oc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)c5)n4)c3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3cccc(Oc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)c3C#N)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3-c3ccccc3Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3C(F)(F)F)c(C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(S(=O)(=O)c4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(S(=O)(=O)c3ccc(S(=O)(=O)c4ccc(S(=O)(=O)c5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Oc4ccc(Sc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(Sc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc(Sc3ccc(Sc4ccc(Sc5ccc(Sc6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)cc4)cc3)cc1)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc3c(c1)-c1cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)ccc1C3)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccc3c(c1)Cc1cc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)ccc1-3)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(-c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5C)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(NC(=O)c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(-c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(C(c5ccc(Oc6ccc(NC(=O)c7cccc(N8C(=O)c9ccc(C(*)(C(F)(F)F)C(F)(F)F)cc9C8=O)c7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)c6)cc5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4cccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)c6)cc5)c4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccccc4Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(C)(C)c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(F)(F)C(F)(F)C(F)(F)c3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(O)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(C(c3ccccc3)(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)C(F)(F)F)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1C)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)ccc3P(=O)(c3ccccc3)c3ccccc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(C(c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Oc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Sc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(N4C(=O)c5ccc(C(F)(F)C(F)(F)C(*)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cccc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(C(F)(F)F)cc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccc(Oc4ccc(C56CC7CC(CC(C7)C5)C6)cc4)cc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(P(=O)(c3ccccc3)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc(S(=O)(=O)c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)c1)C2=O
+*c1ccc2c(c1)C(=O)N(c1cccc3c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cccc13)C2=O
+*c1ccc2c(c1)C(=O)N(c1ccccc1Oc1ccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1)C2=O
+*c1ccc2c(c1)C(CC(CC)CCCC)(CC(CC)CCCC)c1cc(*)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(*)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(CCCCCC)c(*)cc3CCCCCC)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(CCCCCCCC)c(*)cc3CCCCCCCC)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(OCCCCCCCC)c(*)cc3OCCCCCCCC)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3cc(OCc4ccccc4)c(*)cc3OCc3ccccc3)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)C(CC3=NC(Cc5ccccc5)CO3)(CC3=NC(Cc5ccccc5)CO3)c3cc(*)ccc3-4)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccc(C)cc3)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccc(OC)cc3)ccc1-2
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)c3cc(*)ccc3n4-c3ccccc3)ccc1-2
+*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(*)ccc1-2
+*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3c(F)c(F)c(*)c(F)c3F)ccc1-2
+*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3cc(F)c(*)cc3F)ccc1-2
+*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc(*)c4nsnc34)ccc1-2
+*c1ccc2c(c1)C(CCCCCCCC)(CCCCCCCC)c1cc(-c3ccc4c(c3)C(c3ccc(-c5nnc(-c6ccc(C(C)(C)C)cc6)o5)cc3)(c3ccc(-c5nnc(-c6ccc(C(C)(C)C)cc6)o5)cc3)c3cc(*)ccc3-4)ccc1-2
+*c1ccc2c(c1)Cc1cc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)ccc1-2
+*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCC
+*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCCCCCC
+*c1ccc2c(c1)Sc1cc(-c3ccc(-c4ccc(*)s4)s3)ccc1N2CCCCCCCCCCCC
+*c1ccc2c(c1)Sc1cc(-c3sc(/C=C/C4=CC(=C(C#N)C#N)C=C(/C=C/c5sc(*)c(CCCCCC)c5CCCCCC)O4)c(CCCCCC)c3CCCCCC)ccc1N2CCCCCC
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCC
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCCCCCC
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCCCCCCCC
+*c1ccc2c3ccc(-c4ccc(-c5ccc(-c6ccc(*)s6)c6nccnc56)s4)cc3n(C(CCCCCCCC)CCCCCCCC)c2c1
+*c1ccc2c3ccc(-c4ccc(-c5ccc(-c6ccc(*)s6)c6nsnc56)s4)cc3n(C(CCCCCCCC)CCCCCCCC)c2c1
+*c1ccc2cc(-c3nc4ccc(-c5ccc6nc(*)[nH]c6c5)cc4[nH]3)ccc2c1
+*c1ccc2cc(OC(=O)c3ccc(C(=O)Oc4ccc5cc(-c6nc7ccccc7nc6*)ccc5c4)cc3)ccc2c1
+*c1ccc2cc(OC(=O)c3cccc(C(=O)Oc4ccc5cc(-c6nc7ccccc7nc6*)ccc5c4)c3)ccc2c1
+*c1ccc2cc3cc(-c4sc(-c5cc(CCCCCCCCCCCC)c(*)s5)cc4CCCCCCCCCCCC)ccc3cc2c1
+*c1ccc2nc(*)cc(-c3ccccc3)c2c1
+*c1ccc2nc(-c3ccc4c(c3)C(=O)N(c3cc(C(*)(C(F)(F)F)C(F)(F)F)ccc3O)C4=O)oc2c1
+*c1ccc2oc(-c3cc(OCCCCCCCCCC)c(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cc3OCCCCCCCCCC)nc2c1
+*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cc3)nc2c1
+*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)cn3)nc2c1
+*c1ccc2oc(-c3ccc(-c4nc5cc(C(*)(c6ccccc6)C(F)(F)F)ccc5o4)cc3)nc2c1
+*c1ccc2oc(-c3ccc(C(c4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)(C(F)(F)F)C(F)(F)F)cc3)nc2c1
+*c1ccc2oc(-c3ccc(C(c4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)(C(F)(F)F)C(F)(F)F)cc3)nc2c1
+*c1ccc2oc(-c3ccc(C(c4ccccc4)(c4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)C(F)(F)F)cc3)nc2c1
+*c1ccc2oc(-c3ccc(C(c4ccccc4)(c4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)C(F)(F)F)cc3)nc2c1
+*c1ccc2oc(-c3ccc(Oc4ccc(-c5nc6cc(C(*)(C(F)(F)F)C(F)(F)F)ccc6o5)cc4)cc3)nc2c1
+*c1ccc2oc(-c3ccc(Oc4ccc(-c5nc6cc(C(*)(c7ccccc7)C(F)(F)F)ccc6o5)cc4)cc3)nc2c1
+*c1ccc2oc(-c3ccc(Oc4ccc(C(c5ccc(Oc6ccc(-c7nc8cc(C(*)(C(F)(F)F)C(F)(F)F)ccc8o7)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)nc2c1
+*c1ccc2oc(-c3ccc4oc5ccc(-c6nc7cc(C(*)(C(F)(F)F)C(F)(F)F)ccc7o6)cc5c4c3)nc2c1
+*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)c3)nc2c1
+*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)n3)nc2c1
+*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(c6ccccc6)C(F)(F)F)ccc5o4)c3)nc2c1
+*c1ccc2oc(C3CCC(c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)CC3)nc2c1
+*c1ccc2oc3ccc(S(*)(=O)=O)cc3c2c1
+*c1ccc2oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(S(*)(=O)=O)cc5)cc4)cc3c2c1
+*c1cccc(*)c1
+*c1cccc(-c2cc(C(=O)Nc3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc(-c3cccc(N4C(=O)CC(C5C=C(C)C6C(=O)N(*)C(=O)C6C5)C4=O)c3)c2)c1
+*c1cccc(-c2cc(C(=O)Nc3ccc(OC(=O)c4cc(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c(OCCCCCCCCCCCC)c4)cc3)cc(-c3cccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)c3)c2)c1
+*c1cccc(-c2ccc3[nH]c(S(=O)(=O)c4nc5ccc(*)cc5[nH]4)nc3c2)c1
+*c1cccc(-c2cccc(C(F)(F)C(F)(F)C(*)(F)F)c2)c1
+*c1cccc(-c2nc3cc(-c4ccc5[nH]c(*)nc5c4)ccc3[nH]2)c1
+*c1cccc(-c2nc3cc(-c4ccc5c(c4)nc(*)n5C)ccc3[nH]2)c1
+*c1cccc(-c2nc3cc(-c4ccc5nc(*)c(-c6ccc7ccccc7c6)nc5c4)ccc3nc2-c2ccc3ccccc3c2)c1
+*c1cccc(-c2nc3cc(-c4ccc5nc(*)c(-c6ccccc6)nc5c4)ccc3nc2-c2ccccc2)c1
+*c1cccc(-c2nc3cc(-c4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)c1
+*c1cccc(-c2nc3cc(Oc4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)c1
+*c1cccc(-c2nc3cc4nc(*)c(-c5ccccc5)nc4cc3nc2-c2ccccc2)c1
+*c1cccc(-c2nc3cc4nc(-c5ccccc5)c(*)nc4cc3nc2-c2ccccc2)c1
+*c1cccc(-c2nc3ccc(-c4ccc5nc(*)[nH]c5c4)cc3[nH]2)c1
+*c1cccc(-c2nc3ccc(-c4ccc5nc(*)sc5c4)cc3s2)c1
+*c1cccc(-c2nc3ccc(Oc4ccc5nc(*)c(-c6ccccc6)nc5c4)cc3nc2-c2ccccc2)c1
+*c1cccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c1
+*c1cccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)n1
+*c1cccc(-n2nc(-c3ccccc3)c3cc(Oc4ccc(C(C)(C)c5ccc(Oc6ccc7c(-c8ccccc8)nn(*)c(=O)c7c6)cc5)cc4)ccc3c2=O)c1C#N
+*c1cccc(-n2nc(-c3ccccc3)c3cc(Oc4ccc5c(-c6ccccc6)nn(*)c(=O)c5c4)ccc3c2=O)c1C#N
+*c1cccc(C(=O)Nc2ccc(Cc3ccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(c6ccc(Cc8ccc(NC(=O)c9cccc(-c%10nc%11cc(-c%12ccc%13[nH]c(*)nc%13c%12)ccc%11[nH]%10)c9)cc8)cc6)C7=O)cc5C4=O)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(NC(=O)c3cccc(N4C(=O)c5ccc(-c6cccc7c6C(=O)N(*)C7=O)cc5C4=O)c3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(-c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(-c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(C(C)(C)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)c4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7cccc8c7C(=O)N(*)C8=O)cc6C5=O)c4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8cccc9c8C(=O)N(*)C9=O)cc7C6=O)c5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1
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+*c1cccc(C(=O)Nc2ccc(Oc3cccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c5)cc4)c3)cc2)c1
+*c1cccc(C(=O)Nc2ccc(Oc3cccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8cccc9c8C(=O)N(*)C9=O)cc7C6=O)c5)cc4)c3)cc2)c1
+*c1cccc(C(=O)Nc2cccc(S(=O)(=O)c3cccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)c4)c3)c2)c1
+*c1cccc(C(=O)Nc2cccc(S(=O)(=O)c3cccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7cccc8c7C(=O)N(*)C8=O)cc6C5=O)c4)c3)c2)c1
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+*c1cccc(C(=O)c2c(C)cc(C)c(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2C)c1
+*c1cccc(C(=O)c2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3)cc2)c1
+*c1cccc(C(=O)c2ccc(Oc3ccc(N4C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5C4=O)cc3C(F)(F)F)cc2)c1
+*c1cccc(C(=O)c2cccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)c2)c1
+*c1cccc(C(F)(F)C(F)(F)C(*)(F)F)c1
+*c1cccc(C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1
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+*c1cccc(C(O)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1
+*c1cccc(C(O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C)c2cccc(N3C(=O)c4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1
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+*c1cccc(CCCOc2cccc(N3C(=O)c4cccc(-c5ccc6c(c5)C(=O)N(*)C6=O)c4C3=O)c2)c1
+*c1cccc(Cc2cccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)c2)c1
+*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1
+*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1
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+*c1cccc(N/C=C/C(=O)c2cccc(C(=O)/C=C/Nc3cccc(S(*)(=O)=O)c3)c2)c1
+*c1cccc(N2C(=O)C(=O)N(CCCCCCN3C(=O)C(=O)N(*)C3=O)C2=O)n1
+*c1cccc(N2C(=O)C(=O)N(c3ccc(Cc4ccc(N5C(=O)C(=O)N(*)C5=O)cc4)cc3)C2=O)n1
+*c1cccc(N2C(=O)C3CCC(C4CCC5C(=O)N(*)C(=O)C5C4)CC3C2=O)c1
+*c1cccc(N2C(=O)c3c(c(-c4ccccc4)c(-c4ccc(-c5c(-c6ccccc6)c6c(c(-c7ccccc7)c5-c5ccccc5)C(=O)N(*)C6=O)cc4)c(-c4ccccc4)c3-c3ccccc3)C2=O)c1
+*c1cccc(N2C(=O)c3ccc(-c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1
+*c1cccc(N2C(=O)c3ccc(-c4cccc5c4C(=O)N(*)C5=O)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3ccc(Oc4cc5ccccc5cc4Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1
+*c1cccc(N2C(=O)c3ccc(Oc4ccc(-c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1
+*c1cccc(N2C(=O)c3ccc(Oc4ccc(C(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3ccc(Oc4ccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4)cc3C2=O)c1
+*c1cccc(N2C(=O)c3ccc(Oc4ccc(S(=O)(=O)c5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3ccc(Oc4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3ccc(Oc4ccc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5c4)cc3C2=O)c1
+*c1cccc(N2C(=O)c3ccc(Oc4cccc(Oc5ccc6c(c5)C(=O)N(*)C6=O)c4)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3ccc(P(=O)(c4ccccc4)c4ccc5c(c4)C(=O)N(*)C5=O)cc3C2=O)c1
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+*c1cccc(N2C(=O)c3cccc(Oc4ccc(Oc5cccc6c5C(=O)N(*)C6=O)cc4)c3C2=O)c1
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+*c1cccc(N2C(=O)c3cccc(Oc4cccc(Oc5cccc6c5C(=O)N(*)C6=O)c4)c3C2=O)c1
+*c1cccc(NC(=O)CCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1
+*c1cccc(NC(=O)CCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1
+*c1cccc(NC(=O)CCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1
+*c1cccc(NC(=O)CCCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1
+*c1cccc(NC(=O)CCCCCCCCCCC(=O)Nc2cccc(S(*)(=O)=O)c2)c1
+*c1cccc(NC(=O)c2ccc(-c3ccc(C(=O)Nc4cccc(S(*)(=O)=O)c4)c(C)c3)cc2C)c1
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+*c1cn(*)c2ccccc12
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+*c1nc(C)nc(N(CCCCCCCCCCN(*)c2ccccc2)c2ccccc2)n1
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+*c1nnc(-c2cc(CCCCCCCCCC)c(-c3sc(-c4nnc(-c5cc(OCCCCCCCC)c(*)cc5OCCCCCCCC)o4)cc3CCCCCCCCCC)s2)o1
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+*c1nnc(-c2ccc(Oc3ccc(/C=C/c4ccc(Oc5ccc(*)c6ccccc56)cc4)cc3)c3ccccc23)o1
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+*c1nnc(-c2sc(-c3nnc(-c4cc(OCCCCCCCC)c(*)cc4OCCCCCCCC)o3)cc2CCCCCCCC)o1
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diff --git a/simson_modeling/kaggle_comp/train_supplement/dataset3.csv b/simson_modeling/kaggle_comp/train_supplement/dataset3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3061c90352e3aee841b3e7065db5ade3e8531774
--- /dev/null
+++ b/simson_modeling/kaggle_comp/train_supplement/dataset3.csv
@@ -0,0 +1,47 @@
+SMILES,Tg
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+*C(=O)OC(=O)COc1ccc(OCC(=O)OC(=O)c2ccc(*)nc2)cc1,155.9709567
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diff --git a/simson_modeling/kaggle_comp/train_supplement/dataset4.csv b/simson_modeling/kaggle_comp/train_supplement/dataset4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5d9bcbe2291f4813ab9e6e951abdfbaef48d6e2a
--- /dev/null
+++ b/simson_modeling/kaggle_comp/train_supplement/dataset4.csv
@@ -0,0 +1,863 @@
+SMILES,FFV
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+*CC(*)(C)C(=O)OCCCCCCOc1ccc(C(=O)Oc2ccc(C(=O)C=Cc3c(C)c4ccccc4n3CCCC)cc2)cc1,0.36019365
+*CC(*)(C)C(=O)OCCC[Si]12O[Si]3(CC(C)C)O[Si]4(CC(C)C)O[Si](CC(C)C)(O1)O[Si]1(CC(C)C)O[Si](CC(C)C)(O2)O[Si](CC(C)C)(O3)O[Si](CC(C)C)(O4)O1,0.40617403
+*CC(*)(CC(=O)OC1CCCCCCC1)C(=O)OC1CCCCCCC1,0.36818557
+*CC(*)(CC(=O)OCCC1CCCCC1)C(=O)OCCC1CCCCC1,0.3743525
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+*CC(*)C(=O)N1CCCCC1,0.36828508
+*CC(*)C(=O)Nc1ccc2c(c1)C(=O)c1ccccc1C2=O,0.32709343
+*CC(*)C(=O)OCC1(CC)COC(c2ccccc2)OC1,0.34648032
+*CC(*)C(=O)OCCOc1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(OCCCC)cc3)cc2)cc1,0.3436788
+*CC(*)C(=O)OCCOc1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(OCCCCC)cc3)cc2)cc1,0.34788271
+*CC(*)C(=O)OCCOc1ccc(C(C)(C)c2ccc(OCCO)cc2)cc1,0.33916196
+*CC(*)C(=O)Oc1ccc(C(=O)OCc2ccccc2)cc1,0.33941879
+*CC(*)C(=O)Oc1ccc(C(=O)Oc2ccc(OC(=O)c3ccc(OCCCCC)cc3)cc2)cc1,0.34978241
+*CC(*)N1CCCCCC1=O,0.36209846
+*CC(*)c1ccc(C(=O)CCN2CCCCC2)cc1,0.37311156
+*CC(*)c1ccc(C(=O)N(CC)CC)cc1,0.36711341
+*CC(*)c1ccc(C(=O)N2CCOCC2)cc1,0.35986197
+*CC(*)c1ccc(COCCOCCCC)cc1,0.37958253
+*CC(*)c1ccc(COc2ccc(-c3ccc(-c4ccc(C)s4)c4nsnc34)cc2)cc1,0.36773
+*CC(*)n1cncn1,0.33624313
+*CC(=O)Nc1ccc(Oc2cccc(Oc3ccc(NC(=O)CN4C(=O)c5ccc(C(c6ccc7c(c6)C(=O)N(*)C7=O)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)c2)cc1,0.34064566
+*CC(=O)OC(=O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.33458907
+*CC(C)(C)C1C(=O)N(C2CCCCC2)C(=O)C1*,0.36558232
+*CC(C)(C)COC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1,0.3487602
+*CC(C)(C)CS(=O)(=O)CC(C)(C)COC(=O)Nc1ccc(Cc2ccc(NC(=O)O*)cc2)cc1,0.35233663
+*CC(C)N1C(=O)C2C3C=CC(C4C(=O)N(*)C(=O)C34)C2C1=O,0.3634472
+*CC(CCCCCCCCCCCCCCCC)C(CCCCCCCCCCCCCCCC)COC(=O)c1ccc(C(=O)O*)cc1,0.39720442
+*CC(CO)(CCl)COc1ccc(C(c2ccc(O*)cc2)(C(F)(F)F)C(F)(F)F)cc1,0.35329338
+*CC(COc1c(Cl)cc(C(C)(C)c2cc(Cl)c(O*)c(Cl)c2)cc1Cl)OC(C)=O,0.36697598
+*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)C=Cc1ccccc1,0.35543793
+*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)CC,0.35519812
+*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(=O)CCl,0.35599023
+*CC(COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1)OC(C)=O,0.35142781
+*CC(F)(F)C1(F)CC(C(O)(C(F)(F)F)C(F)(F)F)CC1*,0.32084568
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+*CC(O)CN(CCO)CC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.33466845
+*CC(O)COC(=O)/C=C\C(=O)Oc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.33533983
+*CC(O)COC(=O)CCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.33384251
+*CC(O)COC(=O)CCCCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.34100579
+*CC(O)COC(=O)CCCCCCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.3435317
+*CC(O)COC(=O)CCCCCCCCCCC(=O)OCC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.34941747
+*CC(O)COc1c(C)cc(C(C)(C)c2cc(C)c(O*)c(C)c2)cc1C,0.37509406
+*CC(O)COc1c(Cl)cc(C(C)(C)c2cc(Cl)c(O*)c(Cl)c2)cc1Cl,0.36746955
+*CC(O)COc1ccc(C(C)(C)c2ccc(O*)c(Cl)c2)cc1Cl,0.36275869
+*CC(O)COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.35095055
+*CC(O)COc1ccc(C(C)(CC)c2ccc(O*)cc2)cc1,0.35394579
+*CC(O)COc1ccc(Cc2ccc(O*)cc2)cc1,0.34455061
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+*CC(c1ccccn1)C(c1ccccc1)C(*)c1ccccn1,0.39661394
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+*COc1ccc(C(C)(C)c2ccc(O*)cc2)cc1,0.35264631
+*COc1ccc(C(c2ccc(O*)cc2)(C(F)(F)F)C(F)(F)F)cc1,0.34402643
+*Cc1cc2cc(C(=O)Nc3ccc(-c4ccc(NC(=O)c5cc6cc(*)c(OC(=O)COc7ccc8ccc(=O)oc8c7)cc6oc5=O)cc4)cc3)c(=O)oc2cc1OC(=O)COc1ccc2ccc(=O)oc2c1,0.32178966
+*Cc1ccc(COC(=O)c2cccc(C(=O)O*)c2)cc1,0.34174571
+*Cc1ccc(COP(=O)(N=Nc2ccc(-c3ccc(N=NP(=O)(O*)OC)cc3)cc2)OC)cc1,0.35769989
+*Cc1ccc(C[n+]2ccc(-c3cc[n+](*)cc3)cc2)cc1,0.36770634
+*Cc1ccc2nc(-c3cc(-c4nc5ccc(*)cc5c(=O)o4)cc(N4C(=O)c5ccccc5C4=O)c3)oc(=O)c2c1,0.37326662
+*Cc1cccc(C[n+]2ccc(-c3cc[n+](*)cc3)cc2)c1,0.36232627
+*Cc1ccccc1C[n+]1ccc(-c2cc[n+](*)cc2)cc1,0.35795758
+*N/C(C=C)=C/C=C(\C)C1(c2ccc(N*)cc2)CCCCC1,0.35375342
+*N/N=N\c1ccc(N*)c2c1C(=O)c1c(N)ccc(N)c1C2=O,0.34005083
+*N=P(*)(Oc1ccc2ccccc2c1)Oc1ccc2ccccc2c1,0.3648512
+*NC(=C)/C=C\C(=C/C)C1(c2ccc(N*)cc2)CCCCC1,0.3442031
+*NC/C=C(/c1cccc(-c2ccccc2)c1)c1ccccc1-c1ccccc1-c1ccc(C2(c3ccccc3)c3ccccc3-c3ccccc32)cc1N*,0.37006353
+*NC1=C(N)N[C@H](N*)NC1=O,0.28322193
+*NC1=C(c2c(N*)ccc3ccccc23)c2ccccc2CC1,0.35718606
+*NC1=C(c2ccccc2)[C@@](O)(N*)[C@H](N)C=C1,0.32796087
+*NC1=CC=C(c2ccc(N*)cc2)C(C(C)(C)C)(C(C)(C)C)C1,0.34704926
+*NC1=NC(=S)N=C(N)C1N*,0.30874827
+*NCC1(C)CC(N*)CC(C)(C)C1,0.34576246
+*NCC1CCC(CN*)CC1,0.33576539
+*NCCCc1ccc2ccc3ccc(N*)cc3c2c1,0.33634808
+*NCCc1ccc2ccc3ccc(N*)cc3c2c1,0.33458647
+*NC[C@@H]1CCC[C@@H](CN*)C1,0.32836534
+*NNC(=O)C=CC(=O)Nc1cccc(C=C2CCCC(=Cc3cccc(NC(=O)C=CC(*)=O)c3)C2=O)c1,0.3376016
+*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ccc(C)cn2)C3=O)cc(C(=O)NNC(=O)c2ccc(C(*)=O)cc2)c1,0.33692653
+*NNC(=O)c1cc(NC(=O)c2ccc3c(c2)C(=O)N(c2ncccc2C)C3=O)cc(C(=O)NNC(=O)c2ccc(C(*)=O)cc2)c1,0.33234263
+*NNC(=O)c1ccccc1C(=O)Nc1cccc(C=C2CCCC(=Cc3cccc(NC(=O)c4ccccc4C(*)=O)c3)C2=O)c1,0.35281266
+*N[C@H]1C(=O)NC(=O)[C@@](N*)(n2c(C)nc3c(N)c(N)cc(N)c3c2=O)[C@H]1N,0.35197979
+*Nc1c(C)cc(C(c2cc(C)c(N*)c(C)c2)C(c2ccc(N)c(C(C)(C)C)c2)c2ccc(N)c(C(C)(C)C)c2)cc1C,0.39156416
+*Nc1c(C)cc(Cc2cc(C)c(N*)c(CC)c2)cc1CC,0.36832
+*Nc1c(CC)cc(Cc2cc(CC)c(N*)c(CC)c2)cc1CC,0.37359767
+*Nc1c(CC)cc(Cc2cc(CC)c(N*)c(CC)c2Cl)c(Cl)c1CC,0.37886544
+*Nc1c(N)c2c(c(N*)c1NC)C(=O)c1cccc(N)c1C2=O,0.34496829
+*Nc1c(N*)c(-c2ccccc2-c2ccccc2)c(-c2ccccc2)c(-c2ccccc2)c1-c1ccccc1,0.37334739
+*Nc1cc(C(c2ccc(C)c(N*)c2)(C(F)(F)F)C(F)(F)F)ccc1C,0.34739951
+*Nc1cc(C(c2ccc(O)c(N*)c2)(C(F)(F)F)C(F)(F)F)ccc1O,0.32241826
+*Nc1cc(C)c(-c2c(C)cc(N*)cc2-c2ccccc2)c(-c2ccccc2)c1,0.36600387
+*Nc1cc(C)c(C2(c3c(C)cc(N*)c4ccccc34)c3ccccc3-c3ccccc32)c2ccccc12,0.38470754
+*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,0.35997455
+*Nc1cc(C)c(Cc2ccc(CCCCCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,0.35798452
+*Nc1cc(C)c(Cc2ccc(CCCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,0.36147124
+*Nc1cc(C)c(Cc2ccc(CCCCCCCCc3ccc(Cc4c(C)cc(N*)cc4C)cc3)cc2)c(C)c1,0.35897643
+*Nc1cc(C)c(N*)c2c1C(=O)c1c(N)c(C)cc(N)c1C2=O,0.34660788
+*Nc1cc(C2CCCCC2)c2ccc3c(N*)c(-c4ccc(C5(c6ccccc6)c6ccccc6-c6ccccc65)cc4)c(C4CCCCC4)c4ccc1c2c34,0.39403941
+*Nc1cc(N)c(N)c(C)c1C(=O)Oc1cc(N)c(C(=O)Oc2cc(N)c(N)c(C)c2N)c(N)c1N*,0.32137021
+*Nc1cc(N*)c(Cc2cc(C)c(N)cc2N)cc1C,0.35659354
+*Nc1cc(N*)c2c(ccc3ccccc32)c1,0.33406138
+*Nc1cc(N*)c2ccc3ccccc3c2c1,0.33145622
+*Nc1cc(NC(=O)Nc2ccc(Cc3ccc(NC(*)=O)cc3)cc2)cc(C(=O)Nc2ccc3c(c2)C(=O)c2ccccc2C3=O)c1,0.33705899
+*Nc1cc(NC(=O)Nc2ccc(NC(*)=O)cc2)cc(C(=O)Nc2cccc3c2C(=O)c2ccccc2C3=O)c1,0.32684433
+*Nc1cc(NC(=O)c2cc(NC(=O)C(C(C)CC)N3C(=O)c4ccccc4C3=O)cc(C(*)=O)c2)ccc1C,0.34847575
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(C#N)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(C#N)cc2)c1,0.35581869
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(C#N)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc2)c1,0.34405633
+*Nc1cc(NC(=O)c2cc(OCCN(C)c3ccc(S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)cc3)cc(C(*)=O)c2)cc(C(=O)OCCN(C)c2ccc(C#N)cc2)c1,0.3482804
+*Nc1cc(NC(=O)c2ccc(C(*)=O)cc2)cc(C(=O)Oc2cccc3ccccc23)c1,0.34233379
+*Nc1cc(NC(=O)c2ccc3cc(C(*)=O)ccc3c2)cc(C(=O)OCCOc2ccc(C=CC(=O)c3ccccc3)cc2)c1,0.34387133
+*Nc1cc2c(-c3ccc4c(c3)c3ccccc3c3c(N*)c(N)ccc43)cc3c4ccccc4ccc3c2cc1N,0.3459392
+*Nc1cc2c(N*)cccc2c2ccccc12,0.333837
+*Nc1cc2c(cc(N*)c3c(-c4ccc5ccccc5c4)c(-c4ccc5ccccc5c4)c(-c4ccc5ccccc5c4)c(-c4ccc5ccccc5c4)c32)c2ccccc12,0.37064494
+*Nc1cc2c(cc1N*)C(=O)c1cc(N)c(N)cc1C2=O,0.34542857
+*Nc1cc2c3cccc(-c4ccc(C)c(C)c4)c3c(N*)cc2c2ccccc12,0.35695006
+*Nc1cc2cccc(N*)c2c2ccccc12,0.33765318
+*Nc1cc2ccccc2c(N*)c1-c1ccccc1,0.3508717
+*Nc1cc2ccccc2c2c(N*)cccc12,0.33679998
+*Nc1cc2ccccc2c2c1ccc1ccc3c(N*)cc4ccccc4c3c12,0.36566777
+*Nc1ccc(*)cc1OCCCCCCCCCCOc1ccc(C2CCC(CCCCC)CC2)cc1,0.3832015
+*Nc1ccc(-c2c(-c3ccccc3)cc(-c3ccc(-c4cc(-c5ccccc5)c(-c5ccc(NC(=O)c6ccc(C(*)=O)cc6)cc5)c(-c5ccccc5)c4)cc3)cc2-c2ccccc2)cc1,0.38336728
+*Nc1ccc(-c2c(C(C)C)cc(C)cc2C2(c3cc(C)cc(C(C)C)c3-c3ccc(N*)cc3)c3ccccc3-c3ccccc32)cc1,0.39756786
+*Nc1ccc(-c2cc(-c3ccc(-c4ccccc4)cc3)cc(-c3ccc(-c4cc(-c5ccc(N*)cc5)cc(-c5ccc(-c6ccccc6)cc5)c4)cc3)c2)cc1,0.35431916
+*Nc1ccc(-c2cc(-c3ccc(N*)cc3)c3ccc4c(-c5ccc(N)cc5)cc(-c5ccc(N)cc5)c5ccc2c3c54)cc1,0.36303147
+*Nc1ccc(-c2ccc(-c3ccc(N*)c(-c4ccc5ccccc5c4)c3-c3ccc4ccccc4c3)c(-c3ccc4ccccc4c3)c2)c(-c2ccc3ccccc3c2)c1,0.36605712
+*Nc1ccc(-c2ccc(-c3ccc(N*)cc3)c3c2CC2(CCCC2)C3)cc1,0.35946418
+*Nc1ccc(-c2ccc(N*)c(-c3ccc(-c4ccccc4)cc3)c2-c2ccc(-c3ccccc3)cc2)cc1,0.35925281
+*Nc1ccc(-c2ccc(N*)c(-c3cccc(C)c3-c3ccccc3)c2)cc1,0.35748308
+*Nc1ccc(-c2ccc(N*)c(/C=C/c3ccccc3)c2)cc1,0.34484999
+*Nc1ccc(-c2ccc(N*)c(Cc3ccccc3)c2Cc2ccccc2)c(Cc2ccccc2)c1Cc1ccccc1,0.35710982
+*Nc1ccc(-c2ccc(N*)c(N)c2-c2ccccc2)c(-c2ccccc2)c1N,0.35339596
+*Nc1ccc(-c2ccc(N*)cc2-c2cc(N)ccc2-c2ccc(N)cc2)cc1,0.36187072
+*Nc1ccc(-c2ccc(N*)cc2-c2ccc(C)cc2)c(-c2ccc(C)cc2)c1,0.36702255
+*Nc1ccc(-c2ccc(N*)cc2-c2ccc(C)cc2C)c(-c2ccc(C)cc2C)c1,0.37675264
+*Nc1ccc(-c2ccc(N*)cc2-c2ccc(C=C)cc2)c(-c2ccc(C=C)cc2)c1,0.35833444
+*Nc1ccc(-c2ccc(N*)cc2-c2ccc(CC)cc2)c(-c2ccc(CC)cc2)c1,0.36900944
+*Nc1ccc(-c2ccc(N*)cc2-c2cccc(C)c2)c(-c2cccc(C)c2)c1,0.36515839
+*Nc1ccc(-c2ccc(N*)cc2-c2cccc(CC)c2)c(-c2cccc(CC)c2)c1,0.36267578
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2)c(-c2ccccc2)c1,0.35575719
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2-c2ccc(C)cc2)cc1,0.35953405
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2-c2ccccc2)cc1,0.35741644
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2-c2ccccc2C)cc1,0.36316601
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2C(C)(C)C)c(-c2ccccc2C(C)(C)C)c1,0.37633983
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2C(C)C)c(-c2ccccc2C(C)C)c1,0.37695577
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2C)c(-c2ccccc2C)c1,0.36761088
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2CC)c(-c2ccccc2CC)c1,0.3676166
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2CC=C)c(-c2ccccc2CC=C)c1,0.35805994
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2CCC)c(-c2ccccc2CCC)c1,0.36819448
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2CCCC)c(-c2ccccc2CCCC)c1,0.36989526
+*Nc1ccc(-c2ccc(N*)cc2-c2ccccc2[C@@H](C)CC)c(-c2ccccc2[C@@H](C)CC)c1,0.37601542
+*Nc1ccc(-c2ccc(N*)cc2/C(N)=C/c2ccccc2)c(/C(N)=C/c2ccccc2)c1,0.35009849
+*Nc1ccc(-c2ccccc2)c(C2CCCCC2)c1N*,0.37359962
+*Nc1ccc(-c2ccccc2-c2ccccc2-c2ccc(N*)cc2)cc1,0.35490612
+*Nc1ccc(/C(=C(/c2ccc3ccccc3c2)c2ccc(N*)c(-c3ccc4ccccc4c3)c2-c2ccc3ccccc3c2)c2ccc3ccccc3c2)cc1,0.37988811
+*Nc1ccc(/C(C)=C/C(C)(C)c2ccc(N*)cc2)cc1,0.35500937
+*Nc1ccc(/C=C/c2ccc(N*)cc2-c2ccccc2)c(-c2ccccc2)c1,0.35626419
+*Nc1ccc(/C=C/c2ccc3ccccc3c2/C=C/c2ccc(N*)cc2)cc1,0.35372743
+*Nc1ccc(C(=C)C(C)(C)C(C)(C)c2ccc(N*)cc2)cc1,0.34980902
+*Nc1ccc(C(=C)CC(C)(C)c2ccc(N*)cc2)cc1,0.34510572
+*Nc1ccc(C(=C2CCCCC2)c2ccc(N*)cc2)cc1,0.37173273
+*Nc1ccc(C(C)(C)c2cc(C(C)(C)c3ccc(N)cc3)cc(C(C)(C)c3ccc(N*)cc3)c2)cc1,0.36915657
+*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(N*)cc3)cc2)cc1,0.35733218
+*Nc1ccc(C(C)(C)c2ccc(C(C)(C)c3cccc(N*)c3)cc2)cc1,0.35860249
+*Nc1ccc(C(C)(C)c2ccc(N*)cc2)cc1,0.34514842
+*Nc1ccc(C(C)(C=C)c2ccc(N*)cc2)cc1,0.34310285
+*Nc1ccc(C(C)(CC(C)C)c2ccc(N*)cc2)cc1,0.35303022
+*Nc1ccc(C(C)(CC)c2ccc(N*)cc2)cc1,0.35054839
+*Nc1ccc(C(C)(CC=C)c2ccc(N*)cc2)cc1,0.34950176
+*Nc1ccc(C(C)(CCC(C)C)c2ccc(N*)cc2)cc1,0.3593959
+*Nc1ccc(C(C)(CCC)c2ccc(N*)cc2)cc1,0.35256265
+*Nc1ccc(C(C)(CCCC)c2ccc(N*)cc2)cc1,0.35842613
+*Nc1ccc(C(C)(CCCCC)c2ccc(N*)cc2)cc1,0.35858625
+*Nc1ccc(C(C)(CCCCCC)c2ccc(N*)cc2)cc1,0.36233629
+*Nc1ccc(C(C)(c2ccc(N*)cc2)C(C)C)cc1,0.35465545
+*Nc1ccc(C(C)(c2ccc(N*)cc2)[C@H](C)C(=C)C)cc1,0.35652932
+*Nc1ccc(C(CC(C)(C)c2ccc(N*)cc2)=C(C)C)cc1,0.35535977
+*Nc1ccc(C(CC(C)C)(CC(C)C)c2ccc(N*)cc2)cc1,0.36637357
+*Nc1ccc(C(CC)(C/C=C/[C@@H](C)CCC)c2ccc(N*)cc2)cc1,0.36522046
+*Nc1ccc(C(CC)(CC)c2ccc(N*)cc2)cc1,0.3582996
+*Nc1ccc(C(CC)(CCC(C)C)c2ccc(N*)cc2)cc1,0.36819424
+*Nc1ccc(C(CC)(CCC)c2ccc(N*)cc2)cc1,0.35892089
+*Nc1ccc(C(CC)(CCCC)c2ccc(N*)cc2)cc1,0.36309206
+*Nc1ccc(C(CC)(CCCCC)c2ccc(N*)cc2)cc1,0.36534914
+*Nc1ccc(C(CC)(CCCCCC)c2ccc(N*)cc2)cc1,0.36353044
+*Nc1ccc(C(CC)(C[C@H](C)CC)c2ccc(N*)cc2)cc1,0.35671255
+*Nc1ccc(C(CC)c2ccc(N*)c(Cc3ccccc3)c2Cc2ccccc2)c(Cc2ccccc2)c1Cc1ccccc1,0.36855173
+*Nc1ccc(C(CC=C)(CC=C)c2ccc(N*)cc2)cc1,0.34731572
+*Nc1ccc(C(CC=C)c2ccc(N*)cc2)cc1,0.34719504
+*Nc1ccc(C(CCC)(CCC)c2ccc(N*)cc2)cc1,0.36239191
+*Nc1ccc(C(CCCC)(CCCC)c2ccc(N*)cc2)cc1,0.3699291
+*Nc1ccc(C(C[C@@H]2CCC[C@@H](C(c3ccc(N)cc3)c3ccc(N*)cc3)C2)c2ccc(N)cc2)cc1,0.36461546
+*Nc1ccc(C(c2ccc(N*)c(C(C)(C)C)c2)C(c2ccc(N)c(C(C)(C)C)c2)c2ccc(N)c(C(C)(C)C)c2)cc1C(C)(C)C,0.3977767
+*Nc1ccc(C(c2ccc(N*)c(C)c2)C(c2ccc(N)c(C)c2)c2ccc(N)c(C)c2)cc1C,0.38150109
+*Nc1ccc(C(c2ccc(N*)cc2)(C(C)C)C(C)C)cc1,0.36562181
+*Nc1ccc(C(c2ccc(N*)cc2)(C(F)(F)F)C(F)(F)F)cc1,0.34186008
+*Nc1ccc(C(c2ccc(N*)cc2)([C@H](C)CC)[C@H](C)CC)cc1,0.37291553
+*Nc1ccc(C(c2ccc(N*)cc2)C(C)(C)C)cc1,0.3599413
+*Nc1ccc(C(c2ccc(N*)cc2)C(c2ccc(N)cc2)c2ccc(N)cc2)cc1,0.36559722
+*Nc1ccc(C(c2ccc(N*)cc2)C(c2cccc(N)c2)c2cccc(N)c2)cc1,0.35897099
+*Nc1ccc(C(c2ccc(N*)cc2)C(c2ccccc2N)(c2ccccc2N)[C@H](c2ccc(N)cc2)c2ccccc2N)cc1,0.36452149
+*Nc1ccc(C(c2ccc(NC(=O)c3cc(NC(=O)CCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)(C(F)(F)F)C(F)(F)F)cc1,0.33372764
+*Nc1ccc(C2(c3ccc(N*)c(-c4ccc(C)cc4)c3-c3ccc(C)cc3)CCCCC2)c(-c2ccc(C)cc2)c1-c1ccc(C)cc1,0.39222234
+*Nc1ccc(C2(c3ccc(N*)c(C)c3)c3ccccc3Cc3ccccc32)cc1C,0.37721583
+*Nc1ccc(C2(c3ccc(N*)cc3)C=CC=C3C2=Cc2ccccc23)cc1,0.36753725
+*Nc1ccc(C2(c3ccc(N*)cc3)CCCC2)cc1,0.34564076
+*Nc1ccc(C2(c3ccc(N*)cc3)CCc3ccccc32)cc1,0.35743146
+*Nc1ccc(C2(c3ccc(N*)cc3)[C@H]3C[C@@H]4C[C@@H](C[C@H]2C4)C3)cc1,0.36122631
+*Nc1ccc(C2(c3ccc(N*)cc3)c3ccccc3Cc3ccccc32)cc1,0.36815633
+*Nc1ccc(C2=CC=C(c3ccc(N*)cc3)C2(C)C)cc1,0.35499932
+*Nc1ccc(C2=CC[C@](N*)(c3ccccc3-c3ccccc3C)C=C2)cc1,0.35437873
+*Nc1ccc(C2=C[C@@H]3CC[C@H]2C=C3c2ccc(N*)cc2)cc1,0.35798661
+*Nc1ccc(CC[C@@H](Cc2ccc(N*)cc2)[C@@H](Cc2ccc(N)cc2)c2ccccc2)cc1,0.35442602
+*Nc1ccc(CCc2ccc(C3(c4ccc(CCc5ccc(N*)cc5)cc4)CCC(c4ccccc4)CC3)cc2)cc1,0.362202
+*Nc1ccc(Cc2cc(Cc3ccc(N)cc3)cc(Cc3ccc(N*)cc3)c2)cc1,0.36046154
+*Nc1ccc(Cc2ccc(Cc3ccc(N*)c(C)c3C)cc2)c(C)c1C,0.35499508
+*Nc1ccc(Cc2ccc(Cc3ccc(N*)cc3)cc2)cc1,0.34845951
+*Nc1ccc(Cc2ccc(N*)c(C)c2)c(CC)c1,0.3496197
+*Nc1ccc(Cc2ccc(N*)c(C)c2)cc1C,0.35117058
+*Nc1ccc(Cc2ccc(N*)c(C)c2C)c(C)c1,0.35276469
+*Nc1ccc(Cc2ccc(N*)c(C)c2C)c(C)c1C,0.35444913
+*Nc1ccc(Cc2ccc(N*)c(CC)c2CC)c(CC)c1CC,0.37391243
+*Nc1ccc(Cc2ccc(N*)c(CC=C)c2CC=C)c(CC=C)c1CC=C,0.35378076
+*Nc1ccc(Cc2ccc(N*)c(Cl)c2)cc1Cl,0.3514118
+*Nc1ccc(Cc2ccc(N*)cc2)cc1,0.34181931
+*Nc1ccc(Cc2ccc(NC(=O)CCCCCCCC(*)=O)cc2)cc1,0.34905905
+*Nc1ccc(Cc2ccc(NC(=O)CCCCCCCCC(*)=O)cc2)cc1,0.35151514
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCCCCCCCCCNC(*)=O)cc2)cc1,0.37009241
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCCCNC(*)=O)cc2)cc1,0.35680733
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCCNC(*)=O)cc2)cc1,0.35544532
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCCNC(*)=O)cc2)cc1,0.35319219
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCCCNC(*)=O)cc2)cc1,0.35239715
+*Nc1ccc(Cc2ccc(NC(=O)NCCCCCCNC(*)=O)cc2)cc1,0.34117859
+*Nc1ccc(Cc2ccc(NC(=O)Nc3ccccc3CCc3ccc(NC(*)=O)cc3)cc2)cc1,0.35103259
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(C(*)=O)cc(N4C(=O)C5C6C=CC(C6)C5C4=O)c3)cc2)cc1,0.35689692
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)CCCN4C(=O)c5ccccc5C4=O)cc(C(*)=O)c3)cc2)cc1,0.33667621
+*Nc1ccc(Cc2ccc(NC(=O)c3cc(NC(=O)c4ccc(NC(=O)C(CC(C)C)N5C(=O)c6ccccc6C5=O)cc4)cc(C(*)=O)c3)cc2)cc1,0.34741181
+*Nc1ccc(Cc2cccc(N*)c2)cc1,0.33815386
+*Nc1ccc(N*)c(-c2ccc3ccccc3c2)c1,0.34047659
+*Nc1ccc(N*)c(C(c2ccc3ccccc3c2)c2ccc3ccccc3c2)c1,0.36285479
+*Nc1ccc(N*)c2c1ccc1ccccc12,0.33816871
+*Nc1ccc(NC(=O)Cc2cc(C)c(CC(*)=O)cc2C)cc1,0.3373035
+*Nc1ccc(NC(=O)Cc2cc(CC(*)=O)c(C)cc2C)cc1,0.33730529
+*Nc1ccc(NC(=O)c2cc(C(*)=O)cc(S(=O)(=O)c3ccccc3)c2)cc1,0.34297424
+*Nc1ccc(NC(=O)c2ccc(C(=O)c3ccc(C(*)=O)c(C(=O)O)c3)cc2C(=O)O)cc1,0.31794387
+*Nc1ccc(NC(=O)c2ccc(C(=O)c3ccc(C(*)=O)c(C(=O)OCC)c3)cc2C(=O)OCC)cc1,0.34534308
+*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCCCC,0.35844067
+*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCC,0.35882755
+*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCC,0.3595526
+*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCC,0.36201115
+*Nc1ccc(NC(=O)c2ccc(NC(=O)CCCCCCCCCCCCC(=O)Nc3ccc(C(*)=O)cc3)cc2)cc1C(=O)OCCCCCCCCCCCCCCCCCC,0.36659861
+*Nc1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N*)cc4)cc3)cc2)cc1,0.34442362
+*Nc1ccc(Oc2cccc(Oc3ccc(N*)cc3)c2)cc1,0.34111608
+*Nc1ccc(SSc2ccc(N*)cc2)cc1,0.35710288
+*Nc1ccc([C@@H](C)c2ccc(C(C)(C)c3ccc(N*)cc3)cc2)cc1,0.35607415
+*Nc1ccc([C@@H](c2ccccc2)[C@H](c2ccccc2)c2ccc(N*)cc2)cc1,0.36377948
+*Nc1ccc([C@@H]2CC(C)(C)c3cc(N*)ccc32)cc1,0.36237935
+*Nc1ccc([C@@H]2CC(C)(C)c3ccc(N*)cc32)cc1,0.3624745
+*Nc1ccc([C@H](CC)c2ccc([C@@H](CC)c3ccc(N*)cc3)cc2)cc1,0.3623964
+*Nc1ccc([C@H](CCCC)c2ccc(C3(c4ccc([C@@H](CCCC)c5ccc(N*)cc5C)cc4)CCC(C)CC3)cc2)c(C)c1,0.38081893
+*Nc1ccc([C@H](CCCCCC)c2ccc(C3(c4ccc([C@@H](CCCCCC)c5ccc(N*)cc5)cc4)CCC(CCCCC)CC3)cc2)cc1,0.37108303
+*Nc1ccc([C@H](c2ccc(N*)c(C)c2)C(c2ccc(N)c(C)c2)c2ccc(N)c(C)c2)cc1,0.37242979
+*Nc1ccc([C@H](c2cccc(N*)c2)C(c2cccc(N)c2)c2cccc(N)c2)cc1,0.35918801
+*Nc1ccc([C@H](c2ccccc2N*)C(c2ccc(N)cc2)c2ccc(N)cc2)cc1,0.37002899
+*Nc1ccc([C@H]2CC[C@H](c3ccc(N*)cc3)CC2)cc1,0.35770572
+*Nc1ccc2c(N*)cc3ccccc3c2c1,0.334748
+*Nc1ccc2c(c1)-c1ccc3c4ccc5c6c(ccc(c7ccc(c1c73)C2)c64)-c1cc(N*)ccc1C5,0.34349672
+*Nc1ccc2c(c1)C(=O)c1ccc(N*)cc1C2=O,0.33028392
+*Nc1ccc2c(c1)[C@]1(C)CC[C@@]2(C)c2cc(N*)ccc21,0.36507821
+*Nc1ccc2c(c1)c(C)c(C)c1cc(N*)ccc12,0.33627798
+*Nc1ccc2c(c1)c(N*)cc1ccccc12,0.33211721
+*Nc1ccc2c(c1)cc(N*)c1ccccc12,0.33817655
+*Nc1ccc2c(c1N*)CCc1ccccc1-2,0.341292
+*Nc1ccc2c(ccc3c(N*)cccc32)c1,0.3358041
+*Nc1ccc2c(ccc3cc(N*)ccc32)c1,0.3363309
+*Nc1ccc2c(ccc3ccc(N*)cc32)c1,0.33807688
+*Nc1ccc2c(ccc3ccc4ccc5ccc6cc(N*)ccc6c5c4c32)c1,0.3586391
+*Nc1ccc2c(ccc3cccc(N*)c32)c1,0.3331587
+*Nc1ccc2c(ccc3ccccc32)c1N*,0.3361781
+*Nc1ccc2c3cc(-c4ccc(-c5ccccc5)cc4)c(-c4ccc(-c5ccccc5)cc4)c4c(N*)c(-c5ccc(-c6ccccc6)cc5)c(-c5ccc(-c6ccccc6)cc5)c(c5cccc1c25)c43,0.3619219
+*Nc1ccc2cc(N*)c3ccccc3c2c1,0.33919037
+*Nc1ccc2ccc3ccc(N*)cc3c2c1,0.33497194
+*Nc1ccc2ccc3cccc(N*)c3c2c1,0.32965144
+*Nc1ccc2ccc3ccccc3c2c1N*,0.33674811
+*Nc1cccc(-c2cc(N*)ccc2-c2ccccc2-c2ccccc2)c1,0.35340744
+*Nc1cccc(-c2ccccc2-c2cc(N*)ccc2-c2ccccc2)c1,0.35219454
+*Nc1cccc(-c2ccccc2-c2ccccc2-c2cccc(N*)c2)c1,0.35392163
+*Nc1cccc(-c2ccccc2-c2ccccc2-c2ccccc2)c1N*,0.35659206
+*Nc1cccc(C(c2cccc(N*)c2)(C(F)(F)F)C(F)(F)F)c1,0.34059386
+*Nc1cccc(Cc2cccc(N*)c2)c1,0.33697796
+*Nc1cccc(NC(=C(C#N)C#N)c2cccc(C(*)=C(C#N)C#N)c2)c1,0.41003738
+*Nc1cccc(NC(=O)Cc2cc(C)c(CC(*)=O)cc2C)c1,0.3367393
+*Nc1cccc(NC(=O)Cc2cc(CC(*)=O)c(C)cc2C)c1,0.33294467
+*Nc1cccc(NC(=O)c2cc(C(*)=O)cc(S(=O)(=O)c3ccccc3)c2)c1,0.34798034
+*Nc1cccc(NC(=O)c2cc(NC(=O)C(CCSC)N3C(=O)c4ccccc4C3=O)cc(C(*)=O)c2)c1,0.33770588
+*Nc1cccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4cccc(N*)c4)cc3)cc2)c1,0.34393632
+*Nc1cccc(Oc2cccc(Oc3cccc(N*)c3)c2)c1,0.33787009
+*Nc1cccc2c(NC(=O)c3ccc(C(*)=O)cc3)cccc12,0.34599686
+*Nc1cccc2c(NC(=O)c3ccc(NC(=O)c4ccc([Si](C)(C)c5ccc(C(=O)Nc6ccc(C(*)=O)cc6)cc5)cc4)cc3)cccc12,0.3612299
+*Nc1cccc2c1C(=O)c1cccc(N[Se]*)c1C2=O,0.3481788
+*Nc1cccc2c1C1=C(C[C@](N*)(c3ccccc3-c3ccccc3)C=C1)C2(c1ccccc1)c1ccccc1,0.37540911
+*Nc1cccc2c1c(N*)cc1ccccc12,0.33290552
+*Nc1cccc2c1ccc1c(N*)cccc12,0.33410164
+*Nc1cccc2c1ccc1cccc(N*)c12,0.33883964
+*Nc1cccc2ccc3cccc(N*)c3c12,0.34248529
+*Nc1ccccc1-c1cccc(-c2ccccc2-c2cccc(-c3ccccc3)c2)c1N*,0.35563081
+*Nc1ccccc1-c1ccccc1-c1ccccc1-c1ccccc1N*,0.35892837
+*Nc1ccccc1/C=C/c1ccc2ccccc2c1/C=C/c1ccccc1N*,0.35648517
+*Nc1ccccc1CCc1ccccc1NC(=O)Nc1ccc(CCc2ccc(NC(*)=O)cc2)cc1,0.35411182
+*Nc1ccccc1SSc1ccccc1N*,0.35017586
+*Nc1nc(=O)n(C)c(N*)c1N,0.31039992
+*OC(=O)C(Cc1ccccc1)NC(=O)CCCCCCC(=O)NC(Cc1ccccc1)C(=O)OC1COC2C(*)COC12,0.34161233
+*OC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)OC1COC2C(*)COC12,0.34522868
+*OC(=O)C(Cc1ccccc1)NC(=O)CCCCCCCCCCC(=O)NC(Cc1ccccc1)C(=O)OC1COC2C(*)COC12,0.35207165
+*OC(=O)Oc1ccc(C(=O)Oc2ccc(OC(=O)OC3COC4C(*)COC34)cc2)cc1,0.33078823
+*OC(=O)c1ccc(C(=O)OC2COC3C(*)COC23)cc1,0.33842265
+*OC(=O)c1ccc(Cc2ccc(C(*)=O)cc2)cc1,0.34410161
+*OC(COC(=O)c1ccc2cc(C(*)=O)ccc2c1)COc1ccc(N=Nc2ccc(C#N)cc2)cc1,0.35895878
+*OC1C(C)(C)C(OC(=O)C2CCC(C(*)=O)CC2)C1(C)C,0.35756125
+*OS(=O)(=O)c1cccc(S(=O)(=O)Oc2ccc(C(C)(C)c3ccc(*)cc3)cc2)c1,0.36372411
+*OS(=O)(=O)c1cccc(S(=O)(=O)Oc2ccc(C(C)(CC)c3ccc(*)cc3)cc2)c1,0.36537348
+*OS(=O)(=O)c1cccc(S(=O)(=O)Oc2ccc(C3(c4ccc(*)cc4)CCCCC3)cc2)c1,0.36029922
+*O[Si](*)(C)CCCOc1ccc(C(=O)Oc2ccc(C(=O)Oc3ccc(OC)cc3)cc2)cc1,0.34401737
+*O[Si](C)(C)CCCC(=O)Oc1ccc(C=Nc2ccc(Cc3ccc(N=Cc4ccc(OC(=O)CCC[Si](*)(C)C)cc4)cc3)cc2)cc1,0.38299785
+*O[Si](C)(C)CCCN=Cc1cc(Cc2ccc(O)c(C=NCCC[Si](*)(C)C)c2)ccc1O,0.39347596
+*O[Si](C)(C)OC(CCl)COc1ccc(C(C)(C)c2ccc(OCC(*)CCl)cc2)cc1,0.3750933
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12,0.41772931
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12,0.41127913
+*O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)O[Si](C)(C)Oc1c(*)c2ccccc2c2ccccc12,0.40298758
+*O[Si](C)(C)Oc1ccc(C(C)(C)c2ccc(*)cc2)cc1,0.40918281
+*O[Si](C)(C)c1cccc2c1ccc1c([Si](*)(C)C)cccc12,0.40173269
+*O[Si](C)(Oc1ccc(*)cc1)c1ccccc1,0.40342541
+*Oc1c(-c2ccccc2)cc(*)cc1-c1ccccc1-c1ccccc1,0.4050477
+*Oc1c(-c2ccccc2)cc(Cc2cc(-c3ccccc3)c(OC(=O)CCCCC(*)=O)c(-c3ccccc3)c2)cc1-c1ccccc1,0.37195156
+*Oc1c(-c2ccccc2)cc(Cc2cc(-c3ccccc3)c(OC(=O)CCCCCC(*)=O)c(-c3ccccc3)c2)cc1-c1ccccc1,0.37107521
+*Oc1c(-c2ccccc2)cc(Cc2cc(-c3ccccc3)c(OC(=O)CCCCCCCCC(*)=O)c(-c3ccccc3)c2)cc1-c1ccccc1,0.3718338
+*Oc1c(Br)cc(C(C)(C)c2cc(Br)c(OC(*)=O)c(Br)c2)cc1Br,0.4290399
+*Oc1c(Br)cc(C(C)(C)c2cc(Br)c(OC(=O)c3ccc(OCCCCCCCCCCOc4ccc(C(*)=O)cc4)cc3)c(Br)c2)cc1Br,0.39126002
+*Oc1c(Br)cc(C(c2cc(Br)c(OC(*)=O)c(Br)c2)(C(F)(F)F)C(F)(F)F)cc1Br,0.41928552
+*Oc1c(C(C)C)cc(C(=O)c2cccc(C(=O)c3ccc(*)cc3)c2)cc1C(C)C,0.40376992
+*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(*)=O)c(C)c2)cc1C,0.39805007
+*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(*)=O)c(Cl)c2)cc1Cl,0.3979854
+*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)CCCCC(*)=O)c(C)c2)cc1C,0.37611741
+*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)CCCCCC(*)=O)c(C)c2)cc1C,0.37560672
+*Oc1c(C)cc(C(C)(C)c2cc(C)c(OC(=O)CCCCCCCCC(*)=O)c(C)c2)cc1C,0.38134315
+*Oc1c(C)cc(C(c2cc(C)c(OC(*)=O)c(C)c2)(C(F)(F)F)C(F)(F)F)cc1C,0.39598526
+*Oc1c(Cl)cc(C(C)(C)c2cc(Cl)c(OC(*)=O)c(Cl)c2)cc1Cl,0.39482335
+*Oc1c(Cl)cc(C(C)(C)c2cc(Cl)c(OC(=O)CCCCC(*)=O)c(Cl)c2)cc1Cl,0.37453429
+*Oc1c(Cl)cc(C(C)(C)c2cc(Cl)c(OC(=O)c3ccc(OCCCCCCCCCCOc4ccc(C(*)=O)cc4)cc3)c(Cl)c2)cc1Cl,0.37760348
+*Oc1c(Cl)cc(C(c2cc(Cl)c(OC(*)=O)c(Cl)c2)C(Cl)(Cl)Cl)cc1Cl,0.39461971
+*Oc1c(Cl)cc(C2(c3cc(Cl)c(OC(*)=O)c(Cl)c3)CCCCC2)cc1Cl,0.39600978
+*Oc1c(F)c(C#N)c(Oc2ccc(C(C)(C)c3cccc(C(C)(C)c4ccc(*)cc4)c3)cc2)c(F)c1C#N,0.38046147
+*Oc1cc(Br)c(C(C)(C)c2c(Br)cc(OC(*)=O)cc2Br)c(Br)c1,0.3939151
+*Oc1cc(Cl)c(C(C)(C)c2c(Cl)cc(OC(*)=O)cc2Cl)c(Cl)c1,0.37057341
+*Oc1cc(OC(=O)c2ccc(C)cc2)c(OC(=O)CCCCCCCCCCCCCCC(*)=O)cc1OC(=O)c1ccc(C)cc1,0.36849104
+*Oc1cc(OC(=O)c2ccc(OC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OC)cc1,0.33574151
+*Oc1cc(OC(=O)c2ccc(OC)cc2)c(OC(=O)CCCCCCCCCCCCCCC(*)=O)cc1OC(=O)c1ccc(OC)cc1,0.35345176
+*Oc1cc(OC(=O)c2ccc(OCC(C)CC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCC(C)CC)cc1,0.35730159
+*Oc1cc(OC(=O)c2ccc(OCC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCC)cc1,0.33981747
+*Oc1cc(OC(=O)c2ccc(OCC)cc2)c(OC(=O)CCCCCCCCCCCCCCC(*)=O)cc1OC(=O)c1ccc(OCC)cc1,0.36137547
+*Oc1cc(OC(=O)c2ccc(OCCC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCCC)cc1,0.34814804
+*Oc1cc(OC(=O)c2ccc(OCCC)cc2)c(OC(=O)CCCCCCCCCCCCCCC(*)=O)cc1OC(=O)c1ccc(OCCC)cc1,0.36365256
+*Oc1cc(OC(=O)c2ccc(OCCCC)cc2)c(OC(=O)C(C)(C)CCC(*)=O)cc1OC(=O)c1ccc(OCCCC)cc1,0.35709055
+*Oc1cc(OC(=O)c2ccc(OCCCC)cc2)c(OC(=O)C(C)CCC(*)=O)cc1OC(=O)c1ccc(OCCCC)cc1,0.35328505
+*Oc1cc(OC(=O)c2ccc(OCCCC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCCCC)cc1,0.35109057
+*Oc1cc(OC(=O)c2ccc(OCCCC)cc2)c(OC(=O)CCCCCC(*)=O)cc1OC(=O)c1ccc(OCCCC)cc1,0.35336805
+*Oc1cc(OC(=O)c2ccc(OCCCC)cc2)c(OC(=O)CCCCCCCC(*)=O)cc1OC(=O)c1ccc(OCCCC)cc1,0.35540563
+*Oc1cc(OC(=O)c2ccc(OCCCCC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCCCCC)cc1,0.35445196
+*Oc1cc(OC(=O)c2ccc(OCCCCCCCCCCCC)cc2)c(OC(=O)CCCC(*)=O)cc1OC(=O)c1ccc(OCCCCCCCCCCCC)cc1,0.37044637
+*Oc1cc(OC(=O)c2ccc(Oc3ccc(C4(c5ccc(Oc6ccc(C(*)=O)cc6)cc5)CCC(C(C)(C)C)CC4)cc3)cc2)ccc1C12CC3CC(CC(C3)C1)C2,0.38278757
+*Oc1ccc(-c2ccc(OC(=O)OC3C(C)(C)C(OC(*)=O)C3(C)C)cc2)cc1,0.35906343
+*Oc1ccc(-c2ccc(OC3(F)C(*)(F)C(F)(F)C3(F)F)cc2)cc1,0.34511864
+*Oc1ccc(-c2ccc(Oc3cccc(*)n3)cc2)cc1,0.34884407
+*Oc1ccc(C(=O)NNC(=O)c2ccc(*)cc2)cc1,0.34717894
+*Oc1ccc(C(=O)Nc2cc(NC(=O)c3ccc(*)cc3)cc(-c3nc4ccccc4[nH]3)c2)cc1,0.36445259
+*Oc1ccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4ccc(Oc5ccc(C(=O)c6ccc(S(=O)(=O)c7ccc(C(=O)c8ccc(*)cc8)cc7)cc6)cc5)cc4)cc3)cc2)cc1,0.36195969
+*Oc1ccc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(NC(=O)c4ccc(Oc5nc(*)nc(Sc6ccccc6)n5)cc4)cc3)cc2)cc1,0.35315957
+*Oc1ccc(C(=O)OCC(C)(C)COC(=O)c2ccc(*)cc2)cc1,0.35526052
+*Oc1ccc(C(=O)OCCCCCOC(=O)c2ccc(*)cc2)cc1,0.34696395
+*Oc1ccc(C(=O)OCCCOC(=O)c2ccc(*)cc2)cc1,0.34520298
+*Oc1ccc(C(=O)c2ccc(C(=O)c3ccc(Oc4cccc(Cc5cccc(*)c5)c4)cc3)cc2)cc1,0.36639351
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(C(C)C)c3)c2)cc1C,0.39318082
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(C(C)C)c3)c2)cc1CC,0.39239857
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)c(CC)c3)c2)cc1CC,0.38536105
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(*)cc3)c2)cc1,0.36581437
+*Oc1ccc(C(=O)c2cccc(C(=O)c3ccc(Oc4ccc(Cc5ccc(*)cc5)cc4)cc3)c2)cc1,0.37058113
+*Oc1ccc(C(C)(C)c2cc(Cl)c(OC(*)=O)c(Cl)c2)cc1,0.37520677
+*Oc1ccc(C(C)(C)c2ccc(C(C)(C)c3ccc(OC(*)=O)cc3)cc2)cc1,0.36640421
+*Oc1ccc(C(C)(C)c2ccc(OC(*)(Oc3ccccc3)Oc3ccccc3)cc2)cc1,0.37316612
+*Oc1ccc(C(C)(C)c2ccc(OC(*)=O)c(C(C)C)c2)cc1C(C)C,0.40309692
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+*c1ccc(Oc2ccc3ccccc3c2-c2c(Oc3ccc(N4C(=O)c5ccc(NC(=O)Nc6cccc7c(NC(=O)Nc8ccc9c(c8)C(=O)N(*)C9=O)cccc67)cc5C4=O)cc3)ccc3ccccc23)cc1,0.36092398
+*c1ccc(Oc2cccc(N3C(=O)c4ccc(Oc5cccc6c(Oc7ccc8c(c7)C(=O)N(*)C8=O)cccc56)cc4C3=O)c2)cc1,0.37487112
+*c1ccc(Oc2cccc(NC(=O)c3ccc(C(=O)Nc4cccc(Oc5ccc(-c6nnc(*)o6)cc5)c4)c(Oc4ccccc4)c3)c2)cc1,0.36258207
+*c1ccc(Oc2cccc3c(NC(=O)c4ccc([Si](c5ccccc5)(c5ccccc5)c5ccc(C(=O)Nc6cccc7c(Oc8ccc(-c9nnc(*)o9)cc8)cccc67)cc5)cc4)cccc23)cc1,0.37704837
+*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(Oc5ccccc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,0.35809599
+*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(Oc5ccccc5Oc5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2C(F)(F)F)c(C(F)(F)F)c1,0.36055432
+*c1ccc(S(=O)(=O)c2ccc(N3C(=O)c4ccc(-c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)cc2)cc1,0.37525452
+*c1ccc(Sc2ccc(Oc3ccc(Sc4ccc(N5C(=O)c6ccc(Oc7ccc(C(C)(C)c8ccc(Oc9ccc%10c(c9)C(=O)N(*)C%10=O)cc8)cc7)cc6C5=O)cc4)cc3)cc2)cc1,0.37448846
+*c1ccc(Sc2ccc(Sc3ccc(N4C(=O)c5ccc(Sc6ccc(Sc7ccc(Sc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)cc3)cc2)cc1,0.37717954
+*c1ccc(Sc2ccc(Sc3ccc(Sc4ccc(N5C(=O)c6ccc(-c7ccc8c(c7)C(=O)N(*)C8=O)cc6C5=O)cc4)cc3)cc2)cc1,0.37722426
+*c1ccc([Si](c2ccccc2)(c2ccccc2)c2ccc(-c3nnc(-c4ccc(-c5nnc(*)o5)nc4)o3)cc2)cc1,0.46577218
+*c1ccc2c(c1)C(=O)N(C1CCC(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)CC1)C2=O,0.37690953
+*c1ccc2c(c1)C(=O)N(c1c(C)c(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c(C)c1C)C2=O,0.41737364
+*c1ccc2c(c1)C(=O)N(c1c(C)cc(-c3cc(C)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c4ccccc34)c3ccccc13)C2=O,0.41291503
+*c1ccc2c(c1)C(=O)N(c1cc(-c3ccc(O)c(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)ccc1O)C2=O,0.37301864
+*c1ccc2c(c1)C(=O)N(c1cc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5ccc(C)c(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)ccc1C)C2=O,0.36683082
+*c1ccc2c(c1)C(=O)N(c1cc(C)c(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1C)C2=O,0.40933634
+*c1ccc2c(c1)C(=O)N(c1cc(OCCN(CC)c3ccc(N=Nc4ccc([N+](=O)[O-])cc4)cc3)cc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)c1)C2=O,0.37866227
+*c1ccc2c(c1)C(=O)N(c1ccc(-c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c(OCCOc4ccc5c(C)cc(=O)oc5c4)c3)cc1OCCOc1ccc3c(C)cc(=O)oc3c1)C2=O,0.34983336
+*c1ccc2c(c1)C(=O)N(c1ccc(C(=O)Nc3ccc(Oc4cccc(Oc5ccc(NC(=O)c6ccc(N7C(=O)c8ccc(C(*)(C(F)(F)F)C(F)(F)F)cc8C7=O)cc6)cc5)c4)cc3)cc1)C2=O,0.35654385
+*c1ccc2c(c1)C(=O)N(c1ccc(C(c3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)(C(F)(F)F)C(F)(F)F)cc1)C2=O,0.39524704
+*c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4ccc(C(*)(C(F)(F)F)C(F)(F)F)cc4C3=O)cc1)C2=O,0.39320221
+*c1ccc2c(c1)C(=O)N(c1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,0.34222618
+*c1ccc2c(c1)C(=O)N(c1ccc(OCCOCCOCCOCCOc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,0.34187588
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(C(c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5C(F)(F)F)cc4)(C(F)(F)F)C(F)(F)F)cc3)c(C(F)(F)F)c1)C2=O,0.38326805
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,0.38674054
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc(S(=O)(=O)c4ccc(Oc5ccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)cc5)cc4)cc3)cc1)C2=O,0.37337767
+*c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccccc3-c3ccccc3Oc3ccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)cc3)cc1)C2=O,0.37740671
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1)C2=O,0.36118518
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(C(c4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5C)cc4)(C(F)(F)F)C(F)(F)F)cc3)c1C)C2=O,0.36895419
+*c1ccc2c(c1)C(=O)N(c1cccc(C(=O)Nc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,0.35705395
+*c1ccc2c(c1)C(=O)N(c1cccc(C(c3cccc(N4C(=O)c5ccc(C(*)(C(F)(F)F)C(F)(F)F)cc5C4=O)c3)(C(F)(F)F)C(F)(F)F)c1)C2=O,0.38091589
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3cc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)ccc3P(=O)(c3ccccc3)c3ccccc3)c1)C2=O,0.37529286
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(N4C(=O)c5ccc(-c6c(-c7ccccc7)c(-c7ccccc7)c(-c7ccc(Sc8ccc(-c9c(-c%10ccccc%10)c(-c%10ccccc%10)c(*)c(-c%10ccccc%10)c9-c9ccccc9)cc8)cc7)c(-c7ccccc7)c6-c6ccccc6)cc5C4=O)cc3)c1)C2=O,0.4011794
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(Oc4cccc(N5C(=O)c6ccc(C(*)(C(F)(F)F)C(F)(F)F)cc6C5=O)c4)cc3)c1)C2=O,0.37037326
+*c1ccc2c(c1)C(=O)N(c1cccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(Oc5cccc(N6C(=O)c7ccc(C(*)(C(F)(F)F)C(F)(F)F)cc7C6=O)c5)cc4)cc3)c1)C2=O,0.37783134
+*c1ccc2c(c1)C(CCCCCC)(CCCCCC)c1cc(-c3ccc4c(c3)C(CC3=NC(Cc5ccccc5)CO3)(CC3=NC(Cc5ccccc5)CO3)c3cc(*)ccc3-4)ccc1-2,0.40982823
+*c1ccc2c(c1)Sc1cc(-c3sc(C=CC4=CC(=C(C#N)C#N)C=C(C=Cc5sc(*)c(CCCCCC)c5CCCCCC)O4)c(CCCCCC)c3CCCCCC)ccc1N2CCCCCC,0.41634717
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCC,0.52516398
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCCCCCC,0.50803301
+*c1ccc2c(c1)Sc1cc(-c3sc4cc(*)sc4c3CCCCC)ccc1N2CCCCCCCCCCCC,0.4916283
+*c1ccc2c3ccc(-c4c5ccc(C=Cc6ccc(N(CCCCCC)CCCCCC)cc6)cc5c(*)c5ccc(C=Cc6ccc(N(CCCCCC)CCCCCC)cc6)cc45)cc3n(CCCCCCCC)c2c1,0.40341568
+*c1ccc2oc(-c3cccc(-c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)n3)nc2c1,0.45901468
+*c1ccc2oc(C3CCC(c4nc5cc(C(*)(C(F)(F)F)C(F)(F)F)ccc5o4)CC3)nc2c1,0.38067376
+*c1cccc(-c2nc3cc(Oc4ccc5nc(-c6ccccc6)c(*)nc5c4)ccc3nc2-c2ccccc2)c1,0.41748276
+*c1cccc(C(=O)Nc2ccc(Cc3ccc(N4C(=O)c5ccc(S(=O)(=O)c6ccc7c(c6)C(=O)N(c6ccc(Cc8ccc(NC(=O)c9cccc(-c%10nc%11cc(-c%12ccc%13[nH]c(*)nc%13c%12)ccc%11[nH]%10)c9)cc8)cc6)C7=O)cc5C4=O)cc3)cc2)c1,0.37929385
+*c1cccc(C(=O)Nc2ccc(NC(=O)c3cccc(N4C(=O)c5ccc(-c6cccc7c6C(=O)N(*)C7=O)cc5C4=O)c3)cc2)c1,0.34929698
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(-c4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9cccc%10c9C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,0.35563189
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(NC(=O)c5cccc(N6C(=O)c7ccc(-c8ccc9c(c8)C(=O)N(*)C9=O)cc7C6=O)c5)cc4)cc3)cc2)c1,0.35757013
+*c1cccc(C(=O)Nc2ccc(Oc3ccc(Oc4ccc(Oc5ccc(NC(=O)c6cccc(N7C(=O)c8ccc(-c9ccc%10c(c9)C(=O)N(*)C%10=O)cc8C7=O)c6)cc5)cc4)cc3)cc2)c1,0.35608701
+*c1cccc(C(=O)Nc2cccc(S(=O)(=O)c3cccc(NC(=O)c4cccc(N5C(=O)c6ccc(-c7cccc8c7C(=O)N(*)C8=O)cc6C5=O)c4)c3)c2)c1,0.35520964
+*c1cccc(Cc2cccc(N3C(=O)c4ccc([Si](C)(C)O[Si](C)(C)O[Si](C)(C)c5ccc6c(c5)C(=O)N(*)C6=O)cc4C3=O)c2)c1,0.38675185
+*c1cccc(N2C(=O)c3ccc(Oc4ccc(Sc5ccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)cc5)cc4)cc3C2=O)c1,0.36600422
+*c1cccc(NC(=O)c2ccc(-c3ccc(C(=O)Nc4cccc(S(*)(=O)=O)c4)c(C)c3)cc2C)c1,0.3574533
+*c1cccc(NC(=O)c2ccc(OCCOCCOc3ccc(C(=O)Nc4ccc5[nH]c(*)nc5c4)cc3)cc2)c1,0.34893481
+*c1cccc(OCCCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,0.35046399
+*c1cccc(OCCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,0.34909456
+*c1cccc(OCCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,0.35089152
+*c1cccc(OCCCCOc2cccc(N3C(=O)c4ccc(-c5cccc6c5C(=O)N(*)C6=O)cc4C3=O)c2)c1,0.34538642
+*c1cccc(Oc2cccc(Oc3cccc(N4C(=O)c5ccc(Oc6ccc(Sc7ccc(Oc8ccc9c(c8)C(=O)N(*)C9=O)cc7)cc6)cc5C4=O)c3)c2)c1,0.36222427
+*c1cccc(P(C)(=O)c2cccc(N3C(=O)c4ccc(Oc5ccc(C(C)(C)c6ccc(Oc7ccc8c(c7)C(=O)N(*)C8=O)cc6)cc5)cc4C3=O)c2)c1,0.36957365
diff --git a/simson_modeling/moleculenet_eval/.ipynb_checkpoints/better_eval-checkpoint.py b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/better_eval-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..b865b51ba665fa15ce0edf4b84e60daef3f9e2de
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/better_eval-checkpoint.py
@@ -0,0 +1,671 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertConfig, BertModel, AutoTokenizer
+from rdkit import Chem, RDLogger
+from rdkit.Chem.Scaffolds import MurckoScaffold
+import copy
+from tqdm import tqdm
+import os
+from sklearn.metrics import roc_auc_score, root_mean_squared_error, mean_absolute_error
+from itertools import compress
+from collections import defaultdict
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+import optuna
+import warnings
+warnings.filterwarnings("ignore")
+RDLogger.DisableLog('rdApp.*')
+
+torch.set_float32_matmul_precision('high')
+
+# --- 0. Pre-computed Contrastive SMILES Dataset ---
+class PrecomputedContrastiveSmilesDataset(Dataset):
+ """
+ A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.
+ This is significantly faster as it offloads the expensive SMILES randomization
+ to a one-time preprocessing step.
+ """
+ def __init__(self, tokenizer, file_path: str, max_length: int = 512):
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ # Load the entire dataset from the Parquet file into memory.
+ # This is fast and efficient for subsequent access.
+ print(f"Loading pre-computed data from {file_path}...")
+ self.data = pd.read_parquet(file_path)
+ print("Data loaded successfully.")
+
+ def __len__(self):
+ """Returns the total number of pairs in the dataset."""
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ """
+ Retrieves a pre-augmented pair, tokenizes it, and returns it
+ in the format expected by the DataCollator.
+ """
+ # Retrieve the pre-augmented pair from the DataFrame
+ row = self.data.iloc[idx]
+ smiles_1 = row['smiles_1']
+ smiles_2 = row['smiles_2']
+
+ # Tokenize the pair. This operation is fast and remains in the data loader.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+# --- 0a. SMILES enumeration for preprocessing ---
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+def compute_embedding_similarity_precomputed(encoder, dataset, device):
+ """
+ Compute embedding similarity using pre-computed augmented SMILES pairs
+ """
+ encoder.eval()
+ similarities = []
+
+ dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
+
+ with torch.no_grad():
+ for batch in dataloader:
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ emb_1 = encoder(input_ids_1, attention_mask_1).cpu().numpy()
+ emb_2 = encoder(input_ids_2, attention_mask_2).cpu().numpy()
+
+ # Compute cosine similarity for each pair in the batch
+ batch_similarities = []
+ for i in range(len(emb_1)):
+ sim = cosine_similarity([emb_1[i]], [emb_2[i]])[0][0]
+ batch_similarities.append(sim)
+
+ similarities.extend(batch_similarities)
+
+ return np.array(similarities)
+
+def create_augmented_smiles_file(smiles_list, output_path, num_augmentations=1):
+ """
+ Create a parquet file with pre-computed augmented SMILES pairs
+ """
+ enumerator = SmilesEnumerator()
+ pairs = []
+
+ print(f"Generating {num_augmentations} augmentations for {len(smiles_list)} SMILES...")
+
+ for smiles in tqdm(smiles_list):
+ for _ in range(num_augmentations):
+ augmented = enumerator.randomize_smiles(smiles)
+ pairs.append({
+ 'smiles_1': smiles,
+ 'smiles_2': augmented
+ })
+
+ df = pd.DataFrame(pairs)
+ df.to_parquet(output_path, index=False)
+ print(f"Saved {len(pairs)} augmented pairs to {output_path}")
+ return output_path
+
+# --- 1. Data Loading ---
+def load_lists_from_url(data):
+ # Datasets and their splits, all configurations carried over
+ if data == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif data == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif data == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'esol':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')
+ smiles = df.smiles
+ labels = df['ESOL predicted log solubility in mols per litre']
+ elif data == 'freesolv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')
+ smiles = df.smiles
+ labels = df.calc
+ elif data == 'lipophicility':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')
+ smiles, labels = df.smiles, df['exp']
+ elif data == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif data == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ elif data == 'qm8':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)
+ return smiles, labels
+
+# --- 2. Scaffold Splitting ---
+class ScaffoldSplitter:
+ def __init__(self, data, seed, train_frac=0.8, val_frac=0.1, test_frac=0.1, include_chirality=True):
+ self.data = data
+ self.seed = seed
+ self.include_chirality = include_chirality
+ self.train_frac = train_frac
+ self.val_frac = val_frac
+ self.test_frac = test_frac
+
+ def generate_scaffold(self, smiles):
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol, includeChirality=self.include_chirality)
+ return scaffold
+
+ def scaffold_split(self):
+ smiles, labels = load_lists_from_url(self.data)
+ non_null = np.ones(len(smiles)) == 0
+
+ if self.data in {'tox21', 'sider', 'clintox'}:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]) and labels.loc[i].isnull().sum() == 0:
+ non_null[i] = 1
+ else:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]):
+ non_null[i] = 1
+
+ smiles_list = list(compress(enumerate(smiles), non_null))
+ rng = np.random.RandomState(self.seed)
+
+ scaffolds = defaultdict(list)
+ for i, sms in smiles_list:
+ scaffold = self.generate_scaffold(sms)
+ scaffolds[scaffold].append(i)
+
+ scaffold_sets = list(scaffolds.values())
+ rng.shuffle(scaffold_sets)
+ n_total_val = int(np.floor(self.val_frac * len(smiles_list)))
+ n_total_test = int(np.floor(self.test_frac * len(smiles_list)))
+ train_idx, val_idx, test_idx = [], [], []
+
+ for scaffold_set in scaffold_sets:
+ if len(val_idx) + len(scaffold_set) <= n_total_val:
+ val_idx.extend(scaffold_set)
+ elif len(test_idx) + len(scaffold_set) <= n_total_test:
+ test_idx.extend(scaffold_set)
+ else:
+ train_idx.extend(scaffold_set)
+ return train_idx, val_idx, test_idx
+
+# --- 2a. Normal Random Split ---
+def random_split_indices(n, seed=42, train_frac=0.8, val_frac=0.1, test_frac=0.1):
+ np.random.seed(seed)
+ indices = np.random.permutation(n)
+ n_train = int(n * train_frac)
+ n_val = int(n * val_frac)
+ train_idx = indices[:n_train]
+ val_idx = indices[n_train:n_train+n_val]
+ test_idx = indices[n_train+n_val:]
+ return train_idx.tolist(), val_idx.tolist(), test_idx.tolist()
+
+# --- 3. PyTorch Dataset ---
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_len=512):
+ self.smiles_list = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles_list[idx]
+ label = self.labels.iloc[idx]
+
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_len,
+ return_tensors='pt'
+ )
+ item = {key: val.squeeze(0) for key, val in encoding.items()}
+ if isinstance(label, pd.Series):
+ label_values = label.values.astype(np.float32)
+ else:
+ label_values = np.array([label], dtype=np.float32)
+ item['labels'] = torch.tensor(label_values, dtype=torch.float)
+ return item
+
+# --- 4. Model Architecture ---
+def global_ap(x):
+ return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)
+
+class SimSonEncoder(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.config.pad_token_id)
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled = global_ap(hidden_states)
+ return self.linear(pooled)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):
+ super(SimSonClassifier, self).__init__()
+ self.encoder = encoder
+ self.clf = nn.Linear(encoder.max_len, num_labels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ x = self.encoder(input_ids, attention_mask)
+ x = self.relu(self.dropout(x))
+ logits = self.clf(x)
+ return logits
+
+ def load_encoder_params(self, state_dict_path):
+ self.encoder.load_state_dict(torch.load(state_dict_path))
+
+# --- 5. Training, Validation, and Testing Loops ---
+def get_criterion(task_type, num_labels):
+ if task_type == 'classification':
+ return nn.BCEWithLogitsLoss()
+ elif task_type == 'regression':
+ return nn.MSELoss()
+ else:
+ raise ValueError(f"Unknown task type: {task_type}")
+
+def train_epoch(model, dataloader, optimizer, scheduler, criterion, device):
+ model.train()
+ total_loss = 0
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ optimizer.zero_grad()
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ if scheduler is not None:
+ scheduler.step()
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def calc_val_metrics(model, dataloader, criterion, device, task_type):
+ model.eval()
+ all_labels, all_preds = [], []
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ if task_type == 'classification':
+ pred_probs = torch.sigmoid(outputs).cpu().numpy()
+ all_preds.append(pred_probs)
+ all_labels.append(labels.cpu().numpy())
+ else:
+ # Regression
+ preds = outputs.cpu().numpy()
+ all_preds.append(preds)
+ all_labels.append(labels.cpu().numpy())
+ avg_loss = total_loss / len(dataloader)
+ if task_type == 'classification':
+ y_true = np.concatenate(all_labels)
+ y_pred = np.concatenate(all_preds)
+ try:
+ score = roc_auc_score(y_true, y_pred, average='macro')
+ except Exception:
+ score = 0.0
+ return avg_loss, score
+ else:
+ return avg_loss, None
+
+def test_model(model, dataloader, device, task_type):
+ model.eval()
+ all_preds, all_labels = [], []
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels']
+ outputs = model(**inputs)
+ if task_type == 'classification':
+ preds = torch.sigmoid(outputs)
+ else:
+ preds = outputs
+ all_preds.append(preds.cpu().numpy())
+ all_labels.append(labels.numpy())
+ return np.concatenate(all_preds), np.concatenate(all_labels)
+
+# --- 6. Optuna Objective Function ---
+def create_objective(name, info, train_smiles, train_labels, val_smiles, val_labels,
+ test_smiles, test_labels, scaler, tokenizer, encoder_config, device):
+ """Creates objective function for Optuna optimization"""
+
+ def objective(trial):
+ # Suggest hyperparameters
+ lr = trial.suggest_float('lr', 1e-6, 1e-4, log=True)
+ batch_size = trial.suggest_categorical('batch_size', [16, 32, 64, 128, 256])
+ dropout = trial.suggest_float('dropout', 0.1, 0.5)
+ weight_decay = trial.suggest_float('weight_decay', 0.0, 0.1)
+ scheduler_type = trial.suggest_categorical('scheduler', ['plateau', 'cosine', 'step'])
+
+ # Additional hyperparameters for optimization
+ patience_lr = trial.suggest_int('patience_lr', 3, 10)
+ gamma = trial.suggest_float('gamma', 0.5, 0.9) if scheduler_type == 'step' else 0.1
+
+ try:
+ # Create datasets and dataloaders
+ train_dataset = MoleculeDataset(train_smiles, train_labels, tokenizer, 512)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, tokenizer, 512)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, tokenizer, 512)
+
+ train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+ # Create model
+ encoder = SimSonEncoder(encoder_config, 512, dropout=dropout)
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels'], dropout=dropout).to(device)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
+
+ # Create scheduler based on trial suggestion
+ if scheduler_type == 'plateau':
+ scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+ optimizer, mode='max', factor=gamma, patience=patience_lr
+ )
+ elif scheduler_type == 'cosine':
+ scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
+ else: # step
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=gamma)
+
+ # Training loop
+ best_val_metric = -np.inf
+ patience_counter = 0
+ patience = 15
+
+ for epoch in range(50): # Max epochs
+ train_loss = train_epoch(model, train_loader, optimizer,
+ scheduler if scheduler_type == 'cosine' else None,
+ criterion, device)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, device, info['task_type'])
+
+ # Update scheduler
+ if scheduler_type == 'plateau':
+ scheduler.step(val_loss if val_loss is not None else -val_loss)
+ elif scheduler_type == 'step':
+ scheduler.step()
+
+ # Determine metric to optimize
+ if info['task_type'] == 'classification':
+ current_metric = val_loss if val_loss is not None else 0.0
+ else:
+ current_metric = -val_loss # For regression, minimize loss
+
+ # Early stopping and best model tracking
+ if current_metric <= val_loss:
+ best_val_metric = current_metric
+ patience_counter = 0
+ else:
+ patience_counter += 1
+ if patience_counter >= patience:
+ break
+
+ # Optuna pruning
+ trial.report(current_metric, epoch)
+ if trial.should_prune():
+ raise optuna.TrialPruned()
+
+ return best_val_metric
+
+ except Exception as e:
+ print(f"Trial failed with error: {e}")
+ return -np.inf # Return worst possible score for failed trials
+
+ return objective
+
+# --- 7. Main Execution Block ---
+def main():
+ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {DEVICE}")
+
+ DATASETS_TO_RUN = {
+ #'esol': {'task_type': 'regression', 'num_labels': 1, 'split': 'random'},
+ # Add more datasets here, e.g. 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ #'sider': {'task_type': 'classification', 'num_labels': 27, 'split': 'random'},
+ #'bace': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ 'clintox': {'task_type': 'classification', 'num_labels': 2, 'split': 'scaffold'},
+ 'tox21': {'task_type': 'classification', 'num_labels': 12, 'split': 'random'},
+ 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ 'hiv': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ }
+
+ MAX_LEN = 512
+ N_TRIALS = 100 # Number of Optuna trials to run
+
+ TOKENIZER = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ ENCODER_CONFIG = BertConfig(
+ vocab_size=TOKENIZER.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ aggregated_results = {}
+
+ for name, info in DATASETS_TO_RUN.items():
+ print(f"\n{'='*20} Processing Dataset: {name.upper()} ({info['split']} split) {'='*20}")
+ smiles, labels = load_lists_from_url(name)
+
+ # For regression tasks, scale labels and remember scaling transform
+ scaler = None
+ if info["task_type"] == "regression":
+ scaler = StandardScaler()
+ all_labels = labels.values.reshape(-1, 1)
+ scaler.fit(all_labels)
+ labels = pd.Series(scaler.transform(all_labels).flatten(), index=labels.index)
+
+ # Data split
+ if info.get('split', 'scaffold') == 'scaffold':
+ splitter = ScaffoldSplitter(data=name, seed=42)
+ train_idx, val_idx, test_idx = splitter.scaffold_split()
+ elif info['split'] == 'random':
+ train_idx, val_idx, test_idx = random_split_indices(len(smiles), seed=42)
+ else:
+ raise ValueError(f"Unknown split type for {name}: {info['split']}")
+
+ train_smiles = smiles.iloc[train_idx].reset_index(drop=True)
+ train_labels = labels.iloc[train_idx].reset_index(drop=True)
+ val_smiles = smiles.iloc[val_idx].reset_index(drop=True)
+ val_labels = labels.iloc[val_idx].reset_index(drop=True)
+ test_smiles = smiles.iloc[test_idx].reset_index(drop=True)
+ test_labels = labels.iloc[test_idx].reset_index(drop=True)
+ print(f"Data split - Train: {len(train_smiles)}, Val: {len(val_smiles)}, Test: {len(test_smiles)}")
+
+ # Create Optuna study
+ study = optuna.create_study(
+ direction='maximize',
+ pruner=optuna.pruners.MedianPruner(n_startup_trials=5, n_warmup_steps=10)
+ )
+
+ # Create objective function
+ objective_func = create_objective(
+ name, info, train_smiles, train_labels, val_smiles, val_labels,
+ test_smiles, test_labels, scaler, TOKENIZER, ENCODER_CONFIG, DEVICE
+ )
+
+ # Run optimization
+ print(f"Starting Optuna optimization with {N_TRIALS} trials...")
+ study.optimize(objective_func, n_trials=N_TRIALS, timeout=None)
+
+ # Get best parameters
+ best_params = study.best_params
+ best_score = study.best_value
+ print(f"Best parameters: {best_params}")
+ print(f"Best validation score: {0:.4f}")
+
+ # Train final model with best parameters
+ print("Training final model with best parameters...")
+ train_dataset = MoleculeDataset(train_smiles, train_labels, TOKENIZER, MAX_LEN)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, TOKENIZER, MAX_LEN)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, TOKENIZER, MAX_LEN)
+
+ train_loader = DataLoader(train_dataset, batch_size=best_params['batch_size'], shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=best_params['batch_size'], shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=best_params['batch_size'], shuffle=False)
+
+ # Final model training
+ encoder = SimSonEncoder(ENCODER_CONFIG, 512, dropout=best_params['dropout'])
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels'], dropout=best_params['dropout']).to(DEVICE)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=best_params['lr'], weight_decay=best_params['weight_decay'])
+
+ # Set up best scheduler
+ if best_params['scheduler'] == 'plateau':
+ scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+ optimizer, mode='max', factor=best_params.get('gamma', 0.7),
+ patience=best_params.get('patience_lr', 5)
+ )
+ elif best_params['scheduler'] == 'cosine':
+ scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
+ else:
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=best_params.get('gamma', 0.1))
+
+ # Train with best parameters
+ best_val_metric = -np.inf
+ best_model_state = None
+ patience_counter = 0
+ patience = 15
+
+ for epoch in range(50):
+ train_loss = train_epoch(model, train_loader, optimizer,
+ scheduler if best_params['scheduler'] == 'cosine' else None,
+ criterion, DEVICE)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, DEVICE, info['task_type'])
+
+ if best_params['scheduler'] == 'plateau':
+ scheduler.step(val_loss if val_loss is not None else -val_loss)
+ elif best_params['scheduler'] == 'step':
+ scheduler.step()
+
+ if info['task_type'] == 'classification':
+ print(f"Epoch {epoch+1}/50 | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | ROC AUC: {val_metric:.4f}")
+ current_metric = val_metric if val_metric is not None else 0.0
+ else:
+ print(f"Epoch {epoch+1}/50 | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")
+ current_metric = -val_loss
+
+ if current_metric <= val_loss:
+ best_val_metric = current_metric
+ best_model_state = copy.deepcopy(model.state_dict())
+ patience_counter = 0
+ else:
+ patience_counter += 1
+ if patience_counter >= patience:
+ print(f'Early stopping at epoch {epoch+1}')
+ break
+
+ # Test final model
+ if best_model_state is not None:
+ model.load_state_dict(best_model_state)
+
+ test_preds, test_true = test_model(model, test_loader, DEVICE, info['task_type'])
+
+ # Calculate final metrics
+ if info['task_type'] == 'regression' and scaler is not None:
+ test_preds = scaler.inverse_transform(test_preds.reshape(-1, 1)).flatten()
+ test_true = scaler.inverse_transform(test_true.reshape(-1, 1)).flatten()
+ rmse = root_mean_squared_error(test_true, test_preds)
+ mae = mean_absolute_error(test_true, test_preds)
+ final_score = -rmse
+ print(f"Test RMSE: {rmse:.4f}, MAE: {mae:.4f}")
+ else:
+ try:
+ final_score = roc_auc_score(test_true, test_preds, average='macro')
+ print(f"Test ROC AUC: {final_score:.4f}")
+ except Exception:
+ final_score = 0.0
+
+ # Compute embedding similarity using pre-computed augmented SMILES
+ print("Creating pre-computed augmented SMILES for similarity computation...")
+ test_smiles_list = list(test_smiles)
+ similarity_file_path = f"{name}_test_augmented.parquet"
+ create_augmented_smiles_file(test_smiles_list, similarity_file_path, num_augmentations=1)
+
+ # Load pre-computed dataset for similarity computation
+ similarity_dataset = PrecomputedContrastiveSmilesDataset(
+ TOKENIZER, similarity_file_path, max_length=MAX_LEN
+ )
+
+ similarities = compute_embedding_similarity_precomputed(
+ model.encoder, similarity_dataset, DEVICE
+ )
+ print(f"Similarity score: {similarities.mean():.4f}")
+
+ # Clean up temporary file
+ if os.path.exists(similarity_file_path):
+ os.remove(similarity_file_path)
+
+ aggregated_results[name] = {
+ 'best_score': final_score,
+ 'best_params': best_params,
+ 'optuna_trials': len(study.trials),
+ 'study': study,
+ 'similarity_score': similarities.mean()
+ }
+
+ if name == 'do_not_save':
+ torch.save(model.encoder.state_dict(), 'moleculenet_clintox_encoder.bin')
+
+ print(f"\n{'='*20} AGGREGATED RESULTS {'='*20}")
+ for name, result in aggregated_results.items():
+ print(f"{name}: Best score: {result['best_score']:.4f}")
+ print(f" Best parameters: {result['best_params']}")
+ print(f" Total trials: {result['optuna_trials']}")
+ print(f" Similarity score: {result['similarity_score']:.4f}")
+
+ print("\nScript finished.")
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/moleculenet_eval/.ipynb_checkpoints/eval-checkpoint.py b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/eval-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b4eb25054ae7710bbbdc4ce12f907246371b81
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/eval-checkpoint.py
@@ -0,0 +1,457 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertConfig, BertModel, AutoTokenizer
+from rdkit import Chem, RDLogger
+from rdkit.Chem.Scaffolds import MurckoScaffold
+import copy
+from tqdm import tqdm
+import os
+from sklearn.metrics import roc_auc_score, root_mean_squared_error, mean_absolute_error
+from itertools import compress
+from collections import defaultdict
+from sklearn.metrics.pairwise import cosine_similarity
+RDLogger.DisableLog('rdApp.*')
+
+
+torch.set_float32_matmul_precision('high')
+
+# --- 0. Smiles enumeration
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+
+def compute_embedding_similarity(encoder, smiles_list, tokenizer, device, max_len=256):
+ encoder.eval()
+ enumerator = SmilesEnumerator()
+
+ embeddings_orig = []
+ embeddings_aug = []
+
+ with torch.no_grad():
+ for smi in smiles_list:
+ # Original SMILES encoding
+ encoding_orig = tokenizer(
+ smi,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+ # Augmented SMILES encoding
+ smi_aug = enumerator.randomize_smiles(smi)
+ encoding_aug = tokenizer(
+ smi_aug,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+
+ input_ids_orig = encoding_orig.input_ids.to(device)
+ attention_mask_orig = encoding_orig.attention_mask.to(device)
+ input_ids_aug = encoding_aug.input_ids.to(device)
+ attention_mask_aug = encoding_aug.attention_mask.to(device)
+
+ emb_orig = encoder(input_ids_orig, attention_mask_orig).cpu().numpy().flatten()
+ emb_aug = encoder(input_ids_aug, attention_mask_aug).cpu().numpy().flatten()
+
+ embeddings_orig.append(emb_orig)
+ embeddings_aug.append(emb_aug)
+
+ embeddings_orig = np.array(embeddings_orig)
+ embeddings_aug = np.array(embeddings_aug)
+
+ # Cosine similarity between each original and its augmented version
+ similarities = np.array([cosine_similarity([embeddings_orig[i]], [embeddings_aug[i]])[0][0] for i in range(len(embeddings_orig))])
+ return similarities
+
+# --- 1. Data Loading ---
+def load_lists_from_url(data):
+ if data == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif data == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif data == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'esol':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')
+ smiles = df.smiles
+ labels = df['ESOL predicted log solubility in mols per litre']
+ elif data == 'freesolv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')
+ smiles = df.smiles
+ labels = df.calc
+ elif data == 'lipophicility':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')
+ smiles, labels = df.smiles, df['exp']
+ elif data == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif data == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ elif data == 'qm8':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)
+ return smiles, labels
+
+# --- 2. Scaffold Splitting ---
+class ScaffoldSplitter:
+ def __init__(self, data, seed, train_frac=0.8, val_frac=0.1, test_frac=0.1, include_chirality=True):
+ self.data = data
+ self.seed = seed
+ self.include_chirality = include_chirality
+ self.train_frac = train_frac
+ self.val_frac = val_frac
+ self.test_frac = test_frac
+
+ def generate_scaffold(self, smiles):
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol, includeChirality=self.include_chirality)
+ return scaffold
+
+ def scaffold_split(self):
+ smiles, labels = load_lists_from_url(self.data)
+ non_null = np.ones(len(smiles)) == 0
+
+ if self.data in {'tox21', 'sider', 'clintox'}:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]) and labels.loc[i].isnull().sum() == 0:
+ non_null[i] = 1
+ else:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]):
+ non_null[i] = 1
+
+ smiles_list = list(compress(enumerate(smiles), non_null))
+ rng = np.random.RandomState(self.seed)
+
+ scaffolds = defaultdict(list)
+ for i, sms in smiles_list:
+ scaffold = self.generate_scaffold(sms)
+ scaffolds[scaffold].append(i)
+
+ scaffold_sets = list(scaffolds.values())
+ rng.shuffle(scaffold_sets)
+ n_total_val = int(np.floor(self.val_frac * len(smiles_list)))
+ n_total_test = int(np.floor(self.test_frac * len(smiles_list)))
+ train_idx, val_idx, test_idx = [], [], []
+
+ for scaffold_set in scaffold_sets:
+ if len(val_idx) + len(scaffold_set) <= n_total_val:
+ val_idx.extend(scaffold_set)
+ elif len(test_idx) + len(scaffold_set) <= n_total_test:
+ test_idx.extend(scaffold_set)
+ else:
+ train_idx.extend(scaffold_set)
+ return train_idx, val_idx, test_idx
+
+# --- 2a. Normal Random Split ---
+def random_split_indices(n, seed=42, train_frac=0.8, val_frac=0.1, test_frac=0.1):
+ np.random.seed(seed)
+ indices = np.random.permutation(n)
+ n_train = int(n * train_frac)
+ n_val = int(n * val_frac)
+ train_idx = indices[:n_train]
+ val_idx = indices[n_train:n_train+n_val]
+ test_idx = indices[n_train+n_val:]
+ return train_idx.tolist(), val_idx.tolist(), test_idx.tolist()
+
+# --- 3. PyTorch Dataset ---
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_len=512):
+ self.smiles_list = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles_list[idx]
+ label = self.labels.iloc[idx]
+
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_len,
+ return_tensors='pt'
+ )
+ item = {key: val.squeeze(0) for key, val in encoding.items()}
+ if isinstance(label, pd.Series):
+ label_values = label.values.astype(np.float32)
+ else:
+ label_values = np.array([label], dtype=np.float32)
+ item['labels'] = torch.tensor(label_values, dtype=torch.float)
+ return item
+
+# --- 4. Model Architecture ---
+def global_ap(x):
+ return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)
+
+class SimSonEncoder(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.config.pad_token_id)
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled = global_ap(hidden_states)
+ return self.linear(pooled)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):
+ super(SimSonClassifier, self).__init__()
+ self.encoder = encoder
+ self.clf = nn.Linear(encoder.max_len, num_labels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ x = self.encoder(input_ids, attention_mask)
+ x = self.relu(self.dropout(x))
+ logits = self.clf(x)
+ return logits
+
+ def load_encoder_params(self, state_dict_path):
+ self.encoder.load_state_dict(torch.load(state_dict_path))
+ print("Pretrained encoder parameters loaded.")
+
+# --- 5. Training, Validation, and Testing Loops ---
+def get_criterion(task_type, num_labels):
+ if task_type == 'classification':
+ return nn.BCEWithLogitsLoss()
+ elif task_type == 'regression':
+ return nn.MSELoss()
+ else:
+ raise ValueError(f"Unknown task type: {task_type}")
+
+def train_epoch(model, dataloader, optimizer, scheduler, criterion, device):
+ model.train()
+ total_loss = 0
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ optimizer.zero_grad()
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ #scheduler.step()
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def eval_epoch(model, dataloader, criterion, device):
+ model.eval()
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def test_model(model, dataloader, device):
+ model.eval()
+ all_preds, all_labels = [], []
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels']
+ outputs = model(**inputs)
+ preds = torch.sigmoid(outputs)
+ all_preds.append(preds.cpu().numpy())
+ all_labels.append(labels.numpy())
+ return np.concatenate(all_preds), np.concatenate(all_labels)
+
+def calc_val_metrics(model, dataloader, criterion, device, task_type):
+ model.eval()
+ all_labels, all_preds = [], []
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ if task_type == 'classification':
+ pred_probs = torch.sigmoid(outputs).cpu().numpy()
+ all_preds.append(pred_probs)
+ all_labels.append(labels.cpu().numpy())
+ else:
+ # Regression
+ preds = outputs.cpu().numpy()
+ all_preds.append(preds)
+ all_labels.append(labels.cpu().numpy())
+ avg_loss = total_loss / len(dataloader)
+ if task_type == 'classification':
+ y_true = np.concatenate(all_labels)
+ y_pred = np.concatenate(all_preds)
+ try:
+ score = roc_auc_score(y_true, y_pred, average='macro')
+ except Exception:
+ score = 0.0
+ return avg_loss, score
+ else:
+ return avg_loss, None
+
+# --- 6. Main Execution Block ---
+def main():
+ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {DEVICE}")
+
+ DATASETS_TO_RUN = {
+ # 'esol': {'task_type': 'regression', 'num_labels': 1, 'split': 'random'},
+ #'tox21': {'task_type': 'classification', 'num_labels': 12, 'split': 'random'},
+ #'hiv': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ # Add more datasets here, e.g. 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ #'sider': {'task_type': 'classification', 'num_labels': 27, 'split': 'random'},
+ #'bace': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ 'clintox': {'task_type': 'classification', 'num_labels': 2, 'split': 'random'},
+ #'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'}
+ }
+ PATIENCE = 15
+ EPOCHS = 50
+ LEARNING_RATE = 1e-4
+ BATCH_SIZE = 16
+ MAX_LEN = 512
+
+ TOKENIZER = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ ENCODER_CONFIG = BertConfig(
+ vocab_size=TOKENIZER.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ aggregated_results = {}
+
+ for name, info in DATASETS_TO_RUN.items():
+ print(f"\n{'='*20} Processing Dataset: {name.upper()} ({info['split']} split) {'='*20}")
+ smiles, labels = load_lists_from_url(name)
+
+ # Split selection
+ if info.get('split', 'scaffold') == 'scaffold':
+ splitter = ScaffoldSplitter(data=name, seed=42)
+ train_idx, val_idx, test_idx = splitter.scaffold_split()
+ elif info['split'] == 'random':
+ train_idx, val_idx, test_idx = random_split_indices(len(smiles), seed=42)
+ else:
+ raise ValueError(f"Unknown split type for {name}: {info['split']}")
+
+ train_smiles = smiles.iloc[train_idx].reset_index(drop=True)
+ train_labels = labels.iloc[train_idx].reset_index(drop=True)
+ val_smiles = smiles.iloc[val_idx].reset_index(drop=True)
+ val_labels = labels.iloc[val_idx].reset_index(drop=True)
+ test_smiles = smiles.iloc[test_idx].reset_index(drop=True)
+ test_labels = labels.iloc[test_idx].reset_index(drop=True)
+ print(f"Data split - Train: {len(train_smiles)}, Val: {len(val_smiles)}, Test: {len(test_smiles)}")
+
+ train_dataset = MoleculeDataset(train_smiles, train_labels, TOKENIZER, MAX_LEN)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, TOKENIZER, MAX_LEN)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, TOKENIZER, MAX_LEN)
+
+ train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
+
+ encoder = SimSonEncoder(ENCODER_CONFIG, 512)
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels']).to(DEVICE)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=0.0024)
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.59298)
+
+ best_val_loss = float('-inf')
+ best_model_state = None
+ current_patience = 0
+ for epoch in range(EPOCHS):
+ train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, DEVICE)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, 'cuda', info['task_type'])
+ print(f"Epoch {epoch+1}/{EPOCHS} | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | ROC AUC: {val_metric:.4f}")
+
+ if val_metric <= val_loss:
+ best_val_loss = val_loss
+ best_model_state = copy.deepcopy(model.state_dict())
+ print(f" -> New best model saved with validation loss: {best_val_loss:.4f}")
+ current_patience = 0
+ else:
+ current_patience += 1
+ if current_patience >= PATIENCE:
+ print(f'Early stopping at {PATIENCE} epochs')
+ break
+
+ print("\nTesting with the best model...")
+ if not best_model_state is None:
+ model.load_state_dict(best_model_state)
+ test_loss = eval_epoch(model, test_loader, criterion, DEVICE)
+ print(f'Test loss: {test_loss}')
+ test_preds, test_true = test_model(model, test_loader, DEVICE)
+
+ aggregated_results[name] = {
+ 'best_val_loss': best_val_loss,
+ 'test_predictions': test_preds,
+ 'test_labels': test_true
+ }
+ print(f"Finished testing for {name}.")
+ test_smiles_list = list(test_smiles)
+ similarities = compute_embedding_similarity(
+ model.encoder, test_smiles_list, TOKENIZER, DEVICE, MAX_LEN
+ )
+ print(f"Similarity score: {similarities.mean():.4f}")
+ if name == 'do_not_save':
+ torch.save(model.encoder.state_dict(), 'moleculenet_clintox_encoder.bin')
+
+
+
+ print(f"\n{'='*20} AGGREGATED RESULTS {'='*20}")
+ for name, result in aggregated_results.items():
+ if name in ['bbbp', 'tox21', 'sider', 'clintox', 'hiv', 'bace']:
+ auc = roc_auc_score(result['test_labels'], result['test_predictions'], average='macro')
+ print(f'{name} ROC AUC: {auc}')
+
+ if name in ['lipophicility', 'esol', 'qm8']:
+ rmse = root_mean_squared_error(result['test_labels'], result['test_predictions'])
+ mae = mean_absolute_error(result['test_labels'], result['test_predictions'])
+ print(f'{name} MAE: {mae}')
+ print(f'{name} RMSE: {rmse}')
+
+ print("\nScript finished.")
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/moleculenet_eval/.ipynb_checkpoints/showcase-checkpoint.ipynb b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/showcase-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..45a6d679d784a954d193f3cb241c9e388b88176c
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/showcase-checkpoint.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7678e528-e2d6-4ef4-bd11-8c745bbce7c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap.umap_ as umap\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ad8d5a02-6162-41f7-a730-e31e353ed9b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PrecomputedContrastiveSmilesDataset(Dataset):\n",
+ " \"\"\"\n",
+ " A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.\n",
+ " This is significantly faster as it offloads the expensive SMILES randomization\n",
+ " to a one-time preprocessing step.\n",
+ " \"\"\"\n",
+ " def __init__(self, tokenizer, file_path: str, max_length: int = 512):\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Load the entire dataset from the Parquet file into memory.\n",
+ " # This is fast and efficient for subsequent access.\n",
+ " print(f\"Loading pre-computed data from {file_path}...\")\n",
+ " self.data = pd.read_parquet(file_path)\n",
+ " print(\"Data loaded successfully.\")\n",
+ "\n",
+ " def __len__(self):\n",
+ " \"\"\"Returns the total number of pairs in the dataset.\"\"\"\n",
+ " return len(self.data)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " \"\"\"\n",
+ " Retrieves a pre-augmented pair, tokenizes it, and returns it\n",
+ " in the format expected by the DataCollator.\n",
+ " \"\"\"\n",
+ " # Retrieve the pre-augmented pair from the DataFrame\n",
+ " row = self.data.iloc[idx]\n",
+ " smiles_1 = row['smiles_1']\n",
+ " smiles_2 = row['smiles_2']\n",
+ " \n",
+ " # Tokenize the pair. This operation is fast and remains in the data loader.\n",
+ " tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " \n",
+ " return {\n",
+ " 'input_ids_1': torch.tensor(tokens_1['input_ids']),\n",
+ " 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),\n",
+ " 'input_ids_2': torch.tensor(tokens_2['input_ids']),\n",
+ " 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),\n",
+ " }\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "476226dd-54f3-4d3e-adb4-cc30e922fd96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "OptimizedModule(\n",
+ " (_orig_mod): SimSonEncoder(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(591, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): BertEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-3): 4 x BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSdpaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): BertSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): BertIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=2048, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): BertOutput(\n",
+ " (dense): Linear(in_features=2048, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (linear): Linear(in_features=768, out_features=512, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "model_config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "model = SimSonEncoder(config=model_config, max_len=512).to(device)\n",
+ "model = torch.compile(model)\n",
+ "model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_polymer_1M/simson_model_single_gpu.bin'))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "df28c332-c20b-4804-b4ca-97de9d652445",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test dataset shape: (5000, 2)\n",
+ "Columns: ['smiles_1', 'smiles_2']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " smiles_1 | \n",
+ " smiles_2 | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... | \n",
+ " c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... | \n",
+ "
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+ " \n",
+ " | 1 | \n",
+ " C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... | \n",
+ " CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* | \n",
+ " O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC | \n",
+ " C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* | \n",
+ "
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+ " \n",
+ "
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+ ],
+ "text/plain": [
+ " smiles_1 \\\n",
+ "0 c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... \n",
+ "1 C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... \n",
+ "2 C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC \n",
+ "\n",
+ " smiles_2 \n",
+ "0 c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... \n",
+ "1 CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... \n",
+ "2 O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data = pd.read_parquet('/home/jovyan/simson_training_bolgov/data/polymer_splits/test.parquet')\n",
+ "print(f\"Test dataset shape: {test_data.shape}\")\n",
+ "print(f\"Columns: {test_data.columns.tolist()}\")\n",
+ "test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a7f2e2bc-12f5-443b-9d80-899542e07370",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating embeddings for original SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Generating embeddings for augmented SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Original embeddings shape: (5000, 512)\n",
+ "Augmented embeddings shape: (5000, 512)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def generate_embeddings(model, tokenizer, smiles_list, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings for a list of SMILES strings\"\"\"\n",
+ " model.eval()\n",
+ " embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " return np.vstack(embeddings)\n",
+ "\n",
+ "# Generate embeddings for original and augmented SMILES\n",
+ "print(\"Generating embeddings for original SMILES...\")\n",
+ "original_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_1'].tolist())\n",
+ "\n",
+ "print(\"Generating embeddings for augmented SMILES...\")\n",
+ "augmented_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_2'].tolist())\n",
+ "\n",
+ "print(f\"Original embeddings shape: {original_embeddings.shape}\")\n",
+ "print(f\"Augmented embeddings shape: {augmented_embeddings.shape}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c770d7f4-fa37-4519-bda2-9b084d2a4b32",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average cosine similarity between original and augmented SMILES: 0.9874\n",
+ "Standard deviation: 0.0197\n",
+ "Min similarity: 0.7691\n",
+ "Max similarity: 1.0000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def calculate_pairwise_similarities(embeddings1, embeddings2):\n",
+ " \"\"\"Calculate cosine similarities between corresponding pairs\"\"\"\n",
+ " similarities = []\n",
+ " for i in range(len(embeddings1)):\n",
+ " sim = cosine_similarity([embeddings1[i]], [embeddings2[i]])[0][0]\n",
+ " similarities.append(sim)\n",
+ " return np.array(similarities)\n",
+ "\n",
+ "# Calculate cosine similarities\n",
+ "pairwise_similarities = calculate_pairwise_similarities(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "print(f\"Average cosine similarity between original and augmented SMILES: {np.mean(pairwise_similarities):.4f}\")\n",
+ "print(f\"Standard deviation: {np.std(pairwise_similarities):.4f}\")\n",
+ "print(f\"Min similarity: {np.min(pairwise_similarities):.4f}\")\n",
+ "print(f\"Max similarity: {np.max(pairwise_similarities):.4f}\")\n",
+ "\n",
+ "# Plot similarity distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.hist(pairwise_similarities, bins=50, alpha=0.7, edgecolor='black')\n",
+ "plt.axvline(np.mean(pairwise_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(pairwise_similarities):.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Distribution of Cosine Similarities Between Original and Augmented SMILES')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "d7fa6ed4-b546-4544-bb00-4d90a99678da",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== Embedding Space Analysis ===\n",
+ "Embedding dimensionality: 512\n",
+ "Average L2 norm (original): 11.9105\n",
+ "Average L2 norm (augmented): 11.9072\n",
+ "Average intra-class similarity (original): 0.0047\n",
+ "Average intra-class similarity (augmented): 0.0048\n",
+ "Average inter-class similarity: 0.0049\n",
+ "\n",
+ "=== Nearest Neighbor Analysis (k=5) ===\n",
+ "Augmented SMILES in top-5 neighbors: 5000/5000 (100.0%)\n",
+ "Augmented SMILES as top-1 neighbor: 4872/5000 (97.4%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1. Embedding Space Statistics\n",
+ "def analyze_embedding_space(original_emb, augmented_emb):\n",
+ " \"\"\"Analyze the embedding space properties\"\"\"\n",
+ " print(\"=== Embedding Space Analysis ===\")\n",
+ " \n",
+ " # Dimensionality and norms\n",
+ " print(f\"Embedding dimensionality: {original_emb.shape[1]}\")\n",
+ " print(f\"Average L2 norm (original): {np.mean(np.linalg.norm(original_emb, axis=1)):.4f}\")\n",
+ " print(f\"Average L2 norm (augmented): {np.mean(np.linalg.norm(augmented_emb, axis=1)):.4f}\")\n",
+ " \n",
+ " # Intra-class similarities\n",
+ " orig_similarities = cosine_similarity(original_emb)\n",
+ " aug_similarities = cosine_similarity(augmented_emb)\n",
+ " \n",
+ " # Remove diagonal (self-similarity)\n",
+ " orig_similarities_off_diag = orig_similarities[np.triu_indices_from(orig_similarities, k=1)]\n",
+ " aug_similarities_off_diag = aug_similarities[np.triu_indices_from(aug_similarities, k=1)]\n",
+ " \n",
+ " print(f\"Average intra-class similarity (original): {np.mean(orig_similarities_off_diag):.4f}\")\n",
+ " print(f\"Average intra-class similarity (augmented): {np.mean(aug_similarities_off_diag):.4f}\")\n",
+ " \n",
+ " # Inter-class similarities\n",
+ " inter_similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " print(f\"Average inter-class similarity: {np.mean(inter_similarities):.4f}\")\n",
+ "\n",
+ "analyze_embedding_space(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "# 2. Nearest Neighbor Analysis\n",
+ "def nearest_neighbor_analysis(original_emb, augmented_emb, k=5):\n",
+ " \"\"\"Analyze nearest neighbors between original and augmented embeddings\"\"\"\n",
+ " print(f\"\\n=== Nearest Neighbor Analysis (k={k}) ===\")\n",
+ " \n",
+ " # For each original embedding, find its k nearest neighbors in augmented set\n",
+ " similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " \n",
+ " # Find cases where augmented version is among top-k neighbors\n",
+ " correct_matches = 0\n",
+ " top1_matches = 0\n",
+ " \n",
+ " for i in range(len(original_emb)):\n",
+ " # Get similarity scores for i-th original embedding\n",
+ " sim_scores = similarities[i]\n",
+ " top_k_indices = np.argsort(sim_scores)[-k:][::-1]\n",
+ " \n",
+ " if i in top_k_indices:\n",
+ " correct_matches += 1\n",
+ " if np.argmax(sim_scores) == i:\n",
+ " top1_matches += 1\n",
+ " \n",
+ " print(f\"Augmented SMILES in top-{k} neighbors: {correct_matches}/{len(original_emb)} ({100*correct_matches/len(original_emb):.1f}%)\")\n",
+ " print(f\"Augmented SMILES as top-1 neighbor: {top1_matches}/{len(original_emb)} ({100*top1_matches/len(original_emb):.1f}%)\")\n",
+ "\n",
+ "nearest_neighbor_analysis(original_embeddings, augmented_embeddings)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f136743a-c742-469b-a9b8-9a4da8eb4c08",
+ "metadata": {},
+ "source": [
+ "Объяснение\n",
+ "\n",
+ "* Embedding Space Analysis. Показывает, что различные величины (L2 norm - длина вектора, близости) практически идентичны для оригинальных, и для аугментированных молекул (показывая, что они отображаются практически одними и теми же для модели)\n",
+ "* Nearest Neighbor Analysis (k=5). Показывает, что топ 5 ближайших векторов к любому из векторов - всегда его аугментация (кроме его самого, естественно). В 97.4 % случаях вектор, соответствующий аугментации, является ближайшим."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133fb52e-bea8-4159-ab06-386998b71ed0",
+ "metadata": {},
+ "source": [
+ "С разными SMILES - ровно обратная картина, как и должно быть (различные smiles являются очень разными)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "cc48f04e-b8bf-415b-89f0-b51d98f7e6b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "num_molecules = original_embeddings.shape[0]\n",
+ "\n",
+ "# Shuffle indices for unrelated molecules\n",
+ "unrelated_indices = np.random.permutation(num_molecules)\n",
+ "unrelated_embeddings = augmented_embeddings[unrelated_indices]\n",
+ "\n",
+ "# Compute pairwise cosine similarity between original and unrelated\n",
+ "pairwise_unrelated_similarities = np.array([\n",
+ " cosine_similarity([original_embeddings[i]], [unrelated_embeddings[i]])[0][0]\n",
+ " for i in range(num_molecules)\n",
+ "])\n",
+ "\n",
+ "\n",
+ "plt.figure(figsize=(10,6))\n",
+ "plt.hist(pairwise_unrelated_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ "mean_sim = pairwise_unrelated_similarities.mean()\n",
+ "plt.axvline(mean_sim, color='red', linestyle='--', label=f'Mean = {mean_sim:.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Cosine Similarity Distribution of Different Molecules (Unrelated SMILES)')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "881eca62-7bf5-4b84-b0df-659942930a73",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean cosine similarity: 0.0048\n",
+ "Std deviation: 0.1357\n",
+ "Range: -0.4617 to 0.6631\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Mean cosine similarity: {pairwise_unrelated_similarities.mean():.4f}\")\n",
+ "print(f\"Std deviation: {pairwise_unrelated_similarities.std():.4f}\")\n",
+ "print(f\"Range: {pairwise_unrelated_similarities.min():.4f} to {pairwise_unrelated_similarities.max():.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "051f4149-d3eb-472b-b6d0-7060662efb6a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unrelated in top-5 neighbors: 4/5000\n",
+ "Unrelated as top-1 neighbor: 0/5000\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "k = 5\n",
+ "similarities = cosine_similarity(original_embeddings, unrelated_embeddings)\n",
+ "correct_matches = sum(i in np.argsort(similarities[i])[-k:] for i in range(num_molecules))\n",
+ "top1_matches = sum(np.argmax(similarities[i]) == i for i in range(num_molecules))\n",
+ "\n",
+ "print(f\"Unrelated in top-{k} neighbors: {correct_matches}/{num_molecules}\")\n",
+ "print(f\"Unrelated as top-1 neighbor: {top1_matches}/{num_molecules}\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/moleculenet_eval/.ipynb_checkpoints/visualizations-checkpoint.ipynb b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/visualizations-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..961a168ad80e33b521c47f704ca10679647c2cfe
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/.ipynb_checkpoints/visualizations-checkpoint.ipynb
@@ -0,0 +1,568 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "90d8362c-618c-42c9-8f2a-e73d2eb34abc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap\n",
+ "from rdkit import Chem, RDLogger\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "RDLogger.DisableLog('rdApp.*')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n",
+ "\n",
+ "# === HELPER FUNCTIONS AND CLASSES ===\n",
+ "\n",
+ "def load_lists_from_url(data):\n",
+ " if data == 'bbbp':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')\n",
+ " smiles, labels = df.smiles, df.p_np\n",
+ " elif data == 'clintox':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'hiv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')\n",
+ " smiles, labels = df.smiles, df.HIV_active\n",
+ " elif data == 'sider':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'esol':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df['ESOL predicted log solubility in mols per litre']\n",
+ " elif data == 'freesolv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df.calc\n",
+ " elif data == 'lipophicility':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')\n",
+ " smiles, labels = df.smiles, df['exp']\n",
+ " elif data == 'tox21':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['mol_id', 'smiles'], axis=1)\n",
+ " elif data == 'bace':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')\n",
+ " smiles, labels = df.mol, df.Class\n",
+ " elif data == 'qm8':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)\n",
+ " return smiles, labels\n",
+ "\n",
+ "class SmilesEnumerator:\n",
+ " \"\"\"Generates randomized SMILES strings for data augmentation.\"\"\"\n",
+ " def randomize_smiles(self, smiles):\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles\n",
+ " except:\n",
+ " return smiles\n",
+ "\n",
+ "class MoleculeDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_len=512):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.smiles_list[idx]\n",
+ " label = self.labels.iloc[idx]\n",
+ "\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_len,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " item = {key: val.squeeze(0) for key, val in encoding.items()}\n",
+ " if isinstance(label, pd.Series):\n",
+ " label_values = label.values.astype(np.float32)\n",
+ " else:\n",
+ " label_values = np.array([label], dtype=np.float32)\n",
+ " item['labels'] = torch.tensor(label_values, dtype=torch.float)\n",
+ " return item\n",
+ "\n",
+ "# Your model classes (assuming these exist from previous conversation)\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n",
+ "\n",
+ "# === MAIN ANALYSIS FUNCTIONS ===\n",
+ "\n",
+ "def prepare_dataset_with_augmentation(dataset_name, sample_size=1000):\n",
+ " \"\"\"\n",
+ " Load dataset, create augmented SMILES, and prepare for embedding generation\n",
+ " \"\"\"\n",
+ " print(f\"Loading {dataset_name} dataset...\")\n",
+ " smiles, labels = load_lists_from_url(dataset_name)\n",
+ " \n",
+ " # Handle different label structures\n",
+ " if dataset_name == 'bbbp':\n",
+ " # Binary classification - convert to binary labels\n",
+ " label_values = labels.values\n",
+ " label_names = ['Non-permeable', 'Permeable']\n",
+ " elif dataset_name == 'clintox':\n",
+ " # Multi-task - use first task for visualization\n",
+ " label_values = labels.iloc[:, 0].values # Use first toxicity endpoint\n",
+ " label_names = ['Non-toxic', 'Toxic']\n",
+ " \n",
+ " # Remove NaN labels and sample for visualization\n",
+ " valid_mask = ~pd.isna(label_values)\n",
+ " smiles_clean = smiles[valid_mask]\n",
+ " labels_clean = label_values[valid_mask]\n",
+ " \n",
+ " # Sample for manageable visualization\n",
+ " if len(smiles_clean) > sample_size:\n",
+ " indices = np.random.choice(len(smiles_clean), sample_size, replace=False)\n",
+ " smiles_clean = smiles_clean.iloc[indices]\n",
+ " labels_clean = labels_clean[indices]\n",
+ " \n",
+ " # Create augmented SMILES\n",
+ " enumerator = SmilesEnumerator()\n",
+ " print(\"Generating augmented SMILES...\")\n",
+ " augmented_smiles = []\n",
+ " \n",
+ " for smiles_str in smiles_clean:\n",
+ " aug_smiles = enumerator.randomize_smiles(smiles_str)\n",
+ " augmented_smiles.append(aug_smiles)\n",
+ " \n",
+ " print(f\"Dataset: {dataset_name}\")\n",
+ " print(f\"Total molecules: {len(smiles_clean)}\")\n",
+ " print(f\"Label distribution: {np.bincount(labels_clean.astype(int))}\")\n",
+ " \n",
+ " return smiles_clean.tolist(), augmented_smiles, labels_clean, label_names\n",
+ "\n",
+ "def generate_embeddings_for_classes(model, tokenizer, smiles_list, labels, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings and organize by class labels\"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " all_embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " all_embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " embeddings = np.vstack(all_embeddings)\n",
+ " \n",
+ " # Organize by class\n",
+ " class_embeddings = {}\n",
+ " unique_labels = np.unique(labels)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels == label\n",
+ " class_embeddings[label] = embeddings[mask]\n",
+ " \n",
+ " return embeddings, class_embeddings\n",
+ "\n",
+ "def analyze_class_separation(class_embeddings, label_names):\n",
+ " \"\"\"Analyze how well different classes are separated in embedding space\"\"\"\n",
+ " print(\"=== Class Separation Analysis ===\")\n",
+ " \n",
+ " class_keys = list(class_embeddings.keys())\n",
+ " \n",
+ " if len(class_keys) == 2:\n",
+ " # Binary classification analysis\n",
+ " emb1 = class_embeddings[class_keys[0]]\n",
+ " emb2 = class_embeddings[class_keys[1]]\n",
+ " \n",
+ " # Inter-class similarity (between different classes)\n",
+ " inter_sim = cosine_similarity(emb1, emb2)\n",
+ " mean_inter_sim = np.mean(inter_sim)\n",
+ " \n",
+ " # Intra-class similarity (within same class)\n",
+ " intra_sim1 = cosine_similarity(emb1)\n",
+ " intra_sim2 = cosine_similarity(emb2)\n",
+ " \n",
+ " # Remove diagonal for intra-class\n",
+ " intra_sim1_off_diag = intra_sim1[np.triu_indices_from(intra_sim1, k=1)]\n",
+ " intra_sim2_off_diag = intra_sim2[np.triu_indices_from(intra_sim2, k=1)]\n",
+ " \n",
+ " mean_intra_sim1 = np.mean(intra_sim1_off_diag)\n",
+ " mean_intra_sim2 = np.mean(intra_sim2_off_diag)\n",
+ " \n",
+ " print(f\"Class 0 ({label_names[0]}) size: {len(emb1)}\")\n",
+ " print(f\"Class 1 ({label_names[1]}) size: {len(emb2)}\")\n",
+ " print(f\"Mean inter-class similarity: {mean_inter_sim:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 0): {mean_intra_sim1:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 1): {mean_intra_sim2:.4f}\")\n",
+ " print(f\"Separation ratio: {(mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim):.4f}\")\n",
+ " \n",
+ " return {\n",
+ " 'inter_class_sim': mean_inter_sim,\n",
+ " 'intra_class_sim': [mean_intra_sim1, mean_intra_sim2],\n",
+ " 'separation_ratio': (mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim)\n",
+ " }\n",
+ "\n",
+ "def visualize_class_embeddings(embeddings, labels, label_names, dataset_name, sample_size=2000):\n",
+ " \"\"\"Create comprehensive visualizations of class-separated embeddings\"\"\"\n",
+ " \n",
+ " # Sample for visualization if needed\n",
+ " if len(embeddings) > sample_size:\n",
+ " indices = np.random.choice(len(embeddings), sample_size, replace=False)\n",
+ " embeddings_viz = embeddings[indices]\n",
+ " labels_viz = labels[indices]\n",
+ " print(f\"Sampling {sample_size} points for visualization\")\n",
+ " else:\n",
+ " embeddings_viz = embeddings\n",
+ " labels_viz = labels\n",
+ " \n",
+ " # Create color mapping\n",
+ " unique_labels = np.unique(labels_viz)\n",
+ " colors = plt.cm.Set1(np.linspace(0, 1, len(unique_labels)))\n",
+ " color_map = {label: colors[i] for i, label in enumerate(unique_labels)}\n",
+ " \n",
+ " # Create subplots\n",
+ " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+ " fig.suptitle(f'{dataset_name.upper()} Dataset - Class Separation Visualization', fontsize=16, fontweight='bold')\n",
+ " \n",
+ " # PCA\n",
+ " print(\"Computing PCA...\")\n",
+ " pca = PCA(n_components=2, random_state=42)\n",
+ " pca_embeddings = pca.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 0].scatter(pca_embeddings[mask, 0], pca_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 0].set_title('PCA Visualization')\n",
+ " axes[0, 0].set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)')\n",
+ " axes[0, 0].set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)')\n",
+ " axes[0, 0].legend()\n",
+ " axes[0, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # t-SNE\n",
+ " print(\"Computing t-SNE...\")\n",
+ " tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(embeddings_viz)//4))\n",
+ " tsne_embeddings = tsne.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 1].scatter(tsne_embeddings[mask, 0], tsne_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 1].set_title('t-SNE Visualization')\n",
+ " axes[0, 1].set_xlabel('t-SNE 1')\n",
+ " axes[0, 1].set_ylabel('t-SNE 2')\n",
+ " axes[0, 1].legend()\n",
+ " axes[0, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # UMAP\n",
+ " print(\"Computing UMAP...\")\n",
+ " umap_reducer = umap.UMAP(n_components=2, random_state=42, n_neighbors=min(15, len(embeddings_viz)//4))\n",
+ " umap_embeddings = umap_reducer.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 2].scatter(umap_embeddings[mask, 0], umap_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 2].set_title('UMAP Visualization')\n",
+ " axes[0, 2].set_xlabel('UMAP 1')\n",
+ " axes[0, 2].set_ylabel('UMAP 2')\n",
+ " axes[0, 2].legend()\n",
+ " axes[0, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Inter-class distance distribution\n",
+ " if len(unique_labels) == 2:\n",
+ " class_0_emb = embeddings_viz[labels_viz == unique_labels[0]]\n",
+ " class_1_emb = embeddings_viz[labels_viz == unique_labels[1]]\n",
+ " \n",
+ " # Sample for distance calculation if too large\n",
+ " if len(class_0_emb) > 500:\n",
+ " class_0_sample = class_0_emb[np.random.choice(len(class_0_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_0_sample = class_0_emb\n",
+ " \n",
+ " if len(class_1_emb) > 500:\n",
+ " class_1_sample = class_1_emb[np.random.choice(len(class_1_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_1_sample = class_1_emb\n",
+ " \n",
+ " inter_similarities = cosine_similarity(class_0_sample, class_1_sample).flatten()\n",
+ " \n",
+ " axes[1, 0].hist(inter_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ " axes[1, 0].axvline(np.mean(inter_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(inter_similarities):.4f}')\n",
+ " axes[1, 0].set_xlabel('Cosine Similarity')\n",
+ " axes[1, 0].set_ylabel('Frequency')\n",
+ " axes[1, 0].set_title(f'Inter-Class Similarity Distribution')\n",
+ " axes[1, 0].legend()\n",
+ " axes[1, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Embedding norms by class\n",
+ " for i, label in enumerate(unique_labels):\n",
+ " mask = labels_viz == label\n",
+ " norms = np.linalg.norm(embeddings_viz[mask], axis=1)\n",
+ " axes[1, 1].hist(norms, bins=30, alpha=0.6, \n",
+ " color=color_map[label], label=f'{label_names[int(label)]}')\n",
+ " \n",
+ " axes[1, 1].set_xlabel('L2 Norm')\n",
+ " axes[1, 1].set_ylabel('Frequency')\n",
+ " axes[1, 1].set_title('Embedding Norm Distribution by Class')\n",
+ " axes[1, 1].legend()\n",
+ " axes[1, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Class statistics summary\n",
+ " axes[1, 2].axis('off')\n",
+ " stats_text = f\"\"\"\n",
+ " {dataset_name.upper()} Dataset Statistics:\n",
+ " \n",
+ " • Total samples: {len(embeddings_viz):,}\n",
+ " • Embedding dimension: {embeddings_viz.shape[1]}\n",
+ " • Classes: {len(unique_labels)}\n",
+ " \"\"\"\n",
+ " \n",
+ " for i, label in enumerate(unique_labels):\n",
+ " count = np.sum(labels_viz == label)\n",
+ " percentage = 100 * count / len(labels_viz)\n",
+ " stats_text += f\"\\n - {label_names[int(label)]}: {count} ({percentage:.1f}%)\"\n",
+ " \n",
+ " axes[1, 2].text(0.1, 0.9, stats_text, transform=axes[1, 2].transAxes, fontsize=12, \n",
+ " verticalalignment='top', fontfamily='monospace')\n",
+ " \n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ " \n",
+ " return pca_embeddings, tsne_embeddings, umap_embeddings\n",
+ "\n",
+ "def clustering_analysis_for_classes(embeddings, labels, label_names, n_clusters=None):\n",
+ " \"\"\"Perform clustering analysis to evaluate class separation\"\"\"\n",
+ " if n_clusters is None:\n",
+ " n_clusters = len(np.unique(labels))\n",
+ " \n",
+ " print(f\"\\n=== Clustering Analysis (k={n_clusters}) ===\")\n",
+ " \n",
+ " # K-means clustering\n",
+ " kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
+ " cluster_labels = kmeans.fit_predict(embeddings)\n",
+ " \n",
+ " # Calculate cluster purity\n",
+ " cluster_purities = []\n",
+ " for cluster_id in range(n_clusters):\n",
+ " cluster_mask = cluster_labels == cluster_id\n",
+ " cluster_true_labels = labels[cluster_mask]\n",
+ " \n",
+ " if len(cluster_true_labels) > 0:\n",
+ " unique, counts = np.unique(cluster_true_labels, return_counts=True)\n",
+ " purity = np.max(counts) / len(cluster_true_labels)\n",
+ " cluster_purities.append(purity)\n",
+ " print(f\"Cluster {cluster_id}: {len(cluster_true_labels)} samples, purity: {purity:.3f}\")\n",
+ " \n",
+ " avg_purity = np.mean(cluster_purities)\n",
+ " print(f\"Average cluster purity: {avg_purity:.4f}\")\n",
+ " \n",
+ " \n",
+ " return avg_purity\n",
+ "\n",
+ "# === MAIN EXECUTION ===\n",
+ "\n",
+ "def main_class_visualization():\n",
+ " \"\"\"Main function to run the complete class-based visualization analysis\"\"\"\n",
+ " \n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ " config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ " model = SimSonEncoder(config, max_len=512) # Adjust max_len as needed\n",
+ " model = torch.compile(model)\n",
+ " model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_bbp_encoder.bin', map_location=device))\n",
+ " \n",
+ " model.to(device)\n",
+ " model.eval()\n",
+ " ds_name_to_model_path = {'bbbp': '/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_bbp_encoder.bin', 'clintox': ''}\n",
+ " \n",
+ " \n",
+ " # Analyze both datasets\n",
+ " datasets = ['bbbp']\n",
+ " \n",
+ " for dataset_name in datasets:\n",
+ " model.load_state_dict(torch.load(ds_name_to_model_path[dataset_name], map_location=device))\n",
+ " print(f\"\\n{'='*50}\")\n",
+ " print(f\"ANALYZING {dataset_name.upper()} DATASET\")\n",
+ " print(f\"{'='*50}\")\n",
+ " \n",
+ " # Prepare dataset\n",
+ " smiles_list, augmented_smiles, labels, label_names = prepare_dataset_with_augmentation(\n",
+ " dataset_name, sample_size=1000\n",
+ " )\n",
+ " \n",
+ " # Generate embeddings for original SMILES\n",
+ " print(\"Generating embeddings...\")\n",
+ " embeddings, class_embeddings = generate_embeddings_for_classes(\n",
+ " model, tokenizer, smiles_list, labels\n",
+ " )\n",
+ " \n",
+ " # Analyze class separation\n",
+ " separation_stats = analyze_class_separation(class_embeddings, label_names)\n",
+ " \n",
+ " # Create visualizations\n",
+ " pca_emb, tsne_emb, umap_emb = visualize_class_embeddings(\n",
+ " embeddings, labels, label_names, dataset_name\n",
+ " )\n",
+ " \n",
+ " # Clustering analysis\n",
+ " clustering_analysis_for_classes(embeddings, labels, label_names)\n",
+ " \n",
+ " print(f\"\\nCompleted analysis for {dataset_name.upper()} dataset\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "17ada2a4-e44e-4ff1-8b07-cd1f1b53298d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using device: cuda\n",
+ "\n",
+ "==================================================\n",
+ "ANALYZING BBBP DATASET\n",
+ "==================================================\n",
+ "Loading bbbp dataset...\n",
+ "Generating augmented SMILES...\n",
+ "Dataset: bbbp\n",
+ "Total molecules: 1000\n",
+ "Label distribution: [217 783]\n",
+ "Generating embeddings...\n",
+ "=== Class Separation Analysis ===\n",
+ "Class 0 (Non-permeable) size: 217\n",
+ "Class 1 (Permeable) size: 783\n",
+ "Mean inter-class similarity: -0.1213\n",
+ "Mean intra-class similarity (Class 0): 0.3385\n",
+ "Mean intra-class similarity (Class 1): 0.5822\n",
+ "Separation ratio: -3.7953\n",
+ "Computing PCA...\n",
+ "Computing t-SNE...\n",
+ "Computing UMAP...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Clustering Analysis (k=2) ===\n",
+ "Cluster 0: 669 samples, purity: 0.991\n",
+ "Cluster 1: 331 samples, purity: 0.637\n",
+ "Average cluster purity: 0.8142\n",
+ "\n",
+ "Completed analysis for BBBP dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "if __name__ == \"__main__\":\n",
+ " main_class_visualization()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/moleculenet_eval/__pycache__/better_eval.cpython-312.pyc b/simson_modeling/moleculenet_eval/__pycache__/better_eval.cpython-312.pyc
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diff --git a/simson_modeling/moleculenet_eval/__pycache__/eval.cpython-312.pyc b/simson_modeling/moleculenet_eval/__pycache__/eval.cpython-312.pyc
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diff --git a/simson_modeling/moleculenet_eval/better_eval.py b/simson_modeling/moleculenet_eval/better_eval.py
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--- /dev/null
+++ b/simson_modeling/moleculenet_eval/better_eval.py
@@ -0,0 +1,671 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertConfig, BertModel, AutoTokenizer
+from rdkit import Chem, RDLogger
+from rdkit.Chem.Scaffolds import MurckoScaffold
+import copy
+from tqdm import tqdm
+import os
+from sklearn.metrics import roc_auc_score, root_mean_squared_error, mean_absolute_error
+from itertools import compress
+from collections import defaultdict
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+import optuna
+import warnings
+warnings.filterwarnings("ignore")
+RDLogger.DisableLog('rdApp.*')
+
+torch.set_float32_matmul_precision('high')
+
+# --- 0. Pre-computed Contrastive SMILES Dataset ---
+class PrecomputedContrastiveSmilesDataset(Dataset):
+ """
+ A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.
+ This is significantly faster as it offloads the expensive SMILES randomization
+ to a one-time preprocessing step.
+ """
+ def __init__(self, tokenizer, file_path: str, max_length: int = 512):
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ # Load the entire dataset from the Parquet file into memory.
+ # This is fast and efficient for subsequent access.
+ print(f"Loading pre-computed data from {file_path}...")
+ self.data = pd.read_parquet(file_path)
+ print("Data loaded successfully.")
+
+ def __len__(self):
+ """Returns the total number of pairs in the dataset."""
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ """
+ Retrieves a pre-augmented pair, tokenizes it, and returns it
+ in the format expected by the DataCollator.
+ """
+ # Retrieve the pre-augmented pair from the DataFrame
+ row = self.data.iloc[idx]
+ smiles_1 = row['smiles_1']
+ smiles_2 = row['smiles_2']
+
+ # Tokenize the pair. This operation is fast and remains in the data loader.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+# --- 0a. SMILES enumeration for preprocessing ---
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+def compute_embedding_similarity_precomputed(encoder, dataset, device):
+ """
+ Compute embedding similarity using pre-computed augmented SMILES pairs
+ """
+ encoder.eval()
+ similarities = []
+
+ dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
+
+ with torch.no_grad():
+ for batch in dataloader:
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ emb_1 = encoder(input_ids_1, attention_mask_1).cpu().numpy()
+ emb_2 = encoder(input_ids_2, attention_mask_2).cpu().numpy()
+
+ # Compute cosine similarity for each pair in the batch
+ batch_similarities = []
+ for i in range(len(emb_1)):
+ sim = cosine_similarity([emb_1[i]], [emb_2[i]])[0][0]
+ batch_similarities.append(sim)
+
+ similarities.extend(batch_similarities)
+
+ return np.array(similarities)
+
+def create_augmented_smiles_file(smiles_list, output_path, num_augmentations=1):
+ """
+ Create a parquet file with pre-computed augmented SMILES pairs
+ """
+ enumerator = SmilesEnumerator()
+ pairs = []
+
+ print(f"Generating {num_augmentations} augmentations for {len(smiles_list)} SMILES...")
+
+ for smiles in tqdm(smiles_list):
+ for _ in range(num_augmentations):
+ augmented = enumerator.randomize_smiles(smiles)
+ pairs.append({
+ 'smiles_1': smiles,
+ 'smiles_2': augmented
+ })
+
+ df = pd.DataFrame(pairs)
+ df.to_parquet(output_path, index=False)
+ print(f"Saved {len(pairs)} augmented pairs to {output_path}")
+ return output_path
+
+# --- 1. Data Loading ---
+def load_lists_from_url(data):
+ # Datasets and their splits, all configurations carried over
+ if data == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif data == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif data == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'esol':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')
+ smiles = df.smiles
+ labels = df['ESOL predicted log solubility in mols per litre']
+ elif data == 'freesolv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')
+ smiles = df.smiles
+ labels = df.calc
+ elif data == 'lipophicility':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')
+ smiles, labels = df.smiles, df['exp']
+ elif data == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif data == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ elif data == 'qm8':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)
+ return smiles, labels
+
+# --- 2. Scaffold Splitting ---
+class ScaffoldSplitter:
+ def __init__(self, data, seed, train_frac=0.8, val_frac=0.1, test_frac=0.1, include_chirality=True):
+ self.data = data
+ self.seed = seed
+ self.include_chirality = include_chirality
+ self.train_frac = train_frac
+ self.val_frac = val_frac
+ self.test_frac = test_frac
+
+ def generate_scaffold(self, smiles):
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol, includeChirality=self.include_chirality)
+ return scaffold
+
+ def scaffold_split(self):
+ smiles, labels = load_lists_from_url(self.data)
+ non_null = np.ones(len(smiles)) == 0
+
+ if self.data in {'tox21', 'sider', 'clintox'}:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]) and labels.loc[i].isnull().sum() == 0:
+ non_null[i] = 1
+ else:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]):
+ non_null[i] = 1
+
+ smiles_list = list(compress(enumerate(smiles), non_null))
+ rng = np.random.RandomState(self.seed)
+
+ scaffolds = defaultdict(list)
+ for i, sms in smiles_list:
+ scaffold = self.generate_scaffold(sms)
+ scaffolds[scaffold].append(i)
+
+ scaffold_sets = list(scaffolds.values())
+ rng.shuffle(scaffold_sets)
+ n_total_val = int(np.floor(self.val_frac * len(smiles_list)))
+ n_total_test = int(np.floor(self.test_frac * len(smiles_list)))
+ train_idx, val_idx, test_idx = [], [], []
+
+ for scaffold_set in scaffold_sets:
+ if len(val_idx) + len(scaffold_set) <= n_total_val:
+ val_idx.extend(scaffold_set)
+ elif len(test_idx) + len(scaffold_set) <= n_total_test:
+ test_idx.extend(scaffold_set)
+ else:
+ train_idx.extend(scaffold_set)
+ return train_idx, val_idx, test_idx
+
+# --- 2a. Normal Random Split ---
+def random_split_indices(n, seed=42, train_frac=0.8, val_frac=0.1, test_frac=0.1):
+ np.random.seed(seed)
+ indices = np.random.permutation(n)
+ n_train = int(n * train_frac)
+ n_val = int(n * val_frac)
+ train_idx = indices[:n_train]
+ val_idx = indices[n_train:n_train+n_val]
+ test_idx = indices[n_train+n_val:]
+ return train_idx.tolist(), val_idx.tolist(), test_idx.tolist()
+
+# --- 3. PyTorch Dataset ---
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_len=512):
+ self.smiles_list = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles_list[idx]
+ label = self.labels.iloc[idx]
+
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_len,
+ return_tensors='pt'
+ )
+ item = {key: val.squeeze(0) for key, val in encoding.items()}
+ if isinstance(label, pd.Series):
+ label_values = label.values.astype(np.float32)
+ else:
+ label_values = np.array([label], dtype=np.float32)
+ item['labels'] = torch.tensor(label_values, dtype=torch.float)
+ return item
+
+# --- 4. Model Architecture ---
+def global_ap(x):
+ return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)
+
+class SimSonEncoder(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.config.pad_token_id)
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled = global_ap(hidden_states)
+ return self.linear(pooled)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):
+ super(SimSonClassifier, self).__init__()
+ self.encoder = encoder
+ self.clf = nn.Linear(encoder.max_len, num_labels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ x = self.encoder(input_ids, attention_mask)
+ x = self.relu(self.dropout(x))
+ logits = self.clf(x)
+ return logits
+
+ def load_encoder_params(self, state_dict_path):
+ self.encoder.load_state_dict(torch.load(state_dict_path))
+
+# --- 5. Training, Validation, and Testing Loops ---
+def get_criterion(task_type, num_labels):
+ if task_type == 'classification':
+ return nn.BCEWithLogitsLoss()
+ elif task_type == 'regression':
+ return nn.MSELoss()
+ else:
+ raise ValueError(f"Unknown task type: {task_type}")
+
+def train_epoch(model, dataloader, optimizer, scheduler, criterion, device):
+ model.train()
+ total_loss = 0
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ optimizer.zero_grad()
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ if scheduler is not None:
+ scheduler.step()
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def calc_val_metrics(model, dataloader, criterion, device, task_type):
+ model.eval()
+ all_labels, all_preds = [], []
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ if task_type == 'classification':
+ pred_probs = torch.sigmoid(outputs).cpu().numpy()
+ all_preds.append(pred_probs)
+ all_labels.append(labels.cpu().numpy())
+ else:
+ # Regression
+ preds = outputs.cpu().numpy()
+ all_preds.append(preds)
+ all_labels.append(labels.cpu().numpy())
+ avg_loss = total_loss / len(dataloader)
+ if task_type == 'classification':
+ y_true = np.concatenate(all_labels)
+ y_pred = np.concatenate(all_preds)
+ try:
+ score = roc_auc_score(y_true, y_pred, average='macro')
+ except Exception:
+ score = 0.0
+ return avg_loss, score
+ else:
+ return avg_loss, None
+
+def test_model(model, dataloader, device, task_type):
+ model.eval()
+ all_preds, all_labels = [], []
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels']
+ outputs = model(**inputs)
+ if task_type == 'classification':
+ preds = torch.sigmoid(outputs)
+ else:
+ preds = outputs
+ all_preds.append(preds.cpu().numpy())
+ all_labels.append(labels.numpy())
+ return np.concatenate(all_preds), np.concatenate(all_labels)
+
+# --- 6. Optuna Objective Function ---
+def create_objective(name, info, train_smiles, train_labels, val_smiles, val_labels,
+ test_smiles, test_labels, scaler, tokenizer, encoder_config, device):
+ """Creates objective function for Optuna optimization"""
+
+ def objective(trial):
+ # Suggest hyperparameters
+ lr = trial.suggest_float('lr', 1e-6, 1e-4, log=True)
+ batch_size = trial.suggest_categorical('batch_size', [16, 32, 64, 128, 256])
+ dropout = trial.suggest_float('dropout', 0.1, 0.5)
+ weight_decay = trial.suggest_float('weight_decay', 0.0, 0.1)
+ scheduler_type = trial.suggest_categorical('scheduler', ['plateau', 'cosine', 'step'])
+
+ # Additional hyperparameters for optimization
+ patience_lr = trial.suggest_int('patience_lr', 3, 10)
+ gamma = trial.suggest_float('gamma', 0.5, 0.9) if scheduler_type == 'step' else 0.1
+
+ try:
+ # Create datasets and dataloaders
+ train_dataset = MoleculeDataset(train_smiles, train_labels, tokenizer, 512)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, tokenizer, 512)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, tokenizer, 512)
+
+ train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+ # Create model
+ encoder = SimSonEncoder(encoder_config, 512, dropout=dropout)
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels'], dropout=dropout).to(device)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
+
+ # Create scheduler based on trial suggestion
+ if scheduler_type == 'plateau':
+ scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+ optimizer, mode='max', factor=gamma, patience=patience_lr
+ )
+ elif scheduler_type == 'cosine':
+ scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
+ else: # step
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=gamma)
+
+ # Training loop
+ best_val_metric = -np.inf
+ patience_counter = 0
+ patience = 15
+
+ for epoch in range(50): # Max epochs
+ train_loss = train_epoch(model, train_loader, optimizer,
+ scheduler if scheduler_type == 'cosine' else None,
+ criterion, device)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, device, info['task_type'])
+
+ # Update scheduler
+ if scheduler_type == 'plateau':
+ scheduler.step(val_loss if val_loss is not None else -val_loss)
+ elif scheduler_type == 'step':
+ scheduler.step()
+
+ # Determine metric to optimize
+ if info['task_type'] == 'classification':
+ current_metric = val_loss if val_loss is not None else 0.0
+ else:
+ current_metric = -val_loss # For regression, minimize loss
+
+ # Early stopping and best model tracking
+ if current_metric <= val_loss:
+ best_val_metric = current_metric
+ patience_counter = 0
+ else:
+ patience_counter += 1
+ if patience_counter >= patience:
+ break
+
+ # Optuna pruning
+ trial.report(current_metric, epoch)
+ if trial.should_prune():
+ raise optuna.TrialPruned()
+
+ return best_val_metric
+
+ except Exception as e:
+ print(f"Trial failed with error: {e}")
+ return -np.inf # Return worst possible score for failed trials
+
+ return objective
+
+# --- 7. Main Execution Block ---
+def main():
+ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {DEVICE}")
+
+ DATASETS_TO_RUN = {
+ #'esol': {'task_type': 'regression', 'num_labels': 1, 'split': 'random'},
+ # Add more datasets here, e.g. 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ #'sider': {'task_type': 'classification', 'num_labels': 27, 'split': 'random'},
+ #'bace': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ 'clintox': {'task_type': 'classification', 'num_labels': 2, 'split': 'scaffold'},
+ 'tox21': {'task_type': 'classification', 'num_labels': 12, 'split': 'random'},
+ 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ 'hiv': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ }
+
+ MAX_LEN = 512
+ N_TRIALS = 100 # Number of Optuna trials to run
+
+ TOKENIZER = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ ENCODER_CONFIG = BertConfig(
+ vocab_size=TOKENIZER.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ aggregated_results = {}
+
+ for name, info in DATASETS_TO_RUN.items():
+ print(f"\n{'='*20} Processing Dataset: {name.upper()} ({info['split']} split) {'='*20}")
+ smiles, labels = load_lists_from_url(name)
+
+ # For regression tasks, scale labels and remember scaling transform
+ scaler = None
+ if info["task_type"] == "regression":
+ scaler = StandardScaler()
+ all_labels = labels.values.reshape(-1, 1)
+ scaler.fit(all_labels)
+ labels = pd.Series(scaler.transform(all_labels).flatten(), index=labels.index)
+
+ # Data split
+ if info.get('split', 'scaffold') == 'scaffold':
+ splitter = ScaffoldSplitter(data=name, seed=42)
+ train_idx, val_idx, test_idx = splitter.scaffold_split()
+ elif info['split'] == 'random':
+ train_idx, val_idx, test_idx = random_split_indices(len(smiles), seed=42)
+ else:
+ raise ValueError(f"Unknown split type for {name}: {info['split']}")
+
+ train_smiles = smiles.iloc[train_idx].reset_index(drop=True)
+ train_labels = labels.iloc[train_idx].reset_index(drop=True)
+ val_smiles = smiles.iloc[val_idx].reset_index(drop=True)
+ val_labels = labels.iloc[val_idx].reset_index(drop=True)
+ test_smiles = smiles.iloc[test_idx].reset_index(drop=True)
+ test_labels = labels.iloc[test_idx].reset_index(drop=True)
+ print(f"Data split - Train: {len(train_smiles)}, Val: {len(val_smiles)}, Test: {len(test_smiles)}")
+
+ # Create Optuna study
+ study = optuna.create_study(
+ direction='maximize',
+ pruner=optuna.pruners.MedianPruner(n_startup_trials=5, n_warmup_steps=10)
+ )
+
+ # Create objective function
+ objective_func = create_objective(
+ name, info, train_smiles, train_labels, val_smiles, val_labels,
+ test_smiles, test_labels, scaler, TOKENIZER, ENCODER_CONFIG, DEVICE
+ )
+
+ # Run optimization
+ print(f"Starting Optuna optimization with {N_TRIALS} trials...")
+ study.optimize(objective_func, n_trials=N_TRIALS, timeout=None)
+
+ # Get best parameters
+ best_params = study.best_params
+ best_score = study.best_value
+ print(f"Best parameters: {best_params}")
+ print(f"Best validation score: {0:.4f}")
+
+ # Train final model with best parameters
+ print("Training final model with best parameters...")
+ train_dataset = MoleculeDataset(train_smiles, train_labels, TOKENIZER, MAX_LEN)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, TOKENIZER, MAX_LEN)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, TOKENIZER, MAX_LEN)
+
+ train_loader = DataLoader(train_dataset, batch_size=best_params['batch_size'], shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=best_params['batch_size'], shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=best_params['batch_size'], shuffle=False)
+
+ # Final model training
+ encoder = SimSonEncoder(ENCODER_CONFIG, 512, dropout=best_params['dropout'])
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels'], dropout=best_params['dropout']).to(DEVICE)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=best_params['lr'], weight_decay=best_params['weight_decay'])
+
+ # Set up best scheduler
+ if best_params['scheduler'] == 'plateau':
+ scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+ optimizer, mode='max', factor=best_params.get('gamma', 0.7),
+ patience=best_params.get('patience_lr', 5)
+ )
+ elif best_params['scheduler'] == 'cosine':
+ scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
+ else:
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=best_params.get('gamma', 0.1))
+
+ # Train with best parameters
+ best_val_metric = -np.inf
+ best_model_state = None
+ patience_counter = 0
+ patience = 15
+
+ for epoch in range(50):
+ train_loss = train_epoch(model, train_loader, optimizer,
+ scheduler if best_params['scheduler'] == 'cosine' else None,
+ criterion, DEVICE)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, DEVICE, info['task_type'])
+
+ if best_params['scheduler'] == 'plateau':
+ scheduler.step(val_loss if val_loss is not None else -val_loss)
+ elif best_params['scheduler'] == 'step':
+ scheduler.step()
+
+ if info['task_type'] == 'classification':
+ print(f"Epoch {epoch+1}/50 | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | ROC AUC: {val_metric:.4f}")
+ current_metric = val_metric if val_metric is not None else 0.0
+ else:
+ print(f"Epoch {epoch+1}/50 | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")
+ current_metric = -val_loss
+
+ if current_metric <= val_loss:
+ best_val_metric = current_metric
+ best_model_state = copy.deepcopy(model.state_dict())
+ patience_counter = 0
+ else:
+ patience_counter += 1
+ if patience_counter >= patience:
+ print(f'Early stopping at epoch {epoch+1}')
+ break
+
+ # Test final model
+ if best_model_state is not None:
+ model.load_state_dict(best_model_state)
+
+ test_preds, test_true = test_model(model, test_loader, DEVICE, info['task_type'])
+
+ # Calculate final metrics
+ if info['task_type'] == 'regression' and scaler is not None:
+ test_preds = scaler.inverse_transform(test_preds.reshape(-1, 1)).flatten()
+ test_true = scaler.inverse_transform(test_true.reshape(-1, 1)).flatten()
+ rmse = root_mean_squared_error(test_true, test_preds)
+ mae = mean_absolute_error(test_true, test_preds)
+ final_score = -rmse
+ print(f"Test RMSE: {rmse:.4f}, MAE: {mae:.4f}")
+ else:
+ try:
+ final_score = roc_auc_score(test_true, test_preds, average='macro')
+ print(f"Test ROC AUC: {final_score:.4f}")
+ except Exception:
+ final_score = 0.0
+
+ # Compute embedding similarity using pre-computed augmented SMILES
+ print("Creating pre-computed augmented SMILES for similarity computation...")
+ test_smiles_list = list(test_smiles)
+ similarity_file_path = f"{name}_test_augmented.parquet"
+ create_augmented_smiles_file(test_smiles_list, similarity_file_path, num_augmentations=1)
+
+ # Load pre-computed dataset for similarity computation
+ similarity_dataset = PrecomputedContrastiveSmilesDataset(
+ TOKENIZER, similarity_file_path, max_length=MAX_LEN
+ )
+
+ similarities = compute_embedding_similarity_precomputed(
+ model.encoder, similarity_dataset, DEVICE
+ )
+ print(f"Similarity score: {similarities.mean():.4f}")
+
+ # Clean up temporary file
+ if os.path.exists(similarity_file_path):
+ os.remove(similarity_file_path)
+
+ aggregated_results[name] = {
+ 'best_score': final_score,
+ 'best_params': best_params,
+ 'optuna_trials': len(study.trials),
+ 'study': study,
+ 'similarity_score': similarities.mean()
+ }
+
+ if name == 'do_not_save':
+ torch.save(model.encoder.state_dict(), 'moleculenet_clintox_encoder.bin')
+
+ print(f"\n{'='*20} AGGREGATED RESULTS {'='*20}")
+ for name, result in aggregated_results.items():
+ print(f"{name}: Best score: {result['best_score']:.4f}")
+ print(f" Best parameters: {result['best_params']}")
+ print(f" Total trials: {result['optuna_trials']}")
+ print(f" Similarity score: {result['similarity_score']:.4f}")
+
+ print("\nScript finished.")
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/moleculenet_eval/eval.py b/simson_modeling/moleculenet_eval/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b4eb25054ae7710bbbdc4ce12f907246371b81
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/eval.py
@@ -0,0 +1,457 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertConfig, BertModel, AutoTokenizer
+from rdkit import Chem, RDLogger
+from rdkit.Chem.Scaffolds import MurckoScaffold
+import copy
+from tqdm import tqdm
+import os
+from sklearn.metrics import roc_auc_score, root_mean_squared_error, mean_absolute_error
+from itertools import compress
+from collections import defaultdict
+from sklearn.metrics.pairwise import cosine_similarity
+RDLogger.DisableLog('rdApp.*')
+
+
+torch.set_float32_matmul_precision('high')
+
+# --- 0. Smiles enumeration
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+
+def compute_embedding_similarity(encoder, smiles_list, tokenizer, device, max_len=256):
+ encoder.eval()
+ enumerator = SmilesEnumerator()
+
+ embeddings_orig = []
+ embeddings_aug = []
+
+ with torch.no_grad():
+ for smi in smiles_list:
+ # Original SMILES encoding
+ encoding_orig = tokenizer(
+ smi,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+ # Augmented SMILES encoding
+ smi_aug = enumerator.randomize_smiles(smi)
+ encoding_aug = tokenizer(
+ smi_aug,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+
+ input_ids_orig = encoding_orig.input_ids.to(device)
+ attention_mask_orig = encoding_orig.attention_mask.to(device)
+ input_ids_aug = encoding_aug.input_ids.to(device)
+ attention_mask_aug = encoding_aug.attention_mask.to(device)
+
+ emb_orig = encoder(input_ids_orig, attention_mask_orig).cpu().numpy().flatten()
+ emb_aug = encoder(input_ids_aug, attention_mask_aug).cpu().numpy().flatten()
+
+ embeddings_orig.append(emb_orig)
+ embeddings_aug.append(emb_aug)
+
+ embeddings_orig = np.array(embeddings_orig)
+ embeddings_aug = np.array(embeddings_aug)
+
+ # Cosine similarity between each original and its augmented version
+ similarities = np.array([cosine_similarity([embeddings_orig[i]], [embeddings_aug[i]])[0][0] for i in range(len(embeddings_orig))])
+ return similarities
+
+# --- 1. Data Loading ---
+def load_lists_from_url(data):
+ if data == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif data == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif data == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'esol':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')
+ smiles = df.smiles
+ labels = df['ESOL predicted log solubility in mols per litre']
+ elif data == 'freesolv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')
+ smiles = df.smiles
+ labels = df.calc
+ elif data == 'lipophicility':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')
+ smiles, labels = df.smiles, df['exp']
+ elif data == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif data == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ elif data == 'qm8':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)
+ return smiles, labels
+
+# --- 2. Scaffold Splitting ---
+class ScaffoldSplitter:
+ def __init__(self, data, seed, train_frac=0.8, val_frac=0.1, test_frac=0.1, include_chirality=True):
+ self.data = data
+ self.seed = seed
+ self.include_chirality = include_chirality
+ self.train_frac = train_frac
+ self.val_frac = val_frac
+ self.test_frac = test_frac
+
+ def generate_scaffold(self, smiles):
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol, includeChirality=self.include_chirality)
+ return scaffold
+
+ def scaffold_split(self):
+ smiles, labels = load_lists_from_url(self.data)
+ non_null = np.ones(len(smiles)) == 0
+
+ if self.data in {'tox21', 'sider', 'clintox'}:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]) and labels.loc[i].isnull().sum() == 0:
+ non_null[i] = 1
+ else:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]):
+ non_null[i] = 1
+
+ smiles_list = list(compress(enumerate(smiles), non_null))
+ rng = np.random.RandomState(self.seed)
+
+ scaffolds = defaultdict(list)
+ for i, sms in smiles_list:
+ scaffold = self.generate_scaffold(sms)
+ scaffolds[scaffold].append(i)
+
+ scaffold_sets = list(scaffolds.values())
+ rng.shuffle(scaffold_sets)
+ n_total_val = int(np.floor(self.val_frac * len(smiles_list)))
+ n_total_test = int(np.floor(self.test_frac * len(smiles_list)))
+ train_idx, val_idx, test_idx = [], [], []
+
+ for scaffold_set in scaffold_sets:
+ if len(val_idx) + len(scaffold_set) <= n_total_val:
+ val_idx.extend(scaffold_set)
+ elif len(test_idx) + len(scaffold_set) <= n_total_test:
+ test_idx.extend(scaffold_set)
+ else:
+ train_idx.extend(scaffold_set)
+ return train_idx, val_idx, test_idx
+
+# --- 2a. Normal Random Split ---
+def random_split_indices(n, seed=42, train_frac=0.8, val_frac=0.1, test_frac=0.1):
+ np.random.seed(seed)
+ indices = np.random.permutation(n)
+ n_train = int(n * train_frac)
+ n_val = int(n * val_frac)
+ train_idx = indices[:n_train]
+ val_idx = indices[n_train:n_train+n_val]
+ test_idx = indices[n_train+n_val:]
+ return train_idx.tolist(), val_idx.tolist(), test_idx.tolist()
+
+# --- 3. PyTorch Dataset ---
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_len=512):
+ self.smiles_list = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles_list[idx]
+ label = self.labels.iloc[idx]
+
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_len,
+ return_tensors='pt'
+ )
+ item = {key: val.squeeze(0) for key, val in encoding.items()}
+ if isinstance(label, pd.Series):
+ label_values = label.values.astype(np.float32)
+ else:
+ label_values = np.array([label], dtype=np.float32)
+ item['labels'] = torch.tensor(label_values, dtype=torch.float)
+ return item
+
+# --- 4. Model Architecture ---
+def global_ap(x):
+ return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)
+
+class SimSonEncoder(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.config.pad_token_id)
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled = global_ap(hidden_states)
+ return self.linear(pooled)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):
+ super(SimSonClassifier, self).__init__()
+ self.encoder = encoder
+ self.clf = nn.Linear(encoder.max_len, num_labels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ x = self.encoder(input_ids, attention_mask)
+ x = self.relu(self.dropout(x))
+ logits = self.clf(x)
+ return logits
+
+ def load_encoder_params(self, state_dict_path):
+ self.encoder.load_state_dict(torch.load(state_dict_path))
+ print("Pretrained encoder parameters loaded.")
+
+# --- 5. Training, Validation, and Testing Loops ---
+def get_criterion(task_type, num_labels):
+ if task_type == 'classification':
+ return nn.BCEWithLogitsLoss()
+ elif task_type == 'regression':
+ return nn.MSELoss()
+ else:
+ raise ValueError(f"Unknown task type: {task_type}")
+
+def train_epoch(model, dataloader, optimizer, scheduler, criterion, device):
+ model.train()
+ total_loss = 0
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ optimizer.zero_grad()
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ #scheduler.step()
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def eval_epoch(model, dataloader, criterion, device):
+ model.eval()
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def test_model(model, dataloader, device):
+ model.eval()
+ all_preds, all_labels = [], []
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels']
+ outputs = model(**inputs)
+ preds = torch.sigmoid(outputs)
+ all_preds.append(preds.cpu().numpy())
+ all_labels.append(labels.numpy())
+ return np.concatenate(all_preds), np.concatenate(all_labels)
+
+def calc_val_metrics(model, dataloader, criterion, device, task_type):
+ model.eval()
+ all_labels, all_preds = [], []
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ if task_type == 'classification':
+ pred_probs = torch.sigmoid(outputs).cpu().numpy()
+ all_preds.append(pred_probs)
+ all_labels.append(labels.cpu().numpy())
+ else:
+ # Regression
+ preds = outputs.cpu().numpy()
+ all_preds.append(preds)
+ all_labels.append(labels.cpu().numpy())
+ avg_loss = total_loss / len(dataloader)
+ if task_type == 'classification':
+ y_true = np.concatenate(all_labels)
+ y_pred = np.concatenate(all_preds)
+ try:
+ score = roc_auc_score(y_true, y_pred, average='macro')
+ except Exception:
+ score = 0.0
+ return avg_loss, score
+ else:
+ return avg_loss, None
+
+# --- 6. Main Execution Block ---
+def main():
+ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {DEVICE}")
+
+ DATASETS_TO_RUN = {
+ # 'esol': {'task_type': 'regression', 'num_labels': 1, 'split': 'random'},
+ #'tox21': {'task_type': 'classification', 'num_labels': 12, 'split': 'random'},
+ #'hiv': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ # Add more datasets here, e.g. 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ #'sider': {'task_type': 'classification', 'num_labels': 27, 'split': 'random'},
+ #'bace': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ 'clintox': {'task_type': 'classification', 'num_labels': 2, 'split': 'random'},
+ #'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'}
+ }
+ PATIENCE = 15
+ EPOCHS = 50
+ LEARNING_RATE = 1e-4
+ BATCH_SIZE = 16
+ MAX_LEN = 512
+
+ TOKENIZER = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ ENCODER_CONFIG = BertConfig(
+ vocab_size=TOKENIZER.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ aggregated_results = {}
+
+ for name, info in DATASETS_TO_RUN.items():
+ print(f"\n{'='*20} Processing Dataset: {name.upper()} ({info['split']} split) {'='*20}")
+ smiles, labels = load_lists_from_url(name)
+
+ # Split selection
+ if info.get('split', 'scaffold') == 'scaffold':
+ splitter = ScaffoldSplitter(data=name, seed=42)
+ train_idx, val_idx, test_idx = splitter.scaffold_split()
+ elif info['split'] == 'random':
+ train_idx, val_idx, test_idx = random_split_indices(len(smiles), seed=42)
+ else:
+ raise ValueError(f"Unknown split type for {name}: {info['split']}")
+
+ train_smiles = smiles.iloc[train_idx].reset_index(drop=True)
+ train_labels = labels.iloc[train_idx].reset_index(drop=True)
+ val_smiles = smiles.iloc[val_idx].reset_index(drop=True)
+ val_labels = labels.iloc[val_idx].reset_index(drop=True)
+ test_smiles = smiles.iloc[test_idx].reset_index(drop=True)
+ test_labels = labels.iloc[test_idx].reset_index(drop=True)
+ print(f"Data split - Train: {len(train_smiles)}, Val: {len(val_smiles)}, Test: {len(test_smiles)}")
+
+ train_dataset = MoleculeDataset(train_smiles, train_labels, TOKENIZER, MAX_LEN)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, TOKENIZER, MAX_LEN)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, TOKENIZER, MAX_LEN)
+
+ train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
+
+ encoder = SimSonEncoder(ENCODER_CONFIG, 512)
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels']).to(DEVICE)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=0.0024)
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.59298)
+
+ best_val_loss = float('-inf')
+ best_model_state = None
+ current_patience = 0
+ for epoch in range(EPOCHS):
+ train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, DEVICE)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, 'cuda', info['task_type'])
+ print(f"Epoch {epoch+1}/{EPOCHS} | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | ROC AUC: {val_metric:.4f}")
+
+ if val_metric <= val_loss:
+ best_val_loss = val_loss
+ best_model_state = copy.deepcopy(model.state_dict())
+ print(f" -> New best model saved with validation loss: {best_val_loss:.4f}")
+ current_patience = 0
+ else:
+ current_patience += 1
+ if current_patience >= PATIENCE:
+ print(f'Early stopping at {PATIENCE} epochs')
+ break
+
+ print("\nTesting with the best model...")
+ if not best_model_state is None:
+ model.load_state_dict(best_model_state)
+ test_loss = eval_epoch(model, test_loader, criterion, DEVICE)
+ print(f'Test loss: {test_loss}')
+ test_preds, test_true = test_model(model, test_loader, DEVICE)
+
+ aggregated_results[name] = {
+ 'best_val_loss': best_val_loss,
+ 'test_predictions': test_preds,
+ 'test_labels': test_true
+ }
+ print(f"Finished testing for {name}.")
+ test_smiles_list = list(test_smiles)
+ similarities = compute_embedding_similarity(
+ model.encoder, test_smiles_list, TOKENIZER, DEVICE, MAX_LEN
+ )
+ print(f"Similarity score: {similarities.mean():.4f}")
+ if name == 'do_not_save':
+ torch.save(model.encoder.state_dict(), 'moleculenet_clintox_encoder.bin')
+
+
+
+ print(f"\n{'='*20} AGGREGATED RESULTS {'='*20}")
+ for name, result in aggregated_results.items():
+ if name in ['bbbp', 'tox21', 'sider', 'clintox', 'hiv', 'bace']:
+ auc = roc_auc_score(result['test_labels'], result['test_predictions'], average='macro')
+ print(f'{name} ROC AUC: {auc}')
+
+ if name in ['lipophicility', 'esol', 'qm8']:
+ rmse = root_mean_squared_error(result['test_labels'], result['test_predictions'])
+ mae = mean_absolute_error(result['test_labels'], result['test_predictions'])
+ print(f'{name} MAE: {mae}')
+ print(f'{name} RMSE: {rmse}')
+
+ print("\nScript finished.")
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/moleculenet_eval/eval.py.save b/simson_modeling/moleculenet_eval/eval.py.save
new file mode 100644
index 0000000000000000000000000000000000000000..f12a589811697b33d2b4b740369a468bb699c182
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/eval.py.save
@@ -0,0 +1,360 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+import numpy as np
+from sklearn.metrics import roc_auc_score, average_precision_score
+from transformers import BertModel, BertConfig
+import os
+import json
+from collections import defaultdict
+from rdkit import Chem
+from rdkit.Chem import Scaffolds
+import warnings
+warnings.filterwarnings('ignore')
+from transformers import AutoTokenizer
+
+# Global average pooling function (assuming this exists in your codebase)
+def global_ap(x, dim=1):
+ return torch.mean(x, dim=dim)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, num_labels: int, dropout: float = 0.1):
+ super(SimSonClassifier, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.num_labels = num_labels
+
+ # BERT encoder (same as SimSonEncoder)
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(dropout)
+
+ # Classification head
+ self.classifier = nn.Linear(config.hidden_size, num_labels)
+
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(0)
+
+ outputs = self.bert(
+ input_ids=input_ids,
+ attention_mask=attention_mask
+ )
+
+ hidden_states = outputs.last_hidden_state
+ hidden_states = self.dropout(hidden_states)
+
+ # Global average pooling
+ pooled = global_ap(hidden_states)
+
+ # Classification output
+ logits = self.classifier(pooled)
+
+ return logits
+
+ def load_encoder_weights(self, encoder_path):
+ """Load pretrained SimSonEncoder weights into the classifier"""
+ encoder_state = torch.load(encoder_path, map_location='cpu')
+
+ # Create mapping from encoder to classifier state dict
+ classifier_state = {}
+ for key, value in encoder_state.items():
+ if key.startswith('bert.') or key.startswith('dropout.'):
+ classifier_state[key] = value
+
+ # Load only the matching weights
+ self.load_state_dict(classifier_state, strict=False)
+ print(f"Loaded encoder weights from {encoder_path}")
+
+
+
+def load_moleculenet_data(dataset_name):
+ """Load MoleculeNet dataset and return SMILES and labels"""
+ if dataset_name == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif dataset_name == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif dataset_name == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif dataset_name == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif dataset_name == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif dataset_name == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ else:
+ raise ValueError(f"Dataset {dataset_name} not supported")
+
+ return smiles, labels
+
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_length=512):
+ self.smiles = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ def __len__(self):
+ return len(self.smiles)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles[idx]
+
+ # Tokenize SMILES
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_length,
+ return_tensors='pt'
+ )
+
+ # Handle labels
+ if isinstance(self.labels, pd.Series):
+ label = torch.tensor(self.labels.iloc[idx], dtype=torch.float32)
+ else: # DataFrame (multi-label)
+ label = torch.tensor(self.labels.iloc[idx].values, dtype=torch.float32)
+
+ return {
+ 'input_ids': encoding['input_ids'].flatten(),
+ 'attention_mask': encoding['attention_mask'].flatten(),
+ 'labels': label
+ }
+
+def get_loss_fn(num_labels):
+ """Get appropriate loss function based on number of labels"""
+ if num_labels == 1:
+ return nn.BCEWithLogitsLoss()
+ else:
+ return nn.BCEWithLogitsLoss() # Multi-label classification
+
+def compute_metrics(predictions, labels, num_labels):
+ """Compute ROC-AUC for single or multi-label classification"""
+ predictions = torch.sigmoid(predictions).cpu().numpy()
+ labels = labels.cpu().numpy()
+
+ if num_labels == 1:
+ # Single label
+ try:
+ auc = roc_auc_score(labels, predictions)
+ return {'roc_auc': auc}
+ except:
+ return {'roc_auc': 0.5}
+ else:
+ # Multi-label
+ aucs = []
+ for i in range(num_labels):
+ try:
+ auc = roc_auc_score(labels[:, i], predictions[:, i])
+ aucs.append(auc)
+ except:
+ aucs.append(0.5)
+ return {'roc_auc': np.mean(aucs), 'individual_aucs': aucs}
+
+def train_epoch(model, dataloader, optimizer, loss_fn, device):
+ model.train()
+ total_loss = 0
+
+ for batch in dataloader:
+ input_ids = batch['input_ids'].to(device)
+ attention_mask = batch['attention_mask'].to(device)
+ labels = batch['labels'].to(device)
+
+ optimizer.zero_grad()
+
+ outputs = model(input_ids, attention_mask)
+ loss = loss_fn(outputs, labels)
+
+ loss.backward()
+ optimizer.step()
+
+ total_loss += loss.item()
+
+ return total_loss / len(dataloader)
+
+def evaluate(model, dataloader, loss_fn, num_labels, device):
+ model.eval()
+ total_loss = 0
+ all_predictions = []
+ all_labels = []
+
+ with torch.no_grad():
+ for batch in dataloader:
+ input_ids = batch['input_ids'].to(device)
+ attention_mask = batch['attention_mask'].to(device)
+ labels = batch['labels'].to(device)
+
+ outputs = model(input_ids, attention_mask)
+ loss = loss_fn(outputs, labels)
+
+ total_loss += loss.item()
+ all_predictions.append(outputs)
+ all_labels.append(labels)
+
+ all_predictions = torch.cat(all_predictions)
+ all_labels = torch.cat(all_labels)
+
+ metrics = compute_metrics(all_predictions, all_labels, num_labels)
+ avg_loss = total_loss / len(dataloader)
+
+ return avg_loss, metrics
+
+def run_experiment(dataset_name, config, tokenizer, encoder_path=None,
+ batch_size=32, learning_rate=1e-4, epochs=50, device='cuda'):
+ """Run complete experiment for one dataset"""
+ print(f"\n=== Running experiment for {dataset_name.upper()} ===")
+
+ # Load data
+ smiles, labels = load_moleculenet_data(dataset_name)
+ print(f"Loaded {len(smiles)} samples")
+
+ # Determine number of labels
+ if isinstance(labels, pd.Series):
+ num_labels = 1
+ else:
+ num_labels = labels.shape[1]
+ print(f"Number of labels: {num_labels}")
+
+ # Scaffold split
+ smiles_list = smiles.tolist()
+ train_idx, valid_idx, test_idx = scaffold_split(smiles_list)
+
+ print(f"Split sizes - Train: {len(train_idx)}, Valid: {len(valid_idx)}, Test: {len(test_idx)}")
+ # Create datasets
+ train_smiles = [smiles_list[i] for i in train_idx]
+ valid_smiles = [smiles_list[i] for i in valid_idx]
+ test_smiles = [smiles_list[i] for i in test_idx]
+
+ if isinstance(labels, pd.Series):
+ train_labels = labels.iloc[list(train_idx)]
+ valid_labels = labels.iloc[list(valid_idx)]
+ test_labels = labels.iloc[list(test_idx)]
+ else:
+ train_labels = labels.iloc[list(train_idx)]
+ valid_labels = labels.iloc[list(valid_idx)]
+ test_labels = labels.iloc[list(test_idx)]
+
+ # Create data loaders
+ train_dataset = MoleculeDataset(train_smiles, train_labels, tokenizer)
+ valid_dataset = MoleculeDataset(valid_smiles, valid_labels, tokenizer)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, tokenizer)
+
+ train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+ valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+ # Initialize model
+ model = SimSonClassifier(config, max_len=512, num_labels=num_labels).to(device)
+
+ # Load encoder weights if provided
+ if encoder_path:
+ model.load_encoder_weights(encoder_path)
+
+ # Setup training
+ optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+ loss_fn = get_loss_fn(num_labels)
+
+ best_valid_loss = float('inf')
+ best_model_path = f'best_{dataset_name}_model.pth'
+
+ # Training loop
+ for epoch in range(epochs):
+ train_loss = train_epoch(model, train_loader, optimizer, loss_fn, device)
+ valid_loss, valid_metrics = evaluate(model, valid_loader, loss_fn, num_labels, device)
+
+ # Save best model
+ if valid_loss < best_valid_loss:
+ best_valid_loss = valid_loss
+ torch.save(model.state_dict(), best_model_path)
+
+ if epoch % 10 == 0:
+ print(f"Epoch {epoch}: Train Loss = {train_loss:.4f}, "
+ f"Valid Loss = {valid_loss:.4f}, Valid AUC = {valid_metrics['roc_auc']:.4f}")
+
+ # Load best model and test
+ model.load_state_dict(torch.load(best_model_path))
+ test_loss, test_metrics = evaluate(model, test_loader, loss_fn, num_labels, device)
+
+ print(f"Final Test Results - Loss: {test_loss:.4f}, ROC-AUC: {test_metrics['roc_auc']:.4f}")
+
+ # Cleanup
+ os.remove(best_model_path)
+
+ return {
+ 'dataset': dataset_name,
+ 'num_labels': num_labels,
+ 'test_loss': test_loss,
+ 'test_roc_auc': test_metrics['roc_auc'],
+ 'individual_aucs': test_metrics.get('individual_aucs', None)
+ }
+
+def main():
+ """Main function to run all experiments"""
+ # Setup
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {device}")
+
+ # Initialize tokenizer and config (you need to provide these)
+ # tokenizer = your_tokenizer # Replace with your tokenizer
+ # config = BertConfig(...) # Your config from above
+ tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+
+ # Only the hidden size is slightly larger, everything else is the same
+ config = BertConfig(
+ vocab_size=tokenizer.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+ # Datasets to test
+ datasets = ['bbbp', 'tox21', 'sider', 'clintox', 'hiv', 'bace']
+
+ # Path to your pretrained encoder (optional)
+ encoder_path = 'simson_checkpoints_small/simson_model_single_gpu.bin'
+
+ # Run experiments
+ all_results = []
+ for dataset in datasets:
+ try:
+ result = run_experiment(
+ dataset,
+ config,
+ tokenizer,
+ encoder_path=encoder_path,
+ device=device
+ )
+ all_results.append(result)
+ except Exception as e:
+ print(f"Error with {dataset}: {e}")
+
+ # Aggregate and display results
+ print("\n" + "="*60)
+ print("FINAL RESULTS SUMMARY")
+ print("="*60)
+
+ results_df = pd.DataFrame(all_results)
+ print(results_df.to_string(index=False))
+
+ # Save results
+ results_df.to_csv('moleculenet_results.csv', index=False)
+ print(f"\nResults saved to moleculenet_results.csv")
+
+ return results_df
+
+if __name__ == "__main__":
+
+ results = main()
diff --git a/simson_modeling/moleculenet_eval/moleculenet_bbp_encoder.bin b/simson_modeling/moleculenet_eval/moleculenet_bbp_encoder.bin
new file mode 100644
index 0000000000000000000000000000000000000000..f87059a1aebe9d76ba46789717bc204ccac75d91
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/moleculenet_bbp_encoder.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a0ea0313430c287530519402ae5be4ac8c08c00e13adbd1a4d9194c2e7c6d58
+size 93231025
diff --git a/simson_modeling/moleculenet_eval/moleculenet_clintox_encoder.bin b/simson_modeling/moleculenet_eval/moleculenet_clintox_encoder.bin
new file mode 100644
index 0000000000000000000000000000000000000000..2605e598e1bbf42996c1bccb9932f2f48ecc207a
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/moleculenet_clintox_encoder.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a77a3db92e95f35a612131ac786e1c4e3a34fec8263ad50efc3deea6be467614
+size 93231325
diff --git a/simson_modeling/moleculenet_eval/moleculenet_results.csv b/simson_modeling/moleculenet_eval/moleculenet_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/moleculenet_results.csv
@@ -0,0 +1 @@
+
diff --git a/simson_modeling/moleculenet_eval/showcase.ipynb b/simson_modeling/moleculenet_eval/showcase.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..760601f24b129e35574456df61a03e0b7380f206
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/showcase.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7678e528-e2d6-4ef4-bd11-8c745bbce7c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap.umap_ as umap\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ad8d5a02-6162-41f7-a730-e31e353ed9b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PrecomputedContrastiveSmilesDataset(Dataset):\n",
+ " \"\"\"\n",
+ " A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.\n",
+ " This is significantly faster as it offloads the expensive SMILES randomization\n",
+ " to a one-time preprocessing step.\n",
+ " \"\"\"\n",
+ " def __init__(self, tokenizer, file_path: str, max_length: int = 512):\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Load the entire dataset from the Parquet file into memory.\n",
+ " # This is fast and efficient for subsequent access.\n",
+ " print(f\"Loading pre-computed data from {file_path}...\")\n",
+ " self.data = pd.read_parquet(file_path)\n",
+ " print(\"Data loaded successfully.\")\n",
+ "\n",
+ " def __len__(self):\n",
+ " \"\"\"Returns the total number of pairs in the dataset.\"\"\"\n",
+ " return len(self.data)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " \"\"\"\n",
+ " Retrieves a pre-augmented pair, tokenizes it, and returns it\n",
+ " in the format expected by the DataCollator.\n",
+ " \"\"\"\n",
+ " # Retrieve the pre-augmented pair from the DataFrame\n",
+ " row = self.data.iloc[idx]\n",
+ " smiles_1 = row['smiles_1']\n",
+ " smiles_2 = row['smiles_2']\n",
+ " \n",
+ " # Tokenize the pair. This operation is fast and remains in the data loader.\n",
+ " tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " \n",
+ " return {\n",
+ " 'input_ids_1': torch.tensor(tokens_1['input_ids']),\n",
+ " 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),\n",
+ " 'input_ids_2': torch.tensor(tokens_2['input_ids']),\n",
+ " 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),\n",
+ " }\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "476226dd-54f3-4d3e-adb4-cc30e922fd96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "OptimizedModule(\n",
+ " (_orig_mod): SimSonEncoder(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(591, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): BertEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-3): 4 x BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSdpaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): BertSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): BertIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=2048, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): BertOutput(\n",
+ " (dense): Linear(in_features=2048, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (linear): Linear(in_features=768, out_features=512, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "model_config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "model = SimSonEncoder(config=model_config, max_len=512).to(device)\n",
+ "model = torch.compile(model)\n",
+ "model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_polymer_1M/simson_model_single_gpu.bin'))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "df28c332-c20b-4804-b4ca-97de9d652445",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test dataset shape: (5000, 2)\n",
+ "Columns: ['smiles_1', 'smiles_2']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " smiles_1 | \n",
+ " smiles_2 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... | \n",
+ " c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... | \n",
+ " CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* | \n",
+ " O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC | \n",
+ " C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " smiles_1 \\\n",
+ "0 c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... \n",
+ "1 C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... \n",
+ "2 C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC \n",
+ "\n",
+ " smiles_2 \n",
+ "0 c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... \n",
+ "1 CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... \n",
+ "2 O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data = pd.read_parquet('/home/jovyan/simson_training_bolgov/data/polymer_splits/test.parquet')\n",
+ "print(f\"Test dataset shape: {test_data.shape}\")\n",
+ "print(f\"Columns: {test_data.columns.tolist()}\")\n",
+ "test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a7f2e2bc-12f5-443b-9d80-899542e07370",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating embeddings for original SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Generating embeddings for augmented SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Original embeddings shape: (5000, 512)\n",
+ "Augmented embeddings shape: (5000, 512)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def generate_embeddings(model, tokenizer, smiles_list, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings for a list of SMILES strings\"\"\"\n",
+ " model.eval()\n",
+ " embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " return np.vstack(embeddings)\n",
+ "\n",
+ "# Generate embeddings for original and augmented SMILES\n",
+ "print(\"Generating embeddings for original SMILES...\")\n",
+ "original_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_1'].tolist())\n",
+ "\n",
+ "print(\"Generating embeddings for augmented SMILES...\")\n",
+ "augmented_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_2'].tolist())\n",
+ "\n",
+ "print(f\"Original embeddings shape: {original_embeddings.shape}\")\n",
+ "print(f\"Augmented embeddings shape: {augmented_embeddings.shape}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c770d7f4-fa37-4519-bda2-9b084d2a4b32",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average cosine similarity between original and augmented SMILES: 0.9874\n",
+ "Standard deviation: 0.0197\n",
+ "Min similarity: 0.7691\n",
+ "Max similarity: 1.0000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def calculate_pairwise_similarities(embeddings1, embeddings2):\n",
+ " \"\"\"Calculate cosine similarities between corresponding pairs\"\"\"\n",
+ " similarities = []\n",
+ " for i in range(len(embeddings1)):\n",
+ " sim = cosine_similarity([embeddings1[i]], [embeddings2[i]])[0][0]\n",
+ " similarities.append(sim)\n",
+ " return np.array(similarities)\n",
+ "\n",
+ "# Calculate cosine similarities\n",
+ "pairwise_similarities = calculate_pairwise_similarities(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "print(f\"Average cosine similarity between original and augmented SMILES: {np.mean(pairwise_similarities):.4f}\")\n",
+ "print(f\"Standard deviation: {np.std(pairwise_similarities):.4f}\")\n",
+ "print(f\"Min similarity: {np.min(pairwise_similarities):.4f}\")\n",
+ "print(f\"Max similarity: {np.max(pairwise_similarities):.4f}\")\n",
+ "\n",
+ "# Plot similarity distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.hist(pairwise_similarities, bins=50, alpha=0.7, edgecolor='black')\n",
+ "plt.axvline(np.mean(pairwise_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(pairwise_similarities):.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Distribution of Cosine Similarities Between Original and Augmented SMILES')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "d7fa6ed4-b546-4544-bb00-4d90a99678da",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== Embedding Space Analysis ===\n",
+ "Embedding dimensionality: 512\n",
+ "Average L2 norm (original): 11.9105\n",
+ "Average L2 norm (augmented): 11.9072\n",
+ "Average intra-class similarity (original): 0.0047\n",
+ "Average intra-class similarity (augmented): 0.0048\n",
+ "Average inter-class similarity: 0.0049\n",
+ "\n",
+ "=== Nearest Neighbor Analysis (k=5) ===\n",
+ "Augmented SMILES in top-5 neighbors: 5000/5000 (100.0%)\n",
+ "Augmented SMILES as top-1 neighbor: 4872/5000 (97.4%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1. Embedding Space Statistics\n",
+ "def analyze_embedding_space(original_emb, augmented_emb):\n",
+ " \"\"\"Analyze the embedding space properties\"\"\"\n",
+ " print(\"=== Embedding Space Analysis ===\")\n",
+ " \n",
+ " # Dimensionality and norms\n",
+ " print(f\"Embedding dimensionality: {original_emb.shape[1]}\")\n",
+ " print(f\"Average L2 norm (original): {np.mean(np.linalg.norm(original_emb, axis=1)):.4f}\")\n",
+ " print(f\"Average L2 norm (augmented): {np.mean(np.linalg.norm(augmented_emb, axis=1)):.4f}\")\n",
+ " \n",
+ " # Intra-class similarities\n",
+ " orig_similarities = cosine_similarity(original_emb)\n",
+ " aug_similarities = cosine_similarity(augmented_emb)\n",
+ " \n",
+ " # Remove diagonal (self-similarity)\n",
+ " orig_similarities_off_diag = orig_similarities[np.triu_indices_from(orig_similarities, k=1)]\n",
+ " aug_similarities_off_diag = aug_similarities[np.triu_indices_from(aug_similarities, k=1)]\n",
+ " \n",
+ " print(f\"Average intra-class similarity (original): {np.mean(orig_similarities_off_diag):.4f}\")\n",
+ " print(f\"Average intra-class similarity (augmented): {np.mean(aug_similarities_off_diag):.4f}\")\n",
+ " \n",
+ " # Inter-class similarities\n",
+ " inter_similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " print(f\"Average inter-class similarity: {np.mean(inter_similarities):.4f}\")\n",
+ "\n",
+ "analyze_embedding_space(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "# 2. Nearest Neighbor Analysis\n",
+ "def nearest_neighbor_analysis(original_emb, augmented_emb, k=5):\n",
+ " \"\"\"Analyze nearest neighbors between original and augmented embeddings\"\"\"\n",
+ " print(f\"\\n=== Nearest Neighbor Analysis (k={k}) ===\")\n",
+ " \n",
+ " # For each original embedding, find its k nearest neighbors in augmented set\n",
+ " similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " \n",
+ " # Find cases where augmented version is among top-k neighbors\n",
+ " correct_matches = 0\n",
+ " top1_matches = 0\n",
+ " \n",
+ " for i in range(len(original_emb)):\n",
+ " # Get similarity scores for i-th original embedding\n",
+ " sim_scores = similarities[i]\n",
+ " top_k_indices = np.argsort(sim_scores)[-k:][::-1]\n",
+ " \n",
+ " if i in top_k_indices:\n",
+ " correct_matches += 1\n",
+ " if np.argmax(sim_scores) == i:\n",
+ " top1_matches += 1\n",
+ " \n",
+ " print(f\"Augmented SMILES in top-{k} neighbors: {correct_matches}/{len(original_emb)} ({100*correct_matches/len(original_emb):.1f}%)\")\n",
+ " print(f\"Augmented SMILES as top-1 neighbor: {top1_matches}/{len(original_emb)} ({100*top1_matches/len(original_emb):.1f}%)\")\n",
+ "\n",
+ "nearest_neighbor_analysis(original_embeddings, augmented_embeddings)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f136743a-c742-469b-a9b8-9a4da8eb4c08",
+ "metadata": {},
+ "source": [
+ "Объяснение\n",
+ "\n",
+ "* Embedding Space Analysis. Показывает, что различные величины (L2 norm - длина вектора, близости) практически идентичны для оригинальных, и для аугментированных молекул (показывая, что они отображаются практически одними и теми же для модели)\n",
+ "* Nearest Neighbor Analysis (k=5). Показывает, что топ 5 ближайших векторов к любому из векторов - всегда его аугментация (кроме его самого, естественно). В 97.4 % случаях вектор, соответствующий аугментации, является ближайшим."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133fb52e-bea8-4159-ab06-386998b71ed0",
+ "metadata": {},
+ "source": [
+ "С разными SMILES - ровно обратная картина, как и должно быть (различные smiles являются очень разными)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cc48f04e-b8bf-415b-89f0-b51d98f7e6b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "num_molecules = original_embeddings.shape[0]\n",
+ "\n",
+ "# Shuffle indices for unrelated molecules\n",
+ "unrelated_indices = np.random.permutation(num_molecules)\n",
+ "unrelated_embeddings = augmented_embeddings[unrelated_indices]\n",
+ "\n",
+ "# Compute pairwise cosine similarity between original and unrelated\n",
+ "pairwise_unrelated_similarities = np.array([\n",
+ " cosine_similarity([original_embeddings[i]], [unrelated_embeddings[i]])[0][0]\n",
+ " for i in range(num_molecules)\n",
+ "])\n",
+ "\n",
+ "\n",
+ "plt.figure(figsize=(10,6))\n",
+ "plt.hist(pairwise_unrelated_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ "mean_sim = pairwise_unrelated_similarities.mean()\n",
+ "plt.axvline(mean_sim, color='red', linestyle='--', label=f'Mean = {mean_sim:.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Cosine Similarity Distribution of Different Molecules (Unrelated SMILES)')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "881eca62-7bf5-4b84-b0df-659942930a73",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean cosine similarity: 0.0037\n",
+ "Std deviation: 0.1393\n",
+ "Range: -0.4398 to 1.0000\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Mean cosine similarity: {pairwise_unrelated_similarities.mean():.4f}\")\n",
+ "print(f\"Std deviation: {pairwise_unrelated_similarities.std():.4f}\")\n",
+ "print(f\"Range: {pairwise_unrelated_similarities.min():.4f} to {pairwise_unrelated_similarities.max():.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "051f4149-d3eb-472b-b6d0-7060662efb6a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unrelated in top-5 neighbors: 7/5000\n",
+ "Unrelated as top-1 neighbor: 2/5000\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "k = 5\n",
+ "similarities = cosine_similarity(original_embeddings, unrelated_embeddings)\n",
+ "correct_matches = sum(i in np.argsort(similarities[i])[-k:] for i in range(num_molecules))\n",
+ "top1_matches = sum(np.argmax(similarities[i]) == i for i in range(num_molecules))\n",
+ "\n",
+ "print(f\"Unrelated in top-{k} neighbors: {correct_matches}/{num_molecules}\")\n",
+ "print(f\"Unrelated as top-1 neighbor: {top1_matches}/{num_molecules}\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/moleculenet_eval/visualizations.ipynb b/simson_modeling/moleculenet_eval/visualizations.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b6a3ab30e103378cecb86b7c9b9475fa814f5d50
--- /dev/null
+++ b/simson_modeling/moleculenet_eval/visualizations.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "90d8362c-618c-42c9-8f2a-e73d2eb34abc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap\n",
+ "from rdkit import Chem, RDLogger\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "RDLogger.DisableLog('rdApp.*')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n",
+ "\n",
+ "# === HELPER FUNCTIONS AND CLASSES ===\n",
+ "\n",
+ "def load_lists_from_url(data):\n",
+ " if data == 'bbbp':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')\n",
+ " smiles, labels = df.smiles, df.p_np\n",
+ " elif data == 'clintox':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'hiv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')\n",
+ " smiles, labels = df.smiles, df.HIV_active\n",
+ " elif data == 'sider':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'esol':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df['ESOL predicted log solubility in mols per litre']\n",
+ " elif data == 'freesolv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df.calc\n",
+ " elif data == 'lipophicility':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')\n",
+ " smiles, labels = df.smiles, df['exp']\n",
+ " elif data == 'tox21':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['mol_id', 'smiles'], axis=1)\n",
+ " elif data == 'bace':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')\n",
+ " smiles, labels = df.mol, df.Class\n",
+ " elif data == 'qm8':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)\n",
+ " return smiles, labels\n",
+ "\n",
+ "class SmilesEnumerator:\n",
+ " \"\"\"Generates randomized SMILES strings for data augmentation.\"\"\"\n",
+ " def randomize_smiles(self, smiles):\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles\n",
+ " except:\n",
+ " return smiles\n",
+ "\n",
+ "class MoleculeDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_len=512):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.smiles_list[idx]\n",
+ " label = self.labels.iloc[idx]\n",
+ "\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_len,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " item = {key: val.squeeze(0) for key, val in encoding.items()}\n",
+ " if isinstance(label, pd.Series):\n",
+ " label_values = label.values.astype(np.float32)\n",
+ " else:\n",
+ " label_values = np.array([label], dtype=np.float32)\n",
+ " item['labels'] = torch.tensor(label_values, dtype=torch.float)\n",
+ " return item\n",
+ "\n",
+ "# Your model classes (assuming these exist from previous conversation)\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n",
+ "\n",
+ "# === MAIN ANALYSIS FUNCTIONS ===\n",
+ "\n",
+ "def prepare_dataset_with_augmentation(dataset_name, sample_size=1000):\n",
+ " \"\"\"\n",
+ " Load dataset, create augmented SMILES, and prepare for embedding generation\n",
+ " \"\"\"\n",
+ " print(f\"Loading {dataset_name} dataset...\")\n",
+ " smiles, labels = load_lists_from_url(dataset_name)\n",
+ " \n",
+ " # Handle different label structures\n",
+ " if dataset_name == 'bbbp':\n",
+ " # Binary classification - convert to binary labels\n",
+ " label_values = labels.values\n",
+ " label_names = ['Non-permeable', 'Permeable']\n",
+ " elif dataset_name == 'clintox':\n",
+ " # Multi-task - use first task for visualization\n",
+ " label_values = labels.iloc[:, 0].values # Use first toxicity endpoint\n",
+ " label_names = ['Non-toxic', 'Toxic']\n",
+ " \n",
+ " # Remove NaN labels and sample for visualization\n",
+ " valid_mask = ~pd.isna(label_values)\n",
+ " smiles_clean = smiles[valid_mask]\n",
+ " labels_clean = label_values[valid_mask]\n",
+ " \n",
+ " # Sample for manageable visualization\n",
+ " if len(smiles_clean) > sample_size:\n",
+ " indices = np.random.choice(len(smiles_clean), sample_size, replace=False)\n",
+ " smiles_clean = smiles_clean.iloc[indices]\n",
+ " labels_clean = labels_clean[indices]\n",
+ " \n",
+ " # Create augmented SMILES\n",
+ " enumerator = SmilesEnumerator()\n",
+ " print(\"Generating augmented SMILES...\")\n",
+ " augmented_smiles = []\n",
+ " \n",
+ " for smiles_str in smiles_clean:\n",
+ " aug_smiles = enumerator.randomize_smiles(smiles_str)\n",
+ " augmented_smiles.append(aug_smiles)\n",
+ " \n",
+ " print(f\"Dataset: {dataset_name}\")\n",
+ " print(f\"Total molecules: {len(smiles_clean)}\")\n",
+ " print(f\"Label distribution: {np.bincount(labels_clean.astype(int))}\")\n",
+ " \n",
+ " return smiles_clean.tolist(), augmented_smiles, labels_clean, label_names\n",
+ "\n",
+ "def generate_embeddings_for_classes(model, tokenizer, smiles_list, labels, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings and organize by class labels\"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " all_embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " all_embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " embeddings = np.vstack(all_embeddings)\n",
+ " \n",
+ " # Organize by class\n",
+ " class_embeddings = {}\n",
+ " unique_labels = np.unique(labels)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels == label\n",
+ " class_embeddings[label] = embeddings[mask]\n",
+ " \n",
+ " return embeddings, class_embeddings\n",
+ "\n",
+ "def analyze_class_separation(class_embeddings, label_names):\n",
+ " \"\"\"Analyze how well different classes are separated in embedding space\"\"\"\n",
+ " print(\"=== Class Separation Analysis ===\")\n",
+ " \n",
+ " class_keys = list(class_embeddings.keys())\n",
+ " \n",
+ " if len(class_keys) == 2:\n",
+ " # Binary classification analysis\n",
+ " emb1 = class_embeddings[class_keys[0]]\n",
+ " emb2 = class_embeddings[class_keys[1]]\n",
+ " \n",
+ " # Inter-class similarity (between different classes)\n",
+ " inter_sim = cosine_similarity(emb1, emb2)\n",
+ " mean_inter_sim = np.mean(inter_sim)\n",
+ " \n",
+ " # Intra-class similarity (within same class)\n",
+ " intra_sim1 = cosine_similarity(emb1)\n",
+ " intra_sim2 = cosine_similarity(emb2)\n",
+ " \n",
+ " # Remove diagonal for intra-class\n",
+ " intra_sim1_off_diag = intra_sim1[np.triu_indices_from(intra_sim1, k=1)]\n",
+ " intra_sim2_off_diag = intra_sim2[np.triu_indices_from(intra_sim2, k=1)]\n",
+ " \n",
+ " mean_intra_sim1 = np.mean(intra_sim1_off_diag)\n",
+ " mean_intra_sim2 = np.mean(intra_sim2_off_diag)\n",
+ " \n",
+ " print(f\"Class 0 ({label_names[0]}) size: {len(emb1)}\")\n",
+ " print(f\"Class 1 ({label_names[1]}) size: {len(emb2)}\")\n",
+ " print(f\"Mean inter-class similarity: {mean_inter_sim:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 0): {mean_intra_sim1:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 1): {mean_intra_sim2:.4f}\")\n",
+ " print(f\"Separation ratio: {(mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim):.4f}\")\n",
+ " \n",
+ " return {\n",
+ " 'inter_class_sim': mean_inter_sim,\n",
+ " 'intra_class_sim': [mean_intra_sim1, mean_intra_sim2],\n",
+ " 'separation_ratio': (mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim)\n",
+ " }\n",
+ "\n",
+ "def visualize_class_embeddings(embeddings, labels, label_names, dataset_name, sample_size=2000):\n",
+ " \"\"\"Create comprehensive visualizations of class-separated embeddings\"\"\"\n",
+ " \n",
+ " # Sample for visualization if needed\n",
+ " if len(embeddings) > sample_size:\n",
+ " indices = np.random.choice(len(embeddings), sample_size, replace=False)\n",
+ " embeddings_viz = embeddings[indices]\n",
+ " labels_viz = labels[indices]\n",
+ " print(f\"Sampling {sample_size} points for visualization\")\n",
+ " else:\n",
+ " embeddings_viz = embeddings\n",
+ " labels_viz = labels\n",
+ " \n",
+ " # Create color mapping\n",
+ " unique_labels = np.unique(labels_viz)\n",
+ " colors = plt.cm.Set1(np.linspace(0, 1, len(unique_labels)))\n",
+ " color_map = {label: colors[i] for i, label in enumerate(unique_labels)}\n",
+ " \n",
+ " # Create subplots\n",
+ " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+ " fig.suptitle(f'{dataset_name.upper()} Dataset - Class Separation Visualization', fontsize=16, fontweight='bold')\n",
+ " \n",
+ " # PCA\n",
+ " print(\"Computing PCA...\")\n",
+ " pca = PCA(n_components=2, random_state=42)\n",
+ " pca_embeddings = pca.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 0].scatter(pca_embeddings[mask, 0], pca_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 0].set_title('PCA Visualization')\n",
+ " axes[0, 0].set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)')\n",
+ " axes[0, 0].set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)')\n",
+ " axes[0, 0].legend()\n",
+ " axes[0, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # t-SNE\n",
+ " print(\"Computing t-SNE...\")\n",
+ " tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(embeddings_viz)//4))\n",
+ " tsne_embeddings = tsne.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 1].scatter(tsne_embeddings[mask, 0], tsne_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 1].set_title('t-SNE Visualization')\n",
+ " axes[0, 1].set_xlabel('t-SNE 1')\n",
+ " axes[0, 1].set_ylabel('t-SNE 2')\n",
+ " axes[0, 1].legend()\n",
+ " axes[0, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # UMAP\n",
+ " print(\"Computing UMAP...\")\n",
+ " umap_reducer = umap.UMAP(n_components=2, random_state=42, n_neighbors=min(15, len(embeddings_viz)//4))\n",
+ " umap_embeddings = umap_reducer.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 2].scatter(umap_embeddings[mask, 0], umap_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 2].set_title('UMAP Visualization')\n",
+ " axes[0, 2].set_xlabel('UMAP 1')\n",
+ " axes[0, 2].set_ylabel('UMAP 2')\n",
+ " axes[0, 2].legend()\n",
+ " axes[0, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Inter-class distance distribution\n",
+ " if len(unique_labels) == 2:\n",
+ " class_0_emb = embeddings_viz[labels_viz == unique_labels[0]]\n",
+ " class_1_emb = embeddings_viz[labels_viz == unique_labels[1]]\n",
+ " \n",
+ " # Sample for distance calculation if too large\n",
+ " if len(class_0_emb) > 500:\n",
+ " class_0_sample = class_0_emb[np.random.choice(len(class_0_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_0_sample = class_0_emb\n",
+ " \n",
+ " if len(class_1_emb) > 500:\n",
+ " class_1_sample = class_1_emb[np.random.choice(len(class_1_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_1_sample = class_1_emb\n",
+ " \n",
+ " inter_similarities = cosine_similarity(class_0_sample, class_1_sample).flatten()\n",
+ " \n",
+ " axes[1, 0].hist(inter_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ " axes[1, 0].axvline(np.mean(inter_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(inter_similarities):.4f}')\n",
+ " axes[1, 0].set_xlabel('Cosine Similarity')\n",
+ " axes[1, 0].set_ylabel('Frequency')\n",
+ " axes[1, 0].set_title(f'Inter-Class Similarity Distribution')\n",
+ " axes[1, 0].legend()\n",
+ " axes[1, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Embedding norms by class\n",
+ " for i, label in enumerate(unique_labels):\n",
+ " mask = labels_viz == label\n",
+ " norms = np.linalg.norm(embeddings_viz[mask], axis=1)\n",
+ " axes[1, 1].hist(norms, bins=30, alpha=0.6, \n",
+ " color=color_map[label], label=f'{label_names[int(label)]}')\n",
+ " \n",
+ " axes[1, 1].set_xlabel('L2 Norm')\n",
+ " axes[1, 1].set_ylabel('Frequency')\n",
+ " axes[1, 1].set_title('Embedding Norm Distribution by Class')\n",
+ " axes[1, 1].legend()\n",
+ " axes[1, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Class statistics summary\n",
+ " axes[1, 2].axis('off')\n",
+ " stats_text = f\"\"\"\n",
+ " {dataset_name.upper()} Dataset Statistics:\n",
+ " \n",
+ " • Total samples: {len(embeddings_viz):,}\n",
+ " • Embedding dimension: {embeddings_viz.shape[1]}\n",
+ " • Classes: {len(unique_labels)}\n",
+ " \"\"\"\n",
+ " \n",
+ " for i, label in enumerate(unique_labels):\n",
+ " count = np.sum(labels_viz == label)\n",
+ " percentage = 100 * count / len(labels_viz)\n",
+ " stats_text += f\"\\n - {label_names[int(label)]}: {count} ({percentage:.1f}%)\"\n",
+ " \n",
+ " axes[1, 2].text(0.1, 0.9, stats_text, transform=axes[1, 2].transAxes, fontsize=12, \n",
+ " verticalalignment='top', fontfamily='monospace')\n",
+ " \n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ " \n",
+ " return pca_embeddings, tsne_embeddings, umap_embeddings\n",
+ "\n",
+ "def clustering_analysis_for_classes(embeddings, labels, label_names, n_clusters=None):\n",
+ " \"\"\"Perform clustering analysis to evaluate class separation\"\"\"\n",
+ " if n_clusters is None:\n",
+ " n_clusters = len(np.unique(labels))\n",
+ " \n",
+ " print(f\"\\n=== Clustering Analysis (k={n_clusters}) ===\")\n",
+ " \n",
+ " # K-means clustering\n",
+ " kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
+ " cluster_labels = kmeans.fit_predict(embeddings)\n",
+ " \n",
+ " # Calculate cluster purity\n",
+ " cluster_purities = []\n",
+ " for cluster_id in range(n_clusters):\n",
+ " cluster_mask = cluster_labels == cluster_id\n",
+ " cluster_true_labels = labels[cluster_mask]\n",
+ " \n",
+ " if len(cluster_true_labels) > 0:\n",
+ " unique, counts = np.unique(cluster_true_labels, return_counts=True)\n",
+ " purity = np.max(counts) / len(cluster_true_labels)\n",
+ " cluster_purities.append(purity)\n",
+ " print(f\"Cluster {cluster_id}: {len(cluster_true_labels)} samples, purity: {purity:.3f}\")\n",
+ " \n",
+ " avg_purity = np.mean(cluster_purities)\n",
+ " print(f\"Average cluster purity: {avg_purity:.4f}\")\n",
+ " \n",
+ " \n",
+ " return avg_purity\n",
+ "\n",
+ "# === MAIN EXECUTION ===\n",
+ "\n",
+ "def main_class_visualization():\n",
+ " \"\"\"Main function to run the complete class-based visualization analysis\"\"\"\n",
+ " \n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ " config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ " model = SimSonEncoder(config, max_len=512) # Adjust max_len as needed\n",
+ " model = torch.compile(model)\n",
+ " \n",
+ " model.to(device)\n",
+ " model.eval()\n",
+ " ds_name_to_model_path = {'bbbp': '/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_bbp_encoder.bin', 'clintox': '/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_clintox_encoder.bin'}\n",
+ " \n",
+ " \n",
+ " # Analyze both datasets\n",
+ " datasets = ['bbbp', 'clintox']\n",
+ " \n",
+ " for dataset_name in datasets:\n",
+ " model.load_state_dict(torch.load(ds_name_to_model_path[dataset_name], map_location=device))\n",
+ " print(f\"\\n{'='*50}\")\n",
+ " print(f\"ANALYZING {dataset_name.upper()} DATASET\")\n",
+ " print(f\"{'='*50}\")\n",
+ " \n",
+ " # Prepare dataset\n",
+ " smiles_list, augmented_smiles, labels, label_names = prepare_dataset_with_augmentation(\n",
+ " dataset_name, sample_size=1000\n",
+ " )\n",
+ " \n",
+ " # Generate embeddings for original SMILES\n",
+ " print(\"Generating embeddings...\")\n",
+ " embeddings, class_embeddings = generate_embeddings_for_classes(\n",
+ " model, tokenizer, smiles_list, labels\n",
+ " )\n",
+ " \n",
+ " # Analyze class separation\n",
+ " separation_stats = analyze_class_separation(class_embeddings, label_names)\n",
+ " \n",
+ " # Create visualizations\n",
+ " pca_emb, tsne_emb, umap_emb = visualize_class_embeddings(\n",
+ " embeddings, labels, label_names, dataset_name\n",
+ " )\n",
+ " \n",
+ " # Clustering analysis\n",
+ " clustering_analysis_for_classes(embeddings, labels, label_names)\n",
+ " \n",
+ " print(f\"\\nCompleted analysis for {dataset_name.upper()} dataset\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "17ada2a4-e44e-4ff1-8b07-cd1f1b53298d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using device: cuda\n",
+ "\n",
+ "==================================================\n",
+ "ANALYZING BBBP DATASET\n",
+ "==================================================\n",
+ "Loading bbbp dataset...\n",
+ "Generating augmented SMILES...\n",
+ "Dataset: bbbp\n",
+ "Total molecules: 1000\n",
+ "Label distribution: [239 761]\n",
+ "Generating embeddings...\n",
+ "=== Class Separation Analysis ===\n",
+ "Class 0 (Non-permeable) size: 239\n",
+ "Class 1 (Permeable) size: 761\n",
+ "Mean inter-class similarity: -0.1155\n",
+ "Mean intra-class similarity (Class 0): 0.3215\n",
+ "Mean intra-class similarity (Class 1): 0.5806\n",
+ "Separation ratio: -3.9042\n",
+ "Computing PCA...\n",
+ "Computing t-SNE...\n",
+ "Computing UMAP...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Clustering Analysis (k=2) ===\n",
+ "Cluster 0: 353 samples, purity: 0.654\n",
+ "Cluster 1: 647 samples, purity: 0.988\n",
+ "Average cluster purity: 0.8210\n",
+ "\n",
+ "Completed analysis for BBBP dataset\n",
+ "\n",
+ "==================================================\n",
+ "ANALYZING CLINTOX DATASET\n",
+ "==================================================\n",
+ "Loading clintox dataset...\n",
+ "Generating augmented SMILES...\n",
+ "Dataset: clintox\n",
+ "Total molecules: 1000\n",
+ "Label distribution: [ 64 936]\n",
+ "Generating embeddings...\n",
+ "=== Class Separation Analysis ===\n",
+ "Class 0 (Non-toxic) size: 64\n",
+ "Class 1 (Toxic) size: 936\n",
+ "Mean inter-class similarity: -0.0687\n",
+ "Mean intra-class similarity (Class 0): 0.7217\n",
+ "Mean intra-class similarity (Class 1): 0.9002\n",
+ "Separation ratio: -11.8095\n",
+ "Computing PCA...\n",
+ "Computing t-SNE...\n",
+ "Computing UMAP...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Clustering Analysis (k=2) ===\n",
+ "Cluster 0: 921 samples, purity: 0.996\n",
+ "Cluster 1: 79 samples, purity: 0.759\n",
+ "Average cluster purity: 0.8776\n",
+ "\n",
+ "Completed analysis for CLINTOX dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "if __name__ == \"__main__\":\n",
+ " main_class_visualization()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/nohup.out b/simson_modeling/nohup.out
new file mode 100644
index 0000000000000000000000000000000000000000..bc4f41846f8f41449550efa1147c310b32a250a5
--- /dev/null
+++ b/simson_modeling/nohup.out
@@ -0,0 +1,6252 @@
+Loading dataset 'HoangHa/SMILES-250M'...
+Successfully fetched 84345972 SMILES strings.
+Starting augmentation and tokenization with 128 worker processes...
+
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Processing: 0%| | 19017/84345972 [00:04<4:42:38, 4972.68it/s]Process ForkPoolWorker-122:
+Process ForkPoolWorker-112:
+Process ForkPoolWorker-106:
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+Process ForkPoolWorker-85:
+Process ForkPoolWorker-127:
+Process ForkPoolWorker-91:
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+Process ForkPoolWorker-86:
+Process ForkPoolWorker-69:
+Process ForkPoolWorker-121:
+Process ForkPoolWorker-72:
+Process ForkPoolWorker-67:
+Process ForkPoolWorker-87:
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+Process ForkPoolWorker-100:
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+Process ForkPoolWorker-45:
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+Process ForkPoolWorker-51:
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+Process ForkPoolWorker-50:
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+Process ForkPoolWorker-55:
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+Process ForkPoolWorker-1:
+Process ForkPoolWorker-28:
+Process ForkPoolWorker-18:
+Process ForkPoolWorker-88:
+Process ForkPoolWorker-5:
+Process ForkPoolWorker-24:
+Process ForkPoolWorker-14:
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+Process ForkPoolWorker-2:
+Process ForkPoolWorker-62:
+
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+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 399, in put
+ self._writer.send_bytes(obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 200, in send_bytes
+ self._send_bytes(m[offset:offset + size])
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 427, in _send_bytes
+ self._send(header + buf)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 384, in _send
+ n = write(self._handle, buf)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 114, in worker
+ task = get()
+ ^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 389, in get
+ return _ForkingPickler.loads(res)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 399, in put
+ self._writer.send_bytes(obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 200, in send_bytes
+ self._send_bytes(m[offset:offset + size])
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 427, in _send_bytes
+ self._send(header + buf)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 384, in _send
+ n = write(self._handle, buf)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 125, in worker
+ result = (True, func(*args, **kwds))
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 54, in create_and_tokenize_pair
+ tokens_2 = tokenizer(
+ ^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2855, in __call__
+ encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 125, in worker
+ result = (True, func(*args, **kwds))
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 54, in create_and_tokenize_pair
+ tokens_2 = tokenizer(
+ ^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2855, in __call__
+ encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2965, in _call_one
+ return self.encode_plus(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 3040, in encode_plus
+ return self._encode_plus(
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 125, in worker
+ result = (True, func(*args, **kwds))
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 51, in create_and_tokenize_pair
+ tokens_1 = tokenizer(
+ ^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2855, in __call__
+ encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 227, in _encode_plus
+ return super()._encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2965, in _call_one
+ return self.encode_plus(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 627, in _encode_plus
+ batched_output = self._batch_encode_plus(
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 3040, in encode_plus
+ return self._encode_plus(
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 217, in _batch_encode_plus
+ return super()._batch_encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 227, in _encode_plus
+ return super()._encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 553, in _batch_encode_plus
+ encodings = self._tokenizer.encode_batch(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 627, in _encode_plus
+ batched_output = self._batch_encode_plus(
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 217, in _batch_encode_plus
+ return super()._batch_encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 553, in _batch_encode_plus
+ encodings = self._tokenizer.encode_batch(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 2965, in _call_one
+ return self.encode_plus(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 3040, in encode_plus
+ return self._encode_plus(
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 227, in _encode_plus
+ return super()._encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 627, in _encode_plus
+ batched_output = self._batch_encode_plus(
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/roberta/tokenization_roberta_fast.py", line 217, in _batch_encode_plus
+ return super()._batch_encode_plus(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 566, in _batch_encode_plus
+ self._convert_encoding(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py", line 349, in _convert_encoding
+ if return_length:
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Process ForkPoolWorker-92:
+Process ForkPoolWorker-101:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 856, in next
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ item = self._items.popleft()
+ ^^^^^^^^^^^^^^^^^^^^^
+IndexError: pop from an empty deque
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 86, in main
+ results = list(tqdm(p.imap(create_and_tokenize_pair, smiles_list), total=len(smiles_list), desc="Processing"))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 861, in next
+Traceback (most recent call last):
+ self._cond.wait(timeout)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 355, in wait
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ waiter.acquire()
+KeyboardInterrupt
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 101, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 85, in main
+ with Pool(num_workers, initializer=init_worker, initargs=(CONFIG['TOKENIZER_NAME'], CONFIG['MAX_LENGTH'])) as p:
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 739, in __exit__
+ self.terminate()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 657, in terminate
+ self._terminate()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/util.py", line 227, in __call__
+ res = self._callback(*self._args, **self._kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 695, in _terminate_pool
+ cls._help_stuff_finish(inqueue, task_handler, len(pool))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 678, in _help_stuff_finish
+ time.sleep(0)
+object address : 0x7fb691916560
+object refcount : 3
+object type : 0x564f994bb2a0
+object type name: KeyboardInterrupt
+object repr : KeyboardInterrupt()
+lost sys.stderr
+/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python: Error while finding module specification for 'create_augmented_dataset.py' (ModuleNotFoundError: __path__ attribute not found on 'create_augmented_dataset' while trying to find 'create_augmented_dataset.py'). Try using 'create_augmented_dataset' instead of 'create_augmented_dataset.py' as the module name.
+Loading dataset 'HoangHa/SMILES-250M'...
+Successfully fetched 84345972 SMILES strings.
+Starting augmentation and tokenization with 128 worker processes...
+
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Processing: 0%| | 51432/84345972 [00:09<4:50:17, 4839.74it/s]Process ForkPoolWorker-123:
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+Process ForkPoolWorker-4:
+
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+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 98, in __exit__
+ return self._semlock.__exit__(*args)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 399, in put
+ self._writer.send_bytes(obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 200, in send_bytes
+ self._send_bytes(m[offset:offset + size])
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 427, in _send_bytes
+ self._send(header + buf)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 384, in _send
+ n = write(self._handle, buf)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+KeyboardInterrupt
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Process ForkPoolWorker-10:
+Process ForkPoolWorker-146:
+Process ForkPoolWorker-141:
+Process ForkPoolWorker-142:
+Process ForkPoolWorker-145:
+Process ForkPoolWorker-143:
+Process ForkPoolWorker-138:
+Process ForkPoolWorker-137:
+Process ForkPoolWorker-130:
+Process ForkPoolWorker-131:
+Process ForkPoolWorker-136:
+Process ForkPoolWorker-133:
+Process ForkPoolWorker-134:
+Process ForkPoolWorker-135:
+Process ForkPoolWorker-129:
+Process ForkPoolWorker-132:
+Process ForkPoolWorker-140:
+Process ForkPoolWorker-139:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 856, in next
+Process ForkPoolWorker-144:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 131, in worker
+ put((job, i, result))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/queues.py", line 398, in put
+ with self._wlock:
+ ^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/synchronize.py", line 95, in __enter__
+ return self._semlock.__enter__()
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Exception in thread Thread-2 (_handle_workers):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
+Exception in thread Thread-2 (_handle_workers):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1012, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 516, in _handle_workers
+ cls._maintain_pool(ctx, Process, processes, pool, inqueue,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 340, in _maintain_pool
+ Pool._repopulate_pool_static(ctx, Process, processes, pool,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 329, in _repopulate_pool_static
+ w.start()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 121, in start
+ self._popen = self._Popen(self)
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/context.py", line 282, in _Popen
+ return Popen(process_obj)
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 19, in __init__
+ self._launch(process_obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 66, in _launch
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1012, in run
+ self.pid = os.fork()
+ ^^^^^^^^^
+KeyboardInterrupt
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 516, in _handle_workers
+Traceback (most recent call last):
+ cls._maintain_pool(ctx, Process, processes, pool, inqueue,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 340, in _maintain_pool
+ Pool._repopulate_pool_static(ctx, Process, processes, pool,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 329, in _repopulate_pool_static
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 790, in connect
+ sock_and_verified = _ssl_wrap_socket_and_match_hostname(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 969, in _ssl_wrap_socket_and_match_hostname
+ ssl_sock = ssl_wrap_socket(
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/ssl_.py", line 458, in ssl_wrap_socket
+ context.load_verify_locations(ca_certs, ca_cert_dir, ca_cert_data)
+KeyboardInterrupt
+ w.start()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 121, in start
+ self._popen = self._Popen(self)
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/context.py", line 282, in _Popen
+Fatal Python error: init_threadstate: thread state already initialized
+Python runtime state: initialized
+
+ return Popen(process_obj)
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 19, in __init__
+ item = self._items.popleft()
+ ^^^^^^^^^^^^^^^^^^^^^
+IndexError: pop from an empty deque
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 86, in main
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 790, in connect
+ sock_and_verified = _ssl_wrap_socket_and_match_hostname(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 969, in _ssl_wrap_socket_and_match_hostname
+ ssl_sock = ssl_wrap_socket(
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/ssl_.py", line 480, in ssl_wrap_socket
+ ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/ssl_.py", line 524, in _ssl_wrap_socket_impl
+ return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 455, in wrap_socket
+ return self.sslsocket_class._create(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1041, in _create
+ self.do_handshake()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1319, in do_handshake
+ self._sslobj.do_handshake()
+KeyboardInterrupt
+ self._launch(process_obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 66, in _launch
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ self.pid = os.fork()
+ ^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 753, in connect
+ self.sock = sock = self._new_conn()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 198, in _new_conn
+ sock = connection.create_connection(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/connection.py", line 73, in create_connection
+ sock.connect(sa)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Fatal Python error: init_threadstate: thread state already initialized
+Python runtime state: initialized
+
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 753, in connect
+ self.sock = sock = self._new_conn()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 198, in _new_conn
+ sock = connection.create_connection(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/connection.py", line 73, in create_connection
+ sock.connect(sa)
+KeyboardInterrupt
+Traceback (most recent call last):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 753, in connect
+ self.sock = sock = self._new_conn()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 198, in _new_conn
+ sock = connection.create_connection(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/connection.py", line 73, in create_connection
+ sock.connect(sa)
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 753, in connect
+ self.sock = sock = self._new_conn()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 198, in _new_conn
+ sock = connection.create_connection(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/connection.py", line 73, in create_connection
+ sock.connect(sa)
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ results = list(tqdm(p.imap(create_and_tokenize_pair, smiles_list), total=len(smiles_list), desc="Processing"))
+Traceback (most recent call last):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 464, in _make_request
+ self._validate_conn(conn)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 1093, in _validate_conn
+ conn.connect()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 753, in connect
+ self.sock = sock = self._new_conn()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 198, in _new_conn
+ sock = connection.create_connection(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/util/connection.py", line 73, in create_connection
+ sock.connect(sa)
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 861, in next
+ self._cond.wait(timeout)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 355, in wait
+ waiter.acquire()
+KeyboardInterrupt
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 101, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 85, in main
+ with Pool(num_workers, initializer=init_worker, initargs=(CONFIG['TOKENIZER_NAME'], CONFIG['MAX_LENGTH'])) as p:
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 739, in __exit__
+ self.terminate()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 657, in terminate
+ self._terminate()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/util.py", line 227, in __call__
+ res = self._callback(*self._args, **self._kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 695, in _terminate_pool
+ cls._help_stuff_finish(inqueue, task_handler, len(pool))
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 677, in _help_stuff_finish
+ inqueue._reader.recv()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 250, in recv
+ buf = self._recv_bytes()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 430, in _recv_bytes
+ buf = self._recv(4)
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 395, in _recv
+ chunk = read(handle, remaining)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Exception ignored in atexit callback:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/util.py", line 360, in _exit_function
+Exception in thread Thread-2 (_handle_workers):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1012, in run
+Exception in thread Thread-2 (_handle_workers):
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 1012, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 516, in _handle_workers
+ cls._maintain_pool(ctx, Process, processes, pool, inqueue,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 340, in _maintain_pool
+ Pool._repopulate_pool_static(ctx, Process, processes, pool,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 329, in _repopulate_pool_static
+ w.start()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 121, in start
+ self._popen = self._Popen(self)
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/context.py", line 282, in _Popen
+ return Popen(process_obj)
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 19, in __init__
+ self._launch(process_obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 66, in _launch
+ self.pid = os.fork()
+ ^^^^^^^^^
+KeyboardInterrupt
+Fatal Python error: init_threadstate: thread state already initialized
+Python runtime state: initialized
+
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 516, in _handle_workers
+ cls._maintain_pool(ctx, Process, processes, pool, inqueue,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 340, in _maintain_pool
+ Pool._repopulate_pool_static(ctx, Process, processes, pool,
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 329, in _repopulate_pool_static
+ w.start()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 121, in start
+ p.join()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 149, in join
+ self._popen = self._Popen(self)
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/context.py", line 282, in _Popen
+ return Popen(process_obj)
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 19, in __init__
+ self._launch(process_obj)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 66, in _launch
+ self.pid = os.fork()
+ ^^^^^^^^^
+KeyboardInterrupt
+Fatal Python error: init_threadstate: thread state already initialized
+Python runtime state: initialized
+
+ res = self._popen.wait(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 43, in wait
+ return self.poll(os.WNOHANG if timeout == 0.0 else 0)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 27, in poll
+ pid, sts = os.waitpid(self.pid, flag)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt:
+Process ForkPoolWorker-182:
+Process ForkPoolWorker-180:
+Process ForkPoolWorker-179:
+Process ForkPoolWorker-183:
+Process ForkPoolWorker-181:
+Process ForkPoolWorker-184:
+Process ForkPoolWorker-186:
+Process ForkPoolWorker-185:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 305, in _request_wrapper
+ return _request_wrapper(method=method, url=next_url, follow_relative_redirects=True, **params)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 305, in _request_wrapper
+ return _request_wrapper(method=method, url=next_url, follow_relative_redirects=True, **params)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 305, in _request_wrapper
+ return _request_wrapper(method=method, url=next_url, follow_relative_redirects=True, **params)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 1069, in from_pretrained
+ return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 1957, in from_pretrained
+ for template in list_repo_templates(
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 163, in list_repo_templates
+ for entry in list_repo_tree(
+ ^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 3168, in list_repo_tree
+ for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_pagination.py", line 36, in paginate
+ r = session.get(path, params=params, headers=headers)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
+ return self.request("GET", url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 1069, in from_pretrained
+ return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 1877, in from_pretrained
+ if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 187, in is_remote_url
+ parsed = urlparse(url_or_filename)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/urllib/parse.py", line 401, in urlparse
+ result = ParseResult(scheme, netloc, url, params, query, fragment)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "", line 1, in
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 983, in from_pretrained
+ tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 815, in get_tokenizer_config
+ resolved_config_file = cached_file(
+ ^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 312, in cached_file
+ file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 470, in cached_files
+ hf_hub_download(
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1008, in hf_hub_download
+ return _hf_hub_download_to_cache_dir(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1071, in _hf_hub_download_to_cache_dir
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1533, in _get_metadata_or_catch_error
+ metadata = get_hf_file_metadata(
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+ return fn(*args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 1450, in get_hf_file_metadata
+ r = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 286, in _request_wrapper
+ response = _request_wrapper(
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/file_download.py", line 309, in _request_wrapper
+ response = http_backoff(method=method, url=url, **params, retry_on_exceptions=(), retry_on_status_codes=(429,))
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 310, in http_backoff
+ response = session.request(method=method, url=url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
+ self.run()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 108, in run
+ self._target(*self._args, **self._kwargs)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/pool.py", line 109, in worker
+ initializer(*initargs)
+ File "/home/jovyan/simson_training_bolgov/create_augmented_dataset.py", line 35, in init_worker
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/models/auto/tokenization_auto.py", line 1069, in from_pretrained
+ return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 1957, in from_pretrained
+ for template in list_repo_templates(
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/transformers/utils/hub.py", line 163, in list_repo_templates
+ for entry in list_repo_tree(
+ ^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 3168, in list_repo_tree
+ for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_pagination.py", line 36, in paginate
+ r = session.get(path, params=params, headers=headers)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
+ return self.request("GET", url, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
+ resp = self.send(prep, **send_kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
+ r = adapter.send(request, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
+ return super().send(request, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
+ resp = conn.urlopen(
+ ^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
+ response = self._make_request(
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connectionpool.py", line 534, in _make_request
+ response = conn.getresponse()
+ ^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/urllib3/connection.py", line 565, in getresponse
+ httplib_response = super().getresponse()
+ ^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 1430, in getresponse
+ response.begin()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 331, in begin
+ version, status, reason = self._read_status()
+ ^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/http/client.py", line 292, in _read_status
+ line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/socket.py", line 720, in readinto
+ return self._sock.recv_into(b)
+ ^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1251, in recv_into
+ return self.read(nbytes, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/ssl.py", line 1103, in read
+ return self._sslobj.read(len, buffer)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/regression/.ipynb_checkpoints/better_regression-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/better_regression-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..032d707b27602bbceccee17e855b576fdcfb36ee
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/better_regression-checkpoint.ipynb
@@ -0,0 +1,528 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader, Sampler\n",
+ "import torch.nn as nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "# 1. BINNING, SAMPLING, SAMPLE WEIGHTING\n",
+ "def compute_multitarget_sample_weights(labels_unscaled, bins4, bins5):\n",
+ " # Each returns shape (N,)\n",
+ " inds4 = np.digitize(labels_unscaled[:, 0], bins4, right=False) - 1\n",
+ " inds5 = np.digitize(labels_unscaled[:, 1], bins5, right=False) - 1\n",
+ " freq4 = np.bincount(inds4, minlength=len(bins4))\n",
+ " freq5 = np.bincount(inds5, minlength=len(bins5))\n",
+ " w4 = 1.0 / (freq4[inds4] + 1e-8)\n",
+ " w5 = 1.0 / (freq5[inds5] + 1e-8)\n",
+ " main_weights = np.maximum(w4, w5) # Or average, or sum as suits\n",
+ " main_weights /= main_weights.mean() # Normalize for stability\n",
+ " return main_weights # shape (N_samples,)\n",
+ "\n",
+ "\n",
+ "class TargetedSampler(Sampler):\n",
+ " \"\"\"\n",
+ " Enforces a proportion of 'high value' samples for either of the main labels in every batch.\n",
+ " \"\"\"\n",
+ " def __init__(self, inds4, inds5, high_bins4, high_bins5, batch_size, high_frac=0.3, shuffle=True):\n",
+ " # indices for which either label 4 or label 5 is in a 'high' bin\n",
+ " high_mask = (inds4 >= high_bins4) | (inds5 >= high_bins5)\n",
+ " self.high_indices = np.where(high_mask)[0]\n",
+ " self.low_indices = np.where(~high_mask)[0]\n",
+ " self.batch_size = batch_size\n",
+ " self.high_count = int(batch_size * high_frac)\n",
+ " self.low_count = batch_size - self.high_count\n",
+ " self.shuffle = shuffle\n",
+ " \n",
+ " def __iter__(self):\n",
+ " high = np.copy(self.high_indices)\n",
+ " low = np.copy(self.low_indices)\n",
+ " if self.shuffle:\n",
+ " np.random.shuffle(high)\n",
+ " np.random.shuffle(low)\n",
+ " hi_ptr, low_ptr = 0, 0\n",
+ " while hi_ptr < len(high) or low_ptr < len(low):\n",
+ " batch_high = high[hi_ptr: hi_ptr+self.high_count]\n",
+ " batch_low = low[low_ptr: low_ptr+self.low_count]\n",
+ " if len(batch_high) < self.high_count:\n",
+ " np.random.shuffle(high)\n",
+ " hi_ptr = 0\n",
+ " batch_high = high[hi_ptr: hi_ptr+self.high_count]\n",
+ " if len(batch_low) < self.low_count:\n",
+ " np.random.shuffle(low)\n",
+ " low_ptr = 0\n",
+ " batch_low = low[low_ptr: low_ptr+self.low_count]\n",
+ " batch = np.concatenate([batch_high, batch_low])\n",
+ " np.random.shuffle(batch)\n",
+ " yield batch.tolist()\n",
+ " hi_ptr += self.high_count\n",
+ " low_ptr += self.low_count\n",
+ " \n",
+ " def __len__(self):\n",
+ " return (len(self.high_indices) + len(self.low_indices)) // self.batch_size\n",
+ "\n",
+ "class SMILESDataset(torch.utils.data.Dataset):\n",
+ " def __init__(self, smiles_list, labels, sample_weights, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # shape (N, 6), already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " self.sample_weights = sample_weights\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'weight': torch.tensor(self.sample_weights[idx], dtype=torch.float32),\n",
+ " 'index': torch.tensor(idx, dtype=torch.long)\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 6)\n",
+ " labels: Ground truth labels (batch_size, 6)\n",
+ " label_mask: Mask for valid labels (batch_size, 6)\n",
+ " label_weights: Weights for each label (6,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " return losses.mean()\n",
+ "\n",
+ "def stratified_metrics(preds, trues, bins, scalers):\n",
+ " # Only for last two labels\n",
+ " results = {}\n",
+ " for li, label_col in enumerate([4, 5]):\n",
+ " unscaled_pred = scalers[label_col].inverse_transform(preds[:,label_col].reshape(-1,1)).flatten()\n",
+ " unscaled_true = scalers[label_col].inverse_transform(trues[:,label_col].reshape(-1,1)).flatten()\n",
+ " bin_idx = np.digitize(unscaled_true, bins[li])\n",
+ " for i in range(len(bins[li])+1):\n",
+ " in_bin = bin_idx == i\n",
+ " if np.sum(in_bin) == 0:\n",
+ " continue\n",
+ " bin_mae = np.mean(np.abs(unscaled_pred[in_bin] - unscaled_true[in_bin]))\n",
+ " bin_r2 = 1 - (np.mean((unscaled_pred[in_bin] - unscaled_true[in_bin])**2) /\n",
+ " (np.var(unscaled_true[in_bin]) + 1e-8))\n",
+ " results[f'label{label_col}_bin{i}_count'] = np.sum(in_bin)\n",
+ " results[f'label{label_col}_bin{i}_mae'] = bin_mae\n",
+ " return results\n",
+ "\n",
+ "# 5. MAIN TRAIN/VAL LOOP WITH TARGETED SAMPLING AND STRATIFIED EVALUATION\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, model, tokenizer, scalers,\n",
+ " num_epochs=5, learning_rate=1e-5, batch_size=256, validation_steps=500):\n",
+ " # 1. Bins for columns 4 & 5 using **unscaled train-data**\n",
+ " bins_label4 = create_bins(train['unscaled_CO2'], n_bins=10)\n",
+ " bins_label5 = create_bins(train['unscaled_CH4'], n_bins=10)\n",
+ " bins = [bins_label4, bins_label5]\n",
+ " # 2. Bin indicators for each sample in train\n",
+ " inds4 = np.digitize(train['unscaled_CO2'], bins_label4, right=False) - 1\n",
+ " inds5 = np.digitize(train['unscaled_CH4'], bins_label5, right=False) - 1\n",
+ " # 3. Choose high-bin threshold (e.g. top bin, or top 2 bins as \"high\"), adjust as needed\n",
+ " high_bins4 = len(bins_label4) - 1\n",
+ " high_bins5 = len(bins_label5) - 1\n",
+ " # 4. Compute multitarget weights (max rarity of either label-of-interest, UNscaled)\n",
+ " sample_weights = compute_multitarget_sample_weights(\n",
+ " train[['unscaled_CO2', 'unscaled_CH4']].values, bins_label4, bins_label5)\n",
+ " val_sample_weights = compute_multitarget_sample_weights(\n",
+ " test[['unscaled_CO2', 'unscaled_CH4']].values, bins_label4, bins_label5)\n",
+ " # 5. Dataset and batch sampler\n",
+ " targeted_sampler = TargetedSampler(inds4, inds5, high_bins4, high_bins5, batch_size, high_frac=0.3)\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, sample_weights, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, val_sample_weights, tokenizer)\n",
+ " train_loader = DataLoader(train_dataset, batch_sampler=targeted_sampler, num_workers=4)\n",
+ " val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
+ " # 6. Model, optimizer, scheduler\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " model.to(device)\n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
+ " total_steps = len(train_loader)*3\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " best_val_loss = float('inf')\n",
+ " best_state = None\n",
+ " steps_no_improve, patience = 0, 10\n",
+ " global_step, running_train_loss, train_steps_count = 0, 0, 0\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
+ " model.train()\n",
+ " pbar = tqdm(train_loader, desc='Training', total=len(train_loader) * num_epochs)\n",
+ " for batch in pbar:\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " weights = batch['weight'].to(device)\n",
+ " optimizer.zero_grad()\n",
+ " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " loss = calculate_weighted_loss(outputs, labels, weights)\n",
+ " loss.backward()\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " optimizer.step(); scheduler.step()\n",
+ " running_train_loss += loss.item(); train_steps_count += 1; global_step += 1\n",
+ " pbar.set_postfix(loss=f'{loss.item():.3f}')\n",
+ " if global_step % validation_steps == 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " print(f\"Step {global_step}: Mean Weighted Train Loss: {avg_train_loss:.4f}\")\n",
+ " running_train_loss = 0; train_steps_count = 0\n",
+ " # Validation:\n",
+ " all_preds, all_trues, all_weights = [], [], []\n",
+ " model.eval()\n",
+ " with torch.no_grad():\n",
+ " for vb in val_loader:\n",
+ " vi = vb['input_ids'].to(device)\n",
+ " va = vb['attention_mask'].to(device)\n",
+ " vl = vb['labels'].to(device)\n",
+ " vw = vb['weight'].to(device)\n",
+ " out = model(input_ids=vi, attention_mask=va)\n",
+ " all_preds.append(out.cpu()); all_trues.append(vl.cpu()); all_weights.append(vw.cpu())\n",
+ " preds = torch.cat(all_preds).numpy()\n",
+ " trues = torch.cat(all_trues).numpy()\n",
+ " weights = torch.cat(all_weights).numpy()\n",
+ " val_loss = calculate_weighted_loss(torch.tensor(preds), torch.tensor(trues), torch.tensor(weights)).item()\n",
+ " print(f\"Weighted Val MSE (scaled): {val_loss:.4f}\")\n",
+ " metrics = stratified_metrics(preds, trues, bins, scalers)\n",
+ " for k, v in metrics.items():\n",
+ " print(f\"{k}: {v:.4f}\")\n",
+ " if val_loss < best_val_loss:\n",
+ " best_val_loss = val_loss; best_state = model.state_dict().copy(); steps_no_improve = 0\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin')\n",
+ " print(f\"New best val_loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " print(f'Patience meter: {steps_no_improve} out of {patience}')\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping at step {global_step}\")\n",
+ " if best_state: model.load_state_dict(best_state)\n",
+ " return\n",
+ " model.train()\n",
+ " if best_state: model.load_state_dict(best_state)\n",
+ " print(f\"Training completed, best weighted val_loss: {best_val_loss:.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "df['unscaled_CO2'] = df['CO2'].copy()\n",
+ "df['unscaled_CH4'] = df['CH4'].copy()\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl', weights_only=False)\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "\n",
+ "for param in model.encoder.parameters():\n",
+ " param.requires_grad = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "# For your use case:\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['unscaled_CO2', 'unscaled_CH4'], # or actual column names\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_bins(target_values, n_bins=5, strategy='percentile'):\n",
+ " \"\"\"\n",
+ " Create bins for a target based on the specified strategy.\n",
+ " - 'percentile' creates approximately equal-sized groups\n",
+ " - 'uniform' creates equal-width bins\n",
+ " Returns:\n",
+ " bin_edges: array of length n_bins+1\n",
+ " \"\"\"\n",
+ " target_values = target_values[~np.isnan(target_values)]\n",
+ " if strategy == 'percentile':\n",
+ " return np.percentile(target_values, np.linspace(0, 100, n_bins+1))\n",
+ " else:\n",
+ " return np.linspace(np.min(target_values), np.max(target_values), n_bins+1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac00906e-e22a-4f50-94e7-650acf5eec1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/regression_simson.pth'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/6\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 1%| | 1223/149778 [04:01<8:07:04, 5.08it/s, loss=8.268]"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=6, learning_rate=2e-5, batch_size=256, validation_steps=6_500,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64baf577-cfe9-454c-8a2b-71b7a5b12506",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/.ipynb_checkpoints/decoding-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/decoding-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0f35fd9fabd2e185a93977682ac32cc3409a34c4
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/decoding-checkpoint.ipynb
@@ -0,0 +1,4373 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "id": "f72ec62c-c849-45b2-9321-b913c9f32979",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Encoder parameters loaded\n",
+ "Encoder parameters frozen\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from transformers import BertConfig, BertModel, AutoTokenizer\n",
+ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, EarlyStoppingCallback\n",
+ "from torch.utils.data import Dataset\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonDecoder(nn.Module):\n",
+ " def __init__(self, embedding_dim, hidden_dim, vocab_size, max_len):\n",
+ " super(SimSonDecoder, self).__init__()\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.vocab_size = vocab_size\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " # Project embedding to hidden dimension\n",
+ " self.embedding_projection = nn.Linear(embedding_dim, hidden_dim)\n",
+ " \n",
+ " # Token embeddings for decoder input\n",
+ " self.token_embeddings = nn.Embedding(vocab_size, hidden_dim)\n",
+ " self.position_embeddings = nn.Embedding(max_len, hidden_dim)\n",
+ " \n",
+ " # Transformer decoder layers\n",
+ " decoder_layer = nn.TransformerDecoderLayer(\n",
+ " d_model=hidden_dim,\n",
+ " nhead=12,\n",
+ " dim_feedforward=2048,\n",
+ " dropout=0.1,\n",
+ " batch_first=True\n",
+ " )\n",
+ " self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)\n",
+ " \n",
+ " # Output projection to vocabulary\n",
+ " self.output_projection = nn.Linear(hidden_dim, vocab_size)\n",
+ " self.dropout = nn.Dropout(0.1)\n",
+ " \n",
+ " # Layer normalization\n",
+ " self.layer_norm = nn.LayerNorm(hidden_dim)\n",
+ " \n",
+ " def forward(self, molecule_embeddings, decoder_input_ids, decoder_attention_mask=None):\n",
+ " batch_size, seq_len = decoder_input_ids.shape\n",
+ " device = decoder_input_ids.device\n",
+ " \n",
+ " # Project molecule embeddings to memory for cross-attention\n",
+ " memory = self.embedding_projection(molecule_embeddings) # [batch, hidden_dim]\n",
+ " memory = memory.unsqueeze(1) # [batch, 1, hidden_dim]\n",
+ " \n",
+ " # Create decoder input embeddings\n",
+ " token_emb = self.token_embeddings(decoder_input_ids) # [batch, seq_len, hidden_dim]\n",
+ " \n",
+ " # Add positional embeddings\n",
+ " positions = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)\n",
+ " pos_emb = self.position_embeddings(positions)\n",
+ " \n",
+ " decoder_inputs = self.layer_norm(token_emb + pos_emb)\n",
+ " decoder_inputs = self.dropout(decoder_inputs)\n",
+ " \n",
+ " # Create causal mask for decoder\n",
+ " tgt_mask = self._generate_square_subsequent_mask(seq_len).to(device)\n",
+ " \n",
+ " # Create attention mask for decoder inputs\n",
+ " if decoder_attention_mask is not None:\n",
+ " # Convert padding mask to additive mask\n",
+ " tgt_key_padding_mask = (decoder_attention_mask == 0)\n",
+ " else:\n",
+ " tgt_key_padding_mask = None\n",
+ " \n",
+ " # Apply transformer decoder\n",
+ " decoder_output = self.transformer_decoder(\n",
+ " tgt=decoder_inputs,\n",
+ " memory=memory,\n",
+ " tgt_mask=tgt_mask,\n",
+ " tgt_key_padding_mask=tgt_key_padding_mask\n",
+ " )\n",
+ " \n",
+ " # Project to vocabulary\n",
+ " logits = self.output_projection(decoder_output)\n",
+ " \n",
+ " return logits\n",
+ " \n",
+ " def _generate_square_subsequent_mask(self, sz):\n",
+ " \"\"\"Generate causal mask for decoder\"\"\"\n",
+ " mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\n",
+ " return mask\n",
+ "\n",
+ "# Combined Encoder-Decoder Model\n",
+ "class SimSonEncoderDecoder(nn.Module):\n",
+ " def __init__(self, encoder, decoder, tokenizer):\n",
+ " super().__init__()\n",
+ " self.encoder = encoder\n",
+ " self.decoder = decoder\n",
+ " self.tokenizer = tokenizer\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask, labels=None):\n",
+ " # Encode SMILES to embeddings\n",
+ " embeddings = self.encoder(input_ids, attention_mask)\n",
+ " \n",
+ " if labels is not None:\n",
+ " # During training, use teacher forcing\n",
+ " # Shift labels for decoder input (remove last token, add BOS token)\n",
+ " decoder_input_ids = torch.cat([\n",
+ " torch.full((labels.shape[0], 1), self.tokenizer.cls_token_id, \n",
+ " dtype=labels.dtype, device=labels.device),\n",
+ " labels[:, :-1]\n",
+ " ], dim=1)\n",
+ " \n",
+ " # Generate decoder attention mask\n",
+ " decoder_attention_mask = (decoder_input_ids != self.tokenizer.pad_token_id).long()\n",
+ " \n",
+ " # Decode embeddings to SMILES\n",
+ " logits = self.decoder(embeddings, decoder_input_ids, decoder_attention_mask)\n",
+ " \n",
+ " # Calculate loss\n",
+ " loss_fct = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n",
+ " loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))\n",
+ " \n",
+ " return {\"loss\": loss, \"logits\": logits}\n",
+ " else:\n",
+ " # During inference\n",
+ " return self.generate(embeddings)\n",
+ " \n",
+ " def generate(self, embeddings, max_length=512):\n",
+ " \"\"\"Generate SMILES from embeddings\"\"\"\n",
+ " batch_size = embeddings.shape[0]\n",
+ " device = embeddings.device\n",
+ " \n",
+ " # Start with CLS token\n",
+ " generated = torch.full((batch_size, 1), self.tokenizer.cls_token_id, \n",
+ " dtype=torch.long, device=device)\n",
+ " \n",
+ " for _ in range(max_length - 1):\n",
+ " decoder_attention_mask = (generated != self.tokenizer.pad_token_id).long()\n",
+ " logits = self.decoder(embeddings, generated, decoder_attention_mask)\n",
+ " \n",
+ " # Get next token probabilities\n",
+ " next_token_logits = logits[:, -1, :]\n",
+ " next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)\n",
+ " \n",
+ " # Append to generated sequence\n",
+ " generated = torch.cat([generated, next_tokens], dim=1)\n",
+ " \n",
+ " # Stop if all sequences have generated EOS token\n",
+ " if torch.all(next_tokens.squeeze() == self.tokenizer.sep_token_id):\n",
+ " break\n",
+ " \n",
+ " return generated\n",
+ "\n",
+ "# Initialize tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize encoder config\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=4,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize and load encoder\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "# Load encoder parameters (extract encoder from regression model)\n",
+ "regression_state_dict = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin', weights_only=False)\n",
+ "\n",
+ "encoder_state_dict = {}\n",
+ "for key, value in regression_state_dict.items():\n",
+ " key = key[len('_orig_mod.'):]\n",
+ " if key.startswith('encoder.'):\n",
+ " encoder_state_dict[key[8 + len('_orig_mod.'):]] = value\n",
+ "\n",
+ "print(\"Encoder parameters loaded\")\n",
+ "\n",
+ "# Freeze encoder parameters\n",
+ "for param in encoder.parameters():\n",
+ " param.requires_grad = False\n",
+ "print(\"Encoder parameters frozen\")\n",
+ "\n",
+ "# Initialize decoder\n",
+ "decoder = SimSonDecoder(\n",
+ " embedding_dim=512, # encoder.max_len\n",
+ " hidden_dim=768, # config.hidden_size\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " max_len=512\n",
+ ")\n",
+ "\n",
+ "# Create combined model\n",
+ "model = SimSonEncoderDecoder(encoder, decoder, tokenizer)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27946411-ddb4-48f2-ab5d-baec24e53954",
+ "metadata": {},
+ "source": [
+ "regression_params = torch.load('/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder.bin', weights_only=False)\n",
+ "\n",
+ "uncompiled_state_dict = {}\n",
+ "for key, value in regression_params.items():\n",
+ " print(key)\n",
+ " key = key[len('_orig_mod.'):]\n",
+ " uncompiled_state_dict[key] = value\n",
+ " print(key)\n",
+ " break\n",
+ " \n",
+ "torch.save(uncompiled_state_dict, '/home/jovyan/simson_training_bolgov/regression/encoder_decoder_uncompiled.bin')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "id": "5bd4f17d-e369-4e4c-be51-a91cb0b85a0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load dataset\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a40fac8-9c46-4c96-8a47-94ea32803b21",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "5951cb13-e188-4a2c-8b42-9f8125e96165",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training samples: 6390602\n",
+ "Validation samples: 336348\n"
+ ]
+ }
+ ],
+ "source": [
+ "class SMILESReconstructionDataset(Dataset):\n",
+ " def __init__(self, dataframe, tokenizer, max_length=256):\n",
+ " self.data = dataframe\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.data)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " # Get SMILES string (adjust column name as needed)\n",
+ " smiles = self.data.iloc[idx]['smiles'] if 'smiles' in self.data.columns else self.data.iloc[idx]['Smiles']\n",
+ " \n",
+ " # Tokenize input SMILES\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " max_length=self.max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].squeeze(0),\n",
+ " 'attention_mask': encoding['attention_mask'].squeeze(0),\n",
+ " 'labels': encoding['input_ids'].squeeze(0) # Same as input for reconstruction\n",
+ " }\n",
+ "\n",
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Same splits\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['CO2', 'CH4'], \n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")\n",
+ "\n",
+ "train_dataset = SMILESReconstructionDataset(train, tokenizer)\n",
+ "val_dataset = SMILESReconstructionDataset(test, tokenizer)\n",
+ "val_df = test\n",
+ "train_df = train\n",
+ "print(f\"Training samples: {len(train_dataset)}\")\n",
+ "print(f\"Validation samples: {len(val_dataset)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "id": "2b79c8b4-4f5d-4752-b1a7-08ed2d0bb7f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_290780/2978564159.py:51: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Seq2SeqTrainer.__init__`. Use `processing_class` instead.\n",
+ " trainer = Seq2SeqTrainer(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Custom data collator for encoder-decoder\n",
+ "class EncoderDecoderDataCollator:\n",
+ " def __init__(self, tokenizer):\n",
+ " self.tokenizer = tokenizer\n",
+ " \n",
+ " def __call__(self, batch):\n",
+ " # Stack all batch elements\n",
+ " input_ids = torch.stack([item['input_ids'] for item in batch])\n",
+ " attention_mask = torch.stack([item['attention_mask'] for item in batch])\n",
+ " labels = torch.stack([item['labels'] for item in batch])\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': input_ids,\n",
+ " 'attention_mask': attention_mask,\n",
+ " 'labels': labels\n",
+ " }\n",
+ "\n",
+ "\n",
+ "data_collator = EncoderDecoderDataCollator(tokenizer)\n",
+ "\n",
+ "early_stopping_callback = EarlyStoppingCallback(\n",
+ " early_stopping_patience=10,\n",
+ ")\n",
+ "\n",
+ "training_args = Seq2SeqTrainingArguments(\n",
+ " output_dir='/home/jovyan/simson_training_bolgov/regression/decoder_checkpoints',\n",
+ " eval_strategy='steps',\n",
+ " eval_steps=10_000,\n",
+ " logging_steps=10_000,\n",
+ " save_steps=10_000,\n",
+ " save_total_limit=3,\n",
+ " learning_rate=5e-5,\n",
+ " per_device_train_batch_size=256,\n",
+ " per_device_eval_batch_size=256,\n",
+ " gradient_accumulation_steps=1,\n",
+ " num_train_epochs=5,\n",
+ " warmup_steps=50,\n",
+ " weight_decay=0.01,\n",
+ " logging_dir='./logs',\n",
+ " report_to='none',\n",
+ " load_best_model_at_end=True,\n",
+ " metric_for_best_model='eval_loss',\n",
+ " greater_is_better=False,\n",
+ " fp16=True,\n",
+ " dataloader_pin_memory=True,\n",
+ " remove_unused_columns=False,\n",
+ " predict_with_generate=False \n",
+ ")\n",
+ "\n",
+ "# Initialize trainer\n",
+ "trainer = Seq2SeqTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=val_dataset,\n",
+ " data_collator=data_collator,\n",
+ " tokenizer=tokenizer\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "88eccafd-c269-4bc6-8290-0bdb5d184c31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ac6754e-dd6d-4282-b661-0b11e65cadc3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_state = trainer.model.state_dict()\n",
+ "torch.save(final_state, '/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder_better_regression.bin')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "id": "18637615-64f4-42d5-a431-0765df5bc024",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized with 72,264,271 trainable parameters\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonDecoder(nn.Module):\n",
+ " def __init__(self, embedding_dim, hidden_dim, vocab_size, max_len):\n",
+ " super(SimSonDecoder, self).__init__()\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.vocab_size = vocab_size\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " # Project embedding to hidden dimension\n",
+ " self.embedding_projection = nn.Linear(embedding_dim, hidden_dim)\n",
+ " \n",
+ " # Token embeddings for decoder input\n",
+ " self.token_embeddings = nn.Embedding(vocab_size, hidden_dim)\n",
+ " self.position_embeddings = nn.Embedding(max_len, hidden_dim)\n",
+ " \n",
+ " # Transformer decoder layers\n",
+ " decoder_layer = nn.TransformerDecoderLayer(\n",
+ " d_model=hidden_dim,\n",
+ " nhead=12,\n",
+ " dim_feedforward=2048,\n",
+ " dropout=0.1,\n",
+ " batch_first=True\n",
+ " )\n",
+ " self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)\n",
+ " \n",
+ " # Output projection to vocabulary\n",
+ " self.output_projection = nn.Linear(hidden_dim, vocab_size)\n",
+ " self.dropout = nn.Dropout(0.1)\n",
+ " \n",
+ " # Layer normalization\n",
+ " self.layer_norm = nn.LayerNorm(hidden_dim)\n",
+ " \n",
+ " def forward(self, molecule_embeddings, decoder_input_ids, decoder_attention_mask=None):\n",
+ " batch_size, seq_len = decoder_input_ids.shape\n",
+ " device = decoder_input_ids.device\n",
+ " \n",
+ " # Project molecule embeddings to memory for cross-attention\n",
+ " memory = self.embedding_projection(molecule_embeddings) # [batch, hidden_dim]\n",
+ " memory = memory.unsqueeze(1) # [batch, 1, hidden_dim]\n",
+ " \n",
+ " # Create decoder input embeddings\n",
+ " token_emb = self.token_embeddings(decoder_input_ids) # [batch, seq_len, hidden_dim]\n",
+ " \n",
+ " # Add positional embeddings\n",
+ " positions = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)\n",
+ " pos_emb = self.position_embeddings(positions)\n",
+ " \n",
+ " decoder_inputs = self.layer_norm(token_emb + pos_emb)\n",
+ " decoder_inputs = self.dropout(decoder_inputs)\n",
+ " \n",
+ " # Create causal mask for decoder\n",
+ " tgt_mask = self._generate_square_subsequent_mask(seq_len).to(device)\n",
+ " \n",
+ " # Create attention mask for decoder inputs\n",
+ " if decoder_attention_mask is not None:\n",
+ " # Convert padding mask to additive mask\n",
+ " tgt_key_padding_mask = (decoder_attention_mask == 0)\n",
+ " else:\n",
+ " tgt_key_padding_mask = None\n",
+ " \n",
+ " # Apply transformer decoder\n",
+ " decoder_output = self.transformer_decoder(\n",
+ " tgt=decoder_inputs,\n",
+ " memory=memory,\n",
+ " tgt_mask=tgt_mask,\n",
+ " tgt_key_padding_mask=tgt_key_padding_mask\n",
+ " )\n",
+ " \n",
+ " # Project to vocabulary\n",
+ " logits = self.output_projection(decoder_output)\n",
+ " \n",
+ " return logits\n",
+ " \n",
+ " def _generate_square_subsequent_mask(self, sz):\n",
+ " \"\"\"Generate causal mask for decoder\"\"\"\n",
+ " mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\n",
+ " return mask\n",
+ "\n",
+ "# Combined Encoder-Decoder Model\n",
+ "class SimSonEncoderDecoder(nn.Module):\n",
+ " def __init__(self, encoder, decoder, tokenizer):\n",
+ " super().__init__()\n",
+ " self.encoder = encoder\n",
+ " self.decoder = decoder\n",
+ " self.tokenizer = tokenizer\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask, labels=None):\n",
+ " # Encode SMILES to embeddings\n",
+ " embeddings = self.encoder(input_ids, attention_mask)\n",
+ " \n",
+ " if labels is not None:\n",
+ " # During training, use teacher forcing\n",
+ " # Shift labels for decoder input (remove last token, add BOS token)\n",
+ " decoder_input_ids = torch.cat([\n",
+ " torch.full((labels.shape[0], 1), self.tokenizer.cls_token_id, \n",
+ " dtype=labels.dtype, device=labels.device),\n",
+ " labels[:, :-1]\n",
+ " ], dim=1)\n",
+ " \n",
+ " # Generate decoder attention mask\n",
+ " decoder_attention_mask = (decoder_input_ids != self.tokenizer.pad_token_id).long()\n",
+ " \n",
+ " # Decode embeddings to SMILES\n",
+ " logits = self.decoder(embeddings, decoder_input_ids, decoder_attention_mask)\n",
+ " \n",
+ " # Calculate loss\n",
+ " loss_fct = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n",
+ " loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))\n",
+ " \n",
+ " return {\"loss\": loss, \"logits\": logits}\n",
+ " else:\n",
+ " # During inference\n",
+ " return self.generate(embeddings)\n",
+ " \n",
+ " def generate(self, embeddings, max_length=512, temperature=1.0):\n",
+ "\n",
+ " batch_size = embeddings.shape[0]\n",
+ " device = embeddings.device\n",
+ " \n",
+ " # Start with the CLS token for all sequences in the batch\n",
+ " generated = torch.full((batch_size, 1), self.tokenizer.cls_token_id, \n",
+ " dtype=torch.long, device=device)\n",
+ " \n",
+ " # --- THE FIX: Keep track of which sequences have finished ---\n",
+ " # A boolean tensor, initially all False.\n",
+ " is_finished = torch.zeros(batch_size, dtype=torch.bool, device=device)\n",
+ " \n",
+ " for _ in range(max_length - 1):\n",
+ " # Pass the current generated sequences to the decoder\n",
+ " decoder_attention_mask = (generated != self.tokenizer.pad_token_id).long()\n",
+ " logits = self.decoder(embeddings, generated, decoder_attention_mask)\n",
+ " \n",
+ " # Focus on the logits for the last token in each sequence\n",
+ " next_token_logits = logits[:, -1, :]\n",
+ " \n",
+ " # Apply temperature sampling\n",
+ " if temperature == 0.0:\n",
+ " next_tokens = torch.argmax(next_token_logits, dim=-1)\n",
+ " else:\n",
+ " probs = F.softmax(next_token_logits / temperature, dim=-1)\n",
+ " next_tokens = torch.multinomial(probs, num_samples=1).squeeze(-1)\n",
+ " \n",
+ " # --- THE FIX: Prevent finished sequences from generating new tokens ---\n",
+ " # If a sequence is already finished, its next token should be a pad token.\n",
+ " # Otherwise, use the newly generated token.\n",
+ " next_tokens = torch.where(is_finished, self.tokenizer.pad_token_id, next_tokens)\n",
+ " \n",
+ " # Append the new tokens to the generated sequences\n",
+ " generated = torch.cat([generated, next_tokens.unsqueeze(-1)], dim=1)\n",
+ " \n",
+ " # --- THE FIX: Update the `is_finished` status for any sequence that just produced an EOS token ---\n",
+ " is_finished |= (next_tokens == self.tokenizer.sep_token_id)\n",
+ " \n",
+ " # --- THE FIX: Stop if all sequences in the batch are finished ---\n",
+ " if torch.all(is_finished):\n",
+ " break\n",
+ " \n",
+ " return generated\n",
+ "\n",
+ "\n",
+ "# Initialize tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize encoder config\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize and load encoder\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "# Initialize decoder\n",
+ "decoder = SimSonDecoder(\n",
+ " embedding_dim=512, # encoder.max_len\n",
+ " hidden_dim=768, # config.hidden_size\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " max_len=512\n",
+ ")\n",
+ "\n",
+ "# Create combined model\n",
+ "model = SimSonEncoderDecoder(encoder, decoder, tokenizer)\n",
+ "model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder.bin', weights_only=False))\n",
+ "model.cuda()\n",
+ "print(f\"Model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad):,} trainable parameters\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "id": "3f250d5d-0573-4f5b-a665-44ca3ecf135d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "from transformers import BertConfig, AutoTokenizer, BertModel\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "# Load tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize model configuration\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize regression model\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x\n",
+ "\n",
+ "compiling = False\n",
+ "# Load model and parameters\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "if compiling:\n",
+ " encoder = torch.compile(encoder)\n",
+ "encoder_expert = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "regression_model = SimSonClassifier(encoder=encoder, num_labels=6)\n",
+ "regression_expert_model = SimSonClassifier(encoder=encoder_expert, num_labels=6)\n",
+ "\n",
+ "if compiling:\n",
+ " regression_model = torch.compile(regression_model)\n",
+ "\n",
+ "# Load trained parameters\n",
+ "regression_params = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin', weights_only=False)\n",
+ "regression_expert_params = torch.load('/home/jovyan/simson_training_bolgov/regression/high_regression_old_scalers_simson.pth', weights_only=False)\n",
+ "\n",
+ "\n",
+ "regression_model.load_state_dict(regression_params)\n",
+ "regression_expert_model.load_state_dict(regression_expert_params)\n",
+ "\n",
+ "regression_model.eval()\n",
+ "regression_model.cuda()\n",
+ "\n",
+ "regression_expert_model.eval()\n",
+ "regression_expert_model.cuda()\n",
+ "\n",
+ "# Load scalers\n",
+ "scalers = joblib.load('/home/jovyan/simson_training_bolgov/regression/scalers')\n",
+ "scaler_ch4 = scalers[-2] # CH4 scaler\n",
+ "scaler_co2 = scalers[-1] # CO2 scaler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "id": "7a775a04-26de-434f-a138-bafb84b661ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "from tqdm import tqdm\n",
+ "import torch\n",
+ "\n",
+ "def validate_reconstruction(model, tokenizer, test_smiles_list, num_samples=20, max_length=256):\n",
+ " \"\"\"\n",
+ " Evaluate reconstruction quality.\n",
+ " • exact_match ........ literal string equality\n",
+ " • canonical_match .... same molecule after canonicalisation\n",
+ " • generated_valid .... RDKit is able to parse generated SMILES\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ "\n",
+ " results = []\n",
+ " with torch.no_grad():\n",
+ " for i, smiles in enumerate(test_smiles_list[:num_samples]):\n",
+ " # ---------- encode ----------\n",
+ " tokens = tokenizer(\n",
+ " smiles,\n",
+ " max_length=max_length,\n",
+ " truncation=True,\n",
+ " padding=\"max_length\",\n",
+ " return_tensors=\"pt\"\n",
+ " ).to(device)\n",
+ "\n",
+ " embedding = model.encoder(tokens[\"input_ids\"], tokens[\"attention_mask\"])\n",
+ "\n",
+ " # ---------- decode ----------\n",
+ " gen_ids = model.generate(embedding)\n",
+ " gen_smiles = tokenizer.decode(gen_ids[0], skip_special_tokens=True)\n",
+ "\n",
+ " # ---------- validity ----------\n",
+ " mol_orig = Chem.MolFromSmiles(smiles)\n",
+ " mol_gen = Chem.MolFromSmiles(gen_smiles)\n",
+ " orig_valid = mol_orig is not None\n",
+ " gen_valid = mol_gen is not None\n",
+ "\n",
+ " # ---------- canonical comparison ----------\n",
+ " if orig_valid and gen_valid:\n",
+ " can_orig = MolToSmiles(mol_orig, canonical=True)\n",
+ " can_gen = MolToSmiles(mol_gen, canonical=True)\n",
+ " canonical_match = (can_orig == can_gen)\n",
+ " else:\n",
+ " canonical_match = False\n",
+ "\n",
+ " results.append({\n",
+ " \"original\": smiles,\n",
+ " \"reconstructed\": gen_smiles,\n",
+ " \"exact_match\": smiles == gen_smiles,\n",
+ " \"canonical_match\": canonical_match,\n",
+ " \"generated_valid\": gen_valid\n",
+ " })\n",
+ "\n",
+ " print(f\"\\nSample {i+1}\")\n",
+ " print(f\" Original: {smiles}\")\n",
+ " print(f\" Reconstructed: {gen_smiles}\")\n",
+ " print(f\" Valid (gen): {gen_valid}\")\n",
+ " print(f\" Same molecule: {canonical_match}\")\n",
+ " print(\"-\" * 60)\n",
+ "\n",
+ " # ---------- aggregated metrics ----------\n",
+ " exact_acc = sum(r[\"exact_match\"] for r in results) / len(results)\n",
+ " canonical_acc = sum(r[\"canonical_match\"] for r in results) / len(results)\n",
+ " validity_rate = sum(r[\"generated_valid\"] for r in results) / len(results)\n",
+ "\n",
+ " print(f\"\\nExact-string match accuracy ...... {exact_acc:.2%}\")\n",
+ " print(f\"Canonical match accuracy ......... {canonical_acc:.2%}\")\n",
+ " print(f\"Generated validity rate .......... {validity_rate:.2%}\")\n",
+ "\n",
+ " return results\n",
+ "\n",
+ "test_smiles = val_df['smiles' if 'smiles' in val_df.columns else 'Smiles'].tolist()\n",
+ "#validation_results = validate_reconstruction(model, tokenizer, test_smiles, num_samples=20)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "21d72f76-9bc6-480d-8bf1-205c34ed7e2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████| 999/999 [04:08<00:00, 4.02it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import pairwise_distances\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "\n",
+ "\n",
+ "features = ['CO2', 'CH4']\n",
+ "\n",
+ "# --- 3. Max–Min selection ----------------------------------------------------\n",
+ "def select_diverse_maxmin(data, cols, k=1_000):\n",
+ " X = data[cols].to_numpy()\n",
+ " \n",
+ " # scale to [0,1] so CO₂ and CH₄ get equal weight\n",
+ " X = MinMaxScaler().fit_transform(X)\n",
+ " \n",
+ " n = len(X)\n",
+ " if k >= n: # nothing to do\n",
+ " return data\n",
+ " \n",
+ " # start with a random seed point\n",
+ " selected = [np.random.randint(0, n)]\n",
+ " \n",
+ " # pre-allocate distance cache\n",
+ " min_dist = pairwise_distances(\n",
+ " X, X[selected], metric='euclidean'\n",
+ " ).ravel() # distance to first point\n",
+ " \n",
+ " for _ in tqdm(range(1, k)):\n",
+ " # pick the point with the largest distance to the current set\n",
+ " idx = np.argmax(min_dist)\n",
+ " selected.append(idx)\n",
+ " \n",
+ " # update distance cache (keep the shortest distance to any selected pt)\n",
+ " dist_to_new = pairwise_distances(X, X[[idx]], metric='euclidean').ravel()\n",
+ " min_dist = np.minimum(min_dist, dist_to_new)\n",
+ " \n",
+ " return data.iloc[selected].reset_index(drop=True)\n",
+ "\n",
+ "sample_df = select_diverse_maxmin(df, features, k=1_000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "id": "ecae2eaa-dcd0-4a0d-a8d6-347c254285d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "from scipy.stats import pearsonr, spearmanr\n",
+ "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+ "from tqdm import tqdm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "def display_property_fidelity_results(results_df, metrics):\n",
+ " \"\"\"Display comprehensive property fidelity analysis\"\"\"\n",
+ " \n",
+ " valid_results = results_df[results_df['reconstructed_valid']].copy()\n",
+ " total_molecules = len(results_df)\n",
+ " valid_molecules = len(valid_results)\n",
+ " identical_molecules = results_df['chemically_identical'].sum()\n",
+ " \n",
+ " print(f\"\\n{'='*80}\")\n",
+ " print(f\"PROPERTY RECONSTRUCTION FIDELITY ANALYSIS\")\n",
+ " print(f\"{'='*80}\")\n",
+ " \n",
+ " print(f\"Dataset Overview:\")\n",
+ " print(f\" Total molecules tested: {total_molecules}\")\n",
+ " print(f\" Valid reconstructions: {valid_molecules} ({valid_molecules/total_molecules*100:.1f}%)\")\n",
+ " print(f\" Chemically identical: {identical_molecules} ({identical_molecules/total_molecules*100:.1f}%)\")\n",
+ " \n",
+ " if valid_molecules > 0:\n",
+ " print(f\"\\n{'='*60}\")\n",
+ " print(f\"PROPERTY CORRELATION METRICS\")\n",
+ " print(f\"{'='*60}\")\n",
+ " \n",
+ " print(f\"CH₄ Permeability:\")\n",
+ " print(f\" Pearson correlation: {metrics['CH4']['pearson_correlation']:.4f}\")\n",
+ " print(f\" Spearman correlation: {metrics['CH4']['spearman_correlation']:.4f}\")\n",
+ " print(f\" R² score: {metrics['CH4']['r2_score']:.4f}\")\n",
+ " print(f\" MAE: {metrics['CH4']['mae']:.4f}\")\n",
+ " print(f\" RMSE: {metrics['CH4']['rmse']:.4f}\")\n",
+ " print(f\" Mean relative error: {metrics['CH4']['mean_relative_error_pct']:.2f}%\")\n",
+ " print(f\" Median relative error: {metrics['CH4']['median_relative_error_pct']:.2f}%\")\n",
+ " \n",
+ " print(f\"\\nCO₂ Permeability:\")\n",
+ " print(f\" Pearson correlation: {metrics['CO2']['pearson_correlation']:.4f}\")\n",
+ " print(f\" Spearman correlation: {metrics['CO2']['spearman_correlation']:.4f}\")\n",
+ " print(f\" R² score: {metrics['CO2']['r2_score']:.4f}\")\n",
+ " print(f\" MAE: {metrics['CO2']['mae']:.4f}\")\n",
+ " print(f\" RMSE: {metrics['CO2']['rmse']:.4f}\")\n",
+ " print(f\" Mean relative error: {metrics['CO2']['mean_relative_error_pct']:.2f}%\")\n",
+ " print(f\" Median relative error: {metrics['CO2']['median_relative_error_pct']:.2f}%\")\n",
+ " \n",
+ " # Property preservation quality assessment\n",
+ " print(f\"\\n{'='*60}\")\n",
+ " print(f\"PROPERTY PRESERVATION ASSESSMENT\")\n",
+ " print(f\"{'='*60}\")\n",
+ " \n",
+ " # Define quality thresholds\n",
+ " excellent_threshold = 5.0 # <5% relative error\n",
+ " good_threshold = 15.0 # <15% relative error\n",
+ " acceptable_threshold = 30.0 # <30% relative error\n",
+ " \n",
+ " ch4_excellent = (valid_results['CH4_relative_error'] < excellent_threshold).sum()\n",
+ " ch4_good = (valid_results['CH4_relative_error'] < good_threshold).sum()\n",
+ " ch4_acceptable = (valid_results['CH4_relative_error'] < acceptable_threshold).sum()\n",
+ " \n",
+ " co2_excellent = (valid_results['CO2_relative_error'] < excellent_threshold).sum()\n",
+ " co2_good = (valid_results['CO2_relative_error'] < good_threshold).sum()\n",
+ " co2_acceptable = (valid_results['CO2_relative_error'] < acceptable_threshold).sum()\n",
+ " \n",
+ " print(f\"CH₄ Property Preservation Quality:\")\n",
+ " print(f\" Excellent (<{excellent_threshold}% error): {ch4_excellent}/{valid_molecules} ({ch4_excellent/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Good (<{good_threshold}% error): {ch4_good}/{valid_molecules} ({ch4_good/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Acceptable (<{acceptable_threshold}% error): {ch4_acceptable}/{valid_molecules} ({ch4_acceptable/valid_molecules*100:.1f}%)\")\n",
+ " \n",
+ " print(f\"\\nCO₂ Property Preservation Quality:\")\n",
+ " print(f\" Excellent (<{excellent_threshold}% error): {co2_excellent}/{valid_molecules} ({co2_excellent/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Good (<{good_threshold}% error): {co2_good}/{valid_molecules} ({co2_good/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Acceptable (<{acceptable_threshold}% error): {co2_acceptable}/{valid_molecules} ({co2_acceptable/valid_molecules*100:.1f}%)\")\n",
+ "\n",
+ "\n",
+ "def evaluate_property_reconstruction_fidelity(model, regression_model, tokenizer, \n",
+ " scaler_ch4, scaler_co2, test_smiles_list,\n",
+ " batch_size=16, max_length=256):\n",
+ " \"\"\"\n",
+ " Evaluate how well reconstructed SMILES preserve chemical properties\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " regression_model: Trained regression model for property prediction\n",
+ " tokenizer: SMILES tokenizer\n",
+ " scaler_ch4, scaler_co2: Property scalers\n",
+ " test_smiles_list: List of original SMILES to test\n",
+ " batch_size: Batch size for processing\n",
+ " max_length: Maximum SMILES length\n",
+ " \n",
+ " Returns:\n",
+ " results_df: DataFrame with detailed property comparison results\n",
+ " metrics: Dictionary of aggregate evaluation metrics\n",
+ " \"\"\"\n",
+ " \n",
+ " model.eval()\n",
+ " regression_model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " results = []\n",
+ " \n",
+ " print(f\"Evaluating property reconstruction fidelity for {len(test_smiles_list)} molecules...\")\n",
+ " \n",
+ " # Process in batches for memory efficiency\n",
+ " for i in tqdm(range(0, len(test_smiles_list), batch_size), desc=\"Processing batches\"):\n",
+ " batch_end = min(i + batch_size, len(test_smiles_list))\n",
+ " batch_smiles = test_smiles_list[i:batch_end]\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " # Step 1: Generate embeddings from original SMILES\n",
+ " original_tokens = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embeddings\n",
+ " embeddings = model.encoder(original_tokens['input_ids'].cuda(), original_tokens['attention_mask'].cuda())\n",
+ " \n",
+ " # Step 2: Reconstruct SMILES from embeddings\n",
+ " reconstructed_ids = model.generate(embeddings, temperature=1.5)\n",
+ " \n",
+ " # Step 3: Predict properties for both original and reconstructed\n",
+ " # Original properties\n",
+ " orig_predictions = regression_model(original_tokens['input_ids'], original_tokens['attention_mask'])\n",
+ " orig_ch4_scaled = orig_predictions[:, -2].cpu().numpy().reshape(-1, 1)\n",
+ " orig_co2_scaled = orig_predictions[:, -1].cpu().numpy().reshape(-1, 1)\n",
+ " orig_ch4 = scaler_ch4.inverse_transform(orig_ch4_scaled).flatten()\n",
+ " orig_co2 = scaler_co2.inverse_transform(orig_co2_scaled).flatten()\n",
+ " \n",
+ " # Reconstructed properties\n",
+ " reconstructed_smiles = [tokenizer.decode(seq, skip_special_tokens=True) for seq in reconstructed_ids]\n",
+ " # Validate reconstructed SMILES and predict properties\n",
+ " for j, (orig_smiles, recon_smiles) in enumerate(zip(batch_smiles, reconstructed_smiles)):\n",
+ " # Chemical validity checks\n",
+ " orig_mol = Chem.MolFromSmiles(orig_smiles)\n",
+ " recon_mol = Chem.MolFromSmiles(recon_smiles)\n",
+ " \n",
+ " orig_valid = orig_mol is not None\n",
+ " recon_valid = recon_mol is not None\n",
+ " \n",
+ " # Canonical SMILES comparison\n",
+ " if orig_valid and recon_valid:\n",
+ " orig_canonical = MolToSmiles(orig_mol, canonical=True)\n",
+ " recon_canonical = MolToSmiles(recon_mol, canonical=True)\n",
+ " is_identical = orig_canonical == recon_canonical\n",
+ " else:\n",
+ " orig_canonical = \"INVALID\" if not orig_valid else MolToSmiles(orig_mol, canonical=True)\n",
+ " recon_canonical = \"INVALID\" if not recon_valid else MolToSmiles(recon_mol, canonical=True)\n",
+ " is_identical = False\n",
+ " \n",
+ " # Property prediction for reconstructed SMILES\n",
+ " if recon_valid:\n",
+ " recon_tokens = tokenizer(\n",
+ " recon_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " recon_predictions = regression_model(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " recon_ch4_scaled = recon_predictions[0, -2].cpu().numpy().reshape(-1, 1)\n",
+ " recon_co2_scaled = recon_predictions[0, -1].cpu().numpy().reshape(-1, 1)\n",
+ " recon_ch4 = scaler_ch4.inverse_transform(recon_ch4_scaled)[0, 0]\n",
+ " recon_co2 = scaler_co2.inverse_transform(recon_co2_scaled)[0, 0]\n",
+ " else:\n",
+ " recon_ch4 = np.nan\n",
+ " recon_co2 = np.nan\n",
+ " \n",
+ " # Calculate property differences\n",
+ " ch4_absolute_error = abs(orig_ch4[j] - recon_ch4) if recon_valid else np.nan\n",
+ " co2_absolute_error = abs(orig_co2[j] - recon_co2) if recon_valid else np.nan\n",
+ " \n",
+ " ch4_relative_error = (ch4_absolute_error / orig_ch4[j] * 100) if (recon_valid and orig_ch4[j] != 0) else np.nan\n",
+ " co2_relative_error = (co2_absolute_error / orig_co2[j] * 100) if (recon_valid and orig_co2[j] != 0) else np.nan\n",
+ " \n",
+ " results.append({\n",
+ " 'molecule_id': i + j + 1,\n",
+ " 'original_smiles': orig_smiles,\n",
+ " 'reconstructed_smiles': recon_smiles,\n",
+ " 'original_canonical': orig_canonical,\n",
+ " 'reconstructed_canonical': recon_canonical,\n",
+ " 'chemically_identical': is_identical,\n",
+ " 'original_valid': orig_valid,\n",
+ " 'reconstructed_valid': recon_valid,\n",
+ " 'original_CH4': orig_ch4[j],\n",
+ " 'original_CO2': orig_co2[j],\n",
+ " 'reconstructed_CH4': recon_ch4,\n",
+ " 'reconstructed_CO2': recon_co2,\n",
+ " 'CH4_absolute_error': ch4_absolute_error,\n",
+ " 'CO2_absolute_error': co2_absolute_error,\n",
+ " 'CH4_relative_error': ch4_relative_error,\n",
+ " 'CO2_relative_error': co2_relative_error\n",
+ " })\n",
+ " \n",
+ " # Convert to DataFrame\n",
+ " results_df = pd.DataFrame(results)\n",
+ " \n",
+ " # Calculate aggregate metrics\n",
+ " valid_comparisons = results_df[results_df['reconstructed_valid']].copy()\n",
+ " \n",
+ " if len(valid_comparisons) > 0:\n",
+ " metrics = calculate_property_fidelity_metrics(valid_comparisons)\n",
+ " display_property_fidelity_results(results_df, metrics)\n",
+ " else:\n",
+ " print(\"No valid reconstructions for property comparison!\")\n",
+ " metrics = {}\n",
+ " \n",
+ " return results_df, metrics\n",
+ "\n",
+ "def visualize_property_fidelity(results_df, save_plots=True):\n",
+ " \"\"\"Create comprehensive visualizations of property reconstruction fidelity\"\"\"\n",
+ " \n",
+ " valid_results = results_df[results_df['reconstructed_valid']].copy()\n",
+ " \n",
+ " if len(valid_results) == 0:\n",
+ " print(\"No valid results to visualize!\")\n",
+ " return\n",
+ " \n",
+ " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+ " \n",
+ " # 1. CH4 Property Correlation\n",
+ " axes[0, 0].scatter(valid_results['original_CH4'], valid_results['reconstructed_CH4'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " \n",
+ " # Perfect correlation line\n",
+ " min_ch4 = min(valid_results['original_CH4'].min(), valid_results['reconstructed_CH4'].min())\n",
+ " max_ch4 = max(valid_results['original_CH4'].max(), valid_results['reconstructed_CH4'].max())\n",
+ " axes[0, 0].plot([min_ch4, max_ch4], [min_ch4, max_ch4], 'r--', linewidth=2, label='Perfect Correlation')\n",
+ " \n",
+ " # Calculate and display R²\n",
+ " ch4_r2 = r2_score(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " axes[0, 0].set_xlabel('Original CH₄ Permeability')\n",
+ " axes[0, 0].set_ylabel('Reconstructed CH₄ Permeability')\n",
+ " axes[0, 0].set_title(f'CH₄ Property Reconstruction (R² = {ch4_r2:.3f})')\n",
+ " axes[0, 0].legend()\n",
+ " axes[0, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 2. CO2 Property Correlation\n",
+ " axes[0, 1].scatter(valid_results['original_CO2'], valid_results['reconstructed_CO2'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " \n",
+ " min_co2 = min(valid_results['original_CO2'].min(), valid_results['reconstructed_CO2'].min())\n",
+ " max_co2 = max(valid_results['original_CO2'].max(), valid_results['reconstructed_CO2'].max())\n",
+ " axes[0, 1].plot([min_co2, max_co2], [min_co2, max_co2], 'r--', linewidth=2, label='Perfect Correlation')\n",
+ " \n",
+ " co2_r2 = r2_score(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " axes[0, 1].set_xlabel('Original CO₂ Permeability')\n",
+ " axes[0, 1].set_ylabel('Reconstructed CO₂ Permeability')\n",
+ " axes[0, 1].set_title(f'CO₂ Property Reconstruction (R² = {co2_r2:.3f})')\n",
+ " axes[0, 1].legend()\n",
+ " axes[0, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 3. Relative Error Distribution - CH4\n",
+ " axes[0, 2].hist(valid_results['CH4_relative_error'].dropna(), bins=30, alpha=0.7, \n",
+ " edgecolor='black', color='skyblue')\n",
+ " axes[0, 2].axvline(valid_results['CH4_relative_error'].median(), color='red', \n",
+ " linestyle='--', linewidth=2, label=f'Median: {valid_results[\"CH4_relative_error\"].median():.1f}%')\n",
+ " axes[0, 2].set_xlabel('CH₄ Relative Error (%)')\n",
+ " axes[0, 2].set_ylabel('Frequency')\n",
+ " axes[0, 2].set_title('CH₄ Relative Error Distribution')\n",
+ " axes[0, 2].legend()\n",
+ " axes[0, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 4. Relative Error Distribution - CO2\n",
+ " axes[1, 0].hist(valid_results['CO2_relative_error'].dropna(), bins=30, alpha=0.7, \n",
+ " edgecolor='black', color='lightcoral')\n",
+ " axes[1, 0].axvline(valid_results['CO2_relative_error'].median(), color='red', \n",
+ " linestyle='--', linewidth=2, label=f'Median: {valid_results[\"CO2_relative_error\"].median():.1f}%')\n",
+ " axes[1, 0].set_xlabel('CO₂ Relative Error (%)')\n",
+ " axes[1, 0].set_ylabel('Frequency')\n",
+ " axes[1, 0].set_title('CO₂ Relative Error Distribution')\n",
+ " axes[1, 0].legend()\n",
+ " axes[1, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 5. Error Comparison\n",
+ " error_comparison = pd.DataFrame({\n",
+ " 'CH₄_Error': valid_results['CH4_relative_error'].dropna(),\n",
+ " 'CO₂_Error': valid_results['CO2_relative_error'].dropna()\n",
+ " })\n",
+ " \n",
+ " axes[1, 1].scatter(error_comparison['CH₄_Error'], error_comparison['CO₂_Error'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " axes[1, 1].set_xlabel('CH₄ Relative Error (%)')\n",
+ " axes[1, 1].set_ylabel('CO₂ Relative Error (%)')\n",
+ " axes[1, 1].set_title('Property Error Correlation')\n",
+ " axes[1, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 6. Chemical Identity vs Property Preservation\n",
+ " identical_mask = valid_results['chemically_identical']\n",
+ " \n",
+ " ch4_errors_identical = valid_results[identical_mask]['CH4_relative_error'].dropna()\n",
+ " ch4_errors_different = valid_results[~identical_mask]['CH4_relative_error'].dropna()\n",
+ " \n",
+ " axes[1, 2].boxplot([ch4_errors_identical, ch4_errors_different], \n",
+ " labels=['Chemically Identical', 'Chemically Different'])\n",
+ " axes[1, 2].set_ylabel('CH₄ Relative Error (%)')\n",
+ " axes[1, 2].set_title('Property Error by Chemical Identity')\n",
+ " axes[1, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " plt.tight_layout()\n",
+ " \n",
+ " if save_plots:\n",
+ " plt.savefig('property_reconstruction_fidelity.png', dpi=300, bbox_inches='tight')\n",
+ " print(\"Visualization saved as 'property_reconstruction_fidelity.png'\")\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def calculate_property_fidelity_metrics(valid_results):\n",
+ " \"\"\"Calculate comprehensive property fidelity metrics\"\"\"\n",
+ " \n",
+ " metrics = {}\n",
+ " \n",
+ " # Correlation metrics\n",
+ " ch4_corr_pearson, ch4_p_pearson = pearsonr(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_corr_pearson, co2_p_pearson = pearsonr(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_corr_spearman, ch4_p_spearman = spearmanr(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_corr_spearman, co2_p_spearman = spearmanr(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " # Regression metrics\n",
+ " ch4_r2 = r2_score(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_r2 = r2_score(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_mae = mean_absolute_error(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_mae = mean_absolute_error(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_rmse = np.sqrt(mean_squared_error(valid_results['original_CH4'], valid_results['reconstructed_CH4']))\n",
+ " co2_rmse = np.sqrt(mean_squared_error(valid_results['original_CO2'], valid_results['reconstructed_CO2']))\n",
+ " \n",
+ " # Relative error statistics\n",
+ " ch4_mean_rel_error = valid_results['CH4_relative_error'].mean()\n",
+ " co2_mean_rel_error = valid_results['CO2_relative_error'].mean()\n",
+ " \n",
+ " ch4_median_rel_error = valid_results['CH4_relative_error'].median()\n",
+ " co2_median_rel_error = valid_results['CO2_relative_error'].median()\n",
+ " \n",
+ " metrics = {\n",
+ " 'CH4': {\n",
+ " 'pearson_correlation': ch4_corr_pearson,\n",
+ " 'spearman_correlation': ch4_corr_spearman,\n",
+ " 'r2_score': ch4_r2,\n",
+ " 'mae': ch4_mae,\n",
+ " 'rmse': ch4_rmse,\n",
+ " 'mean_relative_error_pct': ch4_mean_rel_error,\n",
+ " 'median_relative_error_pct': ch4_median_rel_error\n",
+ " },\n",
+ " 'CO2': {\n",
+ " 'pearson_correlation': co2_corr_pearson,\n",
+ " 'spearman_correlation': co2_corr_spearman,\n",
+ " 'r2_score': co2_r2,\n",
+ " 'mae': co2_mae,\n",
+ " 'rmse': co2_rmse,\n",
+ " 'mean_relative_error_pct': co2_mean_rel_error,\n",
+ " 'median_relative_error_pct': co2_median_rel_error\n",
+ " }\n",
+ " }\n",
+ " \n",
+ " return metrics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "id": "3e6bcd77-7314-4829-8d26-26eab56f418c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rdkit import RDLogger\n",
+ "\n",
+ "# Disable all RDKit logs\n",
+ "RDLogger.DisableLog('rdApp.*') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "04ecf928-10d8-4366-a9d6-1a646301d387",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "5117051 5117051 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "1654268 1654268 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "6692275 6692275 Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "830270 830270 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "6692276 6692276 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "... ... ... \n",
+ "10950 10950 Ic1cc(Oc2cc(Oc3cc(cc(c3)n3c(=O)c4c(c3=O)cc3c(c... \n",
+ "5309014 5309014 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2598418 2598418 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "746918 746918 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2255 2255 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "5117051 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "1654268 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692275 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "830270 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692276 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "... ... ... ... ... ... ... \n",
+ "10950 538.15 1.23546 0.00105 0.00404 0.00195 0.00785 \n",
+ "5309014 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "2598418 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "746918 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "2255 586.80 1.80544 0.00068 0.00330 0.00184 0.00419 \n",
+ "\n",
+ " synthesizable \n",
+ "5117051 False \n",
+ "1654268 False \n",
+ "6692275 False \n",
+ "830270 False \n",
+ "6692276 False \n",
+ "... ... \n",
+ "10950 True \n",
+ "5309014 False \n",
+ "2598418 False \n",
+ "746918 False \n",
+ "2255 False \n",
+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.sort_values(by=['CO2'], ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "576a2486-6184-4d63-9a07-f545c2551df0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "enc_gen = model.encoder.state_dict()\n",
+ "enc_reg = regression_model.encoder.state_dict()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "19e84d1a-56a8-493e-ba56-5eaa5aebb556",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Evaluating property reconstruction fidelity for 100 molecules...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processing batches: 100%|█████████████████████████| 1/1 [00:16<00:00, 16.36s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "================================================================================\n",
+ "PROPERTY RECONSTRUCTION FIDELITY ANALYSIS\n",
+ "================================================================================\n",
+ "Dataset Overview:\n",
+ " Total molecules tested: 100\n",
+ " Valid reconstructions: 93 (93.0%)\n",
+ " Chemically identical: 0 (0.0%)\n",
+ "\n",
+ "============================================================\n",
+ "PROPERTY CORRELATION METRICS\n",
+ "============================================================\n",
+ "CH₄ Permeability:\n",
+ " Pearson correlation: 0.0400\n",
+ " Spearman correlation: -0.0705\n",
+ " R² score: -3.0056\n",
+ " MAE: 82.2622\n",
+ " RMSE: 103.3733\n",
+ " Mean relative error: 19.19%\n",
+ " Median relative error: 24.48%\n",
+ "\n",
+ "CO₂ Permeability:\n",
+ " Pearson correlation: -0.1054\n",
+ " Spearman correlation: -0.3209\n",
+ " R² score: -3.9737\n",
+ " MAE: 26978.8016\n",
+ " RMSE: 33540.0662\n",
+ " Mean relative error: 20.97%\n",
+ " Median relative error: 26.74%\n",
+ "\n",
+ "============================================================\n",
+ "PROPERTY PRESERVATION ASSESSMENT\n",
+ "============================================================\n",
+ "CH₄ Property Preservation Quality:\n",
+ " Excellent (<5.0% error): 32/93 (34.4%)\n",
+ " Good (<15.0% error): 33/93 (35.5%)\n",
+ " Acceptable (<30.0% error): 76/93 (81.7%)\n",
+ "\n",
+ "CO₂ Property Preservation Quality:\n",
+ " Excellent (<5.0% error): 28/93 (30.1%)\n",
+ " Good (<15.0% error): 33/93 (35.5%)\n",
+ " Acceptable (<30.0% error): 57/93 (61.3%)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_smiles = sample_df['Smiles'].tolist()\n",
+ "test_smiles = df.sort_values(by=['CO2'], ascending=False)['Smiles'].tolist()[:100]\n",
+ "\n",
+ "\n",
+ "results_df, metrics = evaluate_property_reconstruction_fidelity(\n",
+ " model=model, \n",
+ " regression_model=regression_expert_model,\n",
+ " tokenizer=tokenizer,\n",
+ " scaler_ch4=scaler_ch4,\n",
+ " scaler_co2=scaler_co2,\n",
+ " test_smiles_list=test_smiles[:1_000],\n",
+ " batch_size=128\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 165,
+ "id": "62c35e28-78ec-4b04-8211-cdc24ee21969",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from tqdm import tqdm\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "\n",
+ "def generate_base_embeddings(model, tokenizer, val_df, max_length=256, batch_size=128):\n",
+ " \"\"\"\n",
+ " Generate embeddings for molecules in validation dataset using batch processing\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " val_df: Validation dataframe containing SMILES\n",
+ " max_length: Maximum SMILES sequence length \n",
+ " batch_size: Number of molecules to process in each batch\n",
+ " \n",
+ " Returns:\n",
+ " base_embeddings: Tensor of shape [num_molecules, embedding_dim]\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " smiles_list = val_df['Smiles'].tolist()\n",
+ " embeddings_list = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(smiles_list), batch_size), desc=\"Generating base embeddings in batches\"):\n",
+ " # Get current batch of SMILES\n",
+ " batch_end = min(i + batch_size, len(smiles_list))\n",
+ " batch_smiles = smiles_list[i:batch_end]\n",
+ " \n",
+ " # Tokenize entire batch at once\n",
+ " encoding = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " embedding_batch = model.encoder(encoding['input_ids'], encoding['attention_mask'])\n",
+ " \n",
+ " # Move to CPU and store\n",
+ " embeddings_list.append(embedding_batch.cpu().numpy())\n",
+ " \n",
+ " # Stack all batch embeddings into single array\n",
+ " base_embeddings = np.vstack(embeddings_list)\n",
+ " \n",
+ " return torch.tensor(base_embeddings, dtype=torch.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdd463d7-da61-4064-915b-54ab4c3671ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate base embeddings\n",
+ "print(\"Generating 'base' embeddings\")\n",
+ "base_embeddings_s = generate_base_embeddings(regression_model, tokenizer, df[:100])\n",
+ "print(f\"Generated embeddings shape: {base_embeddings.shape}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "9a27dfc8-3510-4339-8799-460f9544a9a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = regression_model.encoder\n",
+ "b = model.encoder\n",
+ "a == b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "3915b796-c57e-44d1-9ff0-7002a7c86499",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/base_embeddings_new_reg.pickle']"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(base_embeddings, '/home/jovyan/simson_training_bolgov/regression/base_embeddings_new_reg.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "id": "a9a8d315-965d-493d-8d9d-7fb096e5c96e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "base_embeddings = joblib.load('/home/jovyan/simson_training_bolgov/regression/sample_embeddings.pickle')\n",
+ "#expert_embeddings = joblib.load('/home/jovyan/simson_training_bolgov/regression/expert_embeddings.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "9778d0de-fc1e-4489-8134-fe50ac868039",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actual_encoder = model.encoder.state_dict()\n",
+ "torch.save(actual_encoder, '/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "id": "2ff3dc41-1501-4ca7-8292-01a58924bb44",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training_smiles_set = set(df['Smiles'].tolist())\n",
+ "\n",
+ "def is_valid_polymer(smiles: str) -> bool:\n",
+ " \"\"\"\n",
+ " Checks if a SMILES string represents a valid polymer according to specific rules.\n",
+ "\n",
+ " A valid polymer must:\n",
+ " 1. Be a chemically valid molecule parsable by RDKit.\n",
+ " 2. Contain exactly two 'I' atoms, representing polymer endpoints.\n",
+ " 3. Have identical bond types connecting to both endpoints.\n",
+ " \"\"\"\n",
+ " if not isinstance(smiles, str):\n",
+ " return False\n",
+ "\n",
+ " # Rule 1: Basic chemical validity\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return False\n",
+ "\n",
+ " # Rule 2: Must contain exactly two endpoints\n",
+ " if smiles.count('I') != 2:\n",
+ " return False\n",
+ "\n",
+ " # Rule 3: Endpoint bonds must match\n",
+ " try:\n",
+ " # Replace 'I' with a standard dummy atom '[*]' for analysis\n",
+ " mol_with_dummy = Chem.MolFromSmiles(smiles.replace('I', '[*]'))\n",
+ " if mol_with_dummy is None:\n",
+ " return False\n",
+ "\n",
+ " # Find the atoms connected to the dummy endpoints\n",
+ " matches = mol_with_dummy.GetSubstructMatches(Chem.MolFromSmarts('[#0]~*'))\n",
+ "\n",
+ " if len(matches) != 2:\n",
+ " return False\n",
+ "\n",
+ " # Get the bonds connecting to the endpoints\n",
+ " bond1 = mol_with_dummy.GetBondBetweenAtoms(matches[0][0], matches[0][1])\n",
+ " bond2 = mol_with_dummy.GetBondBetweenAtoms(matches[1][0], matches[1][1])\n",
+ "\n",
+ " if bond1 is None or bond2 is None:\n",
+ " return False\n",
+ "\n",
+ " # Check if the bond types are identical\n",
+ " if bond1.GetBondType() != bond2.GetBondType():\n",
+ " return False\n",
+ " except Exception:\n",
+ " # Catch any RDKit parsing or processing errors\n",
+ " return False\n",
+ "\n",
+ " return True\n",
+ "\n",
+ "def is_novel_and_valid_polymer(smiles: str, training_set: set) -> bool:\n",
+ " \"\"\"\n",
+ " Performs a final check to ensure a molecule is both novel and a valid polymer.\n",
+ " \"\"\"\n",
+ " # Check 1: Is it new?\n",
+ " if smiles in training_set:\n",
+ " print('NOT NEW')\n",
+ " return False\n",
+ " \n",
+ " # Check 2: Does it meet polymer criteria?\n",
+ " return is_valid_polymer(smiles)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "fe3a75c9-eb84-4854-a477-d401761baba1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " Smiles | \n",
+ " Tg | \n",
+ " He | \n",
+ " N2 | \n",
+ " O2 | \n",
+ " CH4 | \n",
+ " CO2 | \n",
+ " synthesizable | \n",
+ "
\n",
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\n",
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+ " 1 | \n",
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+ " 26.31118 | \n",
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+ " 82.37635 | \n",
+ " False | \n",
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\n",
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+ " | 2 | \n",
+ " 2 | \n",
+ " O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... | \n",
+ " 640.91 | \n",
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+ " False | \n",
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\n",
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+ " | 3 | \n",
+ " 3 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... | \n",
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+ " 0.00191 | \n",
+ " 0.01134 | \n",
+ " 0.00362 | \n",
+ " 0.01418 | \n",
+ " False | \n",
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\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... | \n",
+ " 548.10 | \n",
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\n",
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\n",
+ " \n",
+ " | 6726945 | \n",
+ " 6726945 | \n",
+ " Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... | \n",
+ " 516.27 | \n",
+ " 13.32967 | \n",
+ " 0.11907 | \n",
+ " 1.10144 | \n",
+ " 0.16907 | \n",
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+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726946 | \n",
+ " 6726946 | \n",
+ " Ic1ccc(nc1)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(... | \n",
+ " 510.72 | \n",
+ " 8.96454 | \n",
+ " 7.92257 | \n",
+ " 72.92929 | \n",
+ " 2.17324 | \n",
+ " 247.14446 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726947 | \n",
+ " 6726947 | \n",
+ " Ic1ccc(cn1)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)... | \n",
+ " 610.24 | \n",
+ " 4.90758 | \n",
+ " 20.24364 | \n",
+ " 121.24494 | \n",
+ " 1.01011 | \n",
+ " 650.18364 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726948 | \n",
+ " 6726948 | \n",
+ " Ic1ccc(c(c1)C)Oc1ccc2c(c1)Cc1c2ccc(c1)Oc1ccc(c... | \n",
+ " 510.90 | \n",
+ " 12.40907 | \n",
+ " 0.19307 | \n",
+ " 1.41335 | \n",
+ " 0.11728 | \n",
+ " 4.00573 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " | 6726949 | \n",
+ " 6726949 | \n",
+ " Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... | \n",
+ " 462.31 | \n",
+ " 14.96342 | \n",
+ " 0.13916 | \n",
+ " 1.05252 | \n",
+ " 0.09298 | \n",
+ " 2.54411 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... \n",
+ "... ... ... \n",
+ "6726945 6726945 Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... \n",
+ "6726946 6726946 Ic1ccc(nc1)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(... \n",
+ "6726947 6726947 Ic1ccc(cn1)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)... \n",
+ "6726948 6726948 Ic1ccc(c(c1)C)Oc1ccc2c(c1)Cc1c2ccc(c1)Oc1ccc(c... \n",
+ "6726949 6726949 Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "0 494.84 2.69524 4.75740 42.31847 1.64086 148.43644 \n",
+ "1 508.26 5.33815 2.97239 26.31118 0.86467 82.37635 \n",
+ "2 640.91 20.47515 0.06353 0.90498 0.06905 2.35993 \n",
+ "3 568.04 4.19692 0.00191 0.01134 0.00362 0.01418 \n",
+ "4 548.10 142.68327 0.87380 8.25409 2.52067 30.04739 \n",
+ "... ... ... ... ... ... ... \n",
+ "6726945 516.27 13.32967 0.11907 1.10144 0.16907 2.63642 \n",
+ "6726946 510.72 8.96454 7.92257 72.92929 2.17324 247.14446 \n",
+ "6726947 610.24 4.90758 20.24364 121.24494 1.01011 650.18364 \n",
+ "6726948 510.90 12.40907 0.19307 1.41335 0.11728 4.00573 \n",
+ "6726949 462.31 14.96342 0.13916 1.05252 0.09298 2.54411 \n",
+ "\n",
+ " synthesizable \n",
+ "0 False \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False \n",
+ "... ... \n",
+ "6726945 False \n",
+ "6726946 False \n",
+ "6726947 False \n",
+ "6726948 True \n",
+ "6726949 False \n",
+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "aa424b6f-5cf4-42aa-bdce-83eff2b690c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pred_from_embeds(embeds, regression_model):\n",
+ " \n",
+ " x = regression_model.relu(embeds)\n",
+ " return regression_model.clf(x)\n",
+ "\n",
+ "def gradient_based_extrapolation(\n",
+ " generative_model, regression_model, regression_expert_model, embeddings, scaler, target_idx=-2,\n",
+ " learning_rate=0.0001, steps=50, batch_size=32, best_idx=None, lambda_reg=0.3, scale_factor=1000, expert_threshold=120_000\n",
+ "):\n",
+ " \"\"\"\n",
+ " Use property gradients to guide extrapolation, with checks for novelty and polymer validity.\n",
+ " If an invalid or non-novel molecule is generated, optimization stops.\n",
+ " \"\"\"\n",
+ " device = next(regression_model.parameters()).device\n",
+ "\n",
+ " # --- Freeze models to prevent parameter updates ---\n",
+ " for param in regression_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_model.eval()\n",
+ "\n",
+ " for param in regression_expert_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_expert_model.eval()\n",
+ " \n",
+ " for param in generative_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " generative_model.eval()\n",
+ "\n",
+ " # --- 1. Find the best starting embedding (batched) ---\n",
+ " if best_idx is None:\n",
+ " properties = []\n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(embeddings), batch_size), desc=\"Computing initial properties\"):\n",
+ " batch = embeddings[i:i+batch_size]\n",
+ " preds = regression_model.clf(batch)\n",
+ " properties.append(preds[:, target_idx].cpu())\n",
+ " properties = torch.cat(properties)\n",
+ " best_idx = torch.argmax(properties)\n",
+ "\n",
+ " start_embedding = embeddings[best_idx].cuda().reshape(1, -1).requires_grad_(True)\n",
+ " initial_embedding = start_embedding.clone().cuda()\n",
+ " # This will store the last embedding that successfully passed all checks\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ "\n",
+ " # --- ADDITION: Store initial prediction for MAE calculation ---\n",
+ " with torch.no_grad():\n",
+ " initial_pred_scaled = pred_from_embeds(initial_embedding, regression_model)[:, target_idx]\n",
+ " initial_pred_unscaled = initial_pred_scaled * torch.tensor(scaler.scale_, device=device) + torch.tensor(scaler.mean_, device=device)\n",
+ " initial_prediction_value = initial_pred_unscaled.item()\n",
+ "\n",
+ " optimizer = torch.optim.Adam([start_embedding], lr=learning_rate)\n",
+ "\n",
+ " # --- Extract scaler attributes for PyTorch-based inverse transform ---\n",
+ " scale = torch.tensor(scaler.scale_, device=device, dtype=torch.float32)\n",
+ " mean = torch.tensor(scaler.mean_, device=device, dtype=torch.float32)\n",
+ " # --- 2. Run gradient ascent with step-by-step checks ---\n",
+ " predicting_model = regression_expert_model\n",
+ " pred_unscaled = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " pred = pred_from_embeds(start_embedding.cuda(), regression_model)\n",
+ " sample_scaled = pred[:, target_idx]\n",
+ " sample_unscaled = sample_scaled * scale + mean\n",
+ " print(f'FIRST PRED: {sample_unscaled.detach().item()}') \n",
+ "\n",
+ " for step in range(steps):\n",
+ " if pred_unscaled >= expert_threshold:\n",
+ " predicting_model = regression_expert_model\n",
+ " else:\n",
+ " predicting_model = regression_expert_model\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " pred_scaled = pred_from_embeds(start_embedding.cuda(), regression_model)[:, target_idx]\n",
+ " pred_unscaled = pred_scaled * scale + mean\n",
+ " property_loss = -pred_unscaled.mean()\n",
+ " regularization_loss = torch.norm(start_embedding - initial_embedding, p=2)**2\n",
+ " loss = property_loss + scale_factor * (lambda_reg * regularization_loss)\n",
+ " \n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " start_embedding.clamp_(-20, 20)\n",
+ "\n",
+ " # --- VALIDATION BLOCK: Check molecule after each step ---\n",
+ " with torch.no_grad():\n",
+ " reconstructed_ids = generative_model.generate(start_embedding.cuda(), temperature=1.7)\n",
+ " reconstructed_smiles = generative_model.tokenizer.decode(reconstructed_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " # Use the combined helper function for all checks\n",
+ " is_valid_and_novel = is_novel_and_valid_polymer(reconstructed_smiles, training_smiles_set)\n",
+ " \n",
+ " print(f\"Step {step+1}/{steps} | Value: {pred_unscaled.item():.4f} | SMILES: {reconstructed_smiles} | Novel & Valid Polymer: {is_valid_and_novel}\")\n",
+ "\n",
+ " if is_valid_and_novel:\n",
+ " # Tokenize the reconstructed SMILES\n",
+ " recon_tokens = generative_model.tokenizer(\n",
+ " reconstructed_smiles,\n",
+ " max_length=512,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embedding from the reconstructed SMILES\n",
+ " recon_embedding = generative_model.encoder(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " \n",
+ " CO2_scaled = pred_from_embeds(recon_embedding, predicting_model)[:, target_idx]\n",
+ " CO2_unscaled = CO2_scaled * scale + mean\n",
+ " \n",
+ " print(f\" -> Reconstructed molecule CO2 permeability: {CO2_unscaled.item():.4f}\")\n",
+ " \n",
+ " # --- ADDITION 2: Calculate MAE between initial and reconstructed prediction ---\n",
+ " mae_value = float(abs(CO2_unscaled + property_loss))\n",
+ " print(f\" -> MAE between initial embedding prediction and current prediction: {mae_value:.4f}\")\n",
+ " \n",
+ "\n",
+ " # If it passes all checks, save this as the current best state\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ " else:\n",
+ " pass\n",
+ "\n",
+ " print(f'FINAL VALUE: {pred_unscaled.detach().item():.4f}')\n",
+ " return last_valid_embedding\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53916086-d696-47e4-8984-9e4824bbd4d2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "id": "34e0c67c-ad86-4b7c-9b3c-8132d9aec45b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(161379.46, 5117051)"
+ ]
+ },
+ "execution_count": 169,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_df = df.sort_values(by=['CO2'], ascending=False)\n",
+ "best_idx = sorted_df.index.tolist()[0]\n",
+ "best_co2 = float(sorted_df['CO2'][best_idx])\n",
+ "best_embedding = base_embeddings[best_idx, :].reshape(1, -1).cuda()\n",
+ "best_co2, best_idx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4394402-1921-448d-8bd8-bcca0cd997f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimized_embedding = gradient_based_extrapolation(\n",
+ " model, regression_model, regression_model, base_embeddings, scaler_co2, target_idx=-1, learning_rate=1e-2, steps=30, batch_size=32, best_idx=500, lambda_reg=1, scale_factor=0, expert_threshold=100_000\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1dc47ec-4ce4-44c0-b5da-c14ade1b415b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "example_novel_polymer = 'Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)N1C(=O)c2c(C1=O)c(ccc2)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc2c(c1)C(=O)N(C2=O)I'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "d30a41fe-a8a8-4c6c-9e48-d5403a079f36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson = sample_df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f4443775-23a3-4c34-a8d4-3ba4cd5744d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!cp -r polygnn_kit/polygnn_kit ."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "30876742-2f6e-4564-90e5-6b186a505fac",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "'GT Open Source General Use License.pdf' polygnn_kit.py tests\n",
+ " __init__.py\t\t\t\t __pycache__\t utils.py\n",
+ " poetry.lock\t\t\t\t pyproject.toml\n",
+ " polygnn_kit\t\t\t\t README.md\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cd polygnn_kit && ls"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "2d56df39-af0c-4d87-8966-9a5ee6c776cc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!rm -rf polygnn_kit/polygnn_kit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "id": "d371d73c-6362-4ad7-bf69-2e54ccfac3eb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'exp_perm_CH4__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.07918119430542, min: -3.387216091156006))',\n",
+ " 'exp_perm_CO2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.645422458648682, min: -5.92081880569458))',\n",
+ " 'exp_perm_H2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.22788667678833, min: -1.642065167427063))',\n",
+ " 'exp_perm_He__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.8041393756866455, min: -1.2612193822860718))',\n",
+ " 'exp_perm_N2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.7075700759887695, min: -3.795880079269409))',\n",
+ " 'exp_perm_O2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.931457757949829, min: -6.15490198135376))',\n",
+ " 'exp_solubility__MPa**0.5': 'Forward(MinMaxScaler(dim: 0, max: 29.200000762939453, min: 12.300000190734863))'}"
+ ]
+ },
+ "execution_count": 170,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "import json\n",
+ "\n",
+ "with open('polygnn/trained_models/solubility_and_permeability/metadata/selectors.json', 'r') as file:\n",
+ " a = json.load(file)\n",
+ " selectors = a['exp_perm_CO2__Barrer'][0]\n",
+ "\n",
+ "with open('polygnn/trained_models/solubility_and_permeability/metadata/scalers.json', 'r') as file:\n",
+ " scalers = json.load(file)\n",
+ " polygnn_scaler = scalers['exp_perm_CO2__Barrer']\n",
+ "\n",
+ "scalers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0977829-6ef6-4272-a934-023c73b78f1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "root_dir = 'polygnn/trained_models/solubility_and_permeability'\n",
+ "scaler_dict = pt.load2.load_scalers(root_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "id": "774351fc-6cd0-4626-bcbe-fb6fb0ba4ac7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import pandas as pd\n",
+ "import polygnn\n",
+ "import polygnn_trainer as pt\n",
+ "from tqdm import tqdm\n",
+ "import joblib\n",
+ "import os\n",
+ "\n",
+ "def inverse_minmax(scaled_tensor,\n",
+ " min_val=-5.92081880569458,\n",
+ " max_val= 4.645422458648682):\n",
+ "\n",
+ " return scaled_tensor * (max_val - min_val) + min_val\n",
+ "\n",
+ "def load_polygnn_model(model_path, device='cuda'):\n",
+ " \"\"\"\n",
+ " Load a pre-trained polyGNN model and its configuration dynamically \n",
+ " from pickled files in the metadata folder.\n",
+ " \n",
+ " Args:\n",
+ " model_path (str): Path to the saved model directory.\n",
+ " device (str): Device to load the model on ('cuda' or 'cpu').\n",
+ " \n",
+ " Returns:\n",
+ " tuple: Loaded ensemble model, SMILES featurizer, and the model path.\n",
+ " \"\"\"\n",
+ " metadata_path = os.path.join(model_path, \"metadata\")\n",
+ " if not os.path.exists(metadata_path):\n",
+ " raise FileNotFoundError(f\"Metadata folder not found at {metadata_path}\")\n",
+ "\n",
+ "\n",
+ " \n",
+ " bond_config = polygnn.featurize.BondConfig(True, True, True)\n",
+ " atom_config = polygnn.featurize.AtomConfig(\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " combo_hybrid=False,\n",
+ " aromatic=True,\n",
+ " )\n",
+ " \n",
+ " ensemble = pt.load.load_ensemble(\n",
+ " model_path,\n",
+ " polygnn.models.polyGNN,\n",
+ " device,\n",
+ " submodel_kwargs_dict={\n",
+ " \"node_size\": atom_config.n_features,\n",
+ " \"edge_size\": bond_config.n_features,\n",
+ " \"selector_dim\": len(selectors),\n",
+ " },\n",
+ " \n",
+ " )\n",
+ " \n",
+ " # --- 5. Create the SMILES featurizer with loaded configs ---\n",
+ " import functools\n",
+ " kwargs = dict(bond_config=bond_config,\n",
+ " atom_config=atom_config,\n",
+ " representation=\"monocycle\")\n",
+ " smiles_featurizer = functools.partial(polygnn.featurize.get_minimum_graph_tensor, **kwargs)\n",
+ " \n",
+ " print(\"polyGNN model and configuration loaded successfully from pickle files.\")\n",
+ " return ensemble, smiles_featurizer, model_path\n",
+ "\n",
+ "\n",
+ "def predict_co2_permeability_polygnn(smiles_list, ensemble, smiles_featurizer, model_path, device='cuda'):\n",
+ " \"\"\"\n",
+ " Predict CO2 permeability using polyGNN model\n",
+ " \n",
+ " Args:\n",
+ " smiles_list: List of SMILES strings\n",
+ " ensemble: Loaded polyGNN ensemble model\n",
+ " smiles_featurizer: SMILES featurization function\n",
+ " model_path: Path to model directory (for loading scalers)\n",
+ " device: Device for computation\n",
+ " \n",
+ " Returns:\n",
+ " Tensor of CO2 permeability predictions\n",
+ " \"\"\"\n",
+ " # Create a temporary dataframe for prediction\n",
+ " temp_df = pd.DataFrame({\n",
+ " 'smiles_string': smiles_list,\n",
+ " 'prop': ['exp_perm_CO2__Barrer'] * len(smiles_list), # Adjust property name as needed\n",
+ " })\n",
+ " \n",
+ " # Run predictions\n",
+ " with torch.no_grad():\n",
+ " y, y_mean_hat, y_std_hat, _selectors = pt.infer.eval_ensemble(\n",
+ " model=ensemble,\n",
+ " root_dir=model_path,\n",
+ " dataframe=temp_df,\n",
+ " smiles_featurizer=smiles_featurizer,\n",
+ " device=device,\n",
+ " ensemble_kwargs_dict={\"monte_carlo\": False},\n",
+ " )\n",
+ " return torch.tensor(y_mean_hat, device=device)\n",
+ "\n",
+ "def pred_from_embeds(embeds, regression_model):\n",
+ " x = regression_model.relu(embeds)\n",
+ " return regression_model.clf(x)\n",
+ "\n",
+ "def gradient_based_extrapolation(\n",
+ " generative_model, regression_model, regression_expert_model, embeddings, scaler, target_idx=-2,\n",
+ " learning_rate=0.0001, steps=50, batch_size=32, best_idx=None, lambda_reg=0.3, scale_factor=1000, \n",
+ " expert_threshold=120_000, polygnn_model_path=None\n",
+ "):\n",
+ " \"\"\"\n",
+ " Use property gradients to guide extrapolation, with checks for novelty and polymer validity.\n",
+ " Now includes PolyGNN predictions for CO2 permeability.\n",
+ " \n",
+ " Args:\n",
+ " polygnn_model_path: Path to pre-trained polyGNN model for CO2 permeability prediction\n",
+ " \"\"\"\n",
+ " device = next(regression_model.parameters()).device\n",
+ "\n",
+ " # Load PolyGNN model if path is provided\n",
+ " polygnn_ensemble = None\n",
+ " polygnn_featurizer = None\n",
+ " if polygnn_model_path:\n",
+ " polygnn_ensemble, polygnn_featurizer, _ = load_polygnn_model(polygnn_model_path, device)\n",
+ " \n",
+ "\n",
+ " # --- Freeze models to prevent parameter updates ---\n",
+ " for param in regression_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_model.eval()\n",
+ "\n",
+ " for param in regression_expert_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_expert_model.eval()\n",
+ " \n",
+ " for param in generative_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " generative_model.eval()\n",
+ "\n",
+ " # --- 1. Find the best starting embedding (batched) ---\n",
+ " if best_idx is None:\n",
+ " properties = []\n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(embeddings), batch_size), desc=\"Computing initial properties\"):\n",
+ " batch = embeddings[i:i+batch_size]\n",
+ " preds = regression_model.clf(batch)\n",
+ " properties.append(preds[:, target_idx].cpu())\n",
+ " properties = torch.cat(properties)\n",
+ " best_idx = torch.argmax(properties)\n",
+ "\n",
+ " start_embedding = embeddings[best_idx].cuda().reshape(1, -1).requires_grad_(True)\n",
+ " initial_embedding = start_embedding.clone().cuda()\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ "\n",
+ " # --- Store initial prediction for MAE calculation ---\n",
+ " with torch.no_grad():\n",
+ " initial_pred_scaled = pred_from_embeds(initial_embedding, regression_model)[:, target_idx]\n",
+ " initial_pred_unscaled = initial_pred_scaled * torch.tensor(scaler.scale_, device=device) + torch.tensor(scaler.mean_, device=device)\n",
+ " initial_prediction_value = initial_pred_unscaled.item()\n",
+ "\n",
+ " optimizer = torch.optim.Adam([start_embedding], lr=learning_rate)\n",
+ "\n",
+ " # --- Extract scaler attributes for PyTorch-based inverse transform ---\n",
+ " scale = torch.tensor(scaler.scale_, device=device, dtype=torch.float32)\n",
+ " mean = torch.tensor(scaler.mean_, device=device, dtype=torch.float32)\n",
+ " \n",
+ " # --- 2. Run gradient ascent with step-by-step checks ---\n",
+ " predicting_model = regression_expert_model\n",
+ " pred_unscaled = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " pred = pred_from_embeds(start_embedding.cuda(), regression_model)\n",
+ " sample_scaled = pred[:, target_idx]\n",
+ " sample_unscaled = sample_scaled * scale + mean\n",
+ " print(f'FIRST PRED: {sample_unscaled.detach().item()}') \n",
+ "\n",
+ " for step in range(steps):\n",
+ " if pred_unscaled >= expert_threshold:\n",
+ " predicting_model = regression_expert_model\n",
+ " else:\n",
+ " predicting_model = regression_expert_model\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " pred_scaled = pred_from_embeds(start_embedding.cuda(), regression_model)[:, target_idx]\n",
+ " pred_unscaled = pred_scaled * scale + mean\n",
+ " property_loss = -pred_unscaled.mean()\n",
+ " regularization_loss = torch.norm(start_embedding - initial_embedding, p=2)**2\n",
+ " loss = property_loss + scale_factor * (lambda_reg * regularization_loss)\n",
+ " \n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " start_embedding.clamp_(-20, 20)\n",
+ "\n",
+ " # --- VALIDATION BLOCK: Check molecule after each step ---\n",
+ " with torch.no_grad():\n",
+ " reconstructed_ids = generative_model.generate(start_embedding.cuda(), temperature=1.5)\n",
+ " reconstructed_smiles = generative_model.tokenizer.decode(reconstructed_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " # Use the combined helper function for all checks\n",
+ " is_valid_and_novel = is_novel_and_valid_polymer(reconstructed_smiles, training_smiles_set)\n",
+ " \n",
+ " print(f\"Step {step+1}/{steps} | Value: {pred_unscaled.item():.4f} | SMILES: {reconstructed_smiles} | Novel & Valid Polymer: {is_valid_and_novel}\")\n",
+ "\n",
+ " if is_valid_and_novel:\n",
+ " # Tokenize the reconstructed SMILES\n",
+ " recon_tokens = generative_model.tokenizer(\n",
+ " reconstructed_smiles,\n",
+ " max_length=512,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embedding from the reconstructed SMILES\n",
+ " recon_embedding = generative_model.encoder(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " \n",
+ " CO2_scaled = pred_from_embeds(recon_embedding, predicting_model)[:, target_idx]\n",
+ " CO2_unscaled = CO2_scaled * scale + mean\n",
+ " \n",
+ " print(f\" -> Reconstructed molecule CO2 permeability: {CO2_unscaled.item():.4f}\")\n",
+ " \n",
+ " # --- NEW: PolyGNN CO2 permeability prediction ---\n",
+ " if polygnn_ensemble and polygnn_featurizer:\n",
+ " corrected_smiles = reconstructed_smiles.replace(\"I\", \"[*]\")\n",
+ " print(corrected_smiles)\n",
+ " polygnn_pred = predict_co2_permeability_polygnn(\n",
+ " [corrected_smiles], \n",
+ " polygnn_ensemble, \n",
+ " polygnn_featurizer, \n",
+ " polygnn_model_path, \n",
+ " device\n",
+ " )\n",
+ " polygnn_pred = inverse_minmax(polygnn_pred)\n",
+ " print(f\" -> PolyGNN CO2 permeability prediction: {polygnn_pred.item():.4f}\")\n",
+ " \n",
+ " # Compare predictions\n",
+ " pred_diff = abs(CO2_unscaled.item() - polygnn_pred.item())\n",
+ " print(f\" -> Prediction difference (|Main - PolyGNN|): {pred_diff:.4f}\")\n",
+ " \n",
+ "\n",
+ " \n",
+ " # --- Calculate MAE between initial and reconstructed prediction ---\n",
+ " mae_value = float(abs(CO2_unscaled + property_loss))\n",
+ " print(f\" -> MAE between initial embedding prediction and current prediction: {mae_value:.4f}\")\n",
+ "\n",
+ " # If it passes all checks, save this as the current best state\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ " else:\n",
+ " pass\n",
+ "\n",
+ " print(f'FINAL VALUE: {pred_unscaled.detach().item():.4f}')\n",
+ " return last_valid_embedding\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "dd790c00-2e48-4a51-8195-003933fc92c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I'"
+ ]
+ },
+ "execution_count": 113,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['Smiles'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a0b18aa-e95c-4405-bae3-6f1cc72525ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = gradient_based_extrapolation(\n",
+ " generative_model=model,\n",
+ " regression_model=regression_model,\n",
+ " regression_expert_model=regression_model,\n",
+ " embeddings=base_embeddings,\n",
+ " scaler=scaler_co2,\n",
+ " best_idx=best_idx,\n",
+ " target_idx=-1,\n",
+ " polygnn_model_path=\"polygnn/trained_models/solubility_and_permeability\"\n",
+ " \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "e93b3bd5-89e3-42d9-8a87-46af7bf3c338",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.00419, 166.28537)"
+ ]
+ },
+ "execution_count": 152,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def get_min_max(real_values):\n",
+ " \"\"\"\n",
+ " Derive the Min-Max‐scaler parameters directly from unscaled labels.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " real_values : list | np.ndarray | torch.Tensor\n",
+ " Iterable of ground-truth permeability values (original units).\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " tuple(float, float)\n",
+ " (min_value, max_value) for the dataset.\n",
+ " \"\"\"\n",
+ " arr = np.asarray(real_values, dtype=float)\n",
+ " return float(arr.min()), float(arr.max())\n",
+ "\n",
+ "mi, ma = get_min_max(df.sort_values(by=['CO2'], ascending=True).reset_index(drop=True)[:5_000_000]['CO2'])\n",
+ "mi, ma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "id": "b3c2b0ff-04e9-4274-935f-ab9e8440742b",
+ "metadata": {},
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+ },
+ "execution_count": 151,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.sort_values(by=['CO2'], ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "id": "875a49f5-3aad-466e-a84f-3d77055a5ae4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "polyGNN model and configuration loaded successfully from pickle files.\n",
+ "The following properties will be modeled: ['exp_perm_CO2__Barrer']\n",
+ "Detected 100 data points for exp_perm_CO2__Barrer\n"
+ ]
+ }
+ ],
+ "source": [
+ "polygnn_ensemble, polygnn_featurizer, path = load_polygnn_model(\"polygnn/trained_models/solubility_and_permeability\", 'cuda')\n",
+ "\n",
+ "def make_pred(smiles):\n",
+ " corrected_smiles = [smile.replace(\"I\", \"[*]\") for smile in smiles]\n",
+ " polygnn_preds = predict_co2_permeability_polygnn(\n",
+ " corrected_smiles, \n",
+ " polygnn_ensemble, \n",
+ " polygnn_featurizer, \n",
+ " path, \n",
+ " 'cuda'\n",
+ " )\n",
+ " polygnn_preds = [pred.cpu().item() for pred in polygnn_preds]\n",
+ " return polygnn_preds\n",
+ "\n",
+ "df_sample = df.iloc[:100]\n",
+ "new_preds = make_pred(df_sample['Smiles'].to_list())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "id": "9c0245f1-3a68-4f9b-b799-407e3436b4b5",
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+ "96 96 Ic1cc(Oc2ccc(c(c2)C)Oc2cc(cc(c2)N2C(=O)c3c(C2=... 528.86 \n",
+ "97 97 Cc1cc2c3cc(C)c(cc3S(=O)(=O)c2cc1Oc1ccc(c(c1)C)... 554.76 \n",
+ "98 98 Clc1c(ccc(c1Cl)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1... 575.95 \n",
+ "99 99 Ic1ccc(nc1)S(=O)(=O)c1ccc(c(c1Cl)Cl)S(=O)(=O)c... 639.66 \n",
+ "\n",
+ " He N2 O2 CH4 CO2 synthesizable new_preds \n",
+ "0 2.69524 4.75740 42.31847 1.64086 148.43644 False -0.051322 \n",
+ "1 5.33815 2.97239 26.31118 0.86467 82.37635 False 0.266295 \n",
+ "2 20.47515 0.06353 0.90498 0.06905 2.35993 False 1.326693 \n",
+ "3 4.19692 0.00191 0.01134 0.00362 0.01418 False -0.306186 \n",
+ "4 142.68327 0.87380 8.25409 2.52067 30.04739 False 1.940067 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "95 3.65897 0.00916 0.04655 0.01135 0.10014 True -0.464047 \n",
+ "96 3.65897 0.00916 0.04655 0.01135 0.10014 True -0.464047 \n",
+ "97 27.71933 0.37086 2.36356 0.10131 7.45523 False 0.483150 \n",
+ "98 7.17213 0.01350 0.26675 0.21026 0.83210 False 0.228163 \n",
+ "99 6.86828 2.80338 26.78062 0.40880 117.95785 False 0.578581 \n",
+ "\n",
+ "[100 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 192,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample['new_preds'] = new_preds\n",
+ "df_sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "51a57a9b-e8b6-4c25-a7b0-38463ebed37e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processing batches: 100%|█████████████████████████| 8/8 [00:00<00:00, 18.36it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "CO2s = []\n",
+ "co2_preds = []\n",
+ "ch4_preds = []\n",
+ "\n",
+ "robson_smiles = sample_df['Smiles'].tolist()\n",
+ "\n",
+ "batch_size_robson = 128\n",
+ "for i in tqdm(range(0, len(robson_smiles), batch_size_robson), desc=\"Processing batches\"):\n",
+ " batch_end = min(i + batch_size_robson, len(robson_smiles))\n",
+ " batch_smiles = robson_smiles[i:batch_end]\n",
+ " \n",
+ " # Step 3: Predict properties for both original and reconstructed\n",
+ " # Original properties\n",
+ " encoding = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=256,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " with torch.no_grad():\n",
+ " orig_predictions = regression_model(encoding['input_ids'].cuda(), encoding['attention_mask'].cuda())\n",
+ " \n",
+ " ch4_scaled = orig_predictions[:, -2].cpu().numpy().reshape(-1, 1)\n",
+ " co2_scaled = orig_predictions[:, -1].cpu().numpy().reshape(-1, 1)\n",
+ "\n",
+ " ch4 = scaler_ch4.inverse_transform(ch4_scaled.astype(np.float64)).flatten()\n",
+ " co2 = scaler_co2.inverse_transform(co2_scaled.astype(np.float64)).flatten()\n",
+ "\n",
+ " co2_preds.extend([float(pred) for pred in co2])\n",
+ " ch4_preds.extend([float(pred) for pred in ch4])\n",
+ "\n",
+ "df_robson['CO2_pred'] = co2_preds\n",
+ "df_robson['CH4_pred'] = ch4_preds\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "253af0c3-0ced-417b-a572-034c4cfb601f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "5aec327c-1fd9-4ebc-aa25-74ca8749b477",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if 'P_CO2/P_CH4' not in df_robson.columns:\n",
+ " df_robson['P_CO2/P_CH4'] = df_robson['CO2_pred']/df_robson['CH4_pred']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "7b46c427-5297-4565-b657-27fc7bf5bc38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k_2019 = 2.26e7\n",
+ "n_2019 = -2.401\n",
+ "k_2008 = 5_369_140\n",
+ "n_2008 = -2.636\n",
+ "k_1991 = 1_073_700\n",
+ "n_1991 = -2.6264\n",
+ "\n",
+ "df_robson['upper_bound_Robeson'] = (df_robson['CO2_pred']/k_2019)**(1/n_2019)\n",
+ "df_robson['above_Robeson'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson']\n",
+ "\n",
+ "df_robson['upper_bound_Robeson_2008'] = (df_robson['CO2_pred']/k_2008)**(1/n_2008)\n",
+ "df_robson['above_Robeson_2008'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson_2008']\n",
+ "\n",
+ "df_robson['upper_bound_Robeson_1991'] = (df_robson['CO2_pred']/k_1991)**(1/n_1991)\n",
+ "df_robson['above_Robeson_1991'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson_1991']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "3e2a37c5-9534-4cde-8ae9-bad906c39ae9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(log) x_points_on_Robeson2019_line [np.float64(-0.3245689409002736), np.float64(-0.11311188919602205), np.float64(0.0983451625082295), np.float64(0.30980221421248105), np.float64(0.5212592659167326), np.float64(0.7327163176209841), np.float64(0.9441733693252357), np.float64(1.1556304210294872), np.float64(1.3670874727337388), np.float64(1.5785445244379903), np.float64(1.7900015761422419), np.float64(2.0014586278464934), np.float64(2.212915679550745), np.float64(2.424372731254997), np.float64(2.6358297829592483), np.float64(2.8472868346634996), np.float64(3.0587438863677514), np.float64(3.270200938072003), np.float64(3.4816579897762545), np.float64(3.693115041480506), np.float64(3.9045720931847576)]\n",
+ "x_points_on_Robeson2019_line [0.4736211185063034, 0.7707048833980547, 1.2541375248783457, 2.0408083109233783, 3.3209265166816033, 5.404012160362607, 8.79373490580214, 14.309696443824098, 23.285602136958403, 37.89173788621753, 61.65972396131289, 100.33642612016979, 163.27349134558037, 265.688484302277, 432.3443451174293, 703.5368252632127, 1144.8376047731194, 1862.9488809093987, 3031.502912213795, 4933.044594478952, 8027.3480434650555]\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_bins = 20\n",
+ "x_points_on_Robeson2019_line = [ # референсные значения, использованные при подготовке обучающей выборки для первого этапа обучения\n",
+ " 0.4736211185063034,\n",
+ " 0.7707048833980547,\n",
+ " 1.2541375248783457,\n",
+ " 2.0408083109233783,\n",
+ " 3.3209265166816033,\n",
+ " 5.404012160362607,\n",
+ " 8.79373490580214,\n",
+ " 14.309696443824098,\n",
+ " 23.285602136958403,\n",
+ " 37.89173788621753,\n",
+ " 61.65972396131289,\n",
+ " 100.33642612016979,\n",
+ " 163.27349134558037,\n",
+ " 265.688484302277,\n",
+ " 432.3443451174293,\n",
+ " 703.5368252632127,\n",
+ " 1144.8376047731194,\n",
+ " 1862.9488809093987,\n",
+ " 3031.502912213795,\n",
+ " 4933.044594478952,\n",
+ " 8027.3480434650555]\n",
+ "\n",
+ "log_bin_size = (np.log10(x_points_on_Robeson2019_line[1])-\n",
+ " np.log10(x_points_on_Robeson2019_line[0]))/num_bins\n",
+ "\n",
+ "print('(log) x_points_on_Robeson2019_line', [np.log10(xp) for xp in x_points_on_Robeson2019_line])\n",
+ "print('x_points_on_Robeson2019_line', x_points_on_Robeson2019_line)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "4ec4c897-02fa-41a6-bc42-f353a068bace",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_265640/2901639124.py:11: RuntimeWarning:\n",
+ "\n",
+ "invalid value encountered in log10\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# определение бинов Робсона, для последующей отрисовки матрицы путанности\n",
+ "\n",
+ "def calculate_bin(row, x_col, y_col):\n",
+ " x_point = row[x_col]\n",
+ " y_point = row[y_col]\n",
+ " slope = (1/n_2019)\n",
+ " \n",
+ " bin_for_point = 1\n",
+ " for x_line in x_points_on_Robeson2019_line[1:]:\n",
+ " y_Robeson = (x_line/k_2019)**(1/n_2019) # upper_bound_Robeson_2019\n",
+ " y_bin = 10**(-1/slope * np.log10(x_point/x_line) + np.log10(y_Robeson))\n",
+ " if y_point > y_bin:\n",
+ " break\n",
+ " bin_for_point += 1\n",
+ " return bin_for_point\n",
+ "\n",
+ "df_robson['Robeson_bin'] = df_robson.apply(calculate_bin,\n",
+ " x_col='CO2_pred',\n",
+ " y_col='P_CO2/P_CH4',\n",
+ " axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "576b03a6-8c94-4dfa-ae58-d38652376f69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ipywidgets\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.io as pio\n",
+ "\n",
+ "pio.renderers.default = \"notebook\" # fixes duplicate plotly plots problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "d5c23fc9-926e-4b05-8edb-88c872944f9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " CO2 | \n",
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6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... \n",
+ "... ... ... \n",
+ "6726945 6726945 Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... \n",
+ "6726946 6726946 Ic1ccc(nc1)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(... \n",
+ "6726947 6726947 Ic1ccc(cn1)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)... \n",
+ "6726948 6726948 Ic1ccc(c(c1)C)Oc1ccc2c(c1)Cc1c2ccc(c1)Oc1ccc(c... \n",
+ "6726949 6726949 Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "0 494.84 2.69524 4.75740 42.31847 1.64086 148.43644 \n",
+ "1 508.26 5.33815 2.97239 26.31118 0.86467 82.37635 \n",
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+ "4 548.10 142.68327 0.87380 8.25409 2.52067 30.04739 \n",
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+ "6726949 462.31 14.96342 0.13916 1.05252 0.09298 2.54411 \n",
+ "\n",
+ " synthesizable \n",
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+ "... ... \n",
+ "6726945 False \n",
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+ "6726947 False \n",
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+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "69c7cfef-8075-41eb-aa2d-2d621ce4d1c4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f5c49709dca144f69bb703be1756c165",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "IntSlider(value=10, continuous_update=False, description='Num points', max=1000, min=10, step=10)"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f3aca246b424d2c914e9e1fb84b2429",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# отрисовка троек молекул (2 разгонных + 1 сгенерированная) на диаграмме Робсона\n",
+ "\n",
+ "# new_pi_df_unique = new_pi_df.drop_duplicates(['smiles_start'])\n",
+ "sub_df_preds = df_robson.sample(frac=1, random_state=1010)\n",
+ "\n",
+ "\n",
+ "def plot_with_controls(num_points=1):\n",
+ "\n",
+ "\n",
+ " sub_df_preds_plotted = sub_df_preds.iloc[:num_points]\n",
+ "\n",
+ " x_stuff = 'CO2'\n",
+ " y_stuff = 'P_CO2/P_CH4'\n",
+ "\n",
+ " \n",
+ " log_scale = True\n",
+ " fig = px.scatter(sub_df_preds_plotted, x=x_stuff, y=y_stuff, log_x=log_scale, log_y=log_scale, \n",
+ " color_discrete_sequence=['red'],\n",
+ " )\n",
+ "\n",
+ " \n",
+ " fig2 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson', labels='Robeson_2019')\n",
+ " fig2.update_traces(line_color='red', line_width=1)\n",
+ " fig3 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson_2008', labels='Robeson_2008')\n",
+ " fig3.update_traces(line_color='green', line_width=1)\n",
+ " fig4 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson_1991', labels='Robeson_1991')\n",
+ " fig4.update_traces(line_color='blue', line_width=1)\n",
+ " fig = go.Figure(data=fig.data + fig2.data + fig3.data + fig4.data, layout = fig.layout)\n",
+ " fig.update_layout(fig2.layout)\n",
+ "\n",
+ " fig.update_layout(width=1100, height=550, margin=dict(l=40, r=40, t=10, b=10),)\n",
+ "\n",
+ " list_of_all_arrows = []\n",
+ " \n",
+ " \n",
+ " \n",
+ " fig.show()\n",
+ "\n",
+ "\n",
+ "num_points_slider = ipywidgets.IntSlider(\n",
+ " # value=len(sub_df_preds),\n",
+ " value=10,\n",
+ " # min=1000,\n",
+ " min=10,\n",
+ " # max=len(sub_df_preds),\n",
+ " max=min(len(sub_df_preds),1000),\n",
+ " step=10,\n",
+ " description='Num points',\n",
+ " disabled=False,\n",
+ " continuous_update=False,\n",
+ " orientation='horizontal',\n",
+ " readout=True,\n",
+ " readout_format='d'\n",
+ ")\n",
+ "\n",
+ "\n",
+ "\n",
+ "output = ipywidgets.interactive_output(plot_with_controls,\n",
+ " {\n",
+ " 'num_points': num_points_slider,\n",
+ " })\n",
+ "display(num_points_slider, output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07e0e913-820e-4041-bae4-3be8f85d292c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "reconstructed_ids = model.generate(best_embedding)\n",
+ "reconstructed_smiles = [tokenizer.decode(seq, skip_special_tokens=True) for seq in reconstructed_ids]\n",
+ "mol_gen = Chem.MolFromSmiles(reconstructed_smiles[0])\n",
+ "\n",
+ "print(reconstructed_smiles[0])\n",
+ "\n",
+ "if mol_gen is not None:\n",
+ " print('Valid')\n",
+ "else:\n",
+ " print('Skill issue')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93af70eb-5e42-4372-b0f9-4e03577742bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def comprehensive_generation(model, tokenizer, val_df, base_embeddings):\n",
+ " \"\"\"Generate molecules with different enhancement levels\"\"\"\n",
+ " \n",
+ " all_results = []\n",
+ " extrapolation_factors = [factor / 1000 for factor in range(0, 100, 10)]\n",
+ " print(extrapolation_factors)\n",
+ " for factor in extrapolation_factors:\n",
+ " co2_results = generate_gradient(\n",
+ " model, tokenizer, base_embeddings, val_df, 'CO2', extrapolation_factor=factor)\n",
+ "\n",
+ " all_results.extend(co2_results)# + ch4_results + dual_results)\n",
+ " \n",
+ " return all_results\n",
+ "\n",
+ "# Run comprehensive generation\n",
+ "all_generated_molecules = comprehensive_generation(model, tokenizer, df, base_embeddings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8eeed790-7e65-4ffa-b7cc-c92036ba0aba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict_molecule_properties(smiles_list, regression_model, tokenizer, scaler_ch4, scaler_co2, \n",
+ " batch_size=32, max_length=512, \n",
+ " baseline_ch4=None, baseline_co2=None):\n",
+ " \"\"\"\n",
+ " Predict CO2 and CH4 permeability properties for a list of SMILES\n",
+ "\n",
+ " Returns:\n",
+ " ch4_predictions: Array of CH4 permeability predictions\n",
+ " CO2ictions: Array of CO2 permeability predictions\n",
+ " molecules_exceeding_baselines: List of dicts for molecules exceeding baselines\n",
+ " \"\"\"\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " regression_model.to(device)\n",
+ " regression_model.eval()\n",
+ "\n",
+ " all_predictions = []\n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ "\n",
+ " tokens = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ "\n",
+ " predictions = regression_model(input_ids, attention_mask)\n",
+ " all_predictions.append(predictions.cpu().numpy())\n",
+ "\n",
+ " all_predictions = np.vstack(all_predictions)\n",
+ " ch4_scaled = all_predictions[:, -2].reshape(-1, 1)\n",
+ " co2_scaled = all_predictions[:, -1].reshape(-1, 1)\n",
+ "\n",
+ " ch4_predictions = scaler_ch4.inverse_transform(ch4_scaled).flatten()\n",
+ " CO2ictions = scaler_co2.inverse_transform(co2_scaled).flatten()\n",
+ "\n",
+ " # Use provided baselines or max in predictions\n",
+ " if baseline_ch4 is None:\n",
+ " baseline_ch4 = ch4_predictions.max()\n",
+ " if baseline_co2 is None:\n",
+ " baseline_co2 = CO2ictions.max()\n",
+ "\n",
+ " molecules_exceeding_baselines = []\n",
+ " for idx, (smiles, ch4_pred, CO2) in enumerate(zip(smiles_list, ch4_predictions, CO2ictions)):\n",
+ " exceeds_ch4 = ch4_pred > baseline_ch4\n",
+ " exceeds_co2 = CO2 > baseline_co2\n",
+ " if exceeds_ch4 or exceeds_co2:\n",
+ " molecules_exceeding_baselines.append({\n",
+ " \"index\": idx,\n",
+ " \"smiles\": smiles,\n",
+ " \"predicted_CH4\": ch4_pred,\n",
+ " \"predicted_CO2\": CO2,\n",
+ " \"exceeds_CH4\": exceeds_ch4,\n",
+ " \"exceeds_CO2\": exceeds_co2\n",
+ " })\n",
+ "\n",
+ " return ch4_predictions, CO2ictions, molecules_exceeding_baselines\n",
+ "\n",
+ "\n",
+ "def analyze_generated_molecules_with_properties(generated_molecules, regression_model, tokenizer, \n",
+ " scaler_ch4, scaler_co2, val_df):\n",
+ " \"\"\"\n",
+ " Analyze generated molecules and predict their actual properties\n",
+ " \n",
+ " Args:\n",
+ " generated_molecules: List of generated molecule dictionaries\n",
+ " regression_model: Trained regression model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " scaler_ch4, scaler_co2: Property scalers\n",
+ " val_df: Validation dataframe for baseline comparison\n",
+ " \n",
+ " Returns:\n",
+ " enhanced_results: DataFrame with predicted properties\n",
+ " \"\"\"\n",
+ " \n",
+ " # Extract SMILES from generated molecules\n",
+ " generated_smiles = []\n",
+ " for mol in generated_molecules:\n",
+ " if mol['is_valid']: # Only predict for valid molecules\n",
+ " generated_smiles.append(mol['generated_smiles'])\n",
+ " \n",
+ " if not generated_smiles:\n",
+ " print(\"No valid molecules to analyze!\")\n",
+ " return None\n",
+ " \n",
+ " print(f\"Predicting properties for {len(generated_smiles)} valid generated molecules...\")\n",
+ " \n",
+ " # Predict properties\n",
+ " pred_ch4, pred_co2, molecules_exceeding_baselines = predict_molecule_properties(\n",
+ " generated_smiles, regression_model, tokenizer, scaler_ch4, scaler_co2,\n",
+ " baseline_ch4=val_df['CH4'].max(),\n",
+ " baseline_co2=val_df['CO2'].max()\n",
+ " )\n",
+ " \n",
+ " # Create results dataframe\n",
+ " results_data = []\n",
+ " pred_idx = 0\n",
+ " \n",
+ " for mol in generated_molecules:\n",
+ " if mol['is_valid']:\n",
+ " results_data.append({\n",
+ " 'generated_smiles': mol['generated_smiles'],\n",
+ " 'target_property': mol['target_property'],\n",
+ " 'extrapolation_factor': mol['extrapolation_factor'],\n",
+ " 'predicted_CH4': pred_ch4[pred_idx],\n",
+ " 'predicted_CO2': pred_co2[pred_idx],\n",
+ " 'is_valid': mol['is_valid']\n",
+ " })\n",
+ " pred_idx += 1\n",
+ " else:\n",
+ " # Include invalid molecules with NaN predictions\n",
+ " results_data.append({\n",
+ " 'generated_smiles': mol['generated_smiles'],\n",
+ " 'target_property': mol['target_property'],\n",
+ " 'extrapolation_factor': mol['extrapolation_factor'],\n",
+ " 'predicted_CH4': np.nan,\n",
+ " 'predicted_CO2': np.nan,\n",
+ " 'is_valid': mol['is_valid']\n",
+ " })\n",
+ " \n",
+ " results_df = pd.DataFrame(results_data)\n",
+ " \n",
+ " # Calculate baseline statistics from validation set\n",
+ " baseline_ch4_mean = val_df['CH4'].mean()\n",
+ " baseline_ch4_max = val_df['CH4'].max()\n",
+ " baseline_co2_mean = val_df['CO2'].mean()\n",
+ " baseline_co2_max = val_df['CO2'].max()\n",
+ " \n",
+ " # Calculate enhancement statistics for valid molecules only\n",
+ " valid_results = results_df[results_df['is_valid']].copy()\n",
+ " \n",
+ " if len(valid_results) > 0:\n",
+ " print(f\"\\n{'='*80}\")\n",
+ " print(f\"INDIVIDUAL MOLECULE PROPERTIES\")\n",
+ " print(f\"{'='*80}\")\n",
+ " \n",
+ " # Display properties for each individual molecule\n",
+ " for idx, (_, mol) in enumerate(valid_results.iterrows(), 1):\n",
+ " ch4_vs_baseline = (mol['predicted_CH4'] / baseline_ch4_max - 1) * 100\n",
+ " co2_vs_baseline = (mol['predicted_CO2'] / baseline_co2_max - 1) * 100\n",
+ " \n",
+ " print(f\" SMILES: {mol['generated_smiles']}\")\n",
+ " print(f\" Target: {mol['target_property']} (factor: {mol['extrapolation_factor']})\")\n",
+ " print(f\" CH₄ Permeability: {mol['predicted_CH4']:.4f} ({ch4_vs_baseline:+.1f}% vs baseline max)\")\n",
+ " print(f\" CO₂ Permeability: {mol['predicted_CO2']:.4f} ({co2_vs_baseline:+.1f}% vs baseline max)\")\n",
+ " \n",
+ " # Enhancement indicators\n",
+ " enhancement_flags = []\n",
+ " if mol['predicted_CH4'] > baseline_ch4_max:\n",
+ " enhancement_flags.append(\"CH₄↑\")\n",
+ " if mol['predicted_CO2'] > baseline_co2_max:\n",
+ " enhancement_flags.append(\"CO₂↑\")\n",
+ " if enhancement_flags:\n",
+ " print(f\" Enhancements: {', '.join(enhancement_flags)}\")\n",
+ " \n",
+ " print(\"-\" * 70)\n",
+ " \n",
+ " print(f\"Baseline Dataset Statistics:\")\n",
+ " print(f\" CH4 - Mean: {baseline_ch4_mean:.4f}, Max: {baseline_ch4_max:.4f}\")\n",
+ " print(f\" CO2 - Mean: {baseline_co2_mean:.4f}, Max: {baseline_co2_max:.4f}\")\n",
+ " \n",
+ " print(f\"\\nGenerated Molecules Statistics:\")\n",
+ " print(f\" CH4 - Mean: {valid_results['predicted_CH4'].mean():.4f}, Max: {valid_results['predicted_CH4'].max():.4f}\")\n",
+ " print(f\" CO2 - Mean: {valid_results['predicted_CO2'].mean():.4f}, Max: {valid_results['predicted_CO2'].max():.4f}\")\n",
+ " \n",
+ " # Check for improvements\n",
+ " ch4_improvements = valid_results['predicted_CH4'] > baseline_ch4_max\n",
+ " co2_improvements = valid_results['predicted_CO2'] > baseline_co2_max\n",
+ " print(f\"\\nEnhancement Analysis:\")\n",
+ " print(f\" Molecules exceeding baseline CH4 max: {ch4_improvements.sum()}/{len(valid_results)} ({ch4_improvements.mean()*100:.1f}%)\")\n",
+ " print(f\" Molecules exceeding baseline CO2 max: {co2_improvements.sum()}/{len(valid_results)} ({co2_improvements.mean()*100:.1f}%)\")\n",
+ " # Enhancement by target property\n",
+ " property_analysis = valid_results.groupby('target_property').agg({\n",
+ " 'predicted_CH4': ['mean', 'max', 'count'],\n",
+ " 'predicted_CO2': ['mean', 'max', 'count']\n",
+ " }).round(4)\n",
+ " \n",
+ " print(f\"\\nProperty Enhancement by Generation Target:\")\n",
+ " \n",
+ " # Enhancement by extrapolation factor\n",
+ " factor_analysis = valid_results.groupby('extrapolation_factor').agg({\n",
+ " 'predicted_CH4': ['mean', 'max'],\n",
+ " 'predicted_CO2': ['mean', 'max']\n",
+ " }).round(4)\n",
+ " \n",
+ " print(f\"\\nProperty Enhancement by Extrapolation Factor:\")\n",
+ " print(\"\\nMolecules that exceed baselines:\")\n",
+ " for mol in molecules_exceeding_baselines:\n",
+ " print(f\"SMILES: {mol['smiles']}\")\n",
+ " print(f\" CH₄ Predicted: {mol['predicted_CH4']:.4f} (Exceeds baseline: {mol['exceeds_CH4']})\")\n",
+ " print(f\" CO₂ Predicted: {mol['predicted_CO2']:.4f} (Exceeds baseline: {mol['exceeds_CO2']})\\n\")\n",
+ "\n",
+ " \n",
+ " \n",
+ " else:\n",
+ " print(\"No valid molecules generated for property prediction!\")\n",
+ " \n",
+ " return results_df, molecules_exceeding_baselines\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3df27d92-9da1-433c-b56b-27cd68b8004f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results_df, molecules_exceeding_baselines = analyze_generated_molecules_with_properties(\n",
+ " all_generated_molecules, regression_model, tokenizer, scaler_ch4, scaler_co2, df\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "262b158d-afe5-4f44-85df-c49c175fd454",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exceeding_mols = []\n",
+ "for mol_info in molecules_exceeding_baselines:\n",
+ " if mol_info['exceeds_CH4'] and mol_info['exceeds_CO2']:\n",
+ " exceeding_mols.append(mol_info['smiles'])\n",
+ "\n",
+ "exceeding_mols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c133d4c6-cae8-4a43-be7e-8871d0403f1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smiles_generated_col = 'smiles'\n",
+ "gen_df = pd.DataFrame({smiles_generated_col: exceeding_mols})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "503ba331-1748-430c-96ba-a6de9658b053",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split_dot(smiles):\n",
+ " if '.' in smiles:\n",
+ " parts = smiles.split('.')\n",
+ " # выбираем наиболее длинную отдельную часть молекулы в качестве самой молекулы\n",
+ " smiles = sorted(parts, key=lambda x:len(x), reverse=True)[0]\n",
+ " return smiles\n",
+ "\n",
+ "partial_mols_count = sum(gen_df[smiles_generated_col].str.contains('.', regex=False))\n",
+ "print(f'{partial_mols_count} partial mols fixed')\n",
+ "if partial_mols_count > 0:\n",
+ " gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(split_dot)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ad57b93d-4c4c-4e08-b4ba-6beaa4ab55d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def try_normalize(smiles):\n",
+ " # функция выполняет перевод молекулы в формат rdkit и обратно. Это фильтрует некорректные молекулы и нормализует их, т.е. приводит к единому виду, чтобы затем можно было отфильтровать дубликаты молекул\n",
+ " try:\n",
+ " return Chem.MolToSmiles(Chem.MolFromSmiles(smiles))\n",
+ " except Exception as e:\n",
+ " # print(e)\n",
+ " return None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4bc739e-eb6d-42b7-a8e9-4aeb82d6f93e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "symbols = ['*', '[*]', 'I']\n",
+ "contain_counts = {}\n",
+ "for symbol in symbols:\n",
+ " contain_counts[symbol] = gen_df[smiles_generated_col].apply(lambda smiles: symbol in smiles).sum()\n",
+ " print(f'contain {symbol}: {contain_counts[symbol]} mols')\n",
+ "\n",
+ "most_frequent_symbol = max(contain_counts, key=contain_counts.get)\n",
+ "assert most_frequent_symbol == 'I' # else think about it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11079464-727b-4b44-8fd7-c674edf76b70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(lambda x: x.replace('[*]', 'I'))\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(lambda x: x.replace('*', 'I'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2893bdd-c582-4380-bbed-602c5bc22c22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smiles_normalized_col = 'SMILES_normalized'\n",
+ "\n",
+ "gen_df[smiles_normalized_col] = gen_df[smiles_generated_col].apply(try_normalize)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_normalized_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75d34fa6-1e40-4f63-b134-938d1b9cd309",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temp_smiles_col = smiles_normalized_col+'2'\n",
+ "gen_df[temp_smiles_col] = gen_df[smiles_normalized_col].apply(try_normalize)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[temp_smiles_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "gen_df = gen_df.drop(columns=[temp_smiles_col])\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "32c392d9-145d-46ba-8cb7-9b05dd453514",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.drop_duplicates(subset=[smiles_normalized_col])\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} duplicates'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "740e57e4-ad55-4638-ab0f-f753a242ee0a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def filter_two_endpoints(smiles):\n",
+ " return smiles if smiles.count('I') == 2 else None\n",
+ "\n",
+ "\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].apply(filter_two_endpoints)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_generated_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2cf9b3e8-caa6-4cd3-b76f-c1a3bcfe47db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def filter_matching_endpoint_bonds(smiles):\n",
+ " # фильтрация на предмет того, что типы связей у эндпоинтов должны быть одинаковы\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles.replace('I', '[*]'))\n",
+ " inds = tuple(mol.GetSubstructMatches(Chem.MolFromSmarts(\"[#0]~*\")))\n",
+ " inds = tuple(zip(*inds))\n",
+ " star_inds = list(inds[0])\n",
+ " connector_inds = list(inds[1])\n",
+ " b1_type = mol.GetBondBetweenAtoms(star_inds[0], connector_inds[0]).GetBondType()\n",
+ " b2_type = mol.GetBondBetweenAtoms(star_inds[1], connector_inds[1]).GetBondType()\n",
+ " if b1_type != b2_type:\n",
+ " return None\n",
+ " else:\n",
+ " return smiles\n",
+ " except:\n",
+ " return None\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].apply(filter_matching_endpoint_bonds)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_generated_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a889461-90fd-4e00-a865-8dad2edccc73",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "molecules_exceeding_baselines"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99f92d05-4166-4b2a-a586-78f38e9a3041",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df = gen_df.reset_index(drop=True)\n",
+ "gen_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3e2cdab-226d-421a-99c1-516c6709671f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exceeding_mols_co2 = []\n",
+ "exceeding_mols_ch4 = []\n",
+ "\n",
+ "for mol in gen_df['smiles']:\n",
+ " for candidat_mol_info in molecules_exceeding_baselines:\n",
+ " if mol == candidat_mol_info['smiles']:\n",
+ " exceeding_mols_co2.append(float(candidat_mol_info['predicted_CO2']))\n",
+ " exceeding_mols_ch4.append(float(candidat_mol_info['predicted_CH4']))\n",
+ " break\n",
+ "\n",
+ "exceeding_mols_co2, exceeding_mols_ch4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6fc01a8-9bd8-451a-993e-fe3329e70ee8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df['smiles'].to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed1ae34b-0f9b-4ef3-948e-cec35411764b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df['predicted_CO2'] = exceeding_mols_co2\n",
+ "gen_df['predicted_CH4'] = exceeding_mols_ch4\n",
+ "gen_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db560332-8eeb-438b-86b6-210a64efceea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_output = '/home/jovyan/simson_training_bolgov/regression/exceeding_mols.csv'\n",
+ "gen_df.to_csv(final_output, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ce9bb58-943e-4ee7-b22b-3f7e91cce862",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson = gen_df\n",
+ "df_robson['P_CO2/P_CH4'] = gen_df['predicted_CO2']/gen_df['predicted_CH4']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7f294eef-86cc-4aed-8105-35e3c261bd69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = df_robson['smiles'][0]\n",
+ "df_robson.iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56e42e5e-4988-444f-a476-3ca364fc9d95",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df['Smiles'] == t]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f3cb85b-a441-4491-bd3a-f96f619fd0ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson['in_synth_DB'] = df_robson['smiles'].isin(df['Smiles'])\n",
+ "df_robson"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/.ipynb_checkpoints/decoding_code_dumps-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/decoding_code_dumps-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..763e7d65eea6bcc43eff534e735c5f2a60c6d60e
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/decoding_code_dumps-checkpoint.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73c4a5d6-a444-43c0-9812-298113480923",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "def generate_from_extrapolated_embeddings(model, tokenizer, base_embeddings,\n",
+ " target_properties, extrapolation_factor=0.2):\n",
+ " \"\"\"\n",
+ " Generate molecules from extrapolated embeddings\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " base_embeddings: Base embeddings to extrapolate from\n",
+ " target_properties: Target property values for extrapolation direction\n",
+ " extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " # Calculate property gradient direction in embedding space\n",
+ " # This is a simplified approach - you might want to use more sophisticated methods\n",
+ " mean_embedding = torch.mean(base_embeddings, dim=0)\n",
+ " \n",
+ " # Find direction of increasing properties\n",
+ " high_prop_mask = target_properties > torch.median(target_properties)\n",
+ " low_prop_mask = target_properties < torch.median(target_properties)\n",
+ " \n",
+ " high_prop_embeddings = base_embeddings[high_prop_mask].mean(dim=0)\n",
+ " low_prop_embeddings = base_embeddings[low_prop_mask].mean(dim=0)\n",
+ " \n",
+ " property_direction = high_prop_embeddings - low_prop_embeddings\n",
+ " property_direction = property_direction / torch.norm(property_direction)\n",
+ " \n",
+ " # Generate extrapolated embeddings\n",
+ " extrapolated_embeddings = mean_embedding + extrapolation_factor * property_direction\n",
+ " extrapolated_embeddings = extrapolated_embeddings.unsqueeze(0).to(device)\n",
+ " \n",
+ " # Generate SMILES from extrapolated embeddings\n",
+ " with torch.no_grad():\n",
+ " generated_ids = model.generate(extrapolated_embeddings)\n",
+ " generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " return generated_smiles, extrapolated_embeddings\n",
+ "\n",
+ "def generate_slerp(model, tokenizer, base_embeddings,\n",
+ " df, target_col, extrapolation_factor=0.2):\n",
+ " \"\"\"\n",
+ " Generate molecules from extrapolated embeddings\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " base_embeddings: Base embeddings to extrapolate from\n",
+ " target_properties: Target property values for extrapolation direction\n",
+ " extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ "\n",
+ " co2_properties = torch.tensor(df['CO2'].values, dtype=torch.float32)\n",
+ " ch4_properties = torch.tensor(df['CH4'].values, dtype=torch.float32)\n",
+ " \n",
+ " # Normalize properties to same scale before combining\n",
+ " co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
+ " ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
+ " \n",
+ " # Combined property (equal weighting)\n",
+ " target_properties = co2_norm + ch4_norm\n",
+ " \n",
+ " extrapolated_embeddings = slerp_extrapolation(base_embeddings, target_properties, factor=extrapolation_factor)\n",
+ " generated_molecules = []\n",
+ " # Generate SMILES from extrapolated embeddings\n",
+ " with torch.no_grad():\n",
+ " generated_ids = model.generate(extrapolated_embeddings.cuda())\n",
+ " generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+ "\n",
+ " mol = Chem.MolFromSmiles(generated_smiles)\n",
+ " is_valid = mol is not None\n",
+ " \n",
+ " if is_valid:\n",
+ " canonical_smiles = MolToSmiles(mol, canonical=True)\n",
+ " else:\n",
+ " canonical_smiles = \"INVALID\"\n",
+ " \n",
+ " generated_molecules.append({\n",
+ " 'generated_smiles': generated_smiles,\n",
+ " 'is_valid': is_valid,\n",
+ " 'target_property': 'slerp',\n",
+ " 'extrapolation_factor': extrapolation_factor\n",
+ " })\n",
+ " \n",
+ " print(f\"Generation: {generated_smiles} (Valid: {is_valid})\")\n",
+ " return generated_molecules\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "def generate_dual_enhanced_molecules(model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=0.2, num_generations=10):\n",
+ " \"\"\"Generate molecules with enhanced both CO₂ and CH₄ permeability\"\"\"\n",
+ " \n",
+ " # Combine both properties (you can weight them differently if needed)\n",
+ " co2_properties = torch.tensor(val_df['CO2'].values, dtype=torch.float32)\n",
+ " ch4_properties = torch.tensor(val_df['CH4'].values, dtype=torch.float32)\n",
+ " \n",
+ " # Normalize properties to same scale before combining\n",
+ " co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
+ " ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
+ " \n",
+ " # Combined property (equal weighting)\n",
+ " combined_properties = co2_norm + ch4_norm\n",
+ " \n",
+ " generated_molecules = []\n",
+ " \n",
+ " print(f\"Generating {num_generations} molecules with enhanced dual permeability...\")\n",
+ " print(f\"Extrapolation factor: {extrapolation_factor}\")\n",
+ " \n",
+ " for i in range(num_generations):\n",
+ " generated_smiles, extrapolated_embedding = generate_from_extrapolated_embeddings(\n",
+ " model, tokenizer, base_embeddings, combined_properties, extrapolation_factor\n",
+ " )\n",
+ " \n",
+ " # Validate generated molecule\n",
+ " mol = Chem.MolFromSmiles(generated_smiles)\n",
+ " is_valid = mol is not None\n",
+ " \n",
+ " if is_valid:\n",
+ " canonical_smiles = MolToSmiles(mol, canonical=True)\n",
+ " else:\n",
+ " canonical_smiles = \"INVALID\"\n",
+ " \n",
+ " generated_molecules.append({\n",
+ " 'generation_id': i + 1,\n",
+ " 'generated_smiles': generated_smiles,\n",
+ " 'canonical_smiles': canonical_smiles,\n",
+ " 'is_valid': is_valid,\n",
+ " 'target_property': 'DUAL_enhanced',\n",
+ " 'extrapolation_factor': extrapolation_factor\n",
+ " })\n",
+ " \n",
+ " print(f\"Generation {i+1}: {generated_smiles} (Valid: {is_valid})\")\n",
+ " \n",
+ " return generated_molecules\n",
+ "\n",
+ "ch4_results = generate_enhanced_molecules_ch4(\n",
+ " model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=factor, num_generations=1\n",
+ " )\n",
+ " \n",
+ " # Dual enhanced\n",
+ " dual_results = generate_dual_enhanced_molecules(\n",
+ " model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=factor, num_generations=1\n",
+ " )"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/.ipynb_checkpoints/high_values_regression-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/high_values_regression-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dafbef96817676ce64480e80a2f95e77fb248c5c
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/high_values_regression-checkpoint.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 2) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'both_labels_count': (self.label_masks.sum(axis=1) == 2).sum(),\n",
+ " 'single_label_count': (self.label_masks.sum(axis=1) == 1).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, label_mask, label_weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 2)\n",
+ " labels: Ground truth labels (batch_size, 2)\n",
+ " label_mask: Mask for valid labels (batch_size, 2)\n",
+ " label_weights: Weights for each label (2,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Apply label-specific weights\n",
+ " weighted_losses = masked_losses * label_weights.unsqueeze(0) # Broadcast weights\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = weighted_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "def compute_label_weights(dataset):\n",
+ " \"\"\"\n",
+ " Compute inverse frequency weights based on label availability\n",
+ " \n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " \n",
+ " Returns:\n",
+ " torch.Tensor: Normalized weights for each label\n",
+ " \"\"\"\n",
+ " # Get label counts from dataset\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = len(dataset)\n",
+ " \n",
+ " # Inverse frequency weighting\n",
+ " weights = total_samples / (2 * label_counts) # 2 is the number of labels\n",
+ " \n",
+ " # Normalize weights so they sum to number of labels (2)\n",
+ " weights = weights / weights.sum() * 2\n",
+ " \n",
+ " return torch.tensor(weights, dtype=torch.float32)\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 2).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 2).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 2).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(2):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, label_weights, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with weighted loss for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 2 labels)\n",
+ " train_dataloader: Training data loader\n",
+ " val_dataloader: Validation data loader \n",
+ " label_weights: Tensor with weights for each label\n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " label_weights = label_weights.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Label weights: {label_weights.cpu().numpy()}\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate weighted loss\n",
+ " loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 2) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 2 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 2 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for two-label training (labels assumed pre-scaled)\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Compute label weights based on training data\n",
+ " label_weights = compute_label_weights(train_dataset)\n",
+ " print(f\"Computed label weights: {label_weights.numpy()}\")\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " label_weights=label_weights,\n",
+ " scalers=scalers, # Still pass scalers for true loss calculation\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8e1296a2-551c-48ab-aab3-fcf4b6110d75",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(df['CO2'].to_list())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "model = torch.compile(model, fullgraph=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.99 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/scalers']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(scalers, '/home/jovyan/simson_training_bolgov/regression/scalers')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for two-label training (labels assumed pre-scaled)\n",
+ "Training dataset statistics:\n",
+ " total_samples: 6659681\n",
+ " label_0_count: 6659681\n",
+ " label_1_count: 6659681\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 67270\n",
+ " label_0_count: 67270\n",
+ " label_1_count: 67270\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Computed label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 26015\n",
+ "Total training steps: 78045\n",
+ "Label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Validation will be performed every 7000 steps\n",
+ "\n",
+ "Epoch 1/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 27%|██▍ | 7001/26015 [10:27<10:43:20, 2.03s/it, step=7002, loss=0.0372, true_loss=16.0679, lr=1.84e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 7000 | Train Loss: 0.0618 | Val Loss: 0.1191 | True train loss: 17.4244 | True val loss: 18.3473\n",
+ "New best validation loss: 0.1191\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 54%|████▎ | 14001/26015 [20:40<3:46:01, 1.13s/it, step=14002, loss=0.0315, true_loss=15.1059, lr=1.68e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 14000 | Train Loss: 0.0357 | Val Loss: 0.0652 | True train loss: 16.0534 | True val loss: 17.3651\n",
+ "New best validation loss: 0.0652\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 81%|██████▍ | 21001/26015 [30:53<1:34:27, 1.13s/it, step=21002, loss=0.0348, true_loss=15.9539, lr=1.52e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 21000 | Train Loss: 0.0319 | Val Loss: 0.0438 | True train loss: 15.7137 | True val loss: 16.3045\n",
+ "New best validation loss: 0.0438\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 2/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 8%|▋ | 1987/26015 [02:59<5:37:18, 1.19it/s, step=28002, loss=0.0224, true_loss=14.3285, lr=1.35e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 28000 | Train Loss: 0.0284 | Val Loss: 0.0393 | True train loss: 15.0774 | True val loss: 15.2044\n",
+ "New best validation loss: 0.0393\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 35%|███ | 8987/26015 [13:13<3:55:46, 1.20it/s, step=35002, loss=0.0302, true_loss=13.3737, lr=1.19e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 35000 | Train Loss: 0.0257 | Val Loss: 0.0279 | True train loss: 14.4303 | True val loss: 14.4498\n",
+ "New best validation loss: 0.0279\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 61%|████▉ | 15987/26015 [23:29<2:17:59, 1.21it/s, step=42002, loss=0.0264, true_loss=14.5345, lr=1.03e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 42000 | Train Loss: 0.0245 | Val Loss: 0.0351 | True train loss: 14.1197 | True val loss: 14.2312\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 88%|████████▊ | 22987/26015 [33:46<41:56, 1.20it/s, step=49002, loss=0.0216, true_loss=14.1316, lr=8.70e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 49000 | Train Loss: 0.0233 | Val Loss: 0.0290 | True train loss: 13.9434 | True val loss: 14.4628\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 3/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 15%|█▎ | 3971/26015 [05:52<7:13:46, 1.18s/it, step=56002, loss=0.0254, true_loss=14.4344, lr=7.08e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 56000 | Train Loss: 0.0229 | Val Loss: 0.0479 | True train loss: 13.9115 | True val loss: 14.0929\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 42%|███▎ | 10971/26015 [16:06<4:48:34, 1.15s/it, step=63002, loss=0.0201, true_loss=12.8691, lr=5.47e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 63000 | Train Loss: 0.0219 | Val Loss: 0.0239 | True train loss: 13.6746 | True val loss: 13.5177\n",
+ "New best validation loss: 0.0239\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 69%|██████▏ | 17971/26015 [26:24<2:31:18, 1.13s/it, step=7e+4, loss=0.0248, true_loss=14.9835, lr=3.86e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 70000 | Train Loss: 0.0212 | Val Loss: 0.0259 | True train loss: 13.5072 | True val loss: 13.7410\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 96%|█████████▌| 24971/26015 [36:41<19:36, 1.13s/it, step=77002, loss=0.0228, true_loss=13.9553, lr=2.24e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 77000 | Train Loss: 0.0207 | Val Loss: 0.0267 | True train loss: 13.4052 | True val loss: 13.8021\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded best model with validation loss: 0.0239\n",
+ "Training completed.\n",
+ "Number of validation checkpoints: 11\n",
+ "Final training losses: [0.022863016219410514, 0.02186289042873042, 0.021151691354678145, 0.020719855580878046, 0.020669196563010864]\n",
+ "Best validation loss: 0.0239\n",
+ "Model saved successfully!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=3, learning_rate=2e-5, batch_size=256, validation_steps=7000,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/.ipynb_checkpoints/regression-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/regression-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dafbef96817676ce64480e80a2f95e77fb248c5c
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/regression-checkpoint.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 2) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'both_labels_count': (self.label_masks.sum(axis=1) == 2).sum(),\n",
+ " 'single_label_count': (self.label_masks.sum(axis=1) == 1).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, label_mask, label_weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 2)\n",
+ " labels: Ground truth labels (batch_size, 2)\n",
+ " label_mask: Mask for valid labels (batch_size, 2)\n",
+ " label_weights: Weights for each label (2,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Apply label-specific weights\n",
+ " weighted_losses = masked_losses * label_weights.unsqueeze(0) # Broadcast weights\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = weighted_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "def compute_label_weights(dataset):\n",
+ " \"\"\"\n",
+ " Compute inverse frequency weights based on label availability\n",
+ " \n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " \n",
+ " Returns:\n",
+ " torch.Tensor: Normalized weights for each label\n",
+ " \"\"\"\n",
+ " # Get label counts from dataset\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = len(dataset)\n",
+ " \n",
+ " # Inverse frequency weighting\n",
+ " weights = total_samples / (2 * label_counts) # 2 is the number of labels\n",
+ " \n",
+ " # Normalize weights so they sum to number of labels (2)\n",
+ " weights = weights / weights.sum() * 2\n",
+ " \n",
+ " return torch.tensor(weights, dtype=torch.float32)\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 2).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 2).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 2).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(2):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, label_weights, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with weighted loss for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 2 labels)\n",
+ " train_dataloader: Training data loader\n",
+ " val_dataloader: Validation data loader \n",
+ " label_weights: Tensor with weights for each label\n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " label_weights = label_weights.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Label weights: {label_weights.cpu().numpy()}\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate weighted loss\n",
+ " loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 2) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 2 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 2 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for two-label training (labels assumed pre-scaled)\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Compute label weights based on training data\n",
+ " label_weights = compute_label_weights(train_dataset)\n",
+ " print(f\"Computed label weights: {label_weights.numpy()}\")\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " label_weights=label_weights,\n",
+ " scalers=scalers, # Still pass scalers for true loss calculation\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8e1296a2-551c-48ab-aab3-fcf4b6110d75",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(df['CO2'].to_list())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "model = torch.compile(model, fullgraph=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.99 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/scalers']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(scalers, '/home/jovyan/simson_training_bolgov/regression/scalers')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for two-label training (labels assumed pre-scaled)\n",
+ "Training dataset statistics:\n",
+ " total_samples: 6659681\n",
+ " label_0_count: 6659681\n",
+ " label_1_count: 6659681\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 67270\n",
+ " label_0_count: 67270\n",
+ " label_1_count: 67270\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Computed label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 26015\n",
+ "Total training steps: 78045\n",
+ "Label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Validation will be performed every 7000 steps\n",
+ "\n",
+ "Epoch 1/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 27%|██▍ | 7001/26015 [10:27<10:43:20, 2.03s/it, step=7002, loss=0.0372, true_loss=16.0679, lr=1.84e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 7000 | Train Loss: 0.0618 | Val Loss: 0.1191 | True train loss: 17.4244 | True val loss: 18.3473\n",
+ "New best validation loss: 0.1191\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 54%|████▎ | 14001/26015 [20:40<3:46:01, 1.13s/it, step=14002, loss=0.0315, true_loss=15.1059, lr=1.68e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 14000 | Train Loss: 0.0357 | Val Loss: 0.0652 | True train loss: 16.0534 | True val loss: 17.3651\n",
+ "New best validation loss: 0.0652\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 81%|██████▍ | 21001/26015 [30:53<1:34:27, 1.13s/it, step=21002, loss=0.0348, true_loss=15.9539, lr=1.52e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 21000 | Train Loss: 0.0319 | Val Loss: 0.0438 | True train loss: 15.7137 | True val loss: 16.3045\n",
+ "New best validation loss: 0.0438\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 2/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 8%|▋ | 1987/26015 [02:59<5:37:18, 1.19it/s, step=28002, loss=0.0224, true_loss=14.3285, lr=1.35e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 28000 | Train Loss: 0.0284 | Val Loss: 0.0393 | True train loss: 15.0774 | True val loss: 15.2044\n",
+ "New best validation loss: 0.0393\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 35%|███ | 8987/26015 [13:13<3:55:46, 1.20it/s, step=35002, loss=0.0302, true_loss=13.3737, lr=1.19e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 35000 | Train Loss: 0.0257 | Val Loss: 0.0279 | True train loss: 14.4303 | True val loss: 14.4498\n",
+ "New best validation loss: 0.0279\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 61%|████▉ | 15987/26015 [23:29<2:17:59, 1.21it/s, step=42002, loss=0.0264, true_loss=14.5345, lr=1.03e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 42000 | Train Loss: 0.0245 | Val Loss: 0.0351 | True train loss: 14.1197 | True val loss: 14.2312\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 88%|████████▊ | 22987/26015 [33:46<41:56, 1.20it/s, step=49002, loss=0.0216, true_loss=14.1316, lr=8.70e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 49000 | Train Loss: 0.0233 | Val Loss: 0.0290 | True train loss: 13.9434 | True val loss: 14.4628\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 3/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 15%|█▎ | 3971/26015 [05:52<7:13:46, 1.18s/it, step=56002, loss=0.0254, true_loss=14.4344, lr=7.08e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 56000 | Train Loss: 0.0229 | Val Loss: 0.0479 | True train loss: 13.9115 | True val loss: 14.0929\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 42%|███▎ | 10971/26015 [16:06<4:48:34, 1.15s/it, step=63002, loss=0.0201, true_loss=12.8691, lr=5.47e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 63000 | Train Loss: 0.0219 | Val Loss: 0.0239 | True train loss: 13.6746 | True val loss: 13.5177\n",
+ "New best validation loss: 0.0239\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 69%|██████▏ | 17971/26015 [26:24<2:31:18, 1.13s/it, step=7e+4, loss=0.0248, true_loss=14.9835, lr=3.86e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 70000 | Train Loss: 0.0212 | Val Loss: 0.0259 | True train loss: 13.5072 | True val loss: 13.7410\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 96%|█████████▌| 24971/26015 [36:41<19:36, 1.13s/it, step=77002, loss=0.0228, true_loss=13.9553, lr=2.24e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 77000 | Train Loss: 0.0207 | Val Loss: 0.0267 | True train loss: 13.4052 | True val loss: 13.8021\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded best model with validation loss: 0.0239\n",
+ "Training completed.\n",
+ "Number of validation checkpoints: 11\n",
+ "Final training losses: [0.022863016219410514, 0.02186289042873042, 0.021151691354678145, 0.020719855580878046, 0.020669196563010864]\n",
+ "Best validation loss: 0.0239\n",
+ "Model saved successfully!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=3, learning_rate=2e-5, batch_size=256, validation_steps=7000,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/.ipynb_checkpoints/regression_visualize-checkpoint.ipynb b/simson_modeling/regression/.ipynb_checkpoints/regression_visualize-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bcb1f22ce92eb97609d81e833d8ff461608418aa
--- /dev/null
+++ b/simson_modeling/regression/.ipynb_checkpoints/regression_visualize-checkpoint.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d5165301-7adf-4bf0-8253-59c2a940fb06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import joblib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "import umap\n",
+ "\n",
+ "# For better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_context(\"notebook\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1cbedb53-27e4-47a1-a60a-5e1cc9ac6ef4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bc2a9887-2e5f-48b6-8798-e2b0cdec4db1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load test data\n",
+ "from tqdm import tqdm\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "83ee8d74-a48f-4661-adc6-4d17f7cdcb83",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████████████████████████████████████████████| 999/999 [00:29<00:00, 33.87it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1000, 9)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import pairwise_distances\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "\n",
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "# For your use case:\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['CO2', 'CH4'], # or actual column names\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# --- 2. columns that hold permeability values --------------------------------\n",
+ "features = ['CO2', 'CH4'] # adjust if names differ\n",
+ "\n",
+ "# --- 3. Max–Min selection ----------------------------------------------------\n",
+ "def select_diverse_maxmin(data, cols, k=1_000):\n",
+ " X = data[cols].to_numpy()\n",
+ " \n",
+ " # scale to [0,1] so CO₂ and CH₄ get equal weight\n",
+ " X = MinMaxScaler().fit_transform(X)\n",
+ " \n",
+ " n = len(X)\n",
+ " if k >= n: # nothing to do\n",
+ " return data\n",
+ " \n",
+ " # start with a random seed point\n",
+ " selected = [np.random.randint(0, n)]\n",
+ " \n",
+ " # pre-allocate distance cache\n",
+ " min_dist = pairwise_distances(\n",
+ " X, X[selected], metric='euclidean'\n",
+ " ).ravel() # distance to first point\n",
+ " \n",
+ " for _ in tqdm(range(1, k)):\n",
+ " # pick the point with the largest distance to the current set\n",
+ " idx = np.argmax(min_dist)\n",
+ " selected.append(idx)\n",
+ " \n",
+ " # update distance cache (keep the shortest distance to any selected pt)\n",
+ " dist_to_new = pairwise_distances(X, X[[idx]], metric='euclidean').ravel()\n",
+ " min_dist = np.minimum(min_dist, dist_to_new)\n",
+ " \n",
+ " return data.iloc[selected].reset_index(drop=True)\n",
+ "\n",
+ "diverse_test = select_diverse_maxmin(test, features, k=1_000)\n",
+ "print(diverse_test.shape) # (1000, …)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d251cb4f-a250-4110-ab1c-679532a5d6e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_240462/477882228.py:21: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " simson_params_clf = torch.load('/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "OptimizedModule(\n",
+ " (_orig_mod): SimSonClassifier(\n",
+ " (encoder): OptimizedModule(\n",
+ " (_orig_mod): SimSonEncoder(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(591, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): BertEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-3): 4 x BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSdpaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): BertSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): BertIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=2048, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): BertOutput(\n",
+ " (dense): Linear(in_features=2048, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (linear): Linear(in_features=768, out_features=512, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (clf): Linear(in_features=512, out_features=6, bias=True)\n",
+ " (relu): ReLU()\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Load scalers (list of 6), only last 2 needed for CO2 and CH4 permeability\n",
+ "scalers = joblib.load('/home/jovyan/simson_training_bolgov/regression/scalers')\n",
+ "scaler_co2 = scalers[-1]\n",
+ "scaler_ch4 = scalers[-2]\n",
+ "\n",
+ "# Torch model definitions (SimSonEncoder and SimSonClassifier as in your code)\n",
+ "# --paste classes global_ap, SimSonEncoder, SimSonClassifier here--\n",
+ "\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "simson_params_clf = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=6) # 6 outputs as per full regression\n",
+ "model = torch.compile(model, fullgraph=True)\n",
+ "model.load_state_dict(simson_params_clf)\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "11790c3a-3a8f-4982-94e9-e0134ba69cbe",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing 1000 SMILES in batches of 256...\n",
+ "Final shapes - Embeddings: (1000, 512), Predictions: (1000, 6)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(1000,)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def generate_embeddings_and_predictions(test_smiles, tokenizer, backbone, model, \n",
+ " batch_size=32, max_length=512, device='cpu'):\n",
+ " \"\"\"\n",
+ " Generate embeddings and predictions sequentially in batches to avoid memory issues\n",
+ " \"\"\"\n",
+ " embeddings_list = []\n",
+ " predictions_list = []\n",
+ " \n",
+ " backbone.eval()\n",
+ " model.eval()\n",
+ " \n",
+ " print(f\"Processing {len(test_smiles)} SMILES in batches of {batch_size}...\")\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(test_smiles), batch_size):\n",
+ " batch_smiles = test_smiles[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize current batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings and predictions for current batch\n",
+ " batch_embeddings = backbone(input_ids, attention_mask)\n",
+ " batch_predictions = model(input_ids, attention_mask)\n",
+ " \n",
+ " # Move to CPU and store\n",
+ " embeddings_list.append(batch_embeddings.cpu().numpy())\n",
+ " predictions_list.append(batch_predictions.cpu().numpy())\n",
+ " \n",
+ " # Progress indicator\n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(test_smiles)} SMILES\")\n",
+ " \n",
+ " # Concatenate all batches\n",
+ " embeddings = np.vstack(embeddings_list)\n",
+ " predictions = np.vstack(predictions_list)\n",
+ " \n",
+ " print(f\"Final shapes - Embeddings: {embeddings.shape}, Predictions: {predictions.shape}\")\n",
+ " return embeddings, predictions\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "# Move models to device\n",
+ "backbone.to(device)\n",
+ "model.to(device)\n",
+ "\n",
+ "# Prepare SMILES list from test dataset\n",
+ "smiles_list = diverse_test['Smiles'].tolist() # Note: use correct column name from your dataset\n",
+ "\n",
+ "# Generate embeddings and predictions sequentially\n",
+ "embeddings, predictions_np = generate_embeddings_and_predictions(\n",
+ " smiles_list, \n",
+ " tokenizer, \n",
+ " backbone, \n",
+ " model, \n",
+ " batch_size=256, # Adjust based on your GPU memory\n",
+ " device=device\n",
+ ")\n",
+ "\n",
+ "# Extract CO2 and CH4 predictions (last two columns)\n",
+ "y_pred_co2_unscaled = predictions_np[:, -1] # CO2 permeability\n",
+ "y_pred_ch4_unscaled = predictions_np[:, -2] # CH4 permeability\n",
+ "\n",
+ "y_pred_co2_unscaled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "bfb72f9d-dd28-4fd3-b328-414274c3227b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Inverse transform using the loaded scalers for CO2 and CH4\n",
+ "y_pred_co2 = scaler_co2.inverse_transform(y_pred_co2_unscaled.reshape(-1,1)).flatten()\n",
+ "y_pred_ch4 = scaler_ch4.inverse_transform(y_pred_ch4_unscaled.reshape(-1,1)).flatten()\n",
+ "\n",
+ "# Targets: you may need to change these names based on your test DataFrame\n",
+ "y_true_co2 = diverse_test['CO2'].values\n",
+ "y_true_ch4 = diverse_test['CH4'].values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "7b2ea6e8-21fa-4a2d-a37e-abcea085daee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== CO₂ Permeability Relative MAE ===\n",
+ "Absolute MAE: 2569.0372\n",
+ "MAE as % of data range: 1.59%\n",
+ "MAE as % of mean value: 17.87%\n",
+ "Data range: 0.0854 to 161379.4600\n",
+ "Mean value: 14379.8005\n",
+ "\n",
+ "=== CH₄ Permeability Relative MAE ===\n",
+ "Absolute MAE: 9.0738\n",
+ "MAE as % of data range: 1.72%\n",
+ "MAE as % of mean value: 13.69%\n",
+ "Data range: 0.0003 to 528.1360\n",
+ "Mean value: 66.2984\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import mean_absolute_error\n",
+ "\n",
+ "\n",
+ "def calculate_relative_mae(y_true, y_pred, property_name):\n",
+ " \"\"\"Calculate MAE relative to data range and mean\"\"\"\n",
+ " mae = mean_absolute_error(y_true, y_pred)\n",
+ " data_range = y_true.max() - y_true.min()\n",
+ " mean_value = y_true.mean()\n",
+ " \n",
+ " relative_mae_range = (mae / data_range) * 100\n",
+ " relative_mae_mean = (mae / mean_value) * 100\n",
+ " \n",
+ " print(f\"\\n=== {property_name} Relative MAE ===\")\n",
+ " print(f\"Absolute MAE: {mae:.4f}\")\n",
+ " print(f\"MAE as % of data range: {relative_mae_range:.2f}%\")\n",
+ " print(f\"MAE as % of mean value: {relative_mae_mean:.2f}%\")\n",
+ " print(f\"Data range: {y_true.min():.4f} to {y_true.max():.4f}\")\n",
+ " print(f\"Mean value: {mean_value:.4f}\")\n",
+ " \n",
+ " return mae, relative_mae_range, relative_mae_mean\n",
+ "\n",
+ "# Analyze relative performance\n",
+ "co2_mae_analysis = calculate_relative_mae(y_true_co2, y_pred_co2, \"CO₂ Permeability\")\n",
+ "ch4_mae_analysis = calculate_relative_mae(y_true_ch4, y_pred_ch4, \"CH₄ Permeability\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "c4c74497-0f94-4f88-b7c2-83129b22736f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12,5))\n",
+ "axes[0].scatter(y_true_co2, y_pred_co2, alpha=0.6, edgecolor='k')\n",
+ "axes[0].plot([y_true_co2.min(), y_true_co2.max()], [y_true_co2.min(), y_true_co2.max()], 'r--')\n",
+ "axes[0].set_title('CO₂ Permeability: Prediction vs. True')\n",
+ "axes[0].set_xlabel('True CO₂ Permeability')\n",
+ "axes[0].set_ylabel('Predicted CO₂ Permeability')\n",
+ "\n",
+ "axes[1].scatter(y_true_ch4, y_pred_ch4, alpha=0.6, edgecolor='k')\n",
+ "axes[1].plot([y_true_ch4.min(), y_true_ch4.max()], [y_true_ch4.min(), y_true_ch4.max()], 'r--')\n",
+ "axes[1].set_title('CH₄ Permeability: Prediction vs. True')\n",
+ "axes[1].set_xlabel('True CH₄ Permeability')\n",
+ "axes[1].set_ylabel('Predicted CH₄ Permeability')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "d36e3dd2-64a6-45a5-88a7-32c25cfbbdc3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+ " warn(\n",
+ "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+ " warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Choose dimensionality reducer\n",
+ "import matplotlib.colors as mcolors\n",
+ "import numpy as np\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "embeddings_2d_pca = pca.fit_transform(embeddings)\n",
+ "\n",
+ "tsne = TSNE(n_components=2, random_state=42, perplexity=30)\n",
+ "embeddings_2d_tsne = tsne.fit_transform(embeddings)\n",
+ "\n",
+ "umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
+ "embeddings_2d_umap = umap_reducer.fit_transform(embeddings)\n",
+ "\n",
+ "def plot_embedding_2d_log(emb_2d, vals, title, label):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Add small constant to avoid log(0) issues\n",
+ " vals_positive = vals - vals.min() + 1e-10\n",
+ " vals_log = np.log10(vals_positive)\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals_log, \n",
+ " cmap='plasma', \n",
+ " alpha=0.75, \n",
+ " s=30)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(f'log₁₀({label})', fontsize=12)\n",
+ " plt.title(f'{title} (Log Scale)', fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "def plot_embedding_2d_improved(emb_2d, vals, title, label):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Use percentiles to handle outliers (clips top/bottom 5%)\n",
+ " vmin = np.percentile(vals, 5)\n",
+ " vmax = np.percentile(vals, 95)\n",
+ " \n",
+ " # Use a diverging colormap centered on median\n",
+ " vcenter = np.median(vals)\n",
+ " norm = mcolors.TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals, \n",
+ " cmap='RdYlBu_r', # Better for continuous data\n",
+ " alpha=0.75, \n",
+ " s=30, # Slightly larger points\n",
+ " norm=norm)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(label, fontsize=12)\n",
+ " plt.title(title, fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "def plot_embedding_2d_quantile(emb_2d, vals, title, label, n_bins=10):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Create quantile-based bins\n",
+ " quantiles = np.linspace(0, 100, n_bins + 1)\n",
+ " bin_edges = np.percentile(vals, quantiles)\n",
+ " vals_binned = np.digitize(vals, bin_edges) - 1\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals_binned, \n",
+ " cmap='tab10' if n_bins <= 10 else 'viridis', \n",
+ " alpha=0.75, \n",
+ " s=30)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(f'{label} (Decile)', fontsize=12)\n",
+ " cbar.set_ticks(range(n_bins))\n",
+ " cbar.set_ticklabels([f'Q{i+1}' for i in range(n_bins)])\n",
+ " \n",
+ " plt.title(f'{title} (Quantile Binned)', fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "embeddings_2d_pca = pca.fit_transform(embeddings)\n",
+ "\n",
+ "tsne = TSNE(n_components=2, random_state=42, perplexity=30)\n",
+ "embeddings_2d_tsne = tsne.fit_transform(embeddings)\n",
+ "\n",
+ "umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
+ "embeddings_2d_umap = umap_reducer.fit_transform(embeddings)\n",
+ "\n",
+ "# Method 1: Percentile-based scaling (recommended for most cases)\n",
+ "plot_embedding_2d_improved(embeddings_2d_pca, y_true_co2, 'PCA Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_tsne, y_true_co2, 't-SNE Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_umap, y_true_co2, 'UMAP Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "\n",
+ "# Method 2: Log scale (if values span orders of magnitude)\n",
+ "plot_embedding_2d_improved(embeddings_2d_pca, y_true_ch4, 'PCA Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_tsne, y_true_ch4, 't-SNE Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_umap, y_true_ch4, 'UMAP Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "\n",
+ "# Method 3: Quantile binning (for categorical-like visualization)\n",
+ "# plot_embedding_2d_quantile(embeddings_2d_umap, y_true_co2, 'PCA Embedding (CO₂ permeability)', 'CO₂ permeability', n_bins=8)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e727d25f-70fa-4cfb-ac85-8bd69eff8bef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Correlation between PC1 and CO2 permeability: 0.519\n",
+ "Correlation between PC1 and CH4 permeability: 0.853\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute the first principal component of the embeddings\n",
+ "pc1 = pca.components_[0]\n",
+ "projected = embeddings @ pc1 # Project embeddings onto PC1\n",
+ "\n",
+ "from scipy.stats import pearsonr\n",
+ "corr_co2 = pearsonr(projected, y_true_co2)[0]\n",
+ "corr_ch4 = pearsonr(projected, y_true_ch4)[0]\n",
+ "print(f'Correlation between PC1 and CO2 permeability: {corr_co2:.3f}')\n",
+ "print(f'Correlation between PC1 and CH4 permeability: {corr_ch4:.3f}')\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/PI_Tg_P308K_synth_db_chem.csv b/simson_modeling/regression/PI_Tg_P308K_synth_db_chem.csv
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index 0000000000000000000000000000000000000000..e1fb12e91c6032fca9bf1efd98c430202addb6a5
--- /dev/null
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+size 1371820484
diff --git a/simson_modeling/regression/actual_encoder_state.pkl b/simson_modeling/regression/actual_encoder_state.pkl
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index 0000000000000000000000000000000000000000..a69cd62ce8b73fabe48705eff26a1f4bfdbead5e
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+++ b/simson_modeling/regression/actual_encoder_state.pkl
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diff --git a/simson_modeling/regression/base_embeddings_new_reg.pickle b/simson_modeling/regression/base_embeddings_new_reg.pickle
new file mode 100644
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--- /dev/null
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diff --git a/simson_modeling/regression/better_regression.ipynb b/simson_modeling/regression/better_regression.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e6c0f48ee80adee88e73388ba1d1e3e55f8d3f04
--- /dev/null
+++ b/simson_modeling/regression/better_regression.ipynb
@@ -0,0 +1,670 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset, DataLoader, Sampler\n",
+ "import torch.nn as nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "# 1. BINNING, SAMPLING, SAMPLE WEIGHTING\n",
+ "def compute_multitarget_sample_weights(labels_unscaled, bins4, bins5):\n",
+ " # Each returns shape (N,)\n",
+ " inds4 = np.digitize(labels_unscaled[:, 0], bins4, right=False) - 1\n",
+ " inds5 = np.digitize(labels_unscaled[:, 1], bins5, right=False) - 1\n",
+ " freq4 = np.bincount(inds4, minlength=len(bins4))\n",
+ " freq5 = np.bincount(inds5, minlength=len(bins5))\n",
+ " w4 = 1.0 / (freq4[inds4] + 1e-8)\n",
+ " w5 = 1.0 / (freq5[inds5] + 1e-8)\n",
+ " main_weights = np.maximum(w4, w5) # Or average, or sum as suits\n",
+ " main_weights /= main_weights.mean() # Normalize for stability\n",
+ " return main_weights # shape (N_samples,)\n",
+ "\n",
+ "\n",
+ "class TargetedSampler(Sampler):\n",
+ " \"\"\"\n",
+ " Enforces a proportion of 'high value' samples for either of the main labels in every batch.\n",
+ " \"\"\"\n",
+ " def __init__(self, inds4, inds5, high_bins4, high_bins5, batch_size, high_frac=0.3, shuffle=True):\n",
+ " # indices for which either label 4 or label 5 is in a 'high' bin\n",
+ " high_mask = (inds4 >= high_bins4) | (inds5 >= high_bins5)\n",
+ " self.high_indices = np.where(high_mask)[0]\n",
+ " self.low_indices = np.where(~high_mask)[0]\n",
+ " self.batch_size = batch_size\n",
+ " self.high_count = int(batch_size * high_frac)\n",
+ " self.low_count = batch_size - self.high_count\n",
+ " self.shuffle = shuffle\n",
+ " \n",
+ " def __iter__(self):\n",
+ " high = np.copy(self.high_indices)\n",
+ " low = np.copy(self.low_indices)\n",
+ " if self.shuffle:\n",
+ " np.random.shuffle(high)\n",
+ " np.random.shuffle(low)\n",
+ " hi_ptr, low_ptr = 0, 0\n",
+ " while hi_ptr < len(high) or low_ptr < len(low):\n",
+ " batch_high = high[hi_ptr: hi_ptr+self.high_count]\n",
+ " batch_low = low[low_ptr: low_ptr+self.low_count]\n",
+ " if len(batch_high) < self.high_count:\n",
+ " np.random.shuffle(high)\n",
+ " hi_ptr = 0\n",
+ " batch_high = high[hi_ptr: hi_ptr+self.high_count]\n",
+ " if len(batch_low) < self.low_count:\n",
+ " np.random.shuffle(low)\n",
+ " low_ptr = 0\n",
+ " batch_low = low[low_ptr: low_ptr+self.low_count]\n",
+ " batch = np.concatenate([batch_high, batch_low])\n",
+ " np.random.shuffle(batch)\n",
+ " yield batch.tolist()\n",
+ " hi_ptr += self.high_count\n",
+ " low_ptr += self.low_count\n",
+ " \n",
+ " def __len__(self):\n",
+ " return (len(self.high_indices) + len(self.low_indices)) // self.batch_size\n",
+ "\n",
+ "class SMILESDataset(torch.utils.data.Dataset):\n",
+ " def __init__(self, smiles_list, labels, sample_weights, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # shape (N, 6), already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " self.sample_weights = sample_weights\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'weight': torch.tensor(self.sample_weights[idx], dtype=torch.float32),\n",
+ " 'index': torch.tensor(idx, dtype=torch.long)\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 6)\n",
+ " labels: Ground truth labels (batch_size, 6)\n",
+ " label_mask: Mask for valid labels (batch_size, 6)\n",
+ " label_weights: Weights for each label (6,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " return losses.mean()\n",
+ "\n",
+ "def stratified_metrics(preds, trues, bins, scalers):\n",
+ " # Only for last two labels\n",
+ " results = {}\n",
+ " for li, label_col in enumerate([4, 5]):\n",
+ " unscaled_pred = scalers[label_col].inverse_transform(preds[:,label_col].reshape(-1,1)).flatten()\n",
+ " unscaled_true = scalers[label_col].inverse_transform(trues[:,label_col].reshape(-1,1)).flatten()\n",
+ " bin_idx = np.digitize(unscaled_true, bins[li])\n",
+ " for i in range(len(bins[li])+1):\n",
+ " in_bin = bin_idx == i\n",
+ " if np.sum(in_bin) == 0:\n",
+ " continue\n",
+ " bin_mae = np.mean(np.abs(unscaled_pred[in_bin] - unscaled_true[in_bin]))\n",
+ " bin_r2 = 1 - (np.mean((unscaled_pred[in_bin] - unscaled_true[in_bin])**2) /\n",
+ " (np.var(unscaled_true[in_bin]) + 1e-8))\n",
+ " results[f'label{label_col}_bin{i}_count'] = np.sum(in_bin)\n",
+ " results[f'label{label_col}_bin{i}_mae'] = bin_mae\n",
+ " return results\n",
+ "\n",
+ "# 5. MAIN TRAIN/VAL LOOP WITH TARGETED SAMPLING AND STRATIFIED EVALUATION\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, model, tokenizer, scalers,\n",
+ " num_epochs=5, learning_rate=1e-5, batch_size=256, validation_steps=500):\n",
+ " # 1. Bins for columns 4 & 5 using **unscaled train-data**\n",
+ " bins_label4 = create_bins(train['unscaled_CO2'], n_bins=10)\n",
+ " bins_label5 = create_bins(train['unscaled_CH4'], n_bins=10)\n",
+ " bins = [bins_label4, bins_label5]\n",
+ " # 2. Bin indicators for each sample in train\n",
+ " inds4 = np.digitize(train['unscaled_CO2'], bins_label4, right=False) - 1\n",
+ " inds5 = np.digitize(train['unscaled_CH4'], bins_label5, right=False) - 1\n",
+ " # 3. Choose high-bin threshold (e.g. top bin, or top 2 bins as \"high\"), adjust as needed\n",
+ " high_bins4 = len(bins_label4) - 1\n",
+ " high_bins5 = len(bins_label5) - 1\n",
+ " # 4. Compute multitarget weights (max rarity of either label-of-interest, UNscaled)\n",
+ " sample_weights = compute_multitarget_sample_weights(\n",
+ " train[['unscaled_CO2', 'unscaled_CH4']].values, bins_label4, bins_label5)\n",
+ " val_sample_weights = compute_multitarget_sample_weights(\n",
+ " test[['unscaled_CO2', 'unscaled_CH4']].values, bins_label4, bins_label5)\n",
+ " # 5. Dataset and batch sampler\n",
+ " targeted_sampler = TargetedSampler(inds4, inds5, high_bins4, high_bins5, batch_size, high_frac=0.3)\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, sample_weights, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, val_sample_weights, tokenizer)\n",
+ " train_loader = DataLoader(train_dataset, batch_sampler=targeted_sampler, num_workers=4)\n",
+ " val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
+ " # 6. Model, optimizer, scheduler\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " model.to(device)\n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
+ " total_steps = len(train_loader)*3\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " best_val_loss = float('inf')\n",
+ " best_state = None\n",
+ " steps_no_improve, patience = 0, 10\n",
+ " global_step, running_train_loss, train_steps_count = 0, 0, 0\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
+ " model.train()\n",
+ " pbar = tqdm(train_loader, desc='Training', total=len(train_loader) * num_epochs)\n",
+ " for batch in pbar:\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " weights = batch['weight'].to(device)\n",
+ " optimizer.zero_grad()\n",
+ " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " loss = calculate_weighted_loss(outputs, labels, weights)\n",
+ " loss.backward()\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " optimizer.step(); scheduler.step()\n",
+ " running_train_loss += loss.item(); train_steps_count += 1; global_step += 1\n",
+ " pbar.set_postfix(loss=f'{loss.item():.3f}')\n",
+ " if global_step % validation_steps == 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " print(f\"Step {global_step}: Mean Weighted Train Loss: {avg_train_loss:.4f}\")\n",
+ " running_train_loss = 0; train_steps_count = 0\n",
+ " # Validation:\n",
+ " all_preds, all_trues, all_weights = [], [], []\n",
+ " model.eval()\n",
+ " with torch.no_grad():\n",
+ " for vb in val_loader:\n",
+ " vi = vb['input_ids'].to(device)\n",
+ " va = vb['attention_mask'].to(device)\n",
+ " vl = vb['labels'].to(device)\n",
+ " vw = vb['weight'].to(device)\n",
+ " out = model(input_ids=vi, attention_mask=va)\n",
+ " all_preds.append(out.cpu()); all_trues.append(vl.cpu()); all_weights.append(vw.cpu())\n",
+ " preds = torch.cat(all_preds).numpy()\n",
+ " trues = torch.cat(all_trues).numpy()\n",
+ " weights = torch.cat(all_weights).numpy()\n",
+ " val_loss = calculate_weighted_loss(torch.tensor(preds), torch.tensor(trues), torch.tensor(weights)).item()\n",
+ " print(f\"Weighted Val MSE (scaled): {val_loss:.4f}\")\n",
+ " metrics = stratified_metrics(preds, trues, bins, scalers)\n",
+ " for k, v in metrics.items():\n",
+ " print(f\"{k}: {v:.4f}\")\n",
+ " if val_loss < best_val_loss:\n",
+ " best_val_loss = val_loss; best_state = model.state_dict().copy(); steps_no_improve = 0\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin')\n",
+ " print(f\"New best val_loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " print(f'Patience meter: {steps_no_improve} out of {patience}')\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping at step {global_step}\")\n",
+ " if best_state: model.load_state_dict(best_state)\n",
+ " return\n",
+ " model.train()\n",
+ " if best_state: model.load_state_dict(best_state)\n",
+ " print(f\"Training completed, best weighted val_loss: {best_val_loss:.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "df['unscaled_CO2'] = df['CO2'].copy()\n",
+ "df['unscaled_CH4'] = df['CH4'].copy()\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl', weights_only=False)\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "\n",
+ "for param in model.encoder.parameters():\n",
+ " param.requires_grad = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "# For your use case:\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['unscaled_CO2', 'unscaled_CH4'], # or actual column names\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_bins(target_values, n_bins=5, strategy='percentile'):\n",
+ " \"\"\"\n",
+ " Create bins for a target based on the specified strategy.\n",
+ " - 'percentile' creates approximately equal-sized groups\n",
+ " - 'uniform' creates equal-width bins\n",
+ " Returns:\n",
+ " bin_edges: array of length n_bins+1\n",
+ " \"\"\"\n",
+ " target_values = target_values[~np.isnan(target_values)]\n",
+ " if strategy == 'percentile':\n",
+ " return np.percentile(target_values, np.linspace(0, 100, n_bins+1))\n",
+ " else:\n",
+ " return np.linspace(np.min(target_values), np.max(target_values), n_bins+1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac00906e-e22a-4f50-94e7-650acf5eec1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/regression_simson.pth'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/6\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 4%|▌ | 6499/149778 [21:21<7:51:15, 5.07it/s, loss=0.414]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Step 6500: Mean Weighted Train Loss: 34.2319\n",
+ "Weighted Val MSE (scaled): 0.1242\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 4%|▍ | 6500/149778 [25:38<3067:56:23, 77.08s/it, loss=0.414]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "label4_bin0_count: 1269.0000\n",
+ "label4_bin0_mae: 2.4281\n",
+ "label4_bin1_count: 208626.0000\n",
+ "label4_bin1_mae: 0.8366\n",
+ "label4_bin2_count: 53528.0000\n",
+ "label4_bin2_mae: 1.2479\n",
+ "label4_bin3_count: 37339.0000\n",
+ "label4_bin3_mae: 2.0683\n",
+ "label4_bin4_count: 21793.0000\n",
+ "label4_bin4_mae: 3.2345\n",
+ "label4_bin5_count: 8194.0000\n",
+ "label4_bin5_mae: 5.3938\n",
+ "label4_bin6_count: 3423.0000\n",
+ "label4_bin6_mae: 8.1634\n",
+ "label4_bin7_count: 1506.0000\n",
+ "label4_bin7_mae: 12.3689\n",
+ "label4_bin8_count: 610.0000\n",
+ "label4_bin8_mae: 28.5760\n",
+ "label4_bin9_count: 60.0000\n",
+ "label4_bin9_mae: 29.3493\n",
+ "label5_bin1_count: 299.0000\n",
+ "label5_bin1_mae: 134.7964\n",
+ "label5_bin2_count: 1068.0000\n",
+ "label5_bin2_mae: 116.7105\n",
+ "label5_bin3_count: 1998.0000\n",
+ "label5_bin3_mae: 114.5267\n",
+ "label5_bin4_count: 4626.0000\n",
+ "label5_bin4_mae: 128.3239\n",
+ "label5_bin5_count: 9057.0000\n",
+ "label5_bin5_mae: 140.8095\n",
+ "label5_bin6_count: 12853.0000\n",
+ "label5_bin6_mae: 146.8567\n",
+ "label5_bin7_count: 17785.0000\n",
+ "label5_bin7_mae: 156.6570\n",
+ "label5_bin8_count: 23487.0000\n",
+ "label5_bin8_mae: 177.3546\n",
+ "label5_bin9_count: 32135.0000\n",
+ "label5_bin9_mae: 219.6385\n",
+ "label5_bin10_count: 190443.0000\n",
+ "label5_bin10_mae: 267.8157\n",
+ "label5_bin11_count: 42597.0000\n",
+ "label5_bin11_mae: 1224.3250\n",
+ "New best val_loss: 0.1242\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 9%|█ | 12999/149778 [48:04<7:49:37, 4.85it/s, loss=0.321]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Step 13000: Mean Weighted Train Loss: 0.3521\n",
+ "Weighted Val MSE (scaled): 0.1131\n",
+ "label4_bin0_count: 1269.0000\n",
+ "label4_bin0_mae: 1.1570\n",
+ "label4_bin1_count: 208626.0000\n",
+ "label4_bin1_mae: 0.7505\n",
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+ "label4_bin2_mae: 1.1643\n",
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+ "label4_bin3_mae: 1.9008\n",
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+ "label4_bin4_mae: 3.1142\n",
+ "label4_bin5_count: 8194.0000\n",
+ "label4_bin5_mae: 5.0156\n",
+ "label4_bin6_count: 3423.0000\n",
+ "label4_bin6_mae: 7.3518\n",
+ "label4_bin7_count: 1506.0000\n",
+ "label4_bin7_mae: 9.9343\n",
+ "label4_bin8_count: 610.0000\n",
+ "label4_bin8_mae: 25.9864\n",
+ "label4_bin9_count: 60.0000\n",
+ "label4_bin9_mae: 27.8746\n",
+ "label5_bin1_count: 299.0000\n",
+ "label5_bin1_mae: 86.6469\n",
+ "label5_bin2_count: 1068.0000\n",
+ "label5_bin2_mae: 87.6131\n",
+ "label5_bin3_count: 1998.0000\n",
+ "label5_bin3_mae: 87.2714\n",
+ "label5_bin4_count: 4626.0000\n",
+ "label5_bin4_mae: 90.1329\n",
+ "label5_bin5_count: 9057.0000\n",
+ "label5_bin5_mae: 102.7583\n",
+ "label5_bin6_count: 12853.0000\n",
+ "label5_bin6_mae: 109.2145\n",
+ "label5_bin7_count: 17785.0000\n",
+ "label5_bin7_mae: 125.8342\n",
+ "label5_bin8_count: 23487.0000\n",
+ "label5_bin8_mae: 151.9758\n",
+ "label5_bin9_count: 32135.0000\n",
+ "label5_bin9_mae: 194.6474\n",
+ "label5_bin10_count: 190443.0000\n",
+ "label5_bin10_mae: 236.1328\n",
+ "label5_bin11_count: 42597.0000\n",
+ "label5_bin11_mae: 1116.2136\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 9%|▊ | 13001/149778 [52:21<2053:40:30, 54.05s/it, loss=0.309]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New best val_loss: 0.1131\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 13%|█▎ | 19049/149778 [1:12:14<7:14:15, 5.02it/s, loss=0.264]"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=6, learning_rate=2e-5, batch_size=256, validation_steps=6_500,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64baf577-cfe9-454c-8a2b-71b7a5b12506",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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new file mode 100644
index 0000000000000000000000000000000000000000..7273cc9e35b2432dcdc85390274a881dcb7ace24
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diff --git a/simson_modeling/regression/decoding.ipynb b/simson_modeling/regression/decoding.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aab5210c9a6c724bce9bf408c8174978563e5661
--- /dev/null
+++ b/simson_modeling/regression/decoding.ipynb
@@ -0,0 +1,5344 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f72ec62c-c849-45b2-9321-b913c9f32979",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Encoder parameters loaded\n",
+ "Encoder parameters frozen\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from transformers import BertConfig, BertModel, AutoTokenizer\n",
+ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, EarlyStoppingCallback\n",
+ "from torch.utils.data import Dataset\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonDecoder(nn.Module):\n",
+ " def __init__(self, embedding_dim, hidden_dim, vocab_size, max_len):\n",
+ " super(SimSonDecoder, self).__init__()\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.vocab_size = vocab_size\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " # Project embedding to hidden dimension\n",
+ " self.embedding_projection = nn.Linear(embedding_dim, hidden_dim)\n",
+ " \n",
+ " # Token embeddings for decoder input\n",
+ " self.token_embeddings = nn.Embedding(vocab_size, hidden_dim)\n",
+ " self.position_embeddings = nn.Embedding(max_len, hidden_dim)\n",
+ " \n",
+ " # Transformer decoder layers\n",
+ " decoder_layer = nn.TransformerDecoderLayer(\n",
+ " d_model=hidden_dim,\n",
+ " nhead=12,\n",
+ " dim_feedforward=2048,\n",
+ " dropout=0.1,\n",
+ " batch_first=True\n",
+ " )\n",
+ " self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)\n",
+ " \n",
+ " # Output projection to vocabulary\n",
+ " self.output_projection = nn.Linear(hidden_dim, vocab_size)\n",
+ " self.dropout = nn.Dropout(0.1)\n",
+ " \n",
+ " # Layer normalization\n",
+ " self.layer_norm = nn.LayerNorm(hidden_dim)\n",
+ " \n",
+ " def forward(self, molecule_embeddings, decoder_input_ids, decoder_attention_mask=None):\n",
+ " batch_size, seq_len = decoder_input_ids.shape\n",
+ " device = decoder_input_ids.device\n",
+ " \n",
+ " # Project molecule embeddings to memory for cross-attention\n",
+ " memory = self.embedding_projection(molecule_embeddings) # [batch, hidden_dim]\n",
+ " memory = memory.unsqueeze(1) # [batch, 1, hidden_dim]\n",
+ " \n",
+ " # Create decoder input embeddings\n",
+ " token_emb = self.token_embeddings(decoder_input_ids) # [batch, seq_len, hidden_dim]\n",
+ " \n",
+ " # Add positional embeddings\n",
+ " positions = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)\n",
+ " pos_emb = self.position_embeddings(positions)\n",
+ " \n",
+ " decoder_inputs = self.layer_norm(token_emb + pos_emb)\n",
+ " decoder_inputs = self.dropout(decoder_inputs)\n",
+ " \n",
+ " # Create causal mask for decoder\n",
+ " tgt_mask = self._generate_square_subsequent_mask(seq_len).to(device)\n",
+ " \n",
+ " # Create attention mask for decoder inputs\n",
+ " if decoder_attention_mask is not None:\n",
+ " # Convert padding mask to additive mask\n",
+ " tgt_key_padding_mask = (decoder_attention_mask == 0)\n",
+ " else:\n",
+ " tgt_key_padding_mask = None\n",
+ " \n",
+ " # Apply transformer decoder\n",
+ " decoder_output = self.transformer_decoder(\n",
+ " tgt=decoder_inputs,\n",
+ " memory=memory,\n",
+ " tgt_mask=tgt_mask,\n",
+ " tgt_key_padding_mask=tgt_key_padding_mask\n",
+ " )\n",
+ " \n",
+ " # Project to vocabulary\n",
+ " logits = self.output_projection(decoder_output)\n",
+ " \n",
+ " return logits\n",
+ " \n",
+ " def _generate_square_subsequent_mask(self, sz):\n",
+ " \"\"\"Generate causal mask for decoder\"\"\"\n",
+ " mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\n",
+ " return mask\n",
+ "\n",
+ "# Combined Encoder-Decoder Model\n",
+ "class SimSonEncoderDecoder(nn.Module):\n",
+ " def __init__(self, encoder, decoder, tokenizer):\n",
+ " super().__init__()\n",
+ " self.encoder = encoder\n",
+ " self.decoder = decoder\n",
+ " self.tokenizer = tokenizer\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask, labels=None):\n",
+ " # Encode SMILES to embeddings\n",
+ " embeddings = self.encoder(input_ids, attention_mask)\n",
+ " \n",
+ " if labels is not None:\n",
+ " # During training, use teacher forcing\n",
+ " # Shift labels for decoder input (remove last token, add BOS token)\n",
+ " decoder_input_ids = torch.cat([\n",
+ " torch.full((labels.shape[0], 1), self.tokenizer.cls_token_id, \n",
+ " dtype=labels.dtype, device=labels.device),\n",
+ " labels[:, :-1]\n",
+ " ], dim=1)\n",
+ " \n",
+ " # Generate decoder attention mask\n",
+ " decoder_attention_mask = (decoder_input_ids != self.tokenizer.pad_token_id).long()\n",
+ " \n",
+ " # Decode embeddings to SMILES\n",
+ " logits = self.decoder(embeddings, decoder_input_ids, decoder_attention_mask)\n",
+ " \n",
+ " # Calculate loss\n",
+ " loss_fct = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n",
+ " loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))\n",
+ " \n",
+ " return {\"loss\": loss, \"logits\": logits}\n",
+ " else:\n",
+ " # During inference\n",
+ " return self.generate(embeddings)\n",
+ " \n",
+ " def generate(self, embeddings, max_length=512):\n",
+ " \"\"\"Generate SMILES from embeddings\"\"\"\n",
+ " batch_size = embeddings.shape[0]\n",
+ " device = embeddings.device\n",
+ " \n",
+ " # Start with CLS token\n",
+ " generated = torch.full((batch_size, 1), self.tokenizer.cls_token_id, \n",
+ " dtype=torch.long, device=device)\n",
+ " \n",
+ " for _ in range(max_length - 1):\n",
+ " decoder_attention_mask = (generated != self.tokenizer.pad_token_id).long()\n",
+ " logits = self.decoder(embeddings, generated, decoder_attention_mask)\n",
+ " \n",
+ " # Get next token probabilities\n",
+ " next_token_logits = logits[:, -1, :]\n",
+ " next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)\n",
+ " \n",
+ " # Append to generated sequence\n",
+ " generated = torch.cat([generated, next_tokens], dim=1)\n",
+ " \n",
+ " # Stop if all sequences have generated EOS token\n",
+ " if torch.all(next_tokens.squeeze() == self.tokenizer.sep_token_id):\n",
+ " break\n",
+ " \n",
+ " return generated\n",
+ "\n",
+ "# Initialize tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize encoder config\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=4,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize and load encoder\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "# Load encoder parameters (extract encoder from regression model)\n",
+ "regression_state_dict = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin', weights_only=False)\n",
+ "\n",
+ "encoder_state_dict = {}\n",
+ "for key, value in regression_state_dict.items():\n",
+ " key = key[len('_orig_mod.'):]\n",
+ " if key.startswith('encoder.'):\n",
+ " encoder_state_dict[key[8 + len('_orig_mod.'):]] = value\n",
+ "\n",
+ "print(\"Encoder parameters loaded\")\n",
+ "\n",
+ "# Freeze encoder parameters\n",
+ "for param in encoder.parameters():\n",
+ " param.requires_grad = False\n",
+ "print(\"Encoder parameters frozen\")\n",
+ "\n",
+ "# Initialize decoder\n",
+ "decoder = SimSonDecoder(\n",
+ " embedding_dim=512, # encoder.max_len\n",
+ " hidden_dim=768, # config.hidden_size\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " max_len=512\n",
+ ")\n",
+ "\n",
+ "# Create combined model\n",
+ "model = SimSonEncoderDecoder(encoder, decoder, tokenizer)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27946411-ddb4-48f2-ab5d-baec24e53954",
+ "metadata": {},
+ "source": [
+ "regression_params = torch.load('/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder.bin', weights_only=False)\n",
+ "\n",
+ "uncompiled_state_dict = {}\n",
+ "for key, value in regression_params.items():\n",
+ " print(key)\n",
+ " key = key[len('_orig_mod.'):]\n",
+ " uncompiled_state_dict[key] = value\n",
+ " print(key)\n",
+ " break\n",
+ " \n",
+ "torch.save(uncompiled_state_dict, '/home/jovyan/simson_training_bolgov/regression/encoder_decoder_uncompiled.bin')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5bd4f17d-e369-4e4c-be51-a91cb0b85a0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load dataset\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a40fac8-9c46-4c96-8a47-94ea32803b21",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "5951cb13-e188-4a2c-8b42-9f8125e96165",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training samples: 6390602\n",
+ "Validation samples: 336348\n"
+ ]
+ }
+ ],
+ "source": [
+ "class SMILESReconstructionDataset(Dataset):\n",
+ " def __init__(self, dataframe, tokenizer, max_length=256):\n",
+ " self.data = dataframe\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.data)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " # Get SMILES string (adjust column name as needed)\n",
+ " smiles = self.data.iloc[idx]['smiles'] if 'smiles' in self.data.columns else self.data.iloc[idx]['Smiles']\n",
+ " \n",
+ " # Tokenize input SMILES\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " max_length=self.max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].squeeze(0),\n",
+ " 'attention_mask': encoding['attention_mask'].squeeze(0),\n",
+ " 'labels': encoding['input_ids'].squeeze(0) # Same as input for reconstruction\n",
+ " }\n",
+ "\n",
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Same splits\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['CO2', 'CH4'], \n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")\n",
+ "\n",
+ "train_dataset = SMILESReconstructionDataset(train, tokenizer)\n",
+ "val_dataset = SMILESReconstructionDataset(test, tokenizer)\n",
+ "val_df = test\n",
+ "train_df = train\n",
+ "print(f\"Training samples: {len(train_dataset)}\")\n",
+ "print(f\"Validation samples: {len(val_dataset)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2b79c8b4-4f5d-4752-b1a7-08ed2d0bb7f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_330276/2978564159.py:51: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Seq2SeqTrainer.__init__`. Use `processing_class` instead.\n",
+ " trainer = Seq2SeqTrainer(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Custom data collator for encoder-decoder\n",
+ "class EncoderDecoderDataCollator:\n",
+ " def __init__(self, tokenizer):\n",
+ " self.tokenizer = tokenizer\n",
+ " \n",
+ " def __call__(self, batch):\n",
+ " # Stack all batch elements\n",
+ " input_ids = torch.stack([item['input_ids'] for item in batch])\n",
+ " attention_mask = torch.stack([item['attention_mask'] for item in batch])\n",
+ " labels = torch.stack([item['labels'] for item in batch])\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': input_ids,\n",
+ " 'attention_mask': attention_mask,\n",
+ " 'labels': labels\n",
+ " }\n",
+ "\n",
+ "\n",
+ "data_collator = EncoderDecoderDataCollator(tokenizer)\n",
+ "\n",
+ "early_stopping_callback = EarlyStoppingCallback(\n",
+ " early_stopping_patience=10,\n",
+ ")\n",
+ "\n",
+ "training_args = Seq2SeqTrainingArguments(\n",
+ " output_dir='/home/jovyan/simson_training_bolgov/regression/decoder_checkpoints',\n",
+ " eval_strategy='steps',\n",
+ " eval_steps=10_000,\n",
+ " logging_steps=10_000,\n",
+ " save_steps=10_000,\n",
+ " save_total_limit=3,\n",
+ " learning_rate=5e-5,\n",
+ " per_device_train_batch_size=256,\n",
+ " per_device_eval_batch_size=256,\n",
+ " gradient_accumulation_steps=1,\n",
+ " num_train_epochs=5,\n",
+ " warmup_steps=50,\n",
+ " weight_decay=0.01,\n",
+ " logging_dir='./logs',\n",
+ " report_to='none',\n",
+ " load_best_model_at_end=True,\n",
+ " metric_for_best_model='eval_loss',\n",
+ " greater_is_better=False,\n",
+ " fp16=True,\n",
+ " dataloader_pin_memory=True,\n",
+ " remove_unused_columns=False,\n",
+ " predict_with_generate=False \n",
+ ")\n",
+ "\n",
+ "# Initialize trainer\n",
+ "trainer = Seq2SeqTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=val_dataset,\n",
+ " data_collator=data_collator,\n",
+ " tokenizer=tokenizer\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "88eccafd-c269-4bc6-8290-0bdb5d184c31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ac6754e-dd6d-4282-b661-0b11e65cadc3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_state = trainer.model.state_dict()\n",
+ "torch.save(final_state, '/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder_better_regression.bin')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "18637615-64f4-42d5-a431-0765df5bc024",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized with 72,264,271 trainable parameters\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonDecoder(nn.Module):\n",
+ " def __init__(self, embedding_dim, hidden_dim, vocab_size, max_len):\n",
+ " super(SimSonDecoder, self).__init__()\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.vocab_size = vocab_size\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " # Project embedding to hidden dimension\n",
+ " self.embedding_projection = nn.Linear(embedding_dim, hidden_dim)\n",
+ " \n",
+ " # Token embeddings for decoder input\n",
+ " self.token_embeddings = nn.Embedding(vocab_size, hidden_dim)\n",
+ " self.position_embeddings = nn.Embedding(max_len, hidden_dim)\n",
+ " \n",
+ " # Transformer decoder layers\n",
+ " decoder_layer = nn.TransformerDecoderLayer(\n",
+ " d_model=hidden_dim,\n",
+ " nhead=12,\n",
+ " dim_feedforward=2048,\n",
+ " dropout=0.1,\n",
+ " batch_first=True\n",
+ " )\n",
+ " self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)\n",
+ " \n",
+ " # Output projection to vocabulary\n",
+ " self.output_projection = nn.Linear(hidden_dim, vocab_size)\n",
+ " self.dropout = nn.Dropout(0.1)\n",
+ " \n",
+ " # Layer normalization\n",
+ " self.layer_norm = nn.LayerNorm(hidden_dim)\n",
+ " \n",
+ " def forward(self, molecule_embeddings, decoder_input_ids, decoder_attention_mask=None):\n",
+ " batch_size, seq_len = decoder_input_ids.shape\n",
+ " device = decoder_input_ids.device\n",
+ " \n",
+ " # Project molecule embeddings to memory for cross-attention\n",
+ " memory = self.embedding_projection(molecule_embeddings) # [batch, hidden_dim]\n",
+ " memory = memory.unsqueeze(1) # [batch, 1, hidden_dim]\n",
+ " \n",
+ " # Create decoder input embeddings\n",
+ " token_emb = self.token_embeddings(decoder_input_ids) # [batch, seq_len, hidden_dim]\n",
+ " \n",
+ " # Add positional embeddings\n",
+ " positions = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)\n",
+ " pos_emb = self.position_embeddings(positions)\n",
+ " \n",
+ " decoder_inputs = self.layer_norm(token_emb + pos_emb)\n",
+ " decoder_inputs = self.dropout(decoder_inputs)\n",
+ " \n",
+ " # Create causal mask for decoder\n",
+ " tgt_mask = self._generate_square_subsequent_mask(seq_len).to(device)\n",
+ " \n",
+ " # Create attention mask for decoder inputs\n",
+ " if decoder_attention_mask is not None:\n",
+ " # Convert padding mask to additive mask\n",
+ " tgt_key_padding_mask = (decoder_attention_mask == 0)\n",
+ " else:\n",
+ " tgt_key_padding_mask = None\n",
+ " \n",
+ " # Apply transformer decoder\n",
+ " decoder_output = self.transformer_decoder(\n",
+ " tgt=decoder_inputs,\n",
+ " memory=memory,\n",
+ " tgt_mask=tgt_mask,\n",
+ " tgt_key_padding_mask=tgt_key_padding_mask\n",
+ " )\n",
+ " \n",
+ " # Project to vocabulary\n",
+ " logits = self.output_projection(decoder_output)\n",
+ " \n",
+ " return logits\n",
+ " \n",
+ " def _generate_square_subsequent_mask(self, sz):\n",
+ " \"\"\"Generate causal mask for decoder\"\"\"\n",
+ " mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)\n",
+ " return mask\n",
+ "\n",
+ "# Combined Encoder-Decoder Model\n",
+ "class SimSonEncoderDecoder(nn.Module):\n",
+ " def __init__(self, encoder, decoder, tokenizer):\n",
+ " super().__init__()\n",
+ " self.encoder = encoder\n",
+ " self.decoder = decoder\n",
+ " self.tokenizer = tokenizer\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask, labels=None):\n",
+ " # Encode SMILES to embeddings\n",
+ " embeddings = self.encoder(input_ids, attention_mask)\n",
+ " \n",
+ " if labels is not None:\n",
+ " # During training, use teacher forcing\n",
+ " # Shift labels for decoder input (remove last token, add BOS token)\n",
+ " decoder_input_ids = torch.cat([\n",
+ " torch.full((labels.shape[0], 1), self.tokenizer.cls_token_id, \n",
+ " dtype=labels.dtype, device=labels.device),\n",
+ " labels[:, :-1]\n",
+ " ], dim=1)\n",
+ " \n",
+ " # Generate decoder attention mask\n",
+ " decoder_attention_mask = (decoder_input_ids != self.tokenizer.pad_token_id).long()\n",
+ " \n",
+ " # Decode embeddings to SMILES\n",
+ " logits = self.decoder(embeddings, decoder_input_ids, decoder_attention_mask)\n",
+ " \n",
+ " # Calculate loss\n",
+ " loss_fct = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n",
+ " loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))\n",
+ " \n",
+ " return {\"loss\": loss, \"logits\": logits}\n",
+ " else:\n",
+ " # During inference\n",
+ " return self.generate(embeddings)\n",
+ " \n",
+ " def generate(self, embeddings, max_length=512, temperature=1.0):\n",
+ "\n",
+ " batch_size = embeddings.shape[0]\n",
+ " device = embeddings.device\n",
+ " \n",
+ " # Start with the CLS token for all sequences in the batch\n",
+ " generated = torch.full((batch_size, 1), self.tokenizer.cls_token_id, \n",
+ " dtype=torch.long, device=device)\n",
+ " \n",
+ " # --- THE FIX: Keep track of which sequences have finished ---\n",
+ " # A boolean tensor, initially all False.\n",
+ " is_finished = torch.zeros(batch_size, dtype=torch.bool, device=device)\n",
+ " \n",
+ " for _ in range(max_length - 1):\n",
+ " # Pass the current generated sequences to the decoder\n",
+ " decoder_attention_mask = (generated != self.tokenizer.pad_token_id).long()\n",
+ " logits = self.decoder(embeddings, generated, decoder_attention_mask)\n",
+ " \n",
+ " # Focus on the logits for the last token in each sequence\n",
+ " next_token_logits = logits[:, -1, :]\n",
+ " \n",
+ " # Apply temperature sampling\n",
+ " if temperature == 0.0:\n",
+ " next_tokens = torch.argmax(next_token_logits, dim=-1)\n",
+ " else:\n",
+ " probs = F.softmax(next_token_logits / temperature, dim=-1)\n",
+ " next_tokens = torch.multinomial(probs, num_samples=1).squeeze(-1)\n",
+ " \n",
+ " # --- THE FIX: Prevent finished sequences from generating new tokens ---\n",
+ " # If a sequence is already finished, its next token should be a pad token.\n",
+ " # Otherwise, use the newly generated token.\n",
+ " next_tokens = torch.where(is_finished, self.tokenizer.pad_token_id, next_tokens)\n",
+ " \n",
+ " # Append the new tokens to the generated sequences\n",
+ " generated = torch.cat([generated, next_tokens.unsqueeze(-1)], dim=1)\n",
+ " \n",
+ " # --- THE FIX: Update the `is_finished` status for any sequence that just produced an EOS token ---\n",
+ " is_finished |= (next_tokens == self.tokenizer.sep_token_id)\n",
+ " \n",
+ " # --- THE FIX: Stop if all sequences in the batch are finished ---\n",
+ " if torch.all(is_finished):\n",
+ " break\n",
+ " \n",
+ " return generated\n",
+ "\n",
+ "\n",
+ "# Initialize tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize encoder config\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize and load encoder\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "# Initialize decoder\n",
+ "decoder = SimSonDecoder(\n",
+ " embedding_dim=512, # encoder.max_len\n",
+ " hidden_dim=768, # config.hidden_size\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " max_len=512\n",
+ ")\n",
+ "\n",
+ "# Create combined model\n",
+ "model = SimSonEncoderDecoder(encoder, decoder, tokenizer)\n",
+ "model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/regression/simson_encoder_decoder.bin', weights_only=False))\n",
+ "model.cuda()\n",
+ "print(f\"Model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad):,} trainable parameters\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "3f250d5d-0573-4f5b-a665-44ca3ecf135d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "from transformers import BertConfig, AutoTokenizer, BertModel\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "# Load tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ "# Initialize model configuration\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "# Initialize regression model\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " out = self.linear(pooled)\n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x\n",
+ "\n",
+ "compiling = False\n",
+ "# Load model and parameters\n",
+ "encoder = SimSonEncoder(config=config, max_len=512)\n",
+ "if compiling:\n",
+ " encoder = torch.compile(encoder)\n",
+ "encoder_expert = SimSonEncoder(config=config, max_len=512)\n",
+ "\n",
+ "regression_model = SimSonClassifier(encoder=encoder, num_labels=6)\n",
+ "regression_expert_model = SimSonClassifier(encoder=encoder_expert, num_labels=6)\n",
+ "\n",
+ "if compiling:\n",
+ " regression_model = torch.compile(regression_model)\n",
+ "\n",
+ "# Load trained parameters\n",
+ "regression_params = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin', weights_only=False)\n",
+ "regression_expert_params = torch.load('/home/jovyan/simson_training_bolgov/regression/high_regression_old_scalers_simson.pth', weights_only=False)\n",
+ "\n",
+ "\n",
+ "regression_model.load_state_dict(regression_params)\n",
+ "regression_expert_model.load_state_dict(regression_expert_params)\n",
+ "\n",
+ "regression_model.eval()\n",
+ "regression_model.cuda()\n",
+ "\n",
+ "regression_expert_model.eval()\n",
+ "regression_expert_model.cuda()\n",
+ "\n",
+ "# Load scalers\n",
+ "scalers = joblib.load('/home/jovyan/simson_training_bolgov/regression/scalers')\n",
+ "scaler_ch4 = scalers[-2] # CH4 scaler\n",
+ "scaler_co2 = scalers[-1] # CO2 scaler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7a775a04-26de-434f-a138-bafb84b661ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "from tqdm import tqdm\n",
+ "import torch\n",
+ "\n",
+ "def validate_reconstruction(model, tokenizer, test_smiles_list, num_samples=20, max_length=256):\n",
+ " \"\"\"\n",
+ " Evaluate reconstruction quality.\n",
+ " • exact_match ........ literal string equality\n",
+ " • canonical_match .... same molecule after canonicalisation\n",
+ " • generated_valid .... RDKit is able to parse generated SMILES\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ "\n",
+ " results = []\n",
+ " with torch.no_grad():\n",
+ " for i, smiles in enumerate(test_smiles_list[:num_samples]):\n",
+ " # ---------- encode ----------\n",
+ " tokens = tokenizer(\n",
+ " smiles,\n",
+ " max_length=max_length,\n",
+ " truncation=True,\n",
+ " padding=\"max_length\",\n",
+ " return_tensors=\"pt\"\n",
+ " ).to(device)\n",
+ "\n",
+ " embedding = model.encoder(tokens[\"input_ids\"], tokens[\"attention_mask\"])\n",
+ "\n",
+ " # ---------- decode ----------\n",
+ " gen_ids = model.generate(embedding)\n",
+ " gen_smiles = tokenizer.decode(gen_ids[0], skip_special_tokens=True)\n",
+ "\n",
+ " # ---------- validity ----------\n",
+ " mol_orig = Chem.MolFromSmiles(smiles)\n",
+ " mol_gen = Chem.MolFromSmiles(gen_smiles)\n",
+ " orig_valid = mol_orig is not None\n",
+ " gen_valid = mol_gen is not None\n",
+ "\n",
+ " # ---------- canonical comparison ----------\n",
+ " if orig_valid and gen_valid:\n",
+ " can_orig = MolToSmiles(mol_orig, canonical=True)\n",
+ " can_gen = MolToSmiles(mol_gen, canonical=True)\n",
+ " canonical_match = (can_orig == can_gen)\n",
+ " else:\n",
+ " canonical_match = False\n",
+ "\n",
+ " results.append({\n",
+ " \"original\": smiles,\n",
+ " \"reconstructed\": gen_smiles,\n",
+ " \"exact_match\": smiles == gen_smiles,\n",
+ " \"canonical_match\": canonical_match,\n",
+ " \"generated_valid\": gen_valid\n",
+ " })\n",
+ "\n",
+ " print(f\"\\nSample {i+1}\")\n",
+ " print(f\" Original: {smiles}\")\n",
+ " print(f\" Reconstructed: {gen_smiles}\")\n",
+ " print(f\" Valid (gen): {gen_valid}\")\n",
+ " print(f\" Same molecule: {canonical_match}\")\n",
+ " print(\"-\" * 60)\n",
+ "\n",
+ " # ---------- aggregated metrics ----------\n",
+ " exact_acc = sum(r[\"exact_match\"] for r in results) / len(results)\n",
+ " canonical_acc = sum(r[\"canonical_match\"] for r in results) / len(results)\n",
+ " validity_rate = sum(r[\"generated_valid\"] for r in results) / len(results)\n",
+ "\n",
+ " print(f\"\\nExact-string match accuracy ...... {exact_acc:.2%}\")\n",
+ " print(f\"Canonical match accuracy ......... {canonical_acc:.2%}\")\n",
+ " print(f\"Generated validity rate .......... {validity_rate:.2%}\")\n",
+ "\n",
+ " return results\n",
+ "\n",
+ "test_smiles = val_df['smiles' if 'smiles' in val_df.columns else 'Smiles'].tolist()\n",
+ "#validation_results = validate_reconstruction(model, tokenizer, test_smiles, num_samples=20)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "21d72f76-9bc6-480d-8bf1-205c34ed7e2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████| 999/999 [04:08<00:00, 4.02it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import pairwise_distances\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "\n",
+ "\n",
+ "features = ['CO2', 'CH4']\n",
+ "\n",
+ "# --- 3. Max–Min selection ----------------------------------------------------\n",
+ "def select_diverse_maxmin(data, cols, k=1_000):\n",
+ " X = data[cols].to_numpy()\n",
+ " \n",
+ " # scale to [0,1] so CO₂ and CH₄ get equal weight\n",
+ " X = MinMaxScaler().fit_transform(X)\n",
+ " \n",
+ " n = len(X)\n",
+ " if k >= n: # nothing to do\n",
+ " return data\n",
+ " \n",
+ " # start with a random seed point\n",
+ " selected = [np.random.randint(0, n)]\n",
+ " \n",
+ " # pre-allocate distance cache\n",
+ " min_dist = pairwise_distances(\n",
+ " X, X[selected], metric='euclidean'\n",
+ " ).ravel() # distance to first point\n",
+ " \n",
+ " for _ in tqdm(range(1, k)):\n",
+ " # pick the point with the largest distance to the current set\n",
+ " idx = np.argmax(min_dist)\n",
+ " selected.append(idx)\n",
+ " \n",
+ " # update distance cache (keep the shortest distance to any selected pt)\n",
+ " dist_to_new = pairwise_distances(X, X[[idx]], metric='euclidean').ravel()\n",
+ " min_dist = np.minimum(min_dist, dist_to_new)\n",
+ " \n",
+ " return data.iloc[selected].reset_index(drop=True)\n",
+ "\n",
+ "sample_df = select_diverse_maxmin(df, features, k=1_000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "ecae2eaa-dcd0-4a0d-a8d6-347c254285d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "from scipy.stats import pearsonr, spearmanr\n",
+ "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+ "from tqdm import tqdm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "def display_property_fidelity_results(results_df, metrics):\n",
+ " \"\"\"Display comprehensive property fidelity analysis\"\"\"\n",
+ " \n",
+ " valid_results = results_df[results_df['reconstructed_valid']].copy()\n",
+ " total_molecules = len(results_df)\n",
+ " valid_molecules = len(valid_results)\n",
+ " identical_molecules = results_df['chemically_identical'].sum()\n",
+ " \n",
+ " print(f\"\\n{'='*80}\")\n",
+ " print(f\"PROPERTY RECONSTRUCTION FIDELITY ANALYSIS\")\n",
+ " print(f\"{'='*80}\")\n",
+ " \n",
+ " print(f\"Dataset Overview:\")\n",
+ " print(f\" Total molecules tested: {total_molecules}\")\n",
+ " print(f\" Valid reconstructions: {valid_molecules} ({valid_molecules/total_molecules*100:.1f}%)\")\n",
+ " print(f\" Chemically identical: {identical_molecules} ({identical_molecules/total_molecules*100:.1f}%)\")\n",
+ " \n",
+ " if valid_molecules > 0:\n",
+ " print(f\"\\n{'='*60}\")\n",
+ " print(f\"PROPERTY CORRELATION METRICS\")\n",
+ " print(f\"{'='*60}\")\n",
+ " \n",
+ " print(f\"CH₄ Permeability:\")\n",
+ " print(f\" Pearson correlation: {metrics['CH4']['pearson_correlation']:.4f}\")\n",
+ " print(f\" Spearman correlation: {metrics['CH4']['spearman_correlation']:.4f}\")\n",
+ " print(f\" R² score: {metrics['CH4']['r2_score']:.4f}\")\n",
+ " print(f\" MAE: {metrics['CH4']['mae']:.4f}\")\n",
+ " print(f\" RMSE: {metrics['CH4']['rmse']:.4f}\")\n",
+ " print(f\" Mean relative error: {metrics['CH4']['mean_relative_error_pct']:.2f}%\")\n",
+ " print(f\" Median relative error: {metrics['CH4']['median_relative_error_pct']:.2f}%\")\n",
+ " \n",
+ " print(f\"\\nCO₂ Permeability:\")\n",
+ " print(f\" Pearson correlation: {metrics['CO2']['pearson_correlation']:.4f}\")\n",
+ " print(f\" Spearman correlation: {metrics['CO2']['spearman_correlation']:.4f}\")\n",
+ " print(f\" R² score: {metrics['CO2']['r2_score']:.4f}\")\n",
+ " print(f\" MAE: {metrics['CO2']['mae']:.4f}\")\n",
+ " print(f\" RMSE: {metrics['CO2']['rmse']:.4f}\")\n",
+ " print(f\" Mean relative error: {metrics['CO2']['mean_relative_error_pct']:.2f}%\")\n",
+ " print(f\" Median relative error: {metrics['CO2']['median_relative_error_pct']:.2f}%\")\n",
+ " \n",
+ " # Property preservation quality assessment\n",
+ " print(f\"\\n{'='*60}\")\n",
+ " print(f\"PROPERTY PRESERVATION ASSESSMENT\")\n",
+ " print(f\"{'='*60}\")\n",
+ " \n",
+ " # Define quality thresholds\n",
+ " excellent_threshold = 5.0 # <5% relative error\n",
+ " good_threshold = 15.0 # <15% relative error\n",
+ " acceptable_threshold = 30.0 # <30% relative error\n",
+ " \n",
+ " ch4_excellent = (valid_results['CH4_relative_error'] < excellent_threshold).sum()\n",
+ " ch4_good = (valid_results['CH4_relative_error'] < good_threshold).sum()\n",
+ " ch4_acceptable = (valid_results['CH4_relative_error'] < acceptable_threshold).sum()\n",
+ " \n",
+ " co2_excellent = (valid_results['CO2_relative_error'] < excellent_threshold).sum()\n",
+ " co2_good = (valid_results['CO2_relative_error'] < good_threshold).sum()\n",
+ " co2_acceptable = (valid_results['CO2_relative_error'] < acceptable_threshold).sum()\n",
+ " \n",
+ " print(f\"CH₄ Property Preservation Quality:\")\n",
+ " print(f\" Excellent (<{excellent_threshold}% error): {ch4_excellent}/{valid_molecules} ({ch4_excellent/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Good (<{good_threshold}% error): {ch4_good}/{valid_molecules} ({ch4_good/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Acceptable (<{acceptable_threshold}% error): {ch4_acceptable}/{valid_molecules} ({ch4_acceptable/valid_molecules*100:.1f}%)\")\n",
+ " \n",
+ " print(f\"\\nCO₂ Property Preservation Quality:\")\n",
+ " print(f\" Excellent (<{excellent_threshold}% error): {co2_excellent}/{valid_molecules} ({co2_excellent/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Good (<{good_threshold}% error): {co2_good}/{valid_molecules} ({co2_good/valid_molecules*100:.1f}%)\")\n",
+ " print(f\" Acceptable (<{acceptable_threshold}% error): {co2_acceptable}/{valid_molecules} ({co2_acceptable/valid_molecules*100:.1f}%)\")\n",
+ "\n",
+ "\n",
+ "def evaluate_property_reconstruction_fidelity(model, regression_model, tokenizer, \n",
+ " scaler_ch4, scaler_co2, test_smiles_list,\n",
+ " batch_size=16, max_length=256):\n",
+ " \"\"\"\n",
+ " Evaluate how well reconstructed SMILES preserve chemical properties\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " regression_model: Trained regression model for property prediction\n",
+ " tokenizer: SMILES tokenizer\n",
+ " scaler_ch4, scaler_co2: Property scalers\n",
+ " test_smiles_list: List of original SMILES to test\n",
+ " batch_size: Batch size for processing\n",
+ " max_length: Maximum SMILES length\n",
+ " \n",
+ " Returns:\n",
+ " results_df: DataFrame with detailed property comparison results\n",
+ " metrics: Dictionary of aggregate evaluation metrics\n",
+ " \"\"\"\n",
+ " \n",
+ " model.eval()\n",
+ " regression_model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " results = []\n",
+ " \n",
+ " print(f\"Evaluating property reconstruction fidelity for {len(test_smiles_list)} molecules...\")\n",
+ " \n",
+ " # Process in batches for memory efficiency\n",
+ " for i in tqdm(range(0, len(test_smiles_list), batch_size), desc=\"Processing batches\"):\n",
+ " batch_end = min(i + batch_size, len(test_smiles_list))\n",
+ " batch_smiles = test_smiles_list[i:batch_end]\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " # Step 1: Generate embeddings from original SMILES\n",
+ " original_tokens = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embeddings\n",
+ " embeddings = model.encoder(original_tokens['input_ids'].cuda(), original_tokens['attention_mask'].cuda())\n",
+ " \n",
+ " # Step 2: Reconstruct SMILES from embeddings\n",
+ " reconstructed_ids = model.generate(embeddings, temperature=1.5)\n",
+ " \n",
+ " # Step 3: Predict properties for both original and reconstructed\n",
+ " # Original properties\n",
+ " orig_predictions = regression_model(original_tokens['input_ids'], original_tokens['attention_mask'])\n",
+ " orig_ch4_scaled = orig_predictions[:, -2].cpu().numpy().reshape(-1, 1)\n",
+ " orig_co2_scaled = orig_predictions[:, -1].cpu().numpy().reshape(-1, 1)\n",
+ " orig_ch4 = scaler_ch4.inverse_transform(orig_ch4_scaled).flatten()\n",
+ " orig_co2 = scaler_co2.inverse_transform(orig_co2_scaled).flatten()\n",
+ " \n",
+ " # Reconstructed properties\n",
+ " reconstructed_smiles = [tokenizer.decode(seq, skip_special_tokens=True) for seq in reconstructed_ids]\n",
+ " # Validate reconstructed SMILES and predict properties\n",
+ " for j, (orig_smiles, recon_smiles) in enumerate(zip(batch_smiles, reconstructed_smiles)):\n",
+ " # Chemical validity checks\n",
+ " orig_mol = Chem.MolFromSmiles(orig_smiles)\n",
+ " recon_mol = Chem.MolFromSmiles(recon_smiles)\n",
+ " \n",
+ " orig_valid = orig_mol is not None\n",
+ " recon_valid = recon_mol is not None\n",
+ " \n",
+ " # Canonical SMILES comparison\n",
+ " if orig_valid and recon_valid:\n",
+ " orig_canonical = MolToSmiles(orig_mol, canonical=True)\n",
+ " recon_canonical = MolToSmiles(recon_mol, canonical=True)\n",
+ " is_identical = orig_canonical == recon_canonical\n",
+ " else:\n",
+ " orig_canonical = \"INVALID\" if not orig_valid else MolToSmiles(orig_mol, canonical=True)\n",
+ " recon_canonical = \"INVALID\" if not recon_valid else MolToSmiles(recon_mol, canonical=True)\n",
+ " is_identical = False\n",
+ " \n",
+ " # Property prediction for reconstructed SMILES\n",
+ " if recon_valid:\n",
+ " recon_tokens = tokenizer(\n",
+ " recon_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " recon_predictions = regression_model(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " recon_ch4_scaled = recon_predictions[0, -2].cpu().numpy().reshape(-1, 1)\n",
+ " recon_co2_scaled = recon_predictions[0, -1].cpu().numpy().reshape(-1, 1)\n",
+ " recon_ch4 = scaler_ch4.inverse_transform(recon_ch4_scaled)[0, 0]\n",
+ " recon_co2 = scaler_co2.inverse_transform(recon_co2_scaled)[0, 0]\n",
+ " else:\n",
+ " recon_ch4 = np.nan\n",
+ " recon_co2 = np.nan\n",
+ " \n",
+ " # Calculate property differences\n",
+ " ch4_absolute_error = abs(orig_ch4[j] - recon_ch4) if recon_valid else np.nan\n",
+ " co2_absolute_error = abs(orig_co2[j] - recon_co2) if recon_valid else np.nan\n",
+ " \n",
+ " ch4_relative_error = (ch4_absolute_error / orig_ch4[j] * 100) if (recon_valid and orig_ch4[j] != 0) else np.nan\n",
+ " co2_relative_error = (co2_absolute_error / orig_co2[j] * 100) if (recon_valid and orig_co2[j] != 0) else np.nan\n",
+ " \n",
+ " results.append({\n",
+ " 'molecule_id': i + j + 1,\n",
+ " 'original_smiles': orig_smiles,\n",
+ " 'reconstructed_smiles': recon_smiles,\n",
+ " 'original_canonical': orig_canonical,\n",
+ " 'reconstructed_canonical': recon_canonical,\n",
+ " 'chemically_identical': is_identical,\n",
+ " 'original_valid': orig_valid,\n",
+ " 'reconstructed_valid': recon_valid,\n",
+ " 'original_CH4': orig_ch4[j],\n",
+ " 'original_CO2': orig_co2[j],\n",
+ " 'reconstructed_CH4': recon_ch4,\n",
+ " 'reconstructed_CO2': recon_co2,\n",
+ " 'CH4_absolute_error': ch4_absolute_error,\n",
+ " 'CO2_absolute_error': co2_absolute_error,\n",
+ " 'CH4_relative_error': ch4_relative_error,\n",
+ " 'CO2_relative_error': co2_relative_error\n",
+ " })\n",
+ " \n",
+ " # Convert to DataFrame\n",
+ " results_df = pd.DataFrame(results)\n",
+ " \n",
+ " # Calculate aggregate metrics\n",
+ " valid_comparisons = results_df[results_df['reconstructed_valid']].copy()\n",
+ " \n",
+ " if len(valid_comparisons) > 0:\n",
+ " metrics = calculate_property_fidelity_metrics(valid_comparisons)\n",
+ " display_property_fidelity_results(results_df, metrics)\n",
+ " else:\n",
+ " print(\"No valid reconstructions for property comparison!\")\n",
+ " metrics = {}\n",
+ " \n",
+ " return results_df, metrics\n",
+ "\n",
+ "def visualize_property_fidelity(results_df, save_plots=True):\n",
+ " \"\"\"Create comprehensive visualizations of property reconstruction fidelity\"\"\"\n",
+ " \n",
+ " valid_results = results_df[results_df['reconstructed_valid']].copy()\n",
+ " \n",
+ " if len(valid_results) == 0:\n",
+ " print(\"No valid results to visualize!\")\n",
+ " return\n",
+ " \n",
+ " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+ " \n",
+ " # 1. CH4 Property Correlation\n",
+ " axes[0, 0].scatter(valid_results['original_CH4'], valid_results['reconstructed_CH4'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " \n",
+ " # Perfect correlation line\n",
+ " min_ch4 = min(valid_results['original_CH4'].min(), valid_results['reconstructed_CH4'].min())\n",
+ " max_ch4 = max(valid_results['original_CH4'].max(), valid_results['reconstructed_CH4'].max())\n",
+ " axes[0, 0].plot([min_ch4, max_ch4], [min_ch4, max_ch4], 'r--', linewidth=2, label='Perfect Correlation')\n",
+ " \n",
+ " # Calculate and display R²\n",
+ " ch4_r2 = r2_score(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " axes[0, 0].set_xlabel('Original CH₄ Permeability')\n",
+ " axes[0, 0].set_ylabel('Reconstructed CH₄ Permeability')\n",
+ " axes[0, 0].set_title(f'CH₄ Property Reconstruction (R² = {ch4_r2:.3f})')\n",
+ " axes[0, 0].legend()\n",
+ " axes[0, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 2. CO2 Property Correlation\n",
+ " axes[0, 1].scatter(valid_results['original_CO2'], valid_results['reconstructed_CO2'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " \n",
+ " min_co2 = min(valid_results['original_CO2'].min(), valid_results['reconstructed_CO2'].min())\n",
+ " max_co2 = max(valid_results['original_CO2'].max(), valid_results['reconstructed_CO2'].max())\n",
+ " axes[0, 1].plot([min_co2, max_co2], [min_co2, max_co2], 'r--', linewidth=2, label='Perfect Correlation')\n",
+ " \n",
+ " co2_r2 = r2_score(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " axes[0, 1].set_xlabel('Original CO₂ Permeability')\n",
+ " axes[0, 1].set_ylabel('Reconstructed CO₂ Permeability')\n",
+ " axes[0, 1].set_title(f'CO₂ Property Reconstruction (R² = {co2_r2:.3f})')\n",
+ " axes[0, 1].legend()\n",
+ " axes[0, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 3. Relative Error Distribution - CH4\n",
+ " axes[0, 2].hist(valid_results['CH4_relative_error'].dropna(), bins=30, alpha=0.7, \n",
+ " edgecolor='black', color='skyblue')\n",
+ " axes[0, 2].axvline(valid_results['CH4_relative_error'].median(), color='red', \n",
+ " linestyle='--', linewidth=2, label=f'Median: {valid_results[\"CH4_relative_error\"].median():.1f}%')\n",
+ " axes[0, 2].set_xlabel('CH₄ Relative Error (%)')\n",
+ " axes[0, 2].set_ylabel('Frequency')\n",
+ " axes[0, 2].set_title('CH₄ Relative Error Distribution')\n",
+ " axes[0, 2].legend()\n",
+ " axes[0, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 4. Relative Error Distribution - CO2\n",
+ " axes[1, 0].hist(valid_results['CO2_relative_error'].dropna(), bins=30, alpha=0.7, \n",
+ " edgecolor='black', color='lightcoral')\n",
+ " axes[1, 0].axvline(valid_results['CO2_relative_error'].median(), color='red', \n",
+ " linestyle='--', linewidth=2, label=f'Median: {valid_results[\"CO2_relative_error\"].median():.1f}%')\n",
+ " axes[1, 0].set_xlabel('CO₂ Relative Error (%)')\n",
+ " axes[1, 0].set_ylabel('Frequency')\n",
+ " axes[1, 0].set_title('CO₂ Relative Error Distribution')\n",
+ " axes[1, 0].legend()\n",
+ " axes[1, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 5. Error Comparison\n",
+ " error_comparison = pd.DataFrame({\n",
+ " 'CH₄_Error': valid_results['CH4_relative_error'].dropna(),\n",
+ " 'CO₂_Error': valid_results['CO2_relative_error'].dropna()\n",
+ " })\n",
+ " \n",
+ " axes[1, 1].scatter(error_comparison['CH₄_Error'], error_comparison['CO₂_Error'], \n",
+ " alpha=0.6, s=30, edgecolors='black', linewidth=0.5)\n",
+ " axes[1, 1].set_xlabel('CH₄ Relative Error (%)')\n",
+ " axes[1, 1].set_ylabel('CO₂ Relative Error (%)')\n",
+ " axes[1, 1].set_title('Property Error Correlation')\n",
+ " axes[1, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # 6. Chemical Identity vs Property Preservation\n",
+ " identical_mask = valid_results['chemically_identical']\n",
+ " \n",
+ " ch4_errors_identical = valid_results[identical_mask]['CH4_relative_error'].dropna()\n",
+ " ch4_errors_different = valid_results[~identical_mask]['CH4_relative_error'].dropna()\n",
+ " \n",
+ " axes[1, 2].boxplot([ch4_errors_identical, ch4_errors_different], \n",
+ " labels=['Chemically Identical', 'Chemically Different'])\n",
+ " axes[1, 2].set_ylabel('CH₄ Relative Error (%)')\n",
+ " axes[1, 2].set_title('Property Error by Chemical Identity')\n",
+ " axes[1, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " plt.tight_layout()\n",
+ " \n",
+ " if save_plots:\n",
+ " plt.savefig('property_reconstruction_fidelity.png', dpi=300, bbox_inches='tight')\n",
+ " print(\"Visualization saved as 'property_reconstruction_fidelity.png'\")\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def calculate_property_fidelity_metrics(valid_results):\n",
+ " \"\"\"Calculate comprehensive property fidelity metrics\"\"\"\n",
+ " \n",
+ " metrics = {}\n",
+ " \n",
+ " # Correlation metrics\n",
+ " ch4_corr_pearson, ch4_p_pearson = pearsonr(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_corr_pearson, co2_p_pearson = pearsonr(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_corr_spearman, ch4_p_spearman = spearmanr(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_corr_spearman, co2_p_spearman = spearmanr(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " # Regression metrics\n",
+ " ch4_r2 = r2_score(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_r2 = r2_score(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_mae = mean_absolute_error(valid_results['original_CH4'], valid_results['reconstructed_CH4'])\n",
+ " co2_mae = mean_absolute_error(valid_results['original_CO2'], valid_results['reconstructed_CO2'])\n",
+ " \n",
+ " ch4_rmse = np.sqrt(mean_squared_error(valid_results['original_CH4'], valid_results['reconstructed_CH4']))\n",
+ " co2_rmse = np.sqrt(mean_squared_error(valid_results['original_CO2'], valid_results['reconstructed_CO2']))\n",
+ " \n",
+ " # Relative error statistics\n",
+ " ch4_mean_rel_error = valid_results['CH4_relative_error'].mean()\n",
+ " co2_mean_rel_error = valid_results['CO2_relative_error'].mean()\n",
+ " \n",
+ " ch4_median_rel_error = valid_results['CH4_relative_error'].median()\n",
+ " co2_median_rel_error = valid_results['CO2_relative_error'].median()\n",
+ " \n",
+ " metrics = {\n",
+ " 'CH4': {\n",
+ " 'pearson_correlation': ch4_corr_pearson,\n",
+ " 'spearman_correlation': ch4_corr_spearman,\n",
+ " 'r2_score': ch4_r2,\n",
+ " 'mae': ch4_mae,\n",
+ " 'rmse': ch4_rmse,\n",
+ " 'mean_relative_error_pct': ch4_mean_rel_error,\n",
+ " 'median_relative_error_pct': ch4_median_rel_error\n",
+ " },\n",
+ " 'CO2': {\n",
+ " 'pearson_correlation': co2_corr_pearson,\n",
+ " 'spearman_correlation': co2_corr_spearman,\n",
+ " 'r2_score': co2_r2,\n",
+ " 'mae': co2_mae,\n",
+ " 'rmse': co2_rmse,\n",
+ " 'mean_relative_error_pct': co2_mean_rel_error,\n",
+ " 'median_relative_error_pct': co2_median_rel_error\n",
+ " }\n",
+ " }\n",
+ " \n",
+ " return metrics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "3e6bcd77-7314-4829-8d26-26eab56f418c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rdkit import RDLogger\n",
+ "\n",
+ "# Disable all RDKit logs\n",
+ "RDLogger.DisableLog('rdApp.*') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "04ecf928-10d8-4366-a9d6-1a646301d387",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " Smiles | \n",
+ " Tg | \n",
+ " He | \n",
+ " N2 | \n",
+ " O2 | \n",
+ " CH4 | \n",
+ " CO2 | \n",
+ " synthesizable | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 5117051 | \n",
+ " 5117051 | \n",
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+ " 544.42 | \n",
+ " 343.89398 | \n",
+ " 3284.48720 | \n",
+ " 18341.89400 | \n",
+ " 528.13604 | \n",
+ " 161379.46000 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 1654268 | \n",
+ " 1654268 | \n",
+ " Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... | \n",
+ " 544.42 | \n",
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+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6692275 | \n",
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+ " 3284.48720 | \n",
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+ " False | \n",
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\n",
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+ " 544.42 | \n",
+ " 343.89398 | \n",
+ " 3284.48720 | \n",
+ " 18341.89400 | \n",
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+ " 161379.46000 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6692276 | \n",
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+ " 544.42 | \n",
+ " 343.89398 | \n",
+ " 3284.48720 | \n",
+ " 18341.89400 | \n",
+ " 528.13604 | \n",
+ " 161379.46000 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 10950 | \n",
+ " 10950 | \n",
+ " Ic1cc(Oc2cc(Oc3cc(cc(c3)n3c(=O)c4c(c3=O)cc3c(c... | \n",
+ " 538.15 | \n",
+ " 1.23546 | \n",
+ " 0.00105 | \n",
+ " 0.00404 | \n",
+ " 0.00195 | \n",
+ " 0.00785 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " | 5309014 | \n",
+ " 5309014 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 2598418 | \n",
+ " 2598418 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 746918 | \n",
+ " 746918 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 2255 | \n",
+ " 2255 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 586.80 | \n",
+ " 1.80544 | \n",
+ " 0.00068 | \n",
+ " 0.00330 | \n",
+ " 0.00184 | \n",
+ " 0.00419 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "5117051 5117051 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "1654268 1654268 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "6692275 6692275 Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "830270 830270 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "6692276 6692276 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "... ... ... \n",
+ "10950 10950 Ic1cc(Oc2cc(Oc3cc(cc(c3)n3c(=O)c4c(c3=O)cc3c(c... \n",
+ "5309014 5309014 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2598418 2598418 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "746918 746918 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2255 2255 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "5117051 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "1654268 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692275 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "830270 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692276 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "... ... ... ... ... ... ... \n",
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+ "746918 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "2255 586.80 1.80544 0.00068 0.00330 0.00184 0.00419 \n",
+ "\n",
+ " synthesizable \n",
+ "5117051 False \n",
+ "1654268 False \n",
+ "6692275 False \n",
+ "830270 False \n",
+ "6692276 False \n",
+ "... ... \n",
+ "10950 True \n",
+ "5309014 False \n",
+ "2598418 False \n",
+ "746918 False \n",
+ "2255 False \n",
+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.sort_values(by=['CO2'], ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "576a2486-6184-4d63-9a07-f545c2551df0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "enc_gen = model.encoder.state_dict()\n",
+ "enc_reg = regression_model.encoder.state_dict()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "19e84d1a-56a8-493e-ba56-5eaa5aebb556",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Evaluating property reconstruction fidelity for 100 molecules...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processing batches: 100%|█████████████████████████| 1/1 [00:16<00:00, 16.36s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "================================================================================\n",
+ "PROPERTY RECONSTRUCTION FIDELITY ANALYSIS\n",
+ "================================================================================\n",
+ "Dataset Overview:\n",
+ " Total molecules tested: 100\n",
+ " Valid reconstructions: 93 (93.0%)\n",
+ " Chemically identical: 0 (0.0%)\n",
+ "\n",
+ "============================================================\n",
+ "PROPERTY CORRELATION METRICS\n",
+ "============================================================\n",
+ "CH₄ Permeability:\n",
+ " Pearson correlation: 0.0400\n",
+ " Spearman correlation: -0.0705\n",
+ " R² score: -3.0056\n",
+ " MAE: 82.2622\n",
+ " RMSE: 103.3733\n",
+ " Mean relative error: 19.19%\n",
+ " Median relative error: 24.48%\n",
+ "\n",
+ "CO₂ Permeability:\n",
+ " Pearson correlation: -0.1054\n",
+ " Spearman correlation: -0.3209\n",
+ " R² score: -3.9737\n",
+ " MAE: 26978.8016\n",
+ " RMSE: 33540.0662\n",
+ " Mean relative error: 20.97%\n",
+ " Median relative error: 26.74%\n",
+ "\n",
+ "============================================================\n",
+ "PROPERTY PRESERVATION ASSESSMENT\n",
+ "============================================================\n",
+ "CH₄ Property Preservation Quality:\n",
+ " Excellent (<5.0% error): 32/93 (34.4%)\n",
+ " Good (<15.0% error): 33/93 (35.5%)\n",
+ " Acceptable (<30.0% error): 76/93 (81.7%)\n",
+ "\n",
+ "CO₂ Property Preservation Quality:\n",
+ " Excellent (<5.0% error): 28/93 (30.1%)\n",
+ " Good (<15.0% error): 33/93 (35.5%)\n",
+ " Acceptable (<30.0% error): 57/93 (61.3%)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_smiles = sample_df['Smiles'].tolist()\n",
+ "test_smiles = df.sort_values(by=['CO2'], ascending=False)['Smiles'].tolist()[:100]\n",
+ "\n",
+ "\n",
+ "results_df, metrics = evaluate_property_reconstruction_fidelity(\n",
+ " model=model, \n",
+ " regression_model=regression_expert_model,\n",
+ " tokenizer=tokenizer,\n",
+ " scaler_ch4=scaler_ch4,\n",
+ " scaler_co2=scaler_co2,\n",
+ " test_smiles_list=test_smiles[:1_000],\n",
+ " batch_size=128\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "62c35e28-78ec-4b04-8211-cdc24ee21969",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from tqdm import tqdm\n",
+ "from rdkit import Chem\n",
+ "from rdkit.Chem import MolToSmiles\n",
+ "\n",
+ "def generate_base_embeddings(model, tokenizer, val_df, max_length=256, batch_size=128):\n",
+ " \"\"\"\n",
+ " Generate embeddings for molecules in validation dataset using batch processing\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " val_df: Validation dataframe containing SMILES\n",
+ " max_length: Maximum SMILES sequence length \n",
+ " batch_size: Number of molecules to process in each batch\n",
+ " \n",
+ " Returns:\n",
+ " base_embeddings: Tensor of shape [num_molecules, embedding_dim]\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " smiles_list = val_df['Smiles'].tolist()\n",
+ " embeddings_list = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(smiles_list), batch_size), desc=\"Generating base embeddings in batches\"):\n",
+ " # Get current batch of SMILES\n",
+ " batch_end = min(i + batch_size, len(smiles_list))\n",
+ " batch_smiles = smiles_list[i:batch_end]\n",
+ " \n",
+ " # Tokenize entire batch at once\n",
+ " encoding = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " embedding_batch = model.encoder(encoding['input_ids'], encoding['attention_mask'])\n",
+ " \n",
+ " # Move to CPU and store\n",
+ " embeddings_list.append(embedding_batch.cpu().numpy())\n",
+ " \n",
+ " # Stack all batch embeddings into single array\n",
+ " base_embeddings = np.vstack(embeddings_list)\n",
+ " \n",
+ " return torch.tensor(base_embeddings, dtype=torch.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdd463d7-da61-4064-915b-54ab4c3671ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate base embeddings\n",
+ "print(\"Generating 'base' embeddings\")\n",
+ "base_embeddings_s = generate_base_embeddings(regression_model, tokenizer, df[:100])\n",
+ "print(f\"Generated embeddings shape: {base_embeddings.shape}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "9a27dfc8-3510-4339-8799-460f9544a9a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = regression_model.encoder\n",
+ "b = model.encoder\n",
+ "a == b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "3915b796-c57e-44d1-9ff0-7002a7c86499",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/base_embeddings_new_reg.pickle']"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(base_embeddings, '/home/jovyan/simson_training_bolgov/regression/base_embeddings_new_reg.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a9a8d315-965d-493d-8d9d-7fb096e5c96e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "base_embeddings = joblib.load('/home/jovyan/simson_training_bolgov/regression/sample_embeddings.pickle')\n",
+ "#expert_embeddings = joblib.load('/home/jovyan/simson_training_bolgov/regression/expert_embeddings.pickle')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "9778d0de-fc1e-4489-8134-fe50ac868039",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actual_encoder = model.encoder.state_dict()\n",
+ "torch.save(actual_encoder, '/home/jovyan/simson_training_bolgov/regression/actual_encoder_state.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "2ff3dc41-1501-4ca7-8292-01a58924bb44",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training_smiles_set = set(df['Smiles'].tolist())\n",
+ "\n",
+ "def is_valid_polymer(smiles: str) -> bool:\n",
+ " \"\"\"\n",
+ " Checks if a SMILES string represents a valid polymer according to specific rules.\n",
+ "\n",
+ " A valid polymer must:\n",
+ " 1. Be a chemically valid molecule parsable by RDKit.\n",
+ " 2. Contain exactly two 'I' atoms, representing polymer endpoints.\n",
+ " 3. Have identical bond types connecting to both endpoints.\n",
+ " \"\"\"\n",
+ " if not isinstance(smiles, str):\n",
+ " return False\n",
+ "\n",
+ " # Rule 1: Basic chemical validity\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " if mol is None:\n",
+ " return False\n",
+ "\n",
+ " # Rule 2: Must contain exactly two endpoints\n",
+ " if smiles.count('I') != 2:\n",
+ " return False\n",
+ "\n",
+ " # Rule 3: Endpoint bonds must match\n",
+ " try:\n",
+ " # Replace 'I' with a standard dummy atom '[*]' for analysis\n",
+ " mol_with_dummy = Chem.MolFromSmiles(smiles.replace('I', '[*]'))\n",
+ " if mol_with_dummy is None:\n",
+ " return False\n",
+ "\n",
+ " # Find the atoms connected to the dummy endpoints\n",
+ " matches = mol_with_dummy.GetSubstructMatches(Chem.MolFromSmarts('[#0]~*'))\n",
+ "\n",
+ " if len(matches) != 2:\n",
+ " return False\n",
+ "\n",
+ " # Get the bonds connecting to the endpoints\n",
+ " bond1 = mol_with_dummy.GetBondBetweenAtoms(matches[0][0], matches[0][1])\n",
+ " bond2 = mol_with_dummy.GetBondBetweenAtoms(matches[1][0], matches[1][1])\n",
+ "\n",
+ " if bond1 is None or bond2 is None:\n",
+ " return False\n",
+ "\n",
+ " # Check if the bond types are identical\n",
+ " if bond1.GetBondType() != bond2.GetBondType():\n",
+ " return False\n",
+ " except Exception:\n",
+ " # Catch any RDKit parsing or processing errors\n",
+ " return False\n",
+ "\n",
+ " return True\n",
+ "\n",
+ "def is_novel_and_valid_polymer(smiles: str, training_set: set) -> bool:\n",
+ " \"\"\"\n",
+ " Performs a final check to ensure a molecule is both novel and a valid polymer.\n",
+ " \"\"\"\n",
+ " # Check 1: Is it new?\n",
+ " if smiles in training_set:\n",
+ " print('NOT NEW')\n",
+ " return False\n",
+ " \n",
+ " # Check 2: Does it meet polymer criteria?\n",
+ " return is_valid_polymer(smiles)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "fe3a75c9-eb84-4854-a477-d401761baba1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " Smiles | \n",
+ " Tg | \n",
+ " He | \n",
+ " N2 | \n",
+ " O2 | \n",
+ " CH4 | \n",
+ " CO2 | \n",
+ " synthesizable | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
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+ " Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... | \n",
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+ " 1 | \n",
+ " Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... | \n",
+ " 508.26 | \n",
+ " 5.33815 | \n",
+ " 2.97239 | \n",
+ " 26.31118 | \n",
+ " 0.86467 | \n",
+ " 82.37635 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2 | \n",
+ " O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... | \n",
+ " 640.91 | \n",
+ " 20.47515 | \n",
+ " 0.06353 | \n",
+ " 0.90498 | \n",
+ " 0.06905 | \n",
+ " 2.35993 | \n",
+ " False | \n",
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\n",
+ " \n",
+ " | 3 | \n",
+ " 3 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... | \n",
+ " 568.04 | \n",
+ " 4.19692 | \n",
+ " 0.00191 | \n",
+ " 0.01134 | \n",
+ " 0.00362 | \n",
+ " 0.01418 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... | \n",
+ " 548.10 | \n",
+ " 142.68327 | \n",
+ " 0.87380 | \n",
+ " 8.25409 | \n",
+ " 2.52067 | \n",
+ " 30.04739 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
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+ " ... | \n",
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+ " 6726945 | \n",
+ " Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... | \n",
+ " 516.27 | \n",
+ " 13.32967 | \n",
+ " 0.11907 | \n",
+ " 1.10144 | \n",
+ " 0.16907 | \n",
+ " 2.63642 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726946 | \n",
+ " 6726946 | \n",
+ " Ic1ccc(nc1)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(... | \n",
+ " 510.72 | \n",
+ " 8.96454 | \n",
+ " 7.92257 | \n",
+ " 72.92929 | \n",
+ " 2.17324 | \n",
+ " 247.14446 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726947 | \n",
+ " 6726947 | \n",
+ " Ic1ccc(cn1)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)... | \n",
+ " 610.24 | \n",
+ " 4.90758 | \n",
+ " 20.24364 | \n",
+ " 121.24494 | \n",
+ " 1.01011 | \n",
+ " 650.18364 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 6726948 | \n",
+ " 6726948 | \n",
+ " Ic1ccc(c(c1)C)Oc1ccc2c(c1)Cc1c2ccc(c1)Oc1ccc(c... | \n",
+ " 510.90 | \n",
+ " 12.40907 | \n",
+ " 0.19307 | \n",
+ " 1.41335 | \n",
+ " 0.11728 | \n",
+ " 4.00573 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " | 6726949 | \n",
+ " 6726949 | \n",
+ " Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... | \n",
+ " 462.31 | \n",
+ " 14.96342 | \n",
+ " 0.13916 | \n",
+ " 1.05252 | \n",
+ " 0.09298 | \n",
+ " 2.54411 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... \n",
+ "... ... ... \n",
+ "6726945 6726945 Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... \n",
+ "6726946 6726946 Ic1ccc(nc1)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(... \n",
+ "6726947 6726947 Ic1ccc(cn1)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)... \n",
+ "6726948 6726948 Ic1ccc(c(c1)C)Oc1ccc2c(c1)Cc1c2ccc(c1)Oc1ccc(c... \n",
+ "6726949 6726949 Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "0 494.84 2.69524 4.75740 42.31847 1.64086 148.43644 \n",
+ "1 508.26 5.33815 2.97239 26.31118 0.86467 82.37635 \n",
+ "2 640.91 20.47515 0.06353 0.90498 0.06905 2.35993 \n",
+ "3 568.04 4.19692 0.00191 0.01134 0.00362 0.01418 \n",
+ "4 548.10 142.68327 0.87380 8.25409 2.52067 30.04739 \n",
+ "... ... ... ... ... ... ... \n",
+ "6726945 516.27 13.32967 0.11907 1.10144 0.16907 2.63642 \n",
+ "6726946 510.72 8.96454 7.92257 72.92929 2.17324 247.14446 \n",
+ "6726947 610.24 4.90758 20.24364 121.24494 1.01011 650.18364 \n",
+ "6726948 510.90 12.40907 0.19307 1.41335 0.11728 4.00573 \n",
+ "6726949 462.31 14.96342 0.13916 1.05252 0.09298 2.54411 \n",
+ "\n",
+ " synthesizable \n",
+ "0 False \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False \n",
+ "... ... \n",
+ "6726945 False \n",
+ "6726946 False \n",
+ "6726947 False \n",
+ "6726948 True \n",
+ "6726949 False \n",
+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "aa424b6f-5cf4-42aa-bdce-83eff2b690c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pred_from_embeds(embeds, regression_model):\n",
+ " \n",
+ " x = regression_model.relu(embeds)\n",
+ " return regression_model.clf(x)\n",
+ "\n",
+ "def gradient_based_extrapolation(\n",
+ " generative_model, regression_model, regression_expert_model, embeddings, scaler, target_idx=-2,\n",
+ " learning_rate=0.0001, steps=50, batch_size=32, best_idx=None, lambda_reg=0.3, scale_factor=1000, expert_threshold=120_000\n",
+ "):\n",
+ " \"\"\"\n",
+ " Use property gradients to guide extrapolation, with checks for novelty and polymer validity.\n",
+ " If an invalid or non-novel molecule is generated, optimization stops.\n",
+ " \"\"\"\n",
+ " device = next(regression_model.parameters()).device\n",
+ "\n",
+ " # --- Freeze models to prevent parameter updates ---\n",
+ " for param in regression_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_model.eval()\n",
+ "\n",
+ " for param in regression_expert_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_expert_model.eval()\n",
+ " \n",
+ " for param in generative_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " generative_model.eval()\n",
+ "\n",
+ " # --- 1. Find the best starting embedding (batched) ---\n",
+ " if best_idx is None:\n",
+ " properties = []\n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(embeddings), batch_size), desc=\"Computing initial properties\"):\n",
+ " batch = embeddings[i:i+batch_size]\n",
+ " preds = regression_model.clf(batch)\n",
+ " properties.append(preds[:, target_idx].cpu())\n",
+ " properties = torch.cat(properties)\n",
+ " best_idx = torch.argmax(properties)\n",
+ "\n",
+ " start_embedding = embeddings[best_idx].cuda().reshape(1, -1).requires_grad_(True)\n",
+ " initial_embedding = start_embedding.clone().cuda()\n",
+ " # This will store the last embedding that successfully passed all checks\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ "\n",
+ " # --- ADDITION: Store initial prediction for MAE calculation ---\n",
+ " with torch.no_grad():\n",
+ " initial_pred_scaled = pred_from_embeds(initial_embedding, regression_model)[:, target_idx]\n",
+ " initial_pred_unscaled = initial_pred_scaled * torch.tensor(scaler.scale_, device=device) + torch.tensor(scaler.mean_, device=device)\n",
+ " initial_prediction_value = initial_pred_unscaled.item()\n",
+ "\n",
+ " optimizer = torch.optim.Adam([start_embedding], lr=learning_rate)\n",
+ "\n",
+ " # --- Extract scaler attributes for PyTorch-based inverse transform ---\n",
+ " scale = torch.tensor(scaler.scale_, device=device, dtype=torch.float32)\n",
+ " mean = torch.tensor(scaler.mean_, device=device, dtype=torch.float32)\n",
+ " # --- 2. Run gradient ascent with step-by-step checks ---\n",
+ " predicting_model = regression_expert_model\n",
+ " pred_unscaled = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " pred = pred_from_embeds(start_embedding.cuda(), regression_model)\n",
+ " sample_scaled = pred[:, target_idx]\n",
+ " sample_unscaled = sample_scaled * scale + mean\n",
+ " print(f'FIRST PRED: {sample_unscaled.detach().item()}') \n",
+ "\n",
+ " for step in range(steps):\n",
+ " if pred_unscaled >= expert_threshold:\n",
+ " predicting_model = regression_expert_model\n",
+ " else:\n",
+ " predicting_model = regression_expert_model\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " pred_scaled = pred_from_embeds(start_embedding.cuda(), regression_model)[:, target_idx]\n",
+ " pred_unscaled = pred_scaled * scale + mean\n",
+ " property_loss = -pred_unscaled.mean()\n",
+ " regularization_loss = torch.norm(start_embedding - initial_embedding, p=2)**2\n",
+ " loss = property_loss + scale_factor * (lambda_reg * regularization_loss)\n",
+ " \n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " start_embedding.clamp_(-20, 20)\n",
+ "\n",
+ " # --- VALIDATION BLOCK: Check molecule after each step ---\n",
+ " with torch.no_grad():\n",
+ " reconstructed_ids = generative_model.generate(start_embedding.cuda(), temperature=1.7)\n",
+ " reconstructed_smiles = generative_model.tokenizer.decode(reconstructed_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " # Use the combined helper function for all checks\n",
+ " is_valid_and_novel = is_novel_and_valid_polymer(reconstructed_smiles, training_smiles_set)\n",
+ " \n",
+ " print(f\"Step {step+1}/{steps} | Value: {pred_unscaled.item():.4f} | SMILES: {reconstructed_smiles} | Novel & Valid Polymer: {is_valid_and_novel}\")\n",
+ "\n",
+ " if is_valid_and_novel:\n",
+ " # Tokenize the reconstructed SMILES\n",
+ " recon_tokens = generative_model.tokenizer(\n",
+ " reconstructed_smiles,\n",
+ " max_length=512,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embedding from the reconstructed SMILES\n",
+ " recon_embedding = generative_model.encoder(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " \n",
+ " CO2_scaled = pred_from_embeds(recon_embedding, predicting_model)[:, target_idx]\n",
+ " CO2_unscaled = CO2_scaled * scale + mean\n",
+ " \n",
+ " print(f\" -> Reconstructed molecule CO2 permeability: {CO2_unscaled.item():.4f}\")\n",
+ " \n",
+ " # --- ADDITION 2: Calculate MAE between initial and reconstructed prediction ---\n",
+ " mae_value = float(abs(CO2_unscaled + property_loss))\n",
+ " print(f\" -> MAE between initial embedding prediction and current prediction: {mae_value:.4f}\")\n",
+ " \n",
+ "\n",
+ " # If it passes all checks, save this as the current best state\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ " else:\n",
+ " pass\n",
+ "\n",
+ " print(f'FINAL VALUE: {pred_unscaled.detach().item():.4f}')\n",
+ " return last_valid_embedding\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53916086-d696-47e4-8984-9e4824bbd4d2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "34e0c67c-ad86-4b7c-9b3c-8132d9aec45b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(161379.46, 5117051)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_df = df.sort_values(by=['CO2'], ascending=False)\n",
+ "best_idx = sorted_df.index.tolist()[0]\n",
+ "best_co2 = float(sorted_df['CO2'][best_idx])\n",
+ "best_embedding = base_embeddings[best_idx, :].reshape(1, -1).cuda()\n",
+ "best_co2, best_idx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4394402-1921-448d-8bd8-bcca0cd997f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimized_embedding = gradient_based_extrapolation(\n",
+ " model, regression_model, regression_model, base_embeddings, scaler_co2, target_idx=-1, learning_rate=1e-2, steps=30, batch_size=32, best_idx=500, lambda_reg=1, scale_factor=0, expert_threshold=100_000\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1dc47ec-4ce4-44c0-b5da-c14ade1b415b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "example_novel_polymer = 'Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)N1C(=O)c2c(C1=O)c(ccc2)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc2c(c1)C(=O)N(C2=O)I'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "d30a41fe-a8a8-4c6c-9e48-d5403a079f36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson = sample_df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f4443775-23a3-4c34-a8d4-3ba4cd5744d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!cp -r polygnn_kit/polygnn_kit ."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "30876742-2f6e-4564-90e5-6b186a505fac",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "'GT Open Source General Use License.pdf' polygnn_kit.py tests\n",
+ " __init__.py\t\t\t\t __pycache__\t utils.py\n",
+ " poetry.lock\t\t\t\t pyproject.toml\n",
+ " polygnn_kit\t\t\t\t README.md\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cd polygnn_kit && ls"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "2d56df39-af0c-4d87-8966-9a5ee6c776cc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!rm -rf polygnn_kit/polygnn_kit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d371d73c-6362-4ad7-bf69-2e54ccfac3eb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'exp_perm_CH4__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.07918119430542, min: -3.387216091156006))',\n",
+ " 'exp_perm_CO2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.645422458648682, min: -5.92081880569458))',\n",
+ " 'exp_perm_H2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 4.22788667678833, min: -1.642065167427063))',\n",
+ " 'exp_perm_He__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.8041393756866455, min: -1.2612193822860718))',\n",
+ " 'exp_perm_N2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.7075700759887695, min: -3.795880079269409))',\n",
+ " 'exp_perm_O2__Barrer': 'Forward(MinMaxScaler(dim: 0, max: 3.931457757949829, min: -6.15490198135376))',\n",
+ " 'exp_solubility__MPa**0.5': 'Forward(MinMaxScaler(dim: 0, max: 29.200000762939453, min: 12.300000190734863))'}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "import json\n",
+ "\n",
+ "with open('polygnn/trained_models/solubility_and_permeability/metadata/selectors.json', 'r') as file:\n",
+ " a = json.load(file)\n",
+ " selectors = a['exp_perm_CO2__Barrer'][0]\n",
+ "\n",
+ "with open('polygnn/trained_models/solubility_and_permeability/metadata/scalers.json', 'r') as file:\n",
+ " scalers = json.load(file)\n",
+ " polygnn_scaler = scalers['exp_perm_CO2__Barrer']\n",
+ "\n",
+ "scalers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d0977829-6ef6-4272-a934-023c73b78f1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "root_dir = 'polygnn/trained_models/solubility_and_permeability'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "774351fc-6cd0-4626-bcbe-fb6fb0ba4ac7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import pandas as pd\n",
+ "import polygnn\n",
+ "import polygnn_trainer as pt\n",
+ "from tqdm import tqdm\n",
+ "import joblib\n",
+ "import os\n",
+ "\n",
+ "def inverse_minmax(scaled_tensor,\n",
+ " min_val=-5.92081880569458,\n",
+ " max_val= 4.645422458648682):\n",
+ "\n",
+ " return scaled_tensor * (max_val - min_val) + min_val\n",
+ "\n",
+ "def load_polygnn_model(model_path, device='cuda'):\n",
+ " \"\"\"\n",
+ " Load a pre-trained polyGNN model and its configuration dynamically \n",
+ " from pickled files in the metadata folder.\n",
+ " \n",
+ " Args:\n",
+ " model_path (str): Path to the saved model directory.\n",
+ " device (str): Device to load the model on ('cuda' or 'cpu').\n",
+ " \n",
+ " Returns:\n",
+ " tuple: Loaded ensemble model, SMILES featurizer, and the model path.\n",
+ " \"\"\"\n",
+ " metadata_path = os.path.join(model_path, \"metadata\")\n",
+ " if not os.path.exists(metadata_path):\n",
+ " raise FileNotFoundError(f\"Metadata folder not found at {metadata_path}\")\n",
+ "\n",
+ "\n",
+ " \n",
+ " bond_config = polygnn.featurize.BondConfig(True, True, True)\n",
+ " atom_config = polygnn.featurize.AtomConfig(\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " True,\n",
+ " combo_hybrid=False,\n",
+ " aromatic=True,\n",
+ " )\n",
+ " \n",
+ " ensemble = pt.load.load_ensemble(\n",
+ " model_path,\n",
+ " polygnn.models.polyGNN,\n",
+ " device,\n",
+ " submodel_kwargs_dict={\n",
+ " \"node_size\": atom_config.n_features,\n",
+ " \"edge_size\": bond_config.n_features,\n",
+ " \"selector_dim\": len(selectors),\n",
+ " },\n",
+ " \n",
+ " )\n",
+ " \n",
+ " # --- 5. Create the SMILES featurizer with loaded configs ---\n",
+ " import functools\n",
+ " kwargs = dict(bond_config=bond_config,\n",
+ " atom_config=atom_config,\n",
+ " representation=\"trimer\")\n",
+ " smiles_featurizer = functools.partial(polygnn.featurize.get_minimum_graph_tensor, **kwargs)\n",
+ " \n",
+ " print(\"polyGNN model and configuration loaded successfully from pickle files.\")\n",
+ " return ensemble, smiles_featurizer, model_path\n",
+ "\n",
+ "\n",
+ "def predict_co2_permeability_polygnn(smiles_list, ensemble, smiles_featurizer, model_path, device='cuda'):\n",
+ " \"\"\"\n",
+ " Predict CO2 permeability using polyGNN model\n",
+ " \n",
+ " Args:\n",
+ " smiles_list: List of SMILES strings\n",
+ " ensemble: Loaded polyGNN ensemble model\n",
+ " smiles_featurizer: SMILES featurization function\n",
+ " model_path: Path to model directory (for loading scalers)\n",
+ " device: Device for computation\n",
+ " \n",
+ " Returns:\n",
+ " Tensor of CO2 permeability predictions\n",
+ " \"\"\"\n",
+ " # Create a temporary dataframe for prediction\n",
+ " temp_df = pd.DataFrame({\n",
+ " 'smiles_string': smiles_list,\n",
+ " 'prop': ['exp_perm_CO2__Barrer'] * len(smiles_list), # Adjust property name as needed\n",
+ " })\n",
+ " \n",
+ " # Run predictions\n",
+ " with torch.no_grad():\n",
+ " y, y_mean_hat, y_std_hat, _selectors = pt.infer.eval_ensemble(\n",
+ " model=ensemble,\n",
+ " root_dir=model_path,\n",
+ " dataframe=temp_df,\n",
+ " smiles_featurizer=smiles_featurizer,\n",
+ " device=device,\n",
+ " ensemble_kwargs_dict={\"monte_carlo\": False},\n",
+ " )\n",
+ " return y, y_mean_hat, y_std_hat\n",
+ "\n",
+ "def pred_from_embeds(embeds, regression_model):\n",
+ " x = regression_model.relu(embeds)\n",
+ " return regression_model.clf(x)\n",
+ "\n",
+ "def gradient_based_extrapolation(\n",
+ " generative_model, regression_model, regression_expert_model, embeddings, scaler, target_idx=-2,\n",
+ " learning_rate=0.0001, steps=50, batch_size=32, best_idx=None, lambda_reg=0.3, scale_factor=1000, \n",
+ " expert_threshold=120_000, polygnn_model_path=None\n",
+ "):\n",
+ " \"\"\"\n",
+ " Use property gradients to guide extrapolation, with checks for novelty and polymer validity.\n",
+ " Now includes PolyGNN predictions for CO2 permeability.\n",
+ " \n",
+ " Args:\n",
+ " polygnn_model_path: Path to pre-trained polyGNN model for CO2 permeability prediction\n",
+ " \"\"\"\n",
+ " device = next(regression_model.parameters()).device\n",
+ "\n",
+ " # Load PolyGNN model if path is provided\n",
+ " polygnn_ensemble = None\n",
+ " polygnn_featurizer = None\n",
+ " if polygnn_model_path:\n",
+ " polygnn_ensemble, polygnn_featurizer, _ = load_polygnn_model(polygnn_model_path, device)\n",
+ " \n",
+ "\n",
+ " # --- Freeze models to prevent parameter updates ---\n",
+ " for param in regression_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_model.eval()\n",
+ "\n",
+ " for param in regression_expert_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " regression_expert_model.eval()\n",
+ " \n",
+ " for param in generative_model.parameters():\n",
+ " param.requires_grad = False\n",
+ " generative_model.eval()\n",
+ "\n",
+ " # --- 1. Find the best starting embedding (batched) ---\n",
+ " if best_idx is None:\n",
+ " properties = []\n",
+ " with torch.no_grad():\n",
+ " for i in tqdm(range(0, len(embeddings), batch_size), desc=\"Computing initial properties\"):\n",
+ " batch = embeddings[i:i+batch_size]\n",
+ " preds = regression_model.clf(batch)\n",
+ " properties.append(preds[:, target_idx].cpu())\n",
+ " properties = torch.cat(properties)\n",
+ " best_idx = torch.argmax(properties)\n",
+ "\n",
+ " start_embedding = embeddings[best_idx].cuda().reshape(1, -1).requires_grad_(True)\n",
+ " initial_embedding = start_embedding.clone().cuda()\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ "\n",
+ " # --- Store initial prediction for MAE calculation ---\n",
+ " with torch.no_grad():\n",
+ " initial_pred_scaled = pred_from_embeds(initial_embedding, regression_model)[:, target_idx]\n",
+ " initial_pred_unscaled = initial_pred_scaled * torch.tensor(scaler.scale_, device=device) + torch.tensor(scaler.mean_, device=device)\n",
+ " initial_prediction_value = initial_pred_unscaled.item()\n",
+ "\n",
+ " optimizer = torch.optim.Adam([start_embedding], lr=learning_rate)\n",
+ "\n",
+ " # --- Extract scaler attributes for PyTorch-based inverse transform ---\n",
+ " scale = torch.tensor(scaler.scale_, device=device, dtype=torch.float32)\n",
+ " mean = torch.tensor(scaler.mean_, device=device, dtype=torch.float32)\n",
+ " \n",
+ " # --- 2. Run gradient ascent with step-by-step checks ---\n",
+ " predicting_model = regression_expert_model\n",
+ " pred_unscaled = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " pred = pred_from_embeds(start_embedding.cuda(), regression_model)\n",
+ " sample_scaled = pred[:, target_idx]\n",
+ " sample_unscaled = sample_scaled * scale + mean\n",
+ " print(f'FIRST PRED: {sample_unscaled.detach().item()}') \n",
+ "\n",
+ " for step in range(steps):\n",
+ " if pred_unscaled >= expert_threshold:\n",
+ " predicting_model = regression_expert_model\n",
+ " else:\n",
+ " predicting_model = regression_expert_model\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " pred_scaled = pred_from_embeds(start_embedding.cuda(), regression_model)[:, target_idx]\n",
+ " pred_unscaled = pred_scaled * scale + mean\n",
+ " property_loss = -pred_unscaled.mean()\n",
+ " regularization_loss = torch.norm(start_embedding - initial_embedding, p=2)**2\n",
+ " loss = property_loss + scale_factor * (lambda_reg * regularization_loss)\n",
+ " \n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " start_embedding.clamp_(-20, 20)\n",
+ "\n",
+ " # --- VALIDATION BLOCK: Check molecule after each step ---\n",
+ " with torch.no_grad():\n",
+ " reconstructed_ids = generative_model.generate(start_embedding.cuda(), temperature=1.5)\n",
+ " reconstructed_smiles = generative_model.tokenizer.decode(reconstructed_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " # Use the combined helper function for all checks\n",
+ " is_valid_and_novel = is_novel_and_valid_polymer(reconstructed_smiles, training_smiles_set)\n",
+ " \n",
+ " print(f\"Step {step+1}/{steps} | Value: {pred_unscaled.item():.4f} | SMILES: {reconstructed_smiles} | Novel & Valid Polymer: {is_valid_and_novel}\")\n",
+ "\n",
+ " if is_valid_and_novel:\n",
+ " # Tokenize the reconstructed SMILES\n",
+ " recon_tokens = generative_model.tokenizer(\n",
+ " reconstructed_smiles,\n",
+ " max_length=512,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " ).to(device)\n",
+ " \n",
+ " # Get embedding from the reconstructed SMILES\n",
+ " recon_embedding = generative_model.encoder(recon_tokens['input_ids'], recon_tokens['attention_mask'])\n",
+ " \n",
+ " CO2_scaled = pred_from_embeds(recon_embedding, predicting_model)[:, target_idx]\n",
+ " CO2_unscaled = CO2_scaled * scale + mean\n",
+ " \n",
+ " print(f\" -> Reconstructed molecule CO2 permeability: {CO2_unscaled.item():.4f}\")\n",
+ " \n",
+ " # --- NEW: PolyGNN CO2 permeability prediction ---\n",
+ " if polygnn_ensemble and polygnn_featurizer:\n",
+ " corrected_smiles = reconstructed_smiles.replace(\"I\", \"[*]\")\n",
+ " print(corrected_smiles)\n",
+ " polygnn_pred = predict_co2_permeability_polygnn(\n",
+ " [corrected_smiles], \n",
+ " polygnn_ensemble, \n",
+ " polygnn_featurizer, \n",
+ " polygnn_model_path, \n",
+ " device\n",
+ " )\n",
+ " polygnn_pred = inverse_minmax(polygnn_pred)\n",
+ " print(f\" -> PolyGNN CO2 permeability prediction: {polygnn_pred.item():.4f}\")\n",
+ " \n",
+ " # Compare predictions\n",
+ " pred_diff = abs(CO2_unscaled.item() - polygnn_pred.item())\n",
+ " print(f\" -> Prediction difference (|Main - PolyGNN|): {pred_diff:.4f}\")\n",
+ " \n",
+ "\n",
+ " \n",
+ " # --- Calculate MAE between initial and reconstructed prediction ---\n",
+ " mae_value = float(abs(CO2_unscaled + property_loss))\n",
+ " print(f\" -> MAE between initial embedding prediction and current prediction: {mae_value:.4f}\")\n",
+ "\n",
+ " # If it passes all checks, save this as the current best state\n",
+ " last_valid_embedding = start_embedding.clone().detach()\n",
+ " else:\n",
+ " pass\n",
+ "\n",
+ " print(f'FINAL VALUE: {pred_unscaled.detach().item():.4f}')\n",
+ " return last_valid_embedding\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "dd790c00-2e48-4a51-8195-003933fc92c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I'"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['Smiles'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a0b18aa-e95c-4405-bae3-6f1cc72525ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = gradient_based_extrapolation(\n",
+ " generative_model=model,\n",
+ " regression_model=regression_model,\n",
+ " regression_expert_model=regression_model,\n",
+ " embeddings=base_embeddings,\n",
+ " scaler=scaler_co2,\n",
+ " best_idx=best_idx,\n",
+ " target_idx=-1,\n",
+ " polygnn_model_path=\"polygnn/trained_models/solubility_and_permeability\"\n",
+ " \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "e93b3bd5-89e3-42d9-8a87-46af7bf3c338",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.00419, 166.28537)"
+ ]
+ },
+ "execution_count": 152,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def get_min_max(real_values):\n",
+ " \"\"\"\n",
+ " Derive the Min-Max‐scaler parameters directly from unscaled labels.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " real_values : list | np.ndarray | torch.Tensor\n",
+ " Iterable of ground-truth permeability values (original units).\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " tuple(float, float)\n",
+ " (min_value, max_value) for the dataset.\n",
+ " \"\"\"\n",
+ " arr = np.asarray(real_values, dtype=float)\n",
+ " return float(arr.min()), float(arr.max())\n",
+ "\n",
+ "mi, ma = get_min_max(df.sort_values(by=['CO2'], ascending=True).reset_index(drop=True)[:5_000_000]['CO2'])\n",
+ "mi, ma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "id": "b3c2b0ff-04e9-4274-935f-ab9e8440742b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\n",
+ " \n",
+ " | 10950 | \n",
+ " 10950 | \n",
+ " Ic1cc(Oc2cc(Oc3cc(cc(c3)n3c(=O)c4c(c3=O)cc3c(c... | \n",
+ " 538.15 | \n",
+ " 1.23546 | \n",
+ " 0.00105 | \n",
+ " 0.00404 | \n",
+ " 0.00195 | \n",
+ " 0.00785 | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " | 5309014 | \n",
+ " 5309014 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 2598418 | \n",
+ " 2598418 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 746918 | \n",
+ " 746918 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 552.00 | \n",
+ " 2.56385 | \n",
+ " 0.00138 | \n",
+ " 0.00777 | \n",
+ " 0.00287 | \n",
+ " 0.00772 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ " | 2255 | \n",
+ " 2255 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... | \n",
+ " 586.80 | \n",
+ " 1.80544 | \n",
+ " 0.00068 | \n",
+ " 0.00330 | \n",
+ " 0.00184 | \n",
+ " 0.00419 | \n",
+ " False | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "5117051 5117051 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "1654268 1654268 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "6692275 6692275 Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(cn1)C(C(... \n",
+ "830270 830270 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "6692276 6692276 Ic1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(... \n",
+ "... ... ... \n",
+ "10950 10950 Ic1cc(Oc2cc(Oc3cc(cc(c3)n3c(=O)c4c(c3=O)cc3c(c... \n",
+ "5309014 5309014 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2598418 2598418 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "746918 746918 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "2255 2255 Ic1cc(cc(c1)C(=O)O)C(=O)c1cc(cc(c1)C(=O)O)C(=O... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "5117051 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "1654268 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692275 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "830270 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "6692276 544.42 343.89398 3284.48720 18341.89400 528.13604 161379.46000 \n",
+ "... ... ... ... ... ... ... \n",
+ "10950 538.15 1.23546 0.00105 0.00404 0.00195 0.00785 \n",
+ "5309014 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "2598418 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "746918 552.00 2.56385 0.00138 0.00777 0.00287 0.00772 \n",
+ "2255 586.80 1.80544 0.00068 0.00330 0.00184 0.00419 \n",
+ "\n",
+ " synthesizable \n",
+ "5117051 False \n",
+ "1654268 False \n",
+ "6692275 False \n",
+ "830270 False \n",
+ "6692276 False \n",
+ "... ... \n",
+ "10950 True \n",
+ "5309014 False \n",
+ "2598418 False \n",
+ "746918 False \n",
+ "2255 False \n",
+ "\n",
+ "[6726950 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 151,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.sort_values(by=['CO2'], ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "bfb10438-7d6b-4e73-8856-33fdea15ec72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "25fa51a4-27a0-4f63-872e-3e5f80953efe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['[*]C(=O)c1ccc(N2C(=O)c3ccc(C(=O)c4ccc(C(=O)c5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(C(=O)c8cccc([*])c8C)c(Cl)c6)C7=O)cn5)cn4)cc3C2=O)c(Cl)c1',\n",
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+ " '[*]C(=O)c1ccc2c(c1)C(=O)N(c1ccc3c(c1)Cc1cc(N4C(=O)c5ccc(C(=O)c6ccc7cc([*])ccc7c6)cc5C4=O)ccc1-3)C2=O',\n",
+ " '[*]Sc1cccc(Sc2ccc(C)c(N3C(=O)c4cccc(C(=O)c5cccc(C(=O)c6ccc7c(c6)C(=O)N(c6cc([*])ccc6C)C7=O)c5)c4C3=O)c2)c1',\n",
+ " '[*]C(=O)c1ccc(N2C(=O)c3ccc(Sc4cccc(Sc5ccc6c(c5)C(=O)N(c5ccc(-c7ccc(C)c([*])c7)cc5)C6=O)c4)cc3C2=O)cc1',\n",
+ " '[*]C(=O)c1ccc(N2C(=O)c3ccc(Oc4ccc(Oc5ccc(Oc6ccc7c(c6)C(=O)N(c6ccc(C(=O)c8ccc9c(c8)[nH]c8cc([*])ccc89)c(Cl)c6Cl)C7=O)cc5)cc4)cc3C2=O)c(Cl)c1Cl',\n",
+ " '[*]C(=O)c1ccc2c(c1)C(=O)N(c1ccc(N3C(=O)c4cccc(C(=O)c5ccc(Sc6ccc([*])cc6)cc5)c4C3=O)cc1)C2=O',\n",
+ " '[*]Oc1ccc(Sc2ccc(Oc3ccc4c(c3)C(=O)N(c3cc(Cl)cc(N5C(=O)c6cccc([*])c6C5=O)c3)C4=O)cc2)cc1',\n",
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+ " '[*]Oc1ccc(N2C(=O)c3ccc(C(=O)c4cc5ccccc5cc4C(=O)c4cccc5c4C(=O)N(c4ccc([*])c(C)c4)C5=O)cc3C2=O)c(Cl)c1',\n",
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+ " '[*]C(=O)c1ccc(N2C(=O)c3ccc(C(=O)c4cccc(Oc5cccc(C(=O)c6cccc7c6C(=O)N(c6ccc(C(=O)c8cc([*])cc(C(=O)O)c8)c(Cl)c6Cl)C7=O)c5)c4)cc3C2=O)c(Cl)c1Cl',\n",
+ " '[*]C(=O)c1ccc(N2C(=O)c3ccc(C(=O)c4ccc5ccc(C(=O)c6ccc7c(c6)C(=O)N(c6ccc(C(=O)c8ccc([*])cc8C)c(Cl)c6)C7=O)cc5c4)cc3C2=O)cc1Cl',\n",
+ " '[*]C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Sc3cccc(Sc4ccc(N5C(=O)c6cccc(C(=O)c7ccc(C(=O)c8ccc([*])cn8)nc7)c6C5=O)c(Cl)c4Cl)c3)c(Cl)c1Cl)C2=O',\n",
+ " '[*]C(=O)c1cccc(N2C(=O)c3cccc(C(=O)c4ccc(C(=O)c5ccc(C(=O)c6cccc7c6C(=O)N(c6cccc(C(=O)c8cc([*])ccc8C)c6)C7=O)cn5)nc4)c3C2=O)c1',\n",
+ " '[*]C(=O)c1cccc(C(=O)c2ccc(N3C(=O)c4ccc(Oc5cccc(Oc6ccc7c(c6)C(=O)N(c6ccc([*])cc6C)C7=O)c5)cc4C3=O)cc2C)c1',\n",
+ " '[*]C(=O)c1ccc(C(=O)c2cccc3c2C(=O)N(c2ccc(Cc4cc(-c5ccc(-c6ccc(C(=O)c7ccc([*])cn7)cc6Cl)c(Cl)c5)cc(C(F)(F)F)c4)c(Cl)c2)C3=O)cn1',\n",
+ " '[*]C(=O)c1ccc2c(c1)C(=O)N(c1ccc(Oc3ccc4c(c3)[nH]c3cc(Oc5ccc(N6C(=O)c7ccc(C(=O)c8ccc9ccc([*])cc9c8)cc7C6=O)c(Cl)c5)ccc34)cc1Cl)C2=O',\n",
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+ " '[*]Nc1cccc(C(=O)c2ccc3c(c2)C(=O)N(c2ccc4cc(N5C(=O)c6ccc(C(=O)c7cccc(C([*])=O)c7)cc6C5=O)ccc4c2)C3=O)c1',\n",
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+ "source": [
+ "df_sample = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/polyGNN_combined_mols_.csv')[['SMILES', 'exp_perm_CO2__Barrer_mean']]\n",
+ "df_sample = df_sample.iloc[:100]\n",
+ "df_sample['SMILES'].to_list()"
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+ "id": "875a49f5-3aad-466e-a84f-3d77055a5ae4",
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+ "text": [
+ "polyGNN model and configuration loaded successfully from pickle files.\n",
+ "The following properties will be modeled: ['exp_perm_CO2__Barrer']\n",
+ "Detected 100 data points for exp_perm_CO2__Barrer\n",
+ "[]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "def try_normalize(smiles):\n",
+ " # функция выполняет перевод молекулы в формат rdkit и обратно. Это фильтрует некорректные молекулы и нормализует их, т.е. приводит к единому виду, чтобы затем можно было отфильтровать дубликаты молекул\n",
+ " try:\n",
+ " return Chem.MolToSmiles(Chem.MolFromSmiles(smiles))\n",
+ " except Exception as e:\n",
+ " # print(e)\n",
+ " return None\n",
+ "\n",
+ "polygnn_ensemble, polygnn_featurizer, path = load_polygnn_model(\"polygnn/trained_models/solubility_and_permeability\", 'cuda')\n",
+ "\n",
+ "def make_pred(smiles):\n",
+ " corrected_smiles = [try_normalize(smile) for smile in smiles]\n",
+ " corrected_smiles = [smile.replace(\"I\", \"[*]\") for smile in smiles]\n",
+ " \n",
+ " #corrected_smiles = [smile.replace('*', 'I') for smile in corrected_smiles]\n",
+ " y, mean, dev = predict_co2_permeability_polygnn(\n",
+ " corrected_smiles, \n",
+ " polygnn_ensemble, \n",
+ " polygnn_featurizer, \n",
+ " path, \n",
+ " 'cuda'\n",
+ " )\n",
+ " polygnn_preds = [10**(pred) for pred in mean]\n",
+ " return polygnn_preds\n",
+ "\n",
+ "df_sample = df.iloc[:100]\n",
+ "new_preds = make_pred(df_sample['Smiles'].to_list())"
+ ]
+ },
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+ "execution_count": 75,
+ "id": "9c0245f1-3a68-4f9b-b799-407e3436b4b5",
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+ " 640.91 | \n",
+ " 20.47515 | \n",
+ " 0.06353 | \n",
+ " 0.90498 | \n",
+ " 0.06905 | \n",
+ " 2.35993 | \n",
+ " False | \n",
+ " 21.385293 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 3 | \n",
+ " Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... | \n",
+ " 568.04 | \n",
+ " 4.19692 | \n",
+ " 0.00191 | \n",
+ " 0.01134 | \n",
+ " 0.00362 | \n",
+ " 0.01418 | \n",
+ " False | \n",
+ " 0.802363 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... | \n",
+ " 548.10 | \n",
+ " 142.68327 | \n",
+ " 0.87380 | \n",
+ " 8.25409 | \n",
+ " 2.52067 | \n",
+ " 30.04739 | \n",
+ " False | \n",
+ " 132.455960 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 5 | \n",
+ " Cc1cc(Sc2ccc(nc2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=... | \n",
+ " 501.82 | \n",
+ " 12.37968 | \n",
+ " 1.68540 | \n",
+ " 12.25254 | \n",
+ " 1.03299 | \n",
+ " 67.42722 | \n",
+ " False | \n",
+ " 21.316367 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 6 | \n",
+ " Cc1cc(Sc2ccc(cn2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=... | \n",
+ " 501.82 | \n",
+ " 12.37968 | \n",
+ " 1.68540 | \n",
+ " 12.25254 | \n",
+ " 1.03299 | \n",
+ " 67.42722 | \n",
+ " False | \n",
+ " 21.316372 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 7 | \n",
+ " Clc1cc(ccc1C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1)Cl)... | \n",
+ " 529.64 | \n",
+ " 10.15046 | \n",
+ " 0.02021 | \n",
+ " 0.25611 | \n",
+ " 0.13249 | \n",
+ " 0.51867 | \n",
+ " False | \n",
+ " 1.720072 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 8 | \n",
+ " Ic1ccc(c(c1)Cl)C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1... | \n",
+ " 529.64 | \n",
+ " 10.15046 | \n",
+ " 0.02021 | \n",
+ " 0.25611 | \n",
+ " 0.13249 | \n",
+ " 0.51867 | \n",
+ " False | \n",
+ " 1.720071 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 9 | \n",
+ " Ic1ccc(c(c1)C)C(=O)c1cc2ccccc2cc1C(=O)c1ccc(cc... | \n",
+ " 556.31 | \n",
+ " 20.84491 | \n",
+ " 0.05426 | \n",
+ " 0.43648 | \n",
+ " 0.06503 | \n",
+ " 0.84243 | \n",
+ " False | \n",
+ " 3.767171 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 10 | \n",
+ " Ic1ccc(c(c1)Sc1cc(cc(c1)C(=O)O)Sc1cc(ccc1C)N1C... | \n",
+ " 470.24 | \n",
+ " 3.03201 | \n",
+ " 0.02144 | \n",
+ " 0.15934 | \n",
+ " 0.02558 | \n",
+ " 0.33708 | \n",
+ " True | \n",
+ " 1.093549 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 11 | \n",
+ " Cc1cc(cc(c1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C... | \n",
+ " 575.93 | \n",
+ " 382.37918 | \n",
+ " 4.71113 | \n",
+ " 27.50459 | \n",
+ " 4.06784 | \n",
+ " 134.46742 | \n",
+ " False | \n",
+ " 262.278064 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 12 | \n",
+ " Ic1cc(C)c(c(c1)C)C(c1ccc2c(c1)[nH]c1c2ccc(c1)C... | \n",
+ " 537.39 | \n",
+ " 108.94866 | \n",
+ " 1.07881 | \n",
+ " 8.35180 | \n",
+ " 1.67890 | \n",
+ " 39.29826 | \n",
+ " True | \n",
+ " 206.577505 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 13 | \n",
+ " Ic1cc(Oc2ccc(cc2)C2(c3ccc(cc3)Oc3cc(cc(c3)N3C(... | \n",
+ " 556.56 | \n",
+ " 4.47037 | \n",
+ " 0.04159 | \n",
+ " 0.28007 | \n",
+ " 0.03887 | \n",
+ " 0.73329 | \n",
+ " True | \n",
+ " 9.642768 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 14 | \n",
+ " Ic1ccc(c(c1)Cl)Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)... | \n",
+ " 481.62 | \n",
+ " 4.67411 | \n",
+ " 0.01464 | \n",
+ " 0.17388 | \n",
+ " 0.05333 | \n",
+ " 0.64280 | \n",
+ " False | \n",
+ " 1.471597 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 15 | \n",
+ " Clc1cc(ccc1Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)Cl)I... | \n",
+ " 481.62 | \n",
+ " 4.67411 | \n",
+ " 0.01464 | \n",
+ " 0.17388 | \n",
+ " 0.05333 | \n",
+ " 0.64280 | \n",
+ " False | \n",
+ " 1.471597 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 16 | \n",
+ " Cc1cc(ccc1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O... | \n",
+ " 596.88 | \n",
+ " 18.21650 | \n",
+ " 0.09935 | \n",
+ " 0.68089 | \n",
+ " 0.12015 | \n",
+ " 2.16558 | \n",
+ " True | \n",
+ " 2.989342 | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " 17 | \n",
+ " Ic1ccc(c(c1)C)C(c1ccc(cc1C)C(c1ccc(c(c1)C)N1C(... | \n",
+ " 467.07 | \n",
+ " 58.20149 | \n",
+ " 0.51975 | \n",
+ " 3.97824 | \n",
+ " 0.68842 | \n",
+ " 15.89978 | \n",
+ " True | \n",
+ " 35.433246 | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " 18 | \n",
+ " O=C1c2cccc(c2C(=O)N1c1ccc(c(c1)C)C(c1ccc(cc1C)... | \n",
+ " 467.07 | \n",
+ " 58.20149 | \n",
+ " 0.51975 | \n",
+ " 3.97824 | \n",
+ " 0.68842 | \n",
+ " 15.89978 | \n",
+ " True | \n",
+ " 35.433265 | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " 19 | \n",
+ " Cc1cc(c(cc1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C... | \n",
+ " 668.95 | \n",
+ " 35.18491 | \n",
+ " 0.13804 | \n",
+ " 1.23473 | \n",
+ " 0.07419 | \n",
+ " 4.58177 | \n",
+ " False | \n",
+ " 7.471155 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " 20 | \n",
+ " O=C1c2cc(ccc2c2c1cc(cc2)C(C(F)(F)F)(C(F)(F)F)c... | \n",
+ " 543.52 | \n",
+ " 111.91630 | \n",
+ " 1.33930 | \n",
+ " 9.69874 | \n",
+ " 0.95233 | \n",
+ " 24.32080 | \n",
+ " False | \n",
+ " 241.805804 | \n",
+ "
\n",
+ " \n",
+ " | 21 | \n",
+ " 21 | \n",
+ " Ic1ccc(cn1)C(c1cc(Cl)cc(c1)C(c1ccc(nc1)N1C(=O)... | \n",
+ " 508.98 | \n",
+ " 15.30320 | \n",
+ " 7.27329 | \n",
+ " 75.86246 | \n",
+ " 8.07197 | \n",
+ " 315.19387 | \n",
+ " False | \n",
+ " 8.272223 | \n",
+ "
\n",
+ " \n",
+ " | 22 | \n",
+ " 22 | \n",
+ " O=C1c2cccc(c2C(=O)N1c1cc(C)c(c(c1)C)S(=O)(=O)c... | \n",
+ " 628.48 | \n",
+ " 39.34309 | \n",
+ " 0.18496 | \n",
+ " 1.18768 | \n",
+ " 0.02477 | \n",
+ " 3.25713 | \n",
+ " True | \n",
+ " 25.922022 | \n",
+ "
\n",
+ " \n",
+ " | 23 | \n",
+ " 23 | \n",
+ " Ic1cc(C)c(c(c1)C)S(=O)(=O)c1ccc2c(c1)ccc(c2)S(... | \n",
+ " 628.48 | \n",
+ " 39.34309 | \n",
+ " 0.18496 | \n",
+ " 1.18768 | \n",
+ " 0.02477 | \n",
+ " 3.25713 | \n",
+ " True | \n",
+ " 25.922022 | \n",
+ "
\n",
+ " \n",
+ " | 24 | \n",
+ " 24 | \n",
+ " Ic1cc(Sc2cc(cc(c2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O... | \n",
+ " 564.08 | \n",
+ " 1.07922 | \n",
+ " 0.00146 | \n",
+ " 0.00629 | \n",
+ " 0.00217 | \n",
+ " 0.01674 | \n",
+ " True | \n",
+ " 0.422021 | \n",
+ "
\n",
+ " \n",
+ " | 25 | \n",
+ " 25 | \n",
+ " Ic1ccc(cn1)C(=O)c1cc(C)c(c(c1)C)C(=O)c1ccc(cn1... | \n",
+ " 564.33 | \n",
+ " 11.39561 | \n",
+ " 11.73863 | \n",
+ " 80.00608 | \n",
+ " 8.36759 | \n",
+ " 438.07856 | \n",
+ " False | \n",
+ " 6.608806 | \n",
+ "
\n",
+ " \n",
+ " | 26 | \n",
+ " 26 | \n",
+ " Ic1ccc(nc1)C(=O)c1c(C)cc(cc1C)C(=O)c1ccc(nc1)n... | \n",
+ " 564.33 | \n",
+ " 11.39561 | \n",
+ " 11.73863 | \n",
+ " 80.00608 | \n",
+ " 8.36759 | \n",
+ " 438.07856 | \n",
+ " False | \n",
+ " 6.608806 | \n",
+ "
\n",
+ " \n",
+ " | 27 | \n",
+ " 27 | \n",
+ " Ic1cc(cc(c1)C(F)(F)F)C(=O)c1cc(C)c(cc1C)C(=O)c... | \n",
+ " 526.25 | \n",
+ " 47.17589 | \n",
+ " 0.25597 | \n",
+ " 1.83550 | \n",
+ " 0.38171 | \n",
+ " 4.23498 | \n",
+ " False | \n",
+ " 14.162805 | \n",
+ "
\n",
+ " \n",
+ " | 28 | \n",
+ " 28 | \n",
+ " Ic1ccc(cn1)S(=O)(=O)c1cc(C)c(c(c1)C)c1c(C)cc(c... | \n",
+ " 618.79 | \n",
+ " 21.18174 | \n",
+ " 8.28968 | \n",
+ " 54.04887 | \n",
+ " 0.66770 | \n",
+ " 251.39951 | \n",
+ " False | \n",
+ " 41.374632 | \n",
+ "
\n",
+ " \n",
+ " | 29 | \n",
+ " 29 | \n",
+ " Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1cc(C)c(c(c1... | \n",
+ " 562.21 | \n",
+ " 208.21051 | \n",
+ " 2.21987 | \n",
+ " 15.29097 | \n",
+ " 2.58383 | \n",
+ " 58.80082 | \n",
+ " False | \n",
+ " 373.653450 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles Tg \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... 494.84 \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... 508.26 \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... 640.91 \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... 568.04 \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... 548.10 \n",
+ "5 5 Cc1cc(Sc2ccc(nc2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=... 501.82 \n",
+ "6 6 Cc1cc(Sc2ccc(cn2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=... 501.82 \n",
+ "7 7 Clc1cc(ccc1C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1)Cl)... 529.64 \n",
+ "8 8 Ic1ccc(c(c1)Cl)C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1... 529.64 \n",
+ "9 9 Ic1ccc(c(c1)C)C(=O)c1cc2ccccc2cc1C(=O)c1ccc(cc... 556.31 \n",
+ "10 10 Ic1ccc(c(c1)Sc1cc(cc(c1)C(=O)O)Sc1cc(ccc1C)N1C... 470.24 \n",
+ "11 11 Cc1cc(cc(c1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C... 575.93 \n",
+ "12 12 Ic1cc(C)c(c(c1)C)C(c1ccc2c(c1)[nH]c1c2ccc(c1)C... 537.39 \n",
+ "13 13 Ic1cc(Oc2ccc(cc2)C2(c3ccc(cc3)Oc3cc(cc(c3)N3C(... 556.56 \n",
+ "14 14 Ic1ccc(c(c1)Cl)Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)... 481.62 \n",
+ "15 15 Clc1cc(ccc1Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)Cl)I... 481.62 \n",
+ "16 16 Cc1cc(ccc1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O... 596.88 \n",
+ "17 17 Ic1ccc(c(c1)C)C(c1ccc(cc1C)C(c1ccc(c(c1)C)N1C(... 467.07 \n",
+ "18 18 O=C1c2cccc(c2C(=O)N1c1ccc(c(c1)C)C(c1ccc(cc1C)... 467.07 \n",
+ "19 19 Cc1cc(c(cc1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C... 668.95 \n",
+ "20 20 O=C1c2cc(ccc2c2c1cc(cc2)C(C(F)(F)F)(C(F)(F)F)c... 543.52 \n",
+ "21 21 Ic1ccc(cn1)C(c1cc(Cl)cc(c1)C(c1ccc(nc1)N1C(=O)... 508.98 \n",
+ "22 22 O=C1c2cccc(c2C(=O)N1c1cc(C)c(c(c1)C)S(=O)(=O)c... 628.48 \n",
+ "23 23 Ic1cc(C)c(c(c1)C)S(=O)(=O)c1ccc2c(c1)ccc(c2)S(... 628.48 \n",
+ "24 24 Ic1cc(Sc2cc(cc(c2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O... 564.08 \n",
+ "25 25 Ic1ccc(cn1)C(=O)c1cc(C)c(c(c1)C)C(=O)c1ccc(cn1... 564.33 \n",
+ "26 26 Ic1ccc(nc1)C(=O)c1c(C)cc(cc1C)C(=O)c1ccc(nc1)n... 564.33 \n",
+ "27 27 Ic1cc(cc(c1)C(F)(F)F)C(=O)c1cc(C)c(cc1C)C(=O)c... 526.25 \n",
+ "28 28 Ic1ccc(cn1)S(=O)(=O)c1cc(C)c(c(c1)C)c1c(C)cc(c... 618.79 \n",
+ "29 29 Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1cc(C)c(c(c1... 562.21 \n",
+ "\n",
+ " He N2 O2 CH4 CO2 synthesizable \\\n",
+ "0 2.69524 4.75740 42.31847 1.64086 148.43644 False \n",
+ "1 5.33815 2.97239 26.31118 0.86467 82.37635 False \n",
+ "2 20.47515 0.06353 0.90498 0.06905 2.35993 False \n",
+ "3 4.19692 0.00191 0.01134 0.00362 0.01418 False \n",
+ "4 142.68327 0.87380 8.25409 2.52067 30.04739 False \n",
+ "5 12.37968 1.68540 12.25254 1.03299 67.42722 False \n",
+ "6 12.37968 1.68540 12.25254 1.03299 67.42722 False \n",
+ "7 10.15046 0.02021 0.25611 0.13249 0.51867 False \n",
+ "8 10.15046 0.02021 0.25611 0.13249 0.51867 False \n",
+ "9 20.84491 0.05426 0.43648 0.06503 0.84243 False \n",
+ "10 3.03201 0.02144 0.15934 0.02558 0.33708 True \n",
+ "11 382.37918 4.71113 27.50459 4.06784 134.46742 False \n",
+ "12 108.94866 1.07881 8.35180 1.67890 39.29826 True \n",
+ "13 4.47037 0.04159 0.28007 0.03887 0.73329 True \n",
+ "14 4.67411 0.01464 0.17388 0.05333 0.64280 False \n",
+ "15 4.67411 0.01464 0.17388 0.05333 0.64280 False \n",
+ "16 18.21650 0.09935 0.68089 0.12015 2.16558 True \n",
+ "17 58.20149 0.51975 3.97824 0.68842 15.89978 True \n",
+ "18 58.20149 0.51975 3.97824 0.68842 15.89978 True \n",
+ "19 35.18491 0.13804 1.23473 0.07419 4.58177 False \n",
+ "20 111.91630 1.33930 9.69874 0.95233 24.32080 False \n",
+ "21 15.30320 7.27329 75.86246 8.07197 315.19387 False \n",
+ "22 39.34309 0.18496 1.18768 0.02477 3.25713 True \n",
+ "23 39.34309 0.18496 1.18768 0.02477 3.25713 True \n",
+ "24 1.07922 0.00146 0.00629 0.00217 0.01674 True \n",
+ "25 11.39561 11.73863 80.00608 8.36759 438.07856 False \n",
+ "26 11.39561 11.73863 80.00608 8.36759 438.07856 False \n",
+ "27 47.17589 0.25597 1.83550 0.38171 4.23498 False \n",
+ "28 21.18174 8.28968 54.04887 0.66770 251.39951 False \n",
+ "29 208.21051 2.21987 15.29097 2.58383 58.80082 False \n",
+ "\n",
+ " new_preds \n",
+ "0 1.036345 \n",
+ "1 2.058726 \n",
+ "2 21.385293 \n",
+ "3 0.802363 \n",
+ "4 132.455960 \n",
+ "5 21.316367 \n",
+ "6 21.316372 \n",
+ "7 1.720072 \n",
+ "8 1.720071 \n",
+ "9 3.767171 \n",
+ "10 1.093549 \n",
+ "11 262.278064 \n",
+ "12 206.577505 \n",
+ "13 9.642768 \n",
+ "14 1.471597 \n",
+ "15 1.471597 \n",
+ "16 2.989342 \n",
+ "17 35.433246 \n",
+ "18 35.433265 \n",
+ "19 7.471155 \n",
+ "20 241.805804 \n",
+ "21 8.272223 \n",
+ "22 25.922022 \n",
+ "23 25.922022 \n",
+ "24 0.422021 \n",
+ "25 6.608806 \n",
+ "26 6.608806 \n",
+ "27 14.162805 \n",
+ "28 41.374632 \n",
+ "29 373.653450 "
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample['new_preds'] = new_preds\n",
+ "df_sample.head(30)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "5bf78fae-dd12-4093-a4da-77429214a114",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c1ccc2c(c1)c1cc(ccc1C2(C)C)S(=O)(=O)c1ccc(c(c1Cl)Cl)I)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O)c1cc(cc(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C(=O)O',\n",
+ " 'Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1C)c1cc(C)c(c(c1)C)C(C(F)(F)F)(C(F)(F)F)c1cc(Cl)cc(c1)I)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(Sc2ccc(nc2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Cc1cc(Sc2ccc(cn2)Sc2cc(C)c(c(c2)C)I)cc(c1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Clc1cc(ccc1C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1)Cl)I)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)Cl)C(=O)c1cc(ccc1C)C(=O)c1ccc(c(c1)Cl)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)C)C(=O)c1cc2ccccc2cc1C(=O)c1ccc(cc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)Sc1cc(cc(c1)C(=O)O)Sc1cc(ccc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Cc1cc(cc(c1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C)C(C(F)(F)F)(C(F)(F)F)c1cc(cc(c1)C(F)(F)F)C(C(F)(F)F)(C(F)(F)F)c1cc(C)c(c(c1)C)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)C(c1ccc2c(c1)[nH]c1c2ccc(c1)C(c1c(C)cc(cc1C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)(C)C)(C)C',\n",
+ " 'Ic1cc(Oc2ccc(cc2)C2(c3ccc(cc3)Oc3cc(cc(c3)N3C(=O)c4c(C3=O)cccc4c3ccc4c(c3)C(=O)N(C4=O)I)C(=O)O)c3ccccc3c3c2cccc3)cc(c1)C(=O)O',\n",
+ " 'Ic1ccc(c(c1)Cl)Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)Cl)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Clc1cc(ccc1Sc1ccc2c(c1)ccc(c2)Sc1ccc(c(c1)Cl)I)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Cc1cc(ccc1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C(=O)c1ccc(c(c1)C)I',\n",
+ " 'Ic1ccc(c(c1)C)C(c1ccc(cc1C)C(c1ccc(c(c1)C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)(C)C)(C)C',\n",
+ " 'O=C1c2cccc(c2C(=O)N1c1ccc(c(c1)C)C(c1ccc(cc1C)C(c1ccc(c(c1)C)I)(C)C)(C)C)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(c(cc1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C)S(=O)(=O)c1ccc(c(c1Cl)Cl)S(=O)(=O)c1cc(C)c(cc1C)I',\n",
+ " 'O=C1c2cc(ccc2c2c1cc(cc2)C(C(F)(F)F)(C(F)(F)F)c1cccc(c1C)I)C(C(F)(F)F)(C(F)(F)F)c1cccc(c1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(cn1)C(c1cc(Cl)cc(c1)C(c1ccc(nc1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)(C)C)(C)C',\n",
+ " 'O=C1c2cccc(c2C(=O)N1c1cc(C)c(c(c1)C)S(=O)(=O)c1ccc2c(c1)ccc(c2)S(=O)(=O)c1cc(C)c(c(c1)C)I)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)S(=O)(=O)c1ccc2c(c1)ccc(c2)S(=O)(=O)c1cc(C)c(c(c1)C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(Sc2cc(cc(c2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O)n(c2=O)I)C(=O)O)cc(c1)C(=O)O',\n",
+ " 'Ic1ccc(cn1)C(=O)c1cc(C)c(c(c1)C)C(=O)c1ccc(cn1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(nc1)C(=O)c1c(C)cc(cc1C)C(=O)c1ccc(nc1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1cc(cc(c1)C(F)(F)F)C(=O)c1cc(C)c(cc1C)C(=O)c1cc(cc(c1)C(F)(F)F)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(cn1)S(=O)(=O)c1cc(C)c(c(c1)C)c1c(C)cc(cc1C)S(=O)(=O)c1ccc(nc1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1cc(C)c(c(c1)C)c1c(C)cc(cc1C)C(C(F)(F)F)(C(F)(F)F)c1cc(ccc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Cc1cc(cc(c1c1c(C)cc(cc1C)S(=O)(=O)c1ccc(c(c1)C)I)C)S(=O)(=O)c1ccc(c(c1)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'O=C(c1ccc(c(c1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C)c1ccc(cc1)C1(c2ccc(cc2)C(=O)c2ccc(c(c2)I)C)c2ccccc2c2c1cccc2',\n",
+ " 'Cc1cc(C)c(c(c1C(c1ccc(cc1)C1(c2ccc(cc2)C(c2c(C)cc(c(c2C)I)C)(C)C)c2ccccc2c2c1cccc2)(C)C)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(c(c1)C)Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(c(c1)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(ccc1Cc1ccc2c(c1)c1cc(ccc1C2(C)C)Cc1ccc(c(c1)C)I)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc2c(c1)C(=O)c1c2ccc(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)C',\n",
+ " 'Cc1ccc(cc1C(C(F)(F)F)(C(F)(F)F)c1ccc2c(c1)C(=O)c1c2ccc(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)I)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1cccc(c1)Cc1cccc(c1)Cc1cccc(c1)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)Oc1cc(Oc2ccc(c(c2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O)n(c2=O)I)C)cc(c1)C(F)(F)F)C',\n",
+ " 'Cc1ccc(cc1Oc1cc(Oc2ccc(c(c2)I)C)cc(c1)C(F)(F)F)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(nc1)Oc1ccc(cc1)C1(c2ccc(cc2)Oc2ccc(nc2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O)n(c2=O)I)c2ccccc2c2c1cccc2',\n",
+ " 'Ic1ccc(cn1)Oc1ccc(cc1)C1(c2ccc(cc2)Oc2ccc(cn2)n2c(=O)c3c(c2=O)cc2c(c3)c(=O)n(c2=O)I)c2ccccc2c2c1cccc2',\n",
+ " 'Cc1ccc(cc1I)Oc1ccc(cc1)N(c1ccccc1)c1ccc(cc1)Oc1ccc(c(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C',\n",
+ " 'OC(=O)c1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)I)C)C(C(F)(F)F)(C(F)(F)F)c1cc(ccc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C)C(=O)O)C',\n",
+ " 'O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c1cc2ccccc2cc1S(=O)(=O)c1ccc(c(c1Cl)Cl)I)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(cc(c1C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)Cl)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)c1cc(C)c(c(c1)C)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)Cl)I',\n",
+ " 'Ic1ccc(c(c1)C)Cc1ccc(c(c1)Cc1ccc(c(c1)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C',\n",
+ " 'Cc1cc(ccc1Cc1ccc(c(c1)Cc1ccc(c(c1)C)I)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)S(=O)(=O)c1c(C)cc(cc1C)S(=O)(=O)c1cc(ccc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Ic1ccc(c(c1)S(=O)(=O)c1cc(C)c(c(c1)C)S(=O)(=O)c1cc(ccc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'Ic1ccc(c(c1)Cl)C(=O)c1ccc2c(c1)[nH]c1c2ccc(c1)C(=O)c1ccc(cc1Cl)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Cc1ccc(cc1C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)Cl)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)I)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)Cl)C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)C',\n",
+ " 'Ic1ccc(cc1)C(c1c(C)cc(cc1C)c1cc(C)c(c(c1)C)C(c1ccc(cc1)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)(C)C)(C)C',\n",
+ " 'Cc1ccc(cc1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)Cc1ccc2c(c1)ccc(c2)Cc1ccc(c(c1)I)C',\n",
+ " 'Cc1cc(c(cc1N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)C)C(c1cc(C)c(cc1C)C(c1cc(C)c(cc1C)I)(C)C)(C)C',\n",
+ " 'Ic1ccc(nc1)C(c1ccc2c(c1)ccc(c2)C(c1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)(C)C)(C)C',\n",
+ " 'Ic1ccc(nc1)C(=O)c1ccc(cc1)c1ccc(cc1)C(=O)c1ccc(cn1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(c(c1)Sc1ccc2c(c1)cc(cc2)Sc1ccc(c(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)C',\n",
+ " 'Cc1ccc(cc1Sc1ccc2c(c1)cc(cc2)Sc1ccc(c(c1)I)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(cn1)Oc1cc(C)c(c(c1)C)Oc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(nc1)Oc1c(C)cc(cc1C)Oc1ccc(nc1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(cc1)Oc1cc(C)c(cc1C)Oc1ccc(cc1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)Cl)Oc1cccc2c1cccc2Oc1ccc(c(c1)Cl)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Clc1cc(ccc1Oc1cccc2c1cccc2Oc1ccc(c(c1)Cl)I)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(c(cc1Sc1ccc(cc1)c1ccc(cc1)Sc1cc(C)c(cc1C)I)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(nc1)Sc1cc(ccc1C)Sc1ccc(cn1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(nc1)Sc1ccc(c(c1)Sc1ccc(cn1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C',\n",
+ " 'O=C(c1cc(C)c(c(c1)C)I)c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)C(=O)c1c(C)cc(cc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)C(=O)c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)C(=O)c1cc(C)c(c(c1)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(cc(c1)C(F)(F)F)C(=O)c1ccc2c(c1)c1cc(ccc1C2(C)C)C(=O)c1cc(cc(c1)C(F)(F)F)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Clc1cc(cc(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C(=O)c1ccc(cc1)c1ccc(cc1)C(=O)c1cc(Cl)cc(c1)I',\n",
+ " 'Cc1cc(cc(c1S(=O)(=O)c1cc(ccc1C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)c1cc(C)c(c(c1)C)S(=O)(=O)c1ccc(c(c1)I)C',\n",
+ " 'Ic1ccc(c(c1)S(=O)(=O)c1c(C)cc(cc1C)c1cc(C)c(c(c1)C)S(=O)(=O)c1ccc(c(c1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C)C',\n",
+ " 'Clc1c(ccc(c1Cl)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)Oc1ccc2c(c1)[nH]c1c2ccc(c1)Oc1ccc(c(c1Cl)Cl)I',\n",
+ " 'Ic1ccc(c(c1)C)Sc1ccc(cc1Cl)Sc1ccc(cc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(c(c1)C)Sc1ccc(c(c1)Cl)Sc1ccc(cc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1ccc(cc1)S(=O)(=O)c1ccc2c(c1)c1cc(ccc1C2(C)C)S(=O)(=O)c1ccc(cc1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)Cc1ccc2c(c1)Cc1c2ccc(c1)Cc1cc(C)c(c(c1)C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(cc(c1Cc1ccc2c(c1)Cc1c2ccc(c1)Cc1cc(C)c(c(c1)C)I)C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)Sc1ccc2c(c1)cc(cc2)Sc1cc(C)c(c(c1)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Cc1cc(cc(c1Sc1ccc2c(c1)cc(cc2)Sc1cc(C)c(c(c1)C)I)C)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(nc1)Sc1c(C)cc(c(c1C)Sc1ccc(cn1)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I)C',\n",
+ " 'Ic1cc(C)c(c(c1)C)Oc1ccc(nc1)Oc1c(C)cc(cc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)Oc1ccc(cn1)Oc1c(C)cc(cc1C)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(ccc1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C(C(F)(F)F)(C(F)(F)F)c1cccc2c1cccc2C(C(F)(F)F)(C(F)(F)F)c1ccc(c(c1)C)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)C(c1ccc(c(c1)Cl)C(c1cc(C)c(c(c1)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)(C)C)(C)C',\n",
+ " 'O=C1c2cc(ccc2C(=O)N1c1cc(C)c(c(c1)C)C(c1ccc(c(c1)Cl)C(c1cc(C)c(c(c1)C)I)(C)C)(C)C)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Cc1cc(Cc2ccc(cc2)c2ccc(cc2)Cc2cc(C)c(c(c2)C)I)cc(c1N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C',\n",
+ " 'O=C(c1ccc2c(c1)[nH]c1c2ccc(c1)C(=O)c1ccc(c(c1)Cl)I)c1ccc(c(c1)Cl)n1c(=O)c2c(c1=O)cc1c(c2)c(=O)n(c1=O)I',\n",
+ " 'Ic1ccc(c(c1)Cl)C(=O)c1ccc2c(c1)ccc(c2)C(=O)c1ccc(c(c1)Cl)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Clc1cc(ccc1C(=O)c1ccc2c(c1)ccc(c2)C(=O)c1ccc(c(c1)Cl)I)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(C)c(c(c1)C)Cc1ccc2c(c1)C(=O)c1c2ccc(c1)Cc1c(C)cc(cc1C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I',\n",
+ " 'Ic1cc(cc(c1)C(=O)O)Oc1ccc(cc1C)Oc1cc(cc(c1)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)C(=O)O',\n",
+ " 'Ic1cc(Oc2ccc(c(c2)C)Oc2cc(cc(c2)N2C(=O)c3c(C2=O)cccc3c2ccc3c(c2)C(=O)N(C3=O)I)C(=O)O)cc(c1)C(=O)O',\n",
+ " 'Cc1cc2c3cc(C)c(cc3S(=O)(=O)c2cc1Oc1ccc(c(c1)C)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I)Oc1ccc(c(c1)C)I',\n",
+ " 'Clc1c(ccc(c1Cl)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1)C(=O)N(C2=O)I)Cc1ccc(cc1)Cc1ccc(c(c1Cl)Cl)I',\n",
+ " 'Ic1ccc(nc1)S(=O)(=O)c1ccc(c(c1Cl)Cl)S(=O)(=O)c1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I']"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample['Smiles'].to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "cd87f55e-d88b-4fcf-b2a8-3c7ba6250494",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "\n",
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+ " | \n",
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+ " Tg | \n",
+ " He | \n",
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+ " CH4 | \n",
+ " CO2 | \n",
+ " synthesizable | \n",
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\n",
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\n",
+ "
100 rows × 10 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles Tg \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... 494.84 \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... 508.26 \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... 640.91 \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... 568.04 \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... 548.10 \n",
+ ".. ... ... ... \n",
+ "95 95 Ic1cc(cc(c1)C(=O)O)Oc1ccc(cc1C)Oc1cc(cc(c1)N1C... 528.86 \n",
+ "96 96 Ic1cc(Oc2ccc(c(c2)C)Oc2cc(cc(c2)N2C(=O)c3c(C2=... 528.86 \n",
+ "97 97 Cc1cc2c3cc(C)c(cc3S(=O)(=O)c2cc1Oc1ccc(c(c1)C)... 554.76 \n",
+ "98 98 Clc1c(ccc(c1Cl)N1C(=O)c2c(C1=O)cccc2c1ccc2c(c1... 575.95 \n",
+ "99 99 Ic1ccc(nc1)S(=O)(=O)c1ccc(c(c1Cl)Cl)S(=O)(=O)c... 639.66 \n",
+ "\n",
+ " He N2 O2 CH4 CO2 synthesizable \\\n",
+ "0 2.69524 4.75740 42.31847 1.64086 148.43644 False \n",
+ "1 5.33815 2.97239 26.31118 0.86467 82.37635 False \n",
+ "2 20.47515 0.06353 0.90498 0.06905 2.35993 False \n",
+ "3 4.19692 0.00191 0.01134 0.00362 0.01418 False \n",
+ "4 142.68327 0.87380 8.25409 2.52067 30.04739 False \n",
+ ".. ... ... ... ... ... ... \n",
+ "95 3.65897 0.00916 0.04655 0.01135 0.10014 True \n",
+ "96 3.65897 0.00916 0.04655 0.01135 0.10014 True \n",
+ "97 27.71933 0.37086 2.36356 0.10131 7.45523 False \n",
+ "98 7.17213 0.01350 0.26675 0.21026 0.83210 False \n",
+ "99 6.86828 2.80338 26.78062 0.40880 117.95785 False \n",
+ "\n",
+ " new_preds \n",
+ "0 1.036345 \n",
+ "1 2.058726 \n",
+ "2 21.385310 \n",
+ "3 0.802363 \n",
+ "4 132.455960 \n",
+ ".. ... \n",
+ "95 1.777913 \n",
+ "96 1.777911 \n",
+ "97 10.918775 \n",
+ "98 2.465392 \n",
+ "99 4.053435 \n",
+ "\n",
+ "[100 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample['new_preds'] = new_preds\n",
+ "df_sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "e6d9e29e-eee0-4c5a-8966-58622a4260f8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1=O)cc(cc2)c1ccc2c(c1)C(=O)N(C2=O)I'"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample['Smiles'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "51a57a9b-e8b6-4c25-a7b0-38463ebed37e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processing batches: 100%|█████████████████████████| 8/8 [00:00<00:00, 18.36it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "CO2s = []\n",
+ "co2_preds = []\n",
+ "ch4_preds = []\n",
+ "\n",
+ "robson_smiles = sample_df['Smiles'].tolist()\n",
+ "\n",
+ "batch_size_robson = 128\n",
+ "for i in tqdm(range(0, len(robson_smiles), batch_size_robson), desc=\"Processing batches\"):\n",
+ " batch_end = min(i + batch_size_robson, len(robson_smiles))\n",
+ " batch_smiles = robson_smiles[i:batch_end]\n",
+ " \n",
+ " # Step 3: Predict properties for both original and reconstructed\n",
+ " # Original properties\n",
+ " encoding = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=256,\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " with torch.autocast(dtype=torch.float16, device_type='cuda'):\n",
+ " with torch.no_grad():\n",
+ " orig_predictions = regression_model(encoding['input_ids'].cuda(), encoding['attention_mask'].cuda())\n",
+ " \n",
+ " ch4_scaled = orig_predictions[:, -2].cpu().numpy().reshape(-1, 1)\n",
+ " co2_scaled = orig_predictions[:, -1].cpu().numpy().reshape(-1, 1)\n",
+ "\n",
+ " ch4 = scaler_ch4.inverse_transform(ch4_scaled.astype(np.float64)).flatten()\n",
+ " co2 = scaler_co2.inverse_transform(co2_scaled.astype(np.float64)).flatten()\n",
+ "\n",
+ " co2_preds.extend([float(pred) for pred in co2])\n",
+ " ch4_preds.extend([float(pred) for pred in ch4])\n",
+ "\n",
+ "df_robson['CO2_pred'] = co2_preds\n",
+ "df_robson['CH4_pred'] = ch4_preds\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "253af0c3-0ced-417b-a572-034c4cfb601f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "5aec327c-1fd9-4ebc-aa25-74ca8749b477",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if 'P_CO2/P_CH4' not in df_robson.columns:\n",
+ " df_robson['P_CO2/P_CH4'] = df_robson['CO2_pred']/df_robson['CH4_pred']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "7b46c427-5297-4565-b657-27fc7bf5bc38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k_2019 = 2.26e7\n",
+ "n_2019 = -2.401\n",
+ "k_2008 = 5_369_140\n",
+ "n_2008 = -2.636\n",
+ "k_1991 = 1_073_700\n",
+ "n_1991 = -2.6264\n",
+ "\n",
+ "df_robson['upper_bound_Robeson'] = (df_robson['CO2_pred']/k_2019)**(1/n_2019)\n",
+ "df_robson['above_Robeson'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson']\n",
+ "\n",
+ "df_robson['upper_bound_Robeson_2008'] = (df_robson['CO2_pred']/k_2008)**(1/n_2008)\n",
+ "df_robson['above_Robeson_2008'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson_2008']\n",
+ "\n",
+ "df_robson['upper_bound_Robeson_1991'] = (df_robson['CO2_pred']/k_1991)**(1/n_1991)\n",
+ "df_robson['above_Robeson_1991'] = df_robson['P_CO2/P_CH4'] > df_robson['upper_bound_Robeson_1991']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "3e2a37c5-9534-4cde-8ae9-bad906c39ae9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(log) x_points_on_Robeson2019_line [np.float64(-0.3245689409002736), np.float64(-0.11311188919602205), np.float64(0.0983451625082295), np.float64(0.30980221421248105), np.float64(0.5212592659167326), np.float64(0.7327163176209841), np.float64(0.9441733693252357), np.float64(1.1556304210294872), np.float64(1.3670874727337388), np.float64(1.5785445244379903), np.float64(1.7900015761422419), np.float64(2.0014586278464934), np.float64(2.212915679550745), np.float64(2.424372731254997), np.float64(2.6358297829592483), np.float64(2.8472868346634996), np.float64(3.0587438863677514), np.float64(3.270200938072003), np.float64(3.4816579897762545), np.float64(3.693115041480506), np.float64(3.9045720931847576)]\n",
+ "x_points_on_Robeson2019_line [0.4736211185063034, 0.7707048833980547, 1.2541375248783457, 2.0408083109233783, 3.3209265166816033, 5.404012160362607, 8.79373490580214, 14.309696443824098, 23.285602136958403, 37.89173788621753, 61.65972396131289, 100.33642612016979, 163.27349134558037, 265.688484302277, 432.3443451174293, 703.5368252632127, 1144.8376047731194, 1862.9488809093987, 3031.502912213795, 4933.044594478952, 8027.3480434650555]\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_bins = 20\n",
+ "x_points_on_Robeson2019_line = [ # референсные значения, использованные при подготовке обучающей выборки для первого этапа обучения\n",
+ " 0.4736211185063034,\n",
+ " 0.7707048833980547,\n",
+ " 1.2541375248783457,\n",
+ " 2.0408083109233783,\n",
+ " 3.3209265166816033,\n",
+ " 5.404012160362607,\n",
+ " 8.79373490580214,\n",
+ " 14.309696443824098,\n",
+ " 23.285602136958403,\n",
+ " 37.89173788621753,\n",
+ " 61.65972396131289,\n",
+ " 100.33642612016979,\n",
+ " 163.27349134558037,\n",
+ " 265.688484302277,\n",
+ " 432.3443451174293,\n",
+ " 703.5368252632127,\n",
+ " 1144.8376047731194,\n",
+ " 1862.9488809093987,\n",
+ " 3031.502912213795,\n",
+ " 4933.044594478952,\n",
+ " 8027.3480434650555]\n",
+ "\n",
+ "log_bin_size = (np.log10(x_points_on_Robeson2019_line[1])-\n",
+ " np.log10(x_points_on_Robeson2019_line[0]))/num_bins\n",
+ "\n",
+ "print('(log) x_points_on_Robeson2019_line', [np.log10(xp) for xp in x_points_on_Robeson2019_line])\n",
+ "print('x_points_on_Robeson2019_line', x_points_on_Robeson2019_line)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "4ec4c897-02fa-41a6-bc42-f353a068bace",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_265640/2901639124.py:11: RuntimeWarning:\n",
+ "\n",
+ "invalid value encountered in log10\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# определение бинов Робсона, для последующей отрисовки матрицы путанности\n",
+ "\n",
+ "def calculate_bin(row, x_col, y_col):\n",
+ " x_point = row[x_col]\n",
+ " y_point = row[y_col]\n",
+ " slope = (1/n_2019)\n",
+ " \n",
+ " bin_for_point = 1\n",
+ " for x_line in x_points_on_Robeson2019_line[1:]:\n",
+ " y_Robeson = (x_line/k_2019)**(1/n_2019) # upper_bound_Robeson_2019\n",
+ " y_bin = 10**(-1/slope * np.log10(x_point/x_line) + np.log10(y_Robeson))\n",
+ " if y_point > y_bin:\n",
+ " break\n",
+ " bin_for_point += 1\n",
+ " return bin_for_point\n",
+ "\n",
+ "df_robson['Robeson_bin'] = df_robson.apply(calculate_bin,\n",
+ " x_col='CO2_pred',\n",
+ " y_col='P_CO2/P_CH4',\n",
+ " axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "576b03a6-8c94-4dfa-ae58-d38652376f69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ipywidgets\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.io as pio\n",
+ "\n",
+ "pio.renderers.default = \"notebook\" # fixes duplicate plotly plots problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "d5c23fc9-926e-4b05-8edb-88c872944f9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " CO2 | \n",
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6726950 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Smiles \\\n",
+ "0 0 Ic1ccc(nc1)Cc1cccc(c1)Cc1ccc(cn1)N1C(=O)c2c(C1... \n",
+ "1 1 Ic1ccc(nc1)Cc1ccc2c(c1)cc(cc2)Cc1ccc(cn1)N1C(=... \n",
+ "2 2 O=C1c2cc(ccc2C(=O)N1c1ccc(c(c1Cl)Cl)S(=O)(=O)c... \n",
+ "3 3 Ic1cc(cc(c1)C(=O)O)C(=O)c1ccc2c(c1)ccc(c2)C(=O... \n",
+ "4 4 Clc1cc(cc(c1)C(C(F)(F)F)(C(F)(F)F)c1c(C)cc(cc1... \n",
+ "... ... ... \n",
+ "6726945 6726945 Ic1cccc(c1)Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1c... \n",
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+ "6726949 6726949 Ic1cc(Sc2ccc3c(c2)cc(cc3)Sc2cc(cc(c2)C(F)(F)F)... \n",
+ "\n",
+ " Tg He N2 O2 CH4 CO2 \\\n",
+ "0 494.84 2.69524 4.75740 42.31847 1.64086 148.43644 \n",
+ "1 508.26 5.33815 2.97239 26.31118 0.86467 82.37635 \n",
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+ "3 568.04 4.19692 0.00191 0.01134 0.00362 0.01418 \n",
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+ "\n",
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+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "69c7cfef-8075-41eb-aa2d-2d621ce4d1c4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f5c49709dca144f69bb703be1756c165",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "IntSlider(value=10, continuous_update=False, description='Num points', max=1000, min=10, step=10)"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f3aca246b424d2c914e9e1fb84b2429",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# отрисовка троек молекул (2 разгонных + 1 сгенерированная) на диаграмме Робсона\n",
+ "\n",
+ "# new_pi_df_unique = new_pi_df.drop_duplicates(['smiles_start'])\n",
+ "sub_df_preds = df_robson.sample(frac=1, random_state=1010)\n",
+ "\n",
+ "\n",
+ "def plot_with_controls(num_points=1):\n",
+ "\n",
+ "\n",
+ " sub_df_preds_plotted = sub_df_preds.iloc[:num_points]\n",
+ "\n",
+ " x_stuff = 'CO2'\n",
+ " y_stuff = 'P_CO2/P_CH4'\n",
+ "\n",
+ " \n",
+ " log_scale = True\n",
+ " fig = px.scatter(sub_df_preds_plotted, x=x_stuff, y=y_stuff, log_x=log_scale, log_y=log_scale, \n",
+ " color_discrete_sequence=['red'],\n",
+ " )\n",
+ "\n",
+ " \n",
+ " fig2 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson', labels='Robeson_2019')\n",
+ " fig2.update_traces(line_color='red', line_width=1)\n",
+ " fig3 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson_2008', labels='Robeson_2008')\n",
+ " fig3.update_traces(line_color='green', line_width=1)\n",
+ " fig4 = px.line(sub_df_preds.iloc[:1000],\n",
+ " x='CO2_pred', y='upper_bound_Robeson_1991', labels='Robeson_1991')\n",
+ " fig4.update_traces(line_color='blue', line_width=1)\n",
+ " fig = go.Figure(data=fig.data + fig2.data + fig3.data + fig4.data, layout = fig.layout)\n",
+ " fig.update_layout(fig2.layout)\n",
+ "\n",
+ " fig.update_layout(width=1100, height=550, margin=dict(l=40, r=40, t=10, b=10),)\n",
+ "\n",
+ " list_of_all_arrows = []\n",
+ " \n",
+ " \n",
+ " \n",
+ " fig.show()\n",
+ "\n",
+ "\n",
+ "num_points_slider = ipywidgets.IntSlider(\n",
+ " # value=len(sub_df_preds),\n",
+ " value=10,\n",
+ " # min=1000,\n",
+ " min=10,\n",
+ " # max=len(sub_df_preds),\n",
+ " max=min(len(sub_df_preds),1000),\n",
+ " step=10,\n",
+ " description='Num points',\n",
+ " disabled=False,\n",
+ " continuous_update=False,\n",
+ " orientation='horizontal',\n",
+ " readout=True,\n",
+ " readout_format='d'\n",
+ ")\n",
+ "\n",
+ "\n",
+ "\n",
+ "output = ipywidgets.interactive_output(plot_with_controls,\n",
+ " {\n",
+ " 'num_points': num_points_slider,\n",
+ " })\n",
+ "display(num_points_slider, output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07e0e913-820e-4041-bae4-3be8f85d292c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "reconstructed_ids = model.generate(best_embedding)\n",
+ "reconstructed_smiles = [tokenizer.decode(seq, skip_special_tokens=True) for seq in reconstructed_ids]\n",
+ "mol_gen = Chem.MolFromSmiles(reconstructed_smiles[0])\n",
+ "\n",
+ "print(reconstructed_smiles[0])\n",
+ "\n",
+ "if mol_gen is not None:\n",
+ " print('Valid')\n",
+ "else:\n",
+ " print('Skill issue')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93af70eb-5e42-4372-b0f9-4e03577742bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def comprehensive_generation(model, tokenizer, val_df, base_embeddings):\n",
+ " \"\"\"Generate molecules with different enhancement levels\"\"\"\n",
+ " \n",
+ " all_results = []\n",
+ " extrapolation_factors = [factor / 1000 for factor in range(0, 100, 10)]\n",
+ " print(extrapolation_factors)\n",
+ " for factor in extrapolation_factors:\n",
+ " co2_results = generate_gradient(\n",
+ " model, tokenizer, base_embeddings, val_df, 'CO2', extrapolation_factor=factor)\n",
+ "\n",
+ " all_results.extend(co2_results)# + ch4_results + dual_results)\n",
+ " \n",
+ " return all_results\n",
+ "\n",
+ "# Run comprehensive generation\n",
+ "all_generated_molecules = comprehensive_generation(model, tokenizer, df, base_embeddings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8eeed790-7e65-4ffa-b7cc-c92036ba0aba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict_molecule_properties(smiles_list, regression_model, tokenizer, scaler_ch4, scaler_co2, \n",
+ " batch_size=32, max_length=512, \n",
+ " baseline_ch4=None, baseline_co2=None):\n",
+ " \"\"\"\n",
+ " Predict CO2 and CH4 permeability properties for a list of SMILES\n",
+ "\n",
+ " Returns:\n",
+ " ch4_predictions: Array of CH4 permeability predictions\n",
+ " CO2ictions: Array of CO2 permeability predictions\n",
+ " molecules_exceeding_baselines: List of dicts for molecules exceeding baselines\n",
+ " \"\"\"\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " regression_model.to(device)\n",
+ " regression_model.eval()\n",
+ "\n",
+ " all_predictions = []\n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ "\n",
+ " tokens = tokenizer(\n",
+ " batch_smiles,\n",
+ " max_length=max_length,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ "\n",
+ " predictions = regression_model(input_ids, attention_mask)\n",
+ " all_predictions.append(predictions.cpu().numpy())\n",
+ "\n",
+ " all_predictions = np.vstack(all_predictions)\n",
+ " ch4_scaled = all_predictions[:, -2].reshape(-1, 1)\n",
+ " co2_scaled = all_predictions[:, -1].reshape(-1, 1)\n",
+ "\n",
+ " ch4_predictions = scaler_ch4.inverse_transform(ch4_scaled).flatten()\n",
+ " CO2ictions = scaler_co2.inverse_transform(co2_scaled).flatten()\n",
+ "\n",
+ " # Use provided baselines or max in predictions\n",
+ " if baseline_ch4 is None:\n",
+ " baseline_ch4 = ch4_predictions.max()\n",
+ " if baseline_co2 is None:\n",
+ " baseline_co2 = CO2ictions.max()\n",
+ "\n",
+ " molecules_exceeding_baselines = []\n",
+ " for idx, (smiles, ch4_pred, CO2) in enumerate(zip(smiles_list, ch4_predictions, CO2ictions)):\n",
+ " exceeds_ch4 = ch4_pred > baseline_ch4\n",
+ " exceeds_co2 = CO2 > baseline_co2\n",
+ " if exceeds_ch4 or exceeds_co2:\n",
+ " molecules_exceeding_baselines.append({\n",
+ " \"index\": idx,\n",
+ " \"smiles\": smiles,\n",
+ " \"predicted_CH4\": ch4_pred,\n",
+ " \"predicted_CO2\": CO2,\n",
+ " \"exceeds_CH4\": exceeds_ch4,\n",
+ " \"exceeds_CO2\": exceeds_co2\n",
+ " })\n",
+ "\n",
+ " return ch4_predictions, CO2ictions, molecules_exceeding_baselines\n",
+ "\n",
+ "\n",
+ "def analyze_generated_molecules_with_properties(generated_molecules, regression_model, tokenizer, \n",
+ " scaler_ch4, scaler_co2, val_df):\n",
+ " \"\"\"\n",
+ " Analyze generated molecules and predict their actual properties\n",
+ " \n",
+ " Args:\n",
+ " generated_molecules: List of generated molecule dictionaries\n",
+ " regression_model: Trained regression model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " scaler_ch4, scaler_co2: Property scalers\n",
+ " val_df: Validation dataframe for baseline comparison\n",
+ " \n",
+ " Returns:\n",
+ " enhanced_results: DataFrame with predicted properties\n",
+ " \"\"\"\n",
+ " \n",
+ " # Extract SMILES from generated molecules\n",
+ " generated_smiles = []\n",
+ " for mol in generated_molecules:\n",
+ " if mol['is_valid']: # Only predict for valid molecules\n",
+ " generated_smiles.append(mol['generated_smiles'])\n",
+ " \n",
+ " if not generated_smiles:\n",
+ " print(\"No valid molecules to analyze!\")\n",
+ " return None\n",
+ " \n",
+ " print(f\"Predicting properties for {len(generated_smiles)} valid generated molecules...\")\n",
+ " \n",
+ " # Predict properties\n",
+ " pred_ch4, pred_co2, molecules_exceeding_baselines = predict_molecule_properties(\n",
+ " generated_smiles, regression_model, tokenizer, scaler_ch4, scaler_co2,\n",
+ " baseline_ch4=val_df['CH4'].max(),\n",
+ " baseline_co2=val_df['CO2'].max()\n",
+ " )\n",
+ " \n",
+ " # Create results dataframe\n",
+ " results_data = []\n",
+ " pred_idx = 0\n",
+ " \n",
+ " for mol in generated_molecules:\n",
+ " if mol['is_valid']:\n",
+ " results_data.append({\n",
+ " 'generated_smiles': mol['generated_smiles'],\n",
+ " 'target_property': mol['target_property'],\n",
+ " 'extrapolation_factor': mol['extrapolation_factor'],\n",
+ " 'predicted_CH4': pred_ch4[pred_idx],\n",
+ " 'predicted_CO2': pred_co2[pred_idx],\n",
+ " 'is_valid': mol['is_valid']\n",
+ " })\n",
+ " pred_idx += 1\n",
+ " else:\n",
+ " # Include invalid molecules with NaN predictions\n",
+ " results_data.append({\n",
+ " 'generated_smiles': mol['generated_smiles'],\n",
+ " 'target_property': mol['target_property'],\n",
+ " 'extrapolation_factor': mol['extrapolation_factor'],\n",
+ " 'predicted_CH4': np.nan,\n",
+ " 'predicted_CO2': np.nan,\n",
+ " 'is_valid': mol['is_valid']\n",
+ " })\n",
+ " \n",
+ " results_df = pd.DataFrame(results_data)\n",
+ " \n",
+ " # Calculate baseline statistics from validation set\n",
+ " baseline_ch4_mean = val_df['CH4'].mean()\n",
+ " baseline_ch4_max = val_df['CH4'].max()\n",
+ " baseline_co2_mean = val_df['CO2'].mean()\n",
+ " baseline_co2_max = val_df['CO2'].max()\n",
+ " \n",
+ " # Calculate enhancement statistics for valid molecules only\n",
+ " valid_results = results_df[results_df['is_valid']].copy()\n",
+ " \n",
+ " if len(valid_results) > 0:\n",
+ " print(f\"\\n{'='*80}\")\n",
+ " print(f\"INDIVIDUAL MOLECULE PROPERTIES\")\n",
+ " print(f\"{'='*80}\")\n",
+ " \n",
+ " # Display properties for each individual molecule\n",
+ " for idx, (_, mol) in enumerate(valid_results.iterrows(), 1):\n",
+ " ch4_vs_baseline = (mol['predicted_CH4'] / baseline_ch4_max - 1) * 100\n",
+ " co2_vs_baseline = (mol['predicted_CO2'] / baseline_co2_max - 1) * 100\n",
+ " \n",
+ " print(f\" SMILES: {mol['generated_smiles']}\")\n",
+ " print(f\" Target: {mol['target_property']} (factor: {mol['extrapolation_factor']})\")\n",
+ " print(f\" CH₄ Permeability: {mol['predicted_CH4']:.4f} ({ch4_vs_baseline:+.1f}% vs baseline max)\")\n",
+ " print(f\" CO₂ Permeability: {mol['predicted_CO2']:.4f} ({co2_vs_baseline:+.1f}% vs baseline max)\")\n",
+ " \n",
+ " # Enhancement indicators\n",
+ " enhancement_flags = []\n",
+ " if mol['predicted_CH4'] > baseline_ch4_max:\n",
+ " enhancement_flags.append(\"CH₄↑\")\n",
+ " if mol['predicted_CO2'] > baseline_co2_max:\n",
+ " enhancement_flags.append(\"CO₂↑\")\n",
+ " if enhancement_flags:\n",
+ " print(f\" Enhancements: {', '.join(enhancement_flags)}\")\n",
+ " \n",
+ " print(\"-\" * 70)\n",
+ " \n",
+ " print(f\"Baseline Dataset Statistics:\")\n",
+ " print(f\" CH4 - Mean: {baseline_ch4_mean:.4f}, Max: {baseline_ch4_max:.4f}\")\n",
+ " print(f\" CO2 - Mean: {baseline_co2_mean:.4f}, Max: {baseline_co2_max:.4f}\")\n",
+ " \n",
+ " print(f\"\\nGenerated Molecules Statistics:\")\n",
+ " print(f\" CH4 - Mean: {valid_results['predicted_CH4'].mean():.4f}, Max: {valid_results['predicted_CH4'].max():.4f}\")\n",
+ " print(f\" CO2 - Mean: {valid_results['predicted_CO2'].mean():.4f}, Max: {valid_results['predicted_CO2'].max():.4f}\")\n",
+ " \n",
+ " # Check for improvements\n",
+ " ch4_improvements = valid_results['predicted_CH4'] > baseline_ch4_max\n",
+ " co2_improvements = valid_results['predicted_CO2'] > baseline_co2_max\n",
+ " print(f\"\\nEnhancement Analysis:\")\n",
+ " print(f\" Molecules exceeding baseline CH4 max: {ch4_improvements.sum()}/{len(valid_results)} ({ch4_improvements.mean()*100:.1f}%)\")\n",
+ " print(f\" Molecules exceeding baseline CO2 max: {co2_improvements.sum()}/{len(valid_results)} ({co2_improvements.mean()*100:.1f}%)\")\n",
+ " # Enhancement by target property\n",
+ " property_analysis = valid_results.groupby('target_property').agg({\n",
+ " 'predicted_CH4': ['mean', 'max', 'count'],\n",
+ " 'predicted_CO2': ['mean', 'max', 'count']\n",
+ " }).round(4)\n",
+ " \n",
+ " print(f\"\\nProperty Enhancement by Generation Target:\")\n",
+ " \n",
+ " # Enhancement by extrapolation factor\n",
+ " factor_analysis = valid_results.groupby('extrapolation_factor').agg({\n",
+ " 'predicted_CH4': ['mean', 'max'],\n",
+ " 'predicted_CO2': ['mean', 'max']\n",
+ " }).round(4)\n",
+ " \n",
+ " print(f\"\\nProperty Enhancement by Extrapolation Factor:\")\n",
+ " print(\"\\nMolecules that exceed baselines:\")\n",
+ " for mol in molecules_exceeding_baselines:\n",
+ " print(f\"SMILES: {mol['smiles']}\")\n",
+ " print(f\" CH₄ Predicted: {mol['predicted_CH4']:.4f} (Exceeds baseline: {mol['exceeds_CH4']})\")\n",
+ " print(f\" CO₂ Predicted: {mol['predicted_CO2']:.4f} (Exceeds baseline: {mol['exceeds_CO2']})\\n\")\n",
+ "\n",
+ " \n",
+ " \n",
+ " else:\n",
+ " print(\"No valid molecules generated for property prediction!\")\n",
+ " \n",
+ " return results_df, molecules_exceeding_baselines\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3df27d92-9da1-433c-b56b-27cd68b8004f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results_df, molecules_exceeding_baselines = analyze_generated_molecules_with_properties(\n",
+ " all_generated_molecules, regression_model, tokenizer, scaler_ch4, scaler_co2, df\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "262b158d-afe5-4f44-85df-c49c175fd454",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exceeding_mols = []\n",
+ "for mol_info in molecules_exceeding_baselines:\n",
+ " if mol_info['exceeds_CH4'] and mol_info['exceeds_CO2']:\n",
+ " exceeding_mols.append(mol_info['smiles'])\n",
+ "\n",
+ "exceeding_mols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c133d4c6-cae8-4a43-be7e-8871d0403f1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smiles_generated_col = 'smiles'\n",
+ "gen_df = pd.DataFrame({smiles_generated_col: exceeding_mols})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "503ba331-1748-430c-96ba-a6de9658b053",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split_dot(smiles):\n",
+ " if '.' in smiles:\n",
+ " parts = smiles.split('.')\n",
+ " # выбираем наиболее длинную отдельную часть молекулы в качестве самой молекулы\n",
+ " smiles = sorted(parts, key=lambda x:len(x), reverse=True)[0]\n",
+ " return smiles\n",
+ "\n",
+ "partial_mols_count = sum(gen_df[smiles_generated_col].str.contains('.', regex=False))\n",
+ "print(f'{partial_mols_count} partial mols fixed')\n",
+ "if partial_mols_count > 0:\n",
+ " gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(split_dot)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ad57b93d-4c4c-4e08-b4ba-6beaa4ab55d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def try_normalize(smiles):\n",
+ " # функция выполняет перевод молекулы в формат rdkit и обратно. Это фильтрует некорректные молекулы и нормализует их, т.е. приводит к единому виду, чтобы затем можно было отфильтровать дубликаты молекул\n",
+ " try:\n",
+ " return Chem.MolToSmiles(Chem.MolFromSmiles(smiles))\n",
+ " except Exception as e:\n",
+ " # print(e)\n",
+ " return None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4bc739e-eb6d-42b7-a8e9-4aeb82d6f93e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "symbols = ['*', '[*]', 'I']\n",
+ "contain_counts = {}\n",
+ "for symbol in symbols:\n",
+ " contain_counts[symbol] = gen_df[smiles_generated_col].apply(lambda smiles: symbol in smiles).sum()\n",
+ " print(f'contain {symbol}: {contain_counts[symbol]} mols')\n",
+ "\n",
+ "most_frequent_symbol = max(contain_counts, key=contain_counts.get)\n",
+ "assert most_frequent_symbol == 'I' # else think about it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11079464-727b-4b44-8fd7-c674edf76b70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(lambda x: x.replace('[*]', 'I'))\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].transform(lambda x: x.replace('*', 'I'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2893bdd-c582-4380-bbed-602c5bc22c22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smiles_normalized_col = 'SMILES_normalized'\n",
+ "\n",
+ "gen_df[smiles_normalized_col] = gen_df[smiles_generated_col].apply(try_normalize)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_normalized_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75d34fa6-1e40-4f63-b134-938d1b9cd309",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temp_smiles_col = smiles_normalized_col+'2'\n",
+ "gen_df[temp_smiles_col] = gen_df[smiles_normalized_col].apply(try_normalize)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[temp_smiles_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "gen_df = gen_df.drop(columns=[temp_smiles_col])\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "32c392d9-145d-46ba-8cb7-9b05dd453514",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.drop_duplicates(subset=[smiles_normalized_col])\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} duplicates'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "740e57e4-ad55-4638-ab0f-f753a242ee0a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def filter_two_endpoints(smiles):\n",
+ " return smiles if smiles.count('I') == 2 else None\n",
+ "\n",
+ "\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].apply(filter_two_endpoints)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_generated_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2cf9b3e8-caa6-4cd3-b76f-c1a3bcfe47db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def filter_matching_endpoint_bonds(smiles):\n",
+ " # фильтрация на предмет того, что типы связей у эндпоинтов должны быть одинаковы\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles.replace('I', '[*]'))\n",
+ " inds = tuple(mol.GetSubstructMatches(Chem.MolFromSmarts(\"[#0]~*\")))\n",
+ " inds = tuple(zip(*inds))\n",
+ " star_inds = list(inds[0])\n",
+ " connector_inds = list(inds[1])\n",
+ " b1_type = mol.GetBondBetweenAtoms(star_inds[0], connector_inds[0]).GetBondType()\n",
+ " b2_type = mol.GetBondBetweenAtoms(star_inds[1], connector_inds[1]).GetBondType()\n",
+ " if b1_type != b2_type:\n",
+ " return None\n",
+ " else:\n",
+ " return smiles\n",
+ " except:\n",
+ " return None\n",
+ "gen_df[smiles_generated_col] = gen_df[smiles_generated_col].apply(filter_matching_endpoint_bonds)\n",
+ "\n",
+ "n_before = len(gen_df)\n",
+ "gen_df = gen_df.loc[~gen_df[smiles_generated_col].isnull()]\n",
+ "n_after = len(gen_df)\n",
+ "\n",
+ "print(f'deleted: {n_before-n_after} incorrect mols'\n",
+ " f' (before: {n_before} mols, after: {n_after} mols)')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a889461-90fd-4e00-a865-8dad2edccc73",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "molecules_exceeding_baselines"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99f92d05-4166-4b2a-a586-78f38e9a3041",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df = gen_df.reset_index(drop=True)\n",
+ "gen_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3e2cdab-226d-421a-99c1-516c6709671f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exceeding_mols_co2 = []\n",
+ "exceeding_mols_ch4 = []\n",
+ "\n",
+ "for mol in gen_df['smiles']:\n",
+ " for candidat_mol_info in molecules_exceeding_baselines:\n",
+ " if mol == candidat_mol_info['smiles']:\n",
+ " exceeding_mols_co2.append(float(candidat_mol_info['predicted_CO2']))\n",
+ " exceeding_mols_ch4.append(float(candidat_mol_info['predicted_CH4']))\n",
+ " break\n",
+ "\n",
+ "exceeding_mols_co2, exceeding_mols_ch4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6fc01a8-9bd8-451a-993e-fe3329e70ee8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df['smiles'].to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed1ae34b-0f9b-4ef3-948e-cec35411764b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gen_df['predicted_CO2'] = exceeding_mols_co2\n",
+ "gen_df['predicted_CH4'] = exceeding_mols_ch4\n",
+ "gen_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db560332-8eeb-438b-86b6-210a64efceea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_output = '/home/jovyan/simson_training_bolgov/regression/exceeding_mols.csv'\n",
+ "gen_df.to_csv(final_output, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ce9bb58-943e-4ee7-b22b-3f7e91cce862",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson = gen_df\n",
+ "df_robson['P_CO2/P_CH4'] = gen_df['predicted_CO2']/gen_df['predicted_CH4']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7f294eef-86cc-4aed-8105-35e3c261bd69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = df_robson['smiles'][0]\n",
+ "df_robson.iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56e42e5e-4988-444f-a476-3ca364fc9d95",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[df['Smiles'] == t]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f3cb85b-a441-4491-bd3a-f96f619fd0ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_robson['in_synth_DB'] = df_robson['smiles'].isin(df['Smiles'])\n",
+ "df_robson"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/decoding_code_dumps.ipynb b/simson_modeling/regression/decoding_code_dumps.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d550d08dc79920ed1dedf0bd5c874d887ebb7c94
--- /dev/null
+++ b/simson_modeling/regression/decoding_code_dumps.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73c4a5d6-a444-43c0-9812-298113480923",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "def generate_from_extrapolated_embeddings(model, tokenizer, base_embeddings,\n",
+ " target_properties, extrapolation_factor=0.2):\n",
+ " \"\"\"\n",
+ " Generate molecules from extrapolated embeddings\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " base_embeddings: Base embeddings to extrapolate from\n",
+ " target_properties: Target property values for extrapolation direction\n",
+ " extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " # Calculate property gradient direction in embedding space\n",
+ " # This is a simplified approach - you might want to use more sophisticated methods\n",
+ " mean_embedding = torch.mean(base_embeddings, dim=0)\n",
+ " \n",
+ " # Find direction of increasing properties\n",
+ " high_prop_mask = target_properties > torch.median(target_properties)\n",
+ " low_prop_mask = target_properties < torch.median(target_properties)\n",
+ " \n",
+ " high_prop_embeddings = base_embeddings[high_prop_mask].mean(dim=0)\n",
+ " low_prop_embeddings = base_embeddings[low_prop_mask].mean(dim=0)\n",
+ " \n",
+ " property_direction = high_prop_embeddings - low_prop_embeddings\n",
+ " property_direction = property_direction / torch.norm(property_direction)\n",
+ " \n",
+ " # Generate extrapolated embeddings\n",
+ " extrapolated_embeddings = mean_embedding + extrapolation_factor * property_direction\n",
+ " extrapolated_embeddings = extrapolated_embeddings.unsqueeze(0).to(device)\n",
+ " \n",
+ " # Generate SMILES from extrapolated embeddings\n",
+ " with torch.no_grad():\n",
+ " generated_ids = model.generate(extrapolated_embeddings)\n",
+ " generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+ " \n",
+ " return generated_smiles, extrapolated_embeddings\n",
+ "\n",
+ "def generate_slerp(model, tokenizer, base_embeddings,\n",
+ " df, target_col, extrapolation_factor=0.2):\n",
+ " \"\"\"\n",
+ " Generate molecules from extrapolated embeddings\n",
+ " \n",
+ " Args:\n",
+ " model: Trained encoder-decoder model\n",
+ " tokenizer: SMILES tokenizer\n",
+ " base_embeddings: Base embeddings to extrapolate from\n",
+ " target_properties: Target property values for extrapolation direction\n",
+ " extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
+ " \"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ "\n",
+ " co2_properties = torch.tensor(df['CO2'].values, dtype=torch.float32)\n",
+ " ch4_properties = torch.tensor(df['CH4'].values, dtype=torch.float32)\n",
+ " \n",
+ " # Normalize properties to same scale before combining\n",
+ " co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
+ " ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
+ " \n",
+ " # Combined property (equal weighting)\n",
+ " target_properties = co2_norm + ch4_norm\n",
+ " \n",
+ " extrapolated_embeddings = slerp_extrapolation(base_embeddings, target_properties, factor=extrapolation_factor)\n",
+ " generated_molecules = []\n",
+ " # Generate SMILES from extrapolated embeddings\n",
+ " with torch.no_grad():\n",
+ " generated_ids = model.generate(extrapolated_embeddings.cuda())\n",
+ " generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+ "\n",
+ " mol = Chem.MolFromSmiles(generated_smiles)\n",
+ " is_valid = mol is not None\n",
+ " \n",
+ " if is_valid:\n",
+ " canonical_smiles = MolToSmiles(mol, canonical=True)\n",
+ " else:\n",
+ " canonical_smiles = \"INVALID\"\n",
+ " \n",
+ " generated_molecules.append({\n",
+ " 'generated_smiles': generated_smiles,\n",
+ " 'is_valid': is_valid,\n",
+ " 'target_property': 'slerp',\n",
+ " 'extrapolation_factor': extrapolation_factor\n",
+ " })\n",
+ " \n",
+ " print(f\"Generation: {generated_smiles} (Valid: {is_valid})\")\n",
+ " return generated_molecules\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "def generate_dual_enhanced_molecules(model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=0.2, num_generations=10):\n",
+ " \"\"\"Generate molecules with enhanced both CO₂ and CH₄ permeability\"\"\"\n",
+ " \n",
+ " # Combine both properties (you can weight them differently if needed)\n",
+ " co2_properties = torch.tensor(val_df['CO2'].values, dtype=torch.float32)\n",
+ " ch4_properties = torch.tensor(val_df['CH4'].values, dtype=torch.float32)\n",
+ " \n",
+ " # Normalize properties to same scale before combining\n",
+ " co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
+ " ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
+ " \n",
+ " # Combined property (equal weighting)\n",
+ " combined_properties = co2_norm + ch4_norm\n",
+ " \n",
+ " generated_molecules = []\n",
+ " \n",
+ " print(f\"Generating {num_generations} molecules with enhanced dual permeability...\")\n",
+ " print(f\"Extrapolation factor: {extrapolation_factor}\")\n",
+ " \n",
+ " for i in range(num_generations):\n",
+ " generated_smiles, extrapolated_embedding = generate_from_extrapolated_embeddings(\n",
+ " model, tokenizer, base_embeddings, combined_properties, extrapolation_factor\n",
+ " )\n",
+ " \n",
+ " # Validate generated molecule\n",
+ " mol = Chem.MolFromSmiles(generated_smiles)\n",
+ " is_valid = mol is not None\n",
+ " \n",
+ " if is_valid:\n",
+ " canonical_smiles = MolToSmiles(mol, canonical=True)\n",
+ " else:\n",
+ " canonical_smiles = \"INVALID\"\n",
+ " \n",
+ " generated_molecules.append({\n",
+ " 'generation_id': i + 1,\n",
+ " 'generated_smiles': generated_smiles,\n",
+ " 'canonical_smiles': canonical_smiles,\n",
+ " 'is_valid': is_valid,\n",
+ " 'target_property': 'DUAL_enhanced',\n",
+ " 'extrapolation_factor': extrapolation_factor\n",
+ " })\n",
+ " \n",
+ " print(f\"Generation {i+1}: {generated_smiles} (Valid: {is_valid})\")\n",
+ " \n",
+ " return generated_molecules\n",
+ "\n",
+ "ch4_results = generate_enhanced_molecules_ch4(\n",
+ " model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=factor, num_generations=1\n",
+ " )\n",
+ " \n",
+ " # Dual enhanced\n",
+ " dual_results = generate_dual_enhanced_molecules(\n",
+ " model, tokenizer, val_df, base_embeddings, \n",
+ " extrapolation_factor=factor, num_generations=1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b088097d-26b1-45c8-848f-b02784092e75",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from safetensors import safe_open\n",
+ "\n",
+ "checkpoint_path = '/home/jovyan/simson_training_bolgov/regression/decoder_checkpoints/checkpoint-110000/model.safetensors'\n",
+ "\n",
+ "state_dict = {}\n",
+ "with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n",
+ " for k in f.keys():\n",
+ " state_dict[k] = f.get_tensor(k)\n",
+ "\n",
+ "model.load_state_dict(state_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9abe169-0a17-4728-be0c-4befb2439102",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(property_loss.detach().item(), scale_factor * (lambda_reg * regularization_loss).detach().item())"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/encoder_decoder_uncompiled.bin b/simson_modeling/regression/encoder_decoder_uncompiled.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d00e1ebcdc49d2d0ca48e80cc7c937246a280737
--- /dev/null
+++ b/simson_modeling/regression/encoder_decoder_uncompiled.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cadcfc9174a7f4937cfb9ac9a293c30f62a2cfd8470172a080b8ce007613bbf
+size 1816955
diff --git a/simson_modeling/regression/exceeding_mols.csv b/simson_modeling/regression/exceeding_mols.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ad1e90f0c5bfd08d12e20e44cf8ec86a606e7ee5
--- /dev/null
+++ b/simson_modeling/regression/exceeding_mols.csv
@@ -0,0 +1,9 @@
+smiles,SMILES_normalized,predicted_CO2,predicted_CH4
+Ic1ccc(nc1)C(c1ccc(nc1)C(c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2Oc1cccc(c1)C(C(F)(F)F)(C(F)(F)F)c1cccc(c1)Oc1cccc2c1C(=O)N(C2=O)I)(C)C)(C)C,CC(C)(c1ccc(N2C(=O)c3cccc(Oc4cccc(C(c5cccc(Oc6cccc7c6C(=O)N(I)C7=O)c5)(C(F)(F)F)C(F)(F)F)c4)c3C2=O)nc1)c1ccc(C(C)(C)c2ccc(I)cn2)cn1,1912.1436767578125,27.116050720214844
+Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1cc(ccc1C)N1C(=O)c2c(C1=O)c(ccc2)Cc1ccc(nc1)Cc1ccc(nc1)Cc1ccc2c(c1)C(=O)N(C2=O)I)C,Cc1ccc(I)cc1C(c1ccc(C(c2cc(N3C(=O)c4cccc(Cc5ccc(Cc6ccc(Cc7ccc8c(c7)C(=O)N(I)C8=O)nc6)nc5)c4C3=O)ccc2C)(C(F)(F)F)C(F)(F)F)nc1)(C(F)(F)F)C(F)(F)F,1508.4267578125,22.331361770629883
+Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2Cc1cccc(c1)C(c1cccc(c1)Cc1cccc2c1C(=O)N(C2=O)I)(C)C,CC(C)(c1cccc(Cc2cccc3c2C(=O)N(I)C3=O)c1)c1cccc(Cc2cccc3c2C(=O)N(c2ccc(C(c4ccc(C(c5ccc(I)nc5)(C(F)(F)F)C(F)(F)F)cn4)(C(F)(F)F)C(F)(F)F)cn2)C3=O)c1,1345.258544921875,21.65478515625
+Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2Cc1cc(C)c(c(c1)C)c1c(C)cc(cc1C)Cc1cccc2c1C(=O)N(C2=O)I,Cc1cc(Cc2cccc3c2C(=O)N(I)C3=O)cc(C)c1-c1c(C)cc(Cc2cccc3c2C(=O)N(c2ccc(C(c4ccc(C(c5ccc(I)nc5)(C(F)(F)F)C(F)(F)F)cn4)(C(F)(F)F)C(F)(F)F)cn2)C3=O)cc1C,1693.493896484375,23.38142967224121
+Ic1ccc(c(c1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1cc(ccc1C)N1C(=O)c2c(C1=O)c(ccc2)Cc1ccc(nc1)C(c1ccc(nc1)Cc1cccc2c1C(=O)N(C2=O)I)(C)C)C,Cc1ccc(I)cc1C(c1ccc(C(c2cc(N3C(=O)c4cccc(Cc5ccc(C(C)(C)c6ccc(Cc7cccc8c7C(=O)N(I)C8=O)nc6)nc5)c4C3=O)ccc2C)(C(F)(F)F)C(F)(F)F)nc1)(C(F)(F)F)C(F)(F)F,2024.1002197265625,30.39462661743164
+Ic1ccc(nc1)C(c1ccc(nc1)C(c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2Cc1cccc(c1)C(C(F)(F)F)(C(F)(F)F)c1cccc(c1)Cc1cccc2c1C(=O)N(C2=O)I)(C)C)(C)C,CC(C)(c1ccc(N2C(=O)c3cccc(Cc4cccc(C(c5cccc(Cc6cccc7c6C(=O)N(I)C7=O)c5)(C(F)(F)F)C(F)(F)F)c4)c3C2=O)nc1)c1ccc(C(C)(C)c2ccc(I)cn2)cn1,1698.8431396484375,21.43316078186035
+Ic1ccc(cn1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)C(C(F)(F)F)(C(F)(F)F)c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2C(c1ccc(cc1)Sc1ccc(cc1)C(c1cccc2c1C(=O)N(C2=O)I)(C)C)(C)C,CC(C)(c1ccc(Sc2ccc(C(C)(C)c3cccc4c3C(=O)N(c3ccc(C(c5ccc(C(c6ccc(I)nc6)(C(F)(F)F)C(F)(F)F)cn5)(C(F)(F)F)C(F)(F)F)cn3)C4=O)cc2)cc1)c1cccc2c1C(=O)N(I)C2=O,1995.6099853515625,26.38747215270996
+Ic1ccc(nc1)C(c1ccc(cn1)C(c1ccc(nc1)N1C(=O)c2c(C1=O)cccc2Cc1cccc(c1)C(C(F)(F)F)(C(F)(F)F)c1cccc(c1)Cc1cccc2c1C(=O)N(C2=O)I)(C)C)(C)C,CC(C)(c1ccc(N2C(=O)c3cccc(Cc4cccc(C(c5cccc(Cc6cccc7c6C(=O)N(I)C7=O)c5)(C(F)(F)F)C(F)(F)F)c4)c3C2=O)nc1)c1ccc(C(C)(C)c2ccc(I)cn2)nc1,1781.6085205078125,22.269519805908203
diff --git a/simson_modeling/regression/high_regression_old_scalers_simson.pth b/simson_modeling/regression/high_regression_old_scalers_simson.pth
new file mode 100644
index 0000000000000000000000000000000000000000..6557d5ab8717cd2f882fc74d5bbd7476886e0eeb
--- /dev/null
+++ b/simson_modeling/regression/high_regression_old_scalers_simson.pth
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+version https://git-lfs.github.com/spec/v1
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+size 93244570
diff --git a/simson_modeling/regression/high_regression_simson.pth b/simson_modeling/regression/high_regression_simson.pth
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diff --git a/simson_modeling/regression/high_scalers b/simson_modeling/regression/high_scalers
new file mode 100644
index 0000000000000000000000000000000000000000..49361864eb9aa6c1fe54e234660bb648cca7ada1
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diff --git a/simson_modeling/regression/high_values_regression.ipynb b/simson_modeling/regression/high_values_regression.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dafbef96817676ce64480e80a2f95e77fb248c5c
--- /dev/null
+++ b/simson_modeling/regression/high_values_regression.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 2) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'both_labels_count': (self.label_masks.sum(axis=1) == 2).sum(),\n",
+ " 'single_label_count': (self.label_masks.sum(axis=1) == 1).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, label_mask, label_weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 2)\n",
+ " labels: Ground truth labels (batch_size, 2)\n",
+ " label_mask: Mask for valid labels (batch_size, 2)\n",
+ " label_weights: Weights for each label (2,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Apply label-specific weights\n",
+ " weighted_losses = masked_losses * label_weights.unsqueeze(0) # Broadcast weights\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = weighted_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "def compute_label_weights(dataset):\n",
+ " \"\"\"\n",
+ " Compute inverse frequency weights based on label availability\n",
+ " \n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " \n",
+ " Returns:\n",
+ " torch.Tensor: Normalized weights for each label\n",
+ " \"\"\"\n",
+ " # Get label counts from dataset\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = len(dataset)\n",
+ " \n",
+ " # Inverse frequency weighting\n",
+ " weights = total_samples / (2 * label_counts) # 2 is the number of labels\n",
+ " \n",
+ " # Normalize weights so they sum to number of labels (2)\n",
+ " weights = weights / weights.sum() * 2\n",
+ " \n",
+ " return torch.tensor(weights, dtype=torch.float32)\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 2).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 2).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 2).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(2):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, label_weights, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with weighted loss for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 2 labels)\n",
+ " train_dataloader: Training data loader\n",
+ " val_dataloader: Validation data loader \n",
+ " label_weights: Tensor with weights for each label\n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " label_weights = label_weights.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Label weights: {label_weights.cpu().numpy()}\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate weighted loss\n",
+ " loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 2) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 2 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 2 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for two-label training (labels assumed pre-scaled)\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Compute label weights based on training data\n",
+ " label_weights = compute_label_weights(train_dataset)\n",
+ " print(f\"Computed label weights: {label_weights.numpy()}\")\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " label_weights=label_weights,\n",
+ " scalers=scalers, # Still pass scalers for true loss calculation\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8e1296a2-551c-48ab-aab3-fcf4b6110d75",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(df['CO2'].to_list())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "model = torch.compile(model, fullgraph=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.99 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/scalers']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(scalers, '/home/jovyan/simson_training_bolgov/regression/scalers')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for two-label training (labels assumed pre-scaled)\n",
+ "Training dataset statistics:\n",
+ " total_samples: 6659681\n",
+ " label_0_count: 6659681\n",
+ " label_1_count: 6659681\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 67270\n",
+ " label_0_count: 67270\n",
+ " label_1_count: 67270\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Computed label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 26015\n",
+ "Total training steps: 78045\n",
+ "Label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Validation will be performed every 7000 steps\n",
+ "\n",
+ "Epoch 1/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 27%|██▍ | 7001/26015 [10:27<10:43:20, 2.03s/it, step=7002, loss=0.0372, true_loss=16.0679, lr=1.84e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 7000 | Train Loss: 0.0618 | Val Loss: 0.1191 | True train loss: 17.4244 | True val loss: 18.3473\n",
+ "New best validation loss: 0.1191\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 54%|████▎ | 14001/26015 [20:40<3:46:01, 1.13s/it, step=14002, loss=0.0315, true_loss=15.1059, lr=1.68e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 14000 | Train Loss: 0.0357 | Val Loss: 0.0652 | True train loss: 16.0534 | True val loss: 17.3651\n",
+ "New best validation loss: 0.0652\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 81%|██████▍ | 21001/26015 [30:53<1:34:27, 1.13s/it, step=21002, loss=0.0348, true_loss=15.9539, lr=1.52e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 21000 | Train Loss: 0.0319 | Val Loss: 0.0438 | True train loss: 15.7137 | True val loss: 16.3045\n",
+ "New best validation loss: 0.0438\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 2/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 8%|▋ | 1987/26015 [02:59<5:37:18, 1.19it/s, step=28002, loss=0.0224, true_loss=14.3285, lr=1.35e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 28000 | Train Loss: 0.0284 | Val Loss: 0.0393 | True train loss: 15.0774 | True val loss: 15.2044\n",
+ "New best validation loss: 0.0393\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 35%|███ | 8987/26015 [13:13<3:55:46, 1.20it/s, step=35002, loss=0.0302, true_loss=13.3737, lr=1.19e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 35000 | Train Loss: 0.0257 | Val Loss: 0.0279 | True train loss: 14.4303 | True val loss: 14.4498\n",
+ "New best validation loss: 0.0279\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 61%|████▉ | 15987/26015 [23:29<2:17:59, 1.21it/s, step=42002, loss=0.0264, true_loss=14.5345, lr=1.03e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 42000 | Train Loss: 0.0245 | Val Loss: 0.0351 | True train loss: 14.1197 | True val loss: 14.2312\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 88%|████████▊ | 22987/26015 [33:46<41:56, 1.20it/s, step=49002, loss=0.0216, true_loss=14.1316, lr=8.70e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 49000 | Train Loss: 0.0233 | Val Loss: 0.0290 | True train loss: 13.9434 | True val loss: 14.4628\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 3/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 15%|█▎ | 3971/26015 [05:52<7:13:46, 1.18s/it, step=56002, loss=0.0254, true_loss=14.4344, lr=7.08e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 56000 | Train Loss: 0.0229 | Val Loss: 0.0479 | True train loss: 13.9115 | True val loss: 14.0929\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 42%|███▎ | 10971/26015 [16:06<4:48:34, 1.15s/it, step=63002, loss=0.0201, true_loss=12.8691, lr=5.47e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 63000 | Train Loss: 0.0219 | Val Loss: 0.0239 | True train loss: 13.6746 | True val loss: 13.5177\n",
+ "New best validation loss: 0.0239\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 69%|██████▏ | 17971/26015 [26:24<2:31:18, 1.13s/it, step=7e+4, loss=0.0248, true_loss=14.9835, lr=3.86e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 70000 | Train Loss: 0.0212 | Val Loss: 0.0259 | True train loss: 13.5072 | True val loss: 13.7410\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 96%|█████████▌| 24971/26015 [36:41<19:36, 1.13s/it, step=77002, loss=0.0228, true_loss=13.9553, lr=2.24e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 77000 | Train Loss: 0.0207 | Val Loss: 0.0267 | True train loss: 13.4052 | True val loss: 13.8021\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded best model with validation loss: 0.0239\n",
+ "Training completed.\n",
+ "Number of validation checkpoints: 11\n",
+ "Final training losses: [0.022863016219410514, 0.02186289042873042, 0.021151691354678145, 0.020719855580878046, 0.020669196563010864]\n",
+ "Best validation loss: 0.0239\n",
+ "Model saved successfully!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=3, learning_rate=2e-5, batch_size=256, validation_steps=7000,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/simson_modeling/regression/polyGNN_combined_mols_.csv b/simson_modeling/regression/polyGNN_combined_mols_.csv
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diff --git a/simson_modeling/regression/polygnn_model/model_4.pt b/simson_modeling/regression/polygnn_model/model_4.pt
new file mode 100644
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+size 652461
diff --git a/simson_modeling/regression/regression.ipynb b/simson_modeling/regression/regression.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dafbef96817676ce64480e80a2f95e77fb248c5c
--- /dev/null
+++ b/simson_modeling/regression/regression.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "11ab9ea6-1c5b-4f9b-a6ea-1bc75be56108",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "\n",
+ "import joblib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from torch import nn\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader, Dataset\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (\n",
+ " AutoConfig,\n",
+ " AutoModel,\n",
+ " AutoTokenizer,\n",
+ " BertConfig,\n",
+ " BertModel,\n",
+ " BertTokenizerFast,\n",
+ " PreTrainedModel,\n",
+ ")\n",
+ "from transformers.activations import ACT2FN\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ce760993-fbef-4546-8b2c-1e7a722ad374",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "\n",
+ "class SMILESDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_length=256):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels # Shape: (num_samples, 2) - already scaled\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Create mask for valid (non-NaN) labels\n",
+ " self.label_masks = ~np.isnan(self.labels) # True where label is valid\n",
+ " \n",
+ " # Replace NaNs with 0 for safe tensor conversion (mask will handle exclusion)\n",
+ " self.labels = np.nan_to_num(self.labels, nan=0.0)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.tokenizer.cls_token + self.smiles_list[idx]\n",
+ " \n",
+ " # Tokenize the SMILES string\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_length,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " \n",
+ " return {\n",
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
+ " 'labels': torch.tensor(self.labels[idx], dtype=torch.float32),\n",
+ " 'label_mask': torch.tensor(self.label_masks[idx], dtype=torch.float32)\n",
+ " }\n",
+ " \n",
+ " def get_label_statistics(self):\n",
+ " \"\"\"Return statistics about label availability\"\"\"\n",
+ " label_counts = self.label_masks.sum(axis=0)\n",
+ " total_samples = len(self.smiles_list)\n",
+ " \n",
+ " stats = {\n",
+ " 'total_samples': total_samples,\n",
+ " 'label_0_count': label_counts[0],\n",
+ " 'label_1_count': label_counts[1],\n",
+ " 'label_0_ratio': label_counts[0] / total_samples,\n",
+ " 'label_1_ratio': label_counts[1] / total_samples,\n",
+ " 'both_labels_count': (self.label_masks.sum(axis=1) == 2).sum(),\n",
+ " 'single_label_count': (self.label_masks.sum(axis=1) == 1).sum(),\n",
+ " 'no_labels_count': (self.label_masks.sum(axis=1) == 0).sum()\n",
+ " }\n",
+ " \n",
+ " return stats\n",
+ "\n",
+ "def calculate_weighted_loss(predictions, labels, label_mask, label_weights):\n",
+ " \"\"\"\n",
+ " Calculate weighted loss for two labels with masking\n",
+ " \n",
+ " Args:\n",
+ " predictions: Model outputs (batch_size, 2)\n",
+ " labels: Ground truth labels (batch_size, 2)\n",
+ " label_mask: Mask for valid labels (batch_size, 2)\n",
+ " label_weights: Weights for each label (2,)\n",
+ " \"\"\"\n",
+ " loss_fn = nn.MSELoss(reduction='none')\n",
+ " \n",
+ " # Calculate per-sample, per-label losses\n",
+ " losses = loss_fn(predictions, labels) # Shape: (batch_size, 2)\n",
+ " \n",
+ " # Apply masking to exclude NaN labels\n",
+ " valid_mask = label_mask.bool()\n",
+ " masked_losses = losses * valid_mask.float()\n",
+ " \n",
+ " # Apply label-specific weights\n",
+ " weighted_losses = masked_losses * label_weights.unsqueeze(0) # Broadcast weights\n",
+ " \n",
+ " # Calculate final loss (only over valid predictions)\n",
+ " total_loss = weighted_losses.sum()\n",
+ " total_valid = valid_mask.sum()\n",
+ " \n",
+ " return total_loss / total_valid if total_valid > 0 else torch.tensor(0.0, device=predictions.device, requires_grad=True)\n",
+ "\n",
+ "def compute_label_weights(dataset):\n",
+ " \"\"\"\n",
+ " Compute inverse frequency weights based on label availability\n",
+ " \n",
+ " Args:\n",
+ " dataset: SMILESDataset instance\n",
+ " \n",
+ " Returns:\n",
+ " torch.Tensor: Normalized weights for each label\n",
+ " \"\"\"\n",
+ " # Get label counts from dataset\n",
+ " label_counts = dataset.label_masks.sum(axis=0) # Count valid samples per label\n",
+ " total_samples = len(dataset)\n",
+ " \n",
+ " # Inverse frequency weighting\n",
+ " weights = total_samples / (2 * label_counts) # 2 is the number of labels\n",
+ " \n",
+ " # Normalize weights so they sum to number of labels (2)\n",
+ " weights = weights / weights.sum() * 2\n",
+ " \n",
+ " return torch.tensor(weights, dtype=torch.float32)\n",
+ "\n",
+ "def calculate_true_loss(predictions, labels, label_mask, scalers=None):\n",
+ " \"\"\"\n",
+ " Calculate unscaled MAE loss for monitoring using separate scalers for each label\n",
+ " \n",
+ " Args:\n",
+ " predictions (torch.Tensor): Model outputs of shape (batch_size, 2).\n",
+ " labels (torch.Tensor): Ground truth labels of shape (batch_size, 2).\n",
+ " label_mask (torch.Tensor): Boolean mask for valid labels of shape (batch_size, 2).\n",
+ " scalers: List of scaler objects, one for each label\n",
+ " \"\"\"\n",
+ " # Detach tensors from the computation graph and move to CPU\n",
+ " predictions_np = predictions.cpu().detach().numpy()\n",
+ " labels_np = labels.cpu().numpy()\n",
+ " label_mask_np = label_mask.cpu().numpy().astype(bool)\n",
+ " \n",
+ " total_mae = 0\n",
+ " total_samples = 0\n",
+ " \n",
+ " for label_idx in range(2):\n",
+ " # Get valid samples for this label\n",
+ " valid_mask = label_mask_np[:, label_idx]\n",
+ " \n",
+ " if valid_mask.any():\n",
+ " valid_preds = predictions_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " valid_labels = labels_np[valid_mask, label_idx].reshape(-1, 1)\n",
+ " \n",
+ " if scalers is not None:\n",
+ " # Unscale using the corresponding scaler for this label\n",
+ " unscaled_preds = scalers[label_idx].inverse_transform(valid_preds).flatten()\n",
+ " unscaled_labels = scalers[label_idx].inverse_transform(valid_labels).flatten()\n",
+ " else:\n",
+ " unscaled_preds = valid_preds.flatten()\n",
+ " unscaled_labels = valid_labels.flatten()\n",
+ " \n",
+ " # Calculate MAE for this label\n",
+ " mae = np.mean(np.abs(unscaled_preds - unscaled_labels))\n",
+ " total_mae += mae * len(unscaled_preds)\n",
+ " total_samples += len(unscaled_preds)\n",
+ " \n",
+ " return total_mae / total_samples if total_samples > 0 else 0.0\n",
+ "\n",
+ "\n",
+ "def train_model(model, train_dataloader, val_dataloader, label_weights, \n",
+ " scalers=None, num_epochs=10, learning_rate=2e-5, device='cuda', \n",
+ " patience=3, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Train model with weighted loss for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " model: CustomModel instance (should output 2 labels)\n",
+ " train_dataloader: Training data loader\n",
+ " val_dataloader: Validation data loader \n",
+ " label_weights: Tensor with weights for each label\n",
+ " scalers: List of scalers for unscaled loss monitoring\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " device: Training device\n",
+ " patience: Early stopping patience (in validation steps)\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " model.to(device)\n",
+ " label_weights = label_weights.to(device)\n",
+ " \n",
+ " optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
+ " total_steps = len(train_dataloader) * num_epochs\n",
+ " scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=0.1, total_iters=total_steps)\n",
+ " \n",
+ " train_losses = []\n",
+ " val_losses = []\n",
+ " \n",
+ " # Early stopping initialization\n",
+ " best_val_loss = float('inf')\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = None\n",
+ " \n",
+ " # Training tracking\n",
+ " global_step = 0\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " print(f\"Label weights: {label_weights.cpu().numpy()}\")\n",
+ " print(f\"Validation will be performed every {validation_steps} steps\")\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " for epoch in range(num_epochs):\n",
+ " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n",
+ " \n",
+ " train_progress = tqdm(train_dataloader, desc=\"Training\", leave=False)\n",
+ " \n",
+ " for batch_idx, batch in enumerate(train_progress):\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = batch['input_ids'].to(device)\n",
+ " attention_mask = batch['attention_mask'].to(device)\n",
+ " labels = batch['labels'].to(device)\n",
+ " label_mask = batch['label_mask'].to(device)\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Model forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " # Calculate weighted loss\n",
+ " loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " \n",
+ " # Calculate true loss for monitoring\n",
+ " true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ " \n",
+ " # Accumulate losses for averaging\n",
+ " running_train_loss += loss.item()\n",
+ " running_true_train_loss += true_loss\n",
+ " train_steps_count += 1\n",
+ " \n",
+ " loss.backward()\n",
+ " \n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+ " \n",
+ " optimizer.step()\n",
+ " scheduler.step()\n",
+ " \n",
+ " global_step += 1\n",
+ " \n",
+ " train_progress.set_postfix({\n",
+ " 'step': global_step,\n",
+ " 'loss': f'{loss.item():.4f}',\n",
+ " 'true_loss': f'{true_loss:.4f}',\n",
+ " 'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
+ " })\n",
+ " \n",
+ " # Perform validation every validation_steps\n",
+ " if global_step % validation_steps == 0:\n",
+ " # Calculate average training losses since last validation\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " avg_true_train_loss = running_true_train_loss / train_steps_count\n",
+ " \n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Reset running averages\n",
+ " running_train_loss = 0\n",
+ " running_true_train_loss = 0\n",
+ " train_steps_count = 0\n",
+ " \n",
+ " # Validation\n",
+ " model.eval()\n",
+ " total_val_loss = 0\n",
+ " total_true_val_loss = 0\n",
+ " val_batches = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_batch in val_dataloader:\n",
+ " with torch.autocast(dtype=torch.float16, device_type=\"cuda\"):\n",
+ " input_ids = val_batch['input_ids'].to(device)\n",
+ " attention_mask = val_batch['attention_mask'].to(device)\n",
+ " labels = val_batch['labels'].to(device)\n",
+ " label_mask = val_batch['label_mask'].to(device)\n",
+ " \n",
+ " outputs = model(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask,\n",
+ " )\n",
+ " \n",
+ " val_loss = calculate_weighted_loss(outputs, labels, label_mask, label_weights)\n",
+ " val_true_loss = calculate_true_loss(outputs, labels, label_mask, scalers)\n",
+ "\n",
+ " total_val_loss += val_loss.item()\n",
+ " total_true_val_loss += val_true_loss\n",
+ " val_batches += 1\n",
+ " \n",
+ " avg_val_loss = total_val_loss / val_batches\n",
+ " avg_val_true_loss = total_true_val_loss / val_batches\n",
+ " val_losses.append(avg_val_loss)\n",
+ " \n",
+ " print(f\"\\nStep {global_step} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | True train loss: {avg_true_train_loss:.4f} | True val loss: {avg_val_true_loss:.4f}\")\n",
+ " \n",
+ " # Early stopping check and best model saving\n",
+ " if avg_val_loss < best_val_loss:\n",
+ " best_val_loss = avg_val_loss\n",
+ " steps_no_improve = 0\n",
+ " best_model_state = model.state_dict().copy()\n",
+ " print(f\"New best validation loss: {best_val_loss:.4f}\")\n",
+ " else:\n",
+ " steps_no_improve += 1\n",
+ " if steps_no_improve >= patience:\n",
+ " print(f\"Early stopping triggered after {global_step} steps ({steps_no_improve} validation steps without improvement).\")\n",
+ " # Load best model and return\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " return train_losses, val_losses, best_val_loss\n",
+ " \n",
+ " model.train()\n",
+ " \n",
+ " # Handle any remaining training loss that hasn't been validated\n",
+ " if train_steps_count > 0:\n",
+ " avg_train_loss = running_train_loss / train_steps_count\n",
+ " train_losses.append(avg_train_loss)\n",
+ " \n",
+ " # Load the best model state before returning\n",
+ " if best_model_state is not None:\n",
+ " model.load_state_dict(best_model_state)\n",
+ " print(f\"Loaded best model with validation loss: {best_val_loss:.4f}\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss\n",
+ "\n",
+ "def run_training(smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=5, learning_rate=1e-5, \n",
+ " batch_size=256, validation_steps=500):\n",
+ " \"\"\"\n",
+ " Complete training pipeline for two labels with step-based validation\n",
+ " \n",
+ " Args:\n",
+ " smiles_train, smiles_test: Lists of SMILES strings\n",
+ " labels_train, labels_test: numpy arrays of shape (num_samples, 2) - ALREADY SCALED\n",
+ " model: CustomModel instance (configured for 2 outputs)\n",
+ " tokenizer: Tokenizer instance\n",
+ " scalers: List of 2 scalers, one for each label (for inverse transform only)\n",
+ " num_epochs: Number of training epochs\n",
+ " learning_rate: Learning rate\n",
+ " batch_size: Batch size for training\n",
+ " validation_steps: Perform validation every N training steps\n",
+ " \"\"\"\n",
+ " \n",
+ " print(\"Setting up datasets for two-label training (labels assumed pre-scaled)\")\n",
+ " \n",
+ " # Create datasets - no scaling performed here\n",
+ " train_dataset = SMILESDataset(smiles_train, labels_train, tokenizer)\n",
+ " val_dataset = SMILESDataset(smiles_test, labels_test, tokenizer)\n",
+ " \n",
+ " # Print dataset statistics\n",
+ " train_stats = train_dataset.get_label_statistics()\n",
+ " val_stats = val_dataset.get_label_statistics()\n",
+ " \n",
+ " print(\"Training dataset statistics:\")\n",
+ " for key, value in train_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " print(\"Validation dataset statistics:\")\n",
+ " for key, value in val_stats.items():\n",
+ " print(f\" {key}: {value}\")\n",
+ " \n",
+ " # Compute label weights based on training data\n",
+ " label_weights = compute_label_weights(train_dataset)\n",
+ " print(f\"Computed label weights: {label_weights.numpy()}\")\n",
+ " \n",
+ " # Create data loaders\n",
+ " train_dataloader = DataLoader(\n",
+ " train_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " val_dataloader = DataLoader(\n",
+ " val_dataset,\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " num_workers=4,\n",
+ " pin_memory=True\n",
+ " )\n",
+ " \n",
+ " # Set device\n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " print(f\"Training steps per epoch: {len(train_dataloader)}\")\n",
+ " print(f\"Total training steps: {len(train_dataloader) * num_epochs}\")\n",
+ " \n",
+ " # Train the model\n",
+ " train_losses, val_losses, best_val_loss = train_model(\n",
+ " model=model,\n",
+ " train_dataloader=train_dataloader,\n",
+ " val_dataloader=val_dataloader,\n",
+ " label_weights=label_weights,\n",
+ " scalers=scalers, # Still pass scalers for true loss calculation\n",
+ " num_epochs=num_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " device=device,\n",
+ " patience=10,\n",
+ " validation_steps=validation_steps,\n",
+ " )\n",
+ " \n",
+ " print('Training completed.')\n",
+ " print(f'Number of validation checkpoints: {len(val_losses)}')\n",
+ " print(f'Final training losses: {train_losses[-5:] if len(train_losses) >= 5 else train_losses}')\n",
+ " print(f'Best validation loss: {best_val_loss:.4f}')\n",
+ " \n",
+ " # Save model\n",
+ " torch.save(model.state_dict(), '/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n",
+ " print(\"Model saved successfully!\")\n",
+ " \n",
+ " return train_losses, val_losses, best_val_loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "12a2b8c3-2c4d-4b1b-8cc7-930c9fe68fd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')\n",
+ "targets = ['Tg', 'He', 'N2', 'O2', 'CH4', 'CO2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8e1296a2-551c-48ab-aab3-fcf4b6110d75",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "plt.plot(df['CO2'].to_list())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9946f5cd-3683-49db-8535-393cb04140ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Only the hidden size is slightly larger, everything else is the same\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ "\n",
+ "simson_params = torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "backbone.load_state_dict(simson_params)\n",
+ "\n",
+ "\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=len(targets))\n",
+ "model = torch.compile(model, fullgraph=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "903489f0-9039-4504-894e-6739b4a15371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_splits(df):\n",
+ " length = len(df)\n",
+ " train_length = int(0.99 * length)\n",
+ " train = df.loc[:train_length]\n",
+ " test = df.loc[train_length:]\n",
+ " return train, test\n",
+ "\n",
+ "train, test = create_splits(df)\n",
+ "\n",
+ "train = train.reset_index(drop=True)\n",
+ "test = test.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "00c271f1-bd44-457d-9a0e-7b221871ab78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scalers = []\n",
+ "\n",
+ "for target in targets:\n",
+ " target_scaler = StandardScaler()\n",
+ " train[target] = target_scaler.fit_transform(train[target].to_numpy().reshape(-1, 1))\n",
+ " test[target] = target_scaler.transform(test[target].to_numpy().reshape(-1, 1))\n",
+ " \n",
+ " scalers.append(target_scaler)\n",
+ "\n",
+ "smiles_train = train['Smiles']\n",
+ "smiles_test = test['Smiles']\n",
+ "\n",
+ "labels_train = train[targets].values\n",
+ "labels_test = test[targets].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "01ebce4a-9ac0-4527-a9bd-8d13913f15e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/jovyan/simson_training_bolgov/regression/scalers']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "joblib.dump(scalers, '/home/jovyan/simson_training_bolgov/regression/scalers')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4405c601-f006-4eeb-989e-fb35dd5349ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting up datasets for two-label training (labels assumed pre-scaled)\n",
+ "Training dataset statistics:\n",
+ " total_samples: 6659681\n",
+ " label_0_count: 6659681\n",
+ " label_1_count: 6659681\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Validation dataset statistics:\n",
+ " total_samples: 67270\n",
+ " label_0_count: 67270\n",
+ " label_1_count: 67270\n",
+ " label_0_ratio: 1.0\n",
+ " label_1_ratio: 1.0\n",
+ " both_labels_count: 0\n",
+ " single_label_count: 0\n",
+ " no_labels_count: 0\n",
+ "Computed label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Using device: cuda\n",
+ "Training steps per epoch: 26015\n",
+ "Total training steps: 78045\n",
+ "Label weights: [0.33333334 0.33333334 0.33333334 0.33333334 0.33333334 0.33333334]\n",
+ "Validation will be performed every 7000 steps\n",
+ "\n",
+ "Epoch 1/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 27%|██▍ | 7001/26015 [10:27<10:43:20, 2.03s/it, step=7002, loss=0.0372, true_loss=16.0679, lr=1.84e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 7000 | Train Loss: 0.0618 | Val Loss: 0.1191 | True train loss: 17.4244 | True val loss: 18.3473\n",
+ "New best validation loss: 0.1191\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 54%|████▎ | 14001/26015 [20:40<3:46:01, 1.13s/it, step=14002, loss=0.0315, true_loss=15.1059, lr=1.68e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 14000 | Train Loss: 0.0357 | Val Loss: 0.0652 | True train loss: 16.0534 | True val loss: 17.3651\n",
+ "New best validation loss: 0.0652\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 81%|██████▍ | 21001/26015 [30:53<1:34:27, 1.13s/it, step=21002, loss=0.0348, true_loss=15.9539, lr=1.52e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 21000 | Train Loss: 0.0319 | Val Loss: 0.0438 | True train loss: 15.7137 | True val loss: 16.3045\n",
+ "New best validation loss: 0.0438\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 2/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 8%|▋ | 1987/26015 [02:59<5:37:18, 1.19it/s, step=28002, loss=0.0224, true_loss=14.3285, lr=1.35e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 28000 | Train Loss: 0.0284 | Val Loss: 0.0393 | True train loss: 15.0774 | True val loss: 15.2044\n",
+ "New best validation loss: 0.0393\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 35%|███ | 8987/26015 [13:13<3:55:46, 1.20it/s, step=35002, loss=0.0302, true_loss=13.3737, lr=1.19e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 35000 | Train Loss: 0.0257 | Val Loss: 0.0279 | True train loss: 14.4303 | True val loss: 14.4498\n",
+ "New best validation loss: 0.0279\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 61%|████▉ | 15987/26015 [23:29<2:17:59, 1.21it/s, step=42002, loss=0.0264, true_loss=14.5345, lr=1.03e-05]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 42000 | Train Loss: 0.0245 | Val Loss: 0.0351 | True train loss: 14.1197 | True val loss: 14.2312\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 88%|████████▊ | 22987/26015 [33:46<41:56, 1.20it/s, step=49002, loss=0.0216, true_loss=14.1316, lr=8.70e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 49000 | Train Loss: 0.0233 | Val Loss: 0.0290 | True train loss: 13.9434 | True val loss: 14.4628\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch 3/3\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 15%|█▎ | 3971/26015 [05:52<7:13:46, 1.18s/it, step=56002, loss=0.0254, true_loss=14.4344, lr=7.08e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 56000 | Train Loss: 0.0229 | Val Loss: 0.0479 | True train loss: 13.9115 | True val loss: 14.0929\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 42%|███▎ | 10971/26015 [16:06<4:48:34, 1.15s/it, step=63002, loss=0.0201, true_loss=12.8691, lr=5.47e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 63000 | Train Loss: 0.0219 | Val Loss: 0.0239 | True train loss: 13.6746 | True val loss: 13.5177\n",
+ "New best validation loss: 0.0239\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 69%|██████▏ | 17971/26015 [26:24<2:31:18, 1.13s/it, step=7e+4, loss=0.0248, true_loss=14.9835, lr=3.86e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 70000 | Train Loss: 0.0212 | Val Loss: 0.0259 | True train loss: 13.5072 | True val loss: 13.7410\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Training: 96%|█████████▌| 24971/26015 [36:41<19:36, 1.13s/it, step=77002, loss=0.0228, true_loss=13.9553, lr=2.24e-06]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Step 77000 | Train Loss: 0.0207 | Val Loss: 0.0267 | True train loss: 13.4052 | True val loss: 13.8021\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded best model with validation loss: 0.0239\n",
+ "Training completed.\n",
+ "Number of validation checkpoints: 11\n",
+ "Final training losses: [0.022863016219410514, 0.02186289042873042, 0.021151691354678145, 0.020719855580878046, 0.020669196563010864]\n",
+ "Best validation loss: 0.0239\n",
+ "Model saved successfully!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.optim.lr_scheduler import LinearLR\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "train_losses, val_losses, best_loss = run_training(\n",
+ " smiles_train, smiles_test, labels_train, labels_test, \n",
+ " model, tokenizer, scalers, num_epochs=3, learning_rate=2e-5, batch_size=256, validation_steps=7000,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/regression/regression_simson.pth b/simson_modeling/regression/regression_simson.pth
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+version https://git-lfs.github.com/spec/v1
+oid sha256:0073b873b9a1518a9fa365f4b9b7d9561ed990cf30cc109c7794024b39574ad4
+size 93243197
diff --git a/simson_modeling/regression/regression_simson_uncompiled.pth b/simson_modeling/regression/regression_simson_uncompiled.pth
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed09c827ae131beffe389f0cd622ee2548a3606181cc9c3f9e2aac11f8a01966
+size 93238348
diff --git a/simson_modeling/regression/regression_visualize.ipynb b/simson_modeling/regression/regression_visualize.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bcb1f22ce92eb97609d81e833d8ff461608418aa
--- /dev/null
+++ b/simson_modeling/regression/regression_visualize.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d5165301-7adf-4bf0-8253-59c2a940fb06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import joblib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "import umap\n",
+ "\n",
+ "# For better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_context(\"notebook\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1cbedb53-27e4-47a1-a60a-5e1cc9ac6ef4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "torch.backends.cuda.matmul.allow_tf32 = True\n",
+ "torch.backends.cudnn.allow_tf32 = True\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " \n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " \n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(0)\n",
+ " \n",
+ " outputs = self.bert(\n",
+ " input_ids=input_ids,\n",
+ " attention_mask=attention_mask\n",
+ " )\n",
+ " \n",
+ " hidden_states = outputs.last_hidden_state\n",
+ " \n",
+ " hidden_states = self.dropout(hidden_states)\n",
+ " \n",
+ " pooled = global_ap(hidden_states)\n",
+ " \n",
+ " out = self.linear(pooled)\n",
+ " \n",
+ " return out\n",
+ "\n",
+ "class SimSonClassifier(nn.Module):\n",
+ " def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):\n",
+ " super(SimSonClassifier, self).__init__()\n",
+ " self.encoder = encoder\n",
+ " self.clf = nn.Linear(encoder.max_len, num_labels)\n",
+ " self.relu = nn.ReLU()\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ "\n",
+ " def forward(self, input_ids, attention_mask=None, labels=None):\n",
+ " x = self.encoder(input_ids, attention_mask)\n",
+ " x = self.relu(self.dropout(x))\n",
+ " x = self.clf(x)\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bc2a9887-2e5f-48b6-8798-e2b0cdec4db1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load test data\n",
+ "from tqdm import tqdm\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "\n",
+ "df = pd.read_csv('/home/jovyan/simson_training_bolgov/regression/PI_Tg_P308K_synth_db_chem.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "83ee8d74-a48f-4661-adc6-4d17f7cdcb83",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████████████████████████████████████████████| 999/999 [00:29<00:00, 33.87it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1000, 9)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import pairwise_distances\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "\n",
+ "def create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols,\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ "):\n",
+ " \n",
+ " values = df[label_cols].values\n",
+ " # Each label gets its own bins, based on the overall distribution\n",
+ " bins = [np.unique(np.quantile(values[:,i], np.linspace(0, 1, n_bins+1))) for i in range(len(label_cols))]\n",
+ " # Assign each row to a bin for each label\n",
+ " inds = [\n",
+ " np.digitize(values[:,i], bins[i][1:-1], right=False) # exclude leftmost/rightmost for in-bin, avoids all bin edges as bins\n",
+ " for i in range(len(label_cols))\n",
+ " ]\n",
+ " # Combine into a single integer stratification variable (tuple or max or sum...)\n",
+ " strat_col = np.maximum.reduce(inds) # This ensures high bin in one = high bin overall\n",
+ " # Use sklearn's train_test_split with stratify\n",
+ " train_idx, val_idx = train_test_split(\n",
+ " df.index.values,\n",
+ " test_size=val_frac,\n",
+ " random_state=seed,\n",
+ " shuffle=True,\n",
+ " stratify=strat_col\n",
+ " )\n",
+ " train = df.loc[train_idx].reset_index(drop=True)\n",
+ " val = df.loc[val_idx].reset_index(drop=True)\n",
+ " return train, val\n",
+ "\n",
+ "\n",
+ "# For your use case:\n",
+ "train, test = create_stratified_splits_regression(\n",
+ " df,\n",
+ " label_cols=['CO2', 'CH4'], # or actual column names\n",
+ " n_bins=10,\n",
+ " val_frac=0.05,\n",
+ " seed=42\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# --- 2. columns that hold permeability values --------------------------------\n",
+ "features = ['CO2', 'CH4'] # adjust if names differ\n",
+ "\n",
+ "# --- 3. Max–Min selection ----------------------------------------------------\n",
+ "def select_diverse_maxmin(data, cols, k=1_000):\n",
+ " X = data[cols].to_numpy()\n",
+ " \n",
+ " # scale to [0,1] so CO₂ and CH₄ get equal weight\n",
+ " X = MinMaxScaler().fit_transform(X)\n",
+ " \n",
+ " n = len(X)\n",
+ " if k >= n: # nothing to do\n",
+ " return data\n",
+ " \n",
+ " # start with a random seed point\n",
+ " selected = [np.random.randint(0, n)]\n",
+ " \n",
+ " # pre-allocate distance cache\n",
+ " min_dist = pairwise_distances(\n",
+ " X, X[selected], metric='euclidean'\n",
+ " ).ravel() # distance to first point\n",
+ " \n",
+ " for _ in tqdm(range(1, k)):\n",
+ " # pick the point with the largest distance to the current set\n",
+ " idx = np.argmax(min_dist)\n",
+ " selected.append(idx)\n",
+ " \n",
+ " # update distance cache (keep the shortest distance to any selected pt)\n",
+ " dist_to_new = pairwise_distances(X, X[[idx]], metric='euclidean').ravel()\n",
+ " min_dist = np.minimum(min_dist, dist_to_new)\n",
+ " \n",
+ " return data.iloc[selected].reset_index(drop=True)\n",
+ "\n",
+ "diverse_test = select_diverse_maxmin(test, features, k=1_000)\n",
+ "print(diverse_test.shape) # (1000, …)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d251cb4f-a250-4110-ab1c-679532a5d6e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_240462/477882228.py:21: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " simson_params_clf = torch.load('/home/jovyan/simson_training_bolgov/regression/regression_simson.pth')\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "OptimizedModule(\n",
+ " (_orig_mod): SimSonClassifier(\n",
+ " (encoder): OptimizedModule(\n",
+ " (_orig_mod): SimSonEncoder(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(591, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): BertEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-3): 4 x BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSdpaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): BertSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): BertIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=2048, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): BertOutput(\n",
+ " (dense): Linear(in_features=2048, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (linear): Linear(in_features=768, out_features=512, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (clf): Linear(in_features=512, out_features=6, bias=True)\n",
+ " (relu): ReLU()\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer_path = 'DeepChem/ChemBERTa-77M-MTR'\n",
+ "tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
+ "\n",
+ "# Load scalers (list of 6), only last 2 needed for CO2 and CH4 permeability\n",
+ "scalers = joblib.load('/home/jovyan/simson_training_bolgov/regression/scalers')\n",
+ "scaler_co2 = scalers[-1]\n",
+ "scaler_ch4 = scalers[-2]\n",
+ "\n",
+ "# Torch model definitions (SimSonEncoder and SimSonClassifier as in your code)\n",
+ "# --paste classes global_ap, SimSonEncoder, SimSonClassifier here--\n",
+ "\n",
+ "config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "simson_params_clf = torch.load('/home/jovyan/simson_training_bolgov/regression/better_regression_states/best_state.bin')\n",
+ "\n",
+ "backbone = SimSonEncoder(config=config, max_len=512)\n",
+ "backbone = torch.compile(backbone)\n",
+ "model = SimSonClassifier(encoder=backbone, num_labels=6) # 6 outputs as per full regression\n",
+ "model = torch.compile(model, fullgraph=True)\n",
+ "model.load_state_dict(simson_params_clf)\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "11790c3a-3a8f-4982-94e9-e0134ba69cbe",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing 1000 SMILES in batches of 256...\n",
+ "Final shapes - Embeddings: (1000, 512), Predictions: (1000, 6)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(1000,)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def generate_embeddings_and_predictions(test_smiles, tokenizer, backbone, model, \n",
+ " batch_size=32, max_length=512, device='cpu'):\n",
+ " \"\"\"\n",
+ " Generate embeddings and predictions sequentially in batches to avoid memory issues\n",
+ " \"\"\"\n",
+ " embeddings_list = []\n",
+ " predictions_list = []\n",
+ " \n",
+ " backbone.eval()\n",
+ " model.eval()\n",
+ " \n",
+ " print(f\"Processing {len(test_smiles)} SMILES in batches of {batch_size}...\")\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(test_smiles), batch_size):\n",
+ " batch_smiles = test_smiles[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize current batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings and predictions for current batch\n",
+ " batch_embeddings = backbone(input_ids, attention_mask)\n",
+ " batch_predictions = model(input_ids, attention_mask)\n",
+ " \n",
+ " # Move to CPU and store\n",
+ " embeddings_list.append(batch_embeddings.cpu().numpy())\n",
+ " predictions_list.append(batch_predictions.cpu().numpy())\n",
+ " \n",
+ " # Progress indicator\n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(test_smiles)} SMILES\")\n",
+ " \n",
+ " # Concatenate all batches\n",
+ " embeddings = np.vstack(embeddings_list)\n",
+ " predictions = np.vstack(predictions_list)\n",
+ " \n",
+ " print(f\"Final shapes - Embeddings: {embeddings.shape}, Predictions: {predictions.shape}\")\n",
+ " return embeddings, predictions\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "# Move models to device\n",
+ "backbone.to(device)\n",
+ "model.to(device)\n",
+ "\n",
+ "# Prepare SMILES list from test dataset\n",
+ "smiles_list = diverse_test['Smiles'].tolist() # Note: use correct column name from your dataset\n",
+ "\n",
+ "# Generate embeddings and predictions sequentially\n",
+ "embeddings, predictions_np = generate_embeddings_and_predictions(\n",
+ " smiles_list, \n",
+ " tokenizer, \n",
+ " backbone, \n",
+ " model, \n",
+ " batch_size=256, # Adjust based on your GPU memory\n",
+ " device=device\n",
+ ")\n",
+ "\n",
+ "# Extract CO2 and CH4 predictions (last two columns)\n",
+ "y_pred_co2_unscaled = predictions_np[:, -1] # CO2 permeability\n",
+ "y_pred_ch4_unscaled = predictions_np[:, -2] # CH4 permeability\n",
+ "\n",
+ "y_pred_co2_unscaled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "bfb72f9d-dd28-4fd3-b328-414274c3227b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Inverse transform using the loaded scalers for CO2 and CH4\n",
+ "y_pred_co2 = scaler_co2.inverse_transform(y_pred_co2_unscaled.reshape(-1,1)).flatten()\n",
+ "y_pred_ch4 = scaler_ch4.inverse_transform(y_pred_ch4_unscaled.reshape(-1,1)).flatten()\n",
+ "\n",
+ "# Targets: you may need to change these names based on your test DataFrame\n",
+ "y_true_co2 = diverse_test['CO2'].values\n",
+ "y_true_ch4 = diverse_test['CH4'].values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "7b2ea6e8-21fa-4a2d-a37e-abcea085daee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== CO₂ Permeability Relative MAE ===\n",
+ "Absolute MAE: 2569.0372\n",
+ "MAE as % of data range: 1.59%\n",
+ "MAE as % of mean value: 17.87%\n",
+ "Data range: 0.0854 to 161379.4600\n",
+ "Mean value: 14379.8005\n",
+ "\n",
+ "=== CH₄ Permeability Relative MAE ===\n",
+ "Absolute MAE: 9.0738\n",
+ "MAE as % of data range: 1.72%\n",
+ "MAE as % of mean value: 13.69%\n",
+ "Data range: 0.0003 to 528.1360\n",
+ "Mean value: 66.2984\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import mean_absolute_error\n",
+ "\n",
+ "\n",
+ "def calculate_relative_mae(y_true, y_pred, property_name):\n",
+ " \"\"\"Calculate MAE relative to data range and mean\"\"\"\n",
+ " mae = mean_absolute_error(y_true, y_pred)\n",
+ " data_range = y_true.max() - y_true.min()\n",
+ " mean_value = y_true.mean()\n",
+ " \n",
+ " relative_mae_range = (mae / data_range) * 100\n",
+ " relative_mae_mean = (mae / mean_value) * 100\n",
+ " \n",
+ " print(f\"\\n=== {property_name} Relative MAE ===\")\n",
+ " print(f\"Absolute MAE: {mae:.4f}\")\n",
+ " print(f\"MAE as % of data range: {relative_mae_range:.2f}%\")\n",
+ " print(f\"MAE as % of mean value: {relative_mae_mean:.2f}%\")\n",
+ " print(f\"Data range: {y_true.min():.4f} to {y_true.max():.4f}\")\n",
+ " print(f\"Mean value: {mean_value:.4f}\")\n",
+ " \n",
+ " return mae, relative_mae_range, relative_mae_mean\n",
+ "\n",
+ "# Analyze relative performance\n",
+ "co2_mae_analysis = calculate_relative_mae(y_true_co2, y_pred_co2, \"CO₂ Permeability\")\n",
+ "ch4_mae_analysis = calculate_relative_mae(y_true_ch4, y_pred_ch4, \"CH₄ Permeability\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "c4c74497-0f94-4f88-b7c2-83129b22736f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12,5))\n",
+ "axes[0].scatter(y_true_co2, y_pred_co2, alpha=0.6, edgecolor='k')\n",
+ "axes[0].plot([y_true_co2.min(), y_true_co2.max()], [y_true_co2.min(), y_true_co2.max()], 'r--')\n",
+ "axes[0].set_title('CO₂ Permeability: Prediction vs. True')\n",
+ "axes[0].set_xlabel('True CO₂ Permeability')\n",
+ "axes[0].set_ylabel('Predicted CO₂ Permeability')\n",
+ "\n",
+ "axes[1].scatter(y_true_ch4, y_pred_ch4, alpha=0.6, edgecolor='k')\n",
+ "axes[1].plot([y_true_ch4.min(), y_true_ch4.max()], [y_true_ch4.min(), y_true_ch4.max()], 'r--')\n",
+ "axes[1].set_title('CH₄ Permeability: Prediction vs. True')\n",
+ "axes[1].set_xlabel('True CH₄ Permeability')\n",
+ "axes[1].set_ylabel('Predicted CH₄ Permeability')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "d36e3dd2-64a6-45a5-88a7-32c25cfbbdc3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+ " warn(\n",
+ "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+ " warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Choose dimensionality reducer\n",
+ "import matplotlib.colors as mcolors\n",
+ "import numpy as np\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "embeddings_2d_pca = pca.fit_transform(embeddings)\n",
+ "\n",
+ "tsne = TSNE(n_components=2, random_state=42, perplexity=30)\n",
+ "embeddings_2d_tsne = tsne.fit_transform(embeddings)\n",
+ "\n",
+ "umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
+ "embeddings_2d_umap = umap_reducer.fit_transform(embeddings)\n",
+ "\n",
+ "def plot_embedding_2d_log(emb_2d, vals, title, label):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Add small constant to avoid log(0) issues\n",
+ " vals_positive = vals - vals.min() + 1e-10\n",
+ " vals_log = np.log10(vals_positive)\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals_log, \n",
+ " cmap='plasma', \n",
+ " alpha=0.75, \n",
+ " s=30)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(f'log₁₀({label})', fontsize=12)\n",
+ " plt.title(f'{title} (Log Scale)', fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "def plot_embedding_2d_improved(emb_2d, vals, title, label):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Use percentiles to handle outliers (clips top/bottom 5%)\n",
+ " vmin = np.percentile(vals, 5)\n",
+ " vmax = np.percentile(vals, 95)\n",
+ " \n",
+ " # Use a diverging colormap centered on median\n",
+ " vcenter = np.median(vals)\n",
+ " norm = mcolors.TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals, \n",
+ " cmap='RdYlBu_r', # Better for continuous data\n",
+ " alpha=0.75, \n",
+ " s=30, # Slightly larger points\n",
+ " norm=norm)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(label, fontsize=12)\n",
+ " plt.title(title, fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "def plot_embedding_2d_quantile(emb_2d, vals, title, label, n_bins=10):\n",
+ " plt.figure(figsize=(10, 7))\n",
+ " \n",
+ " # Create quantile-based bins\n",
+ " quantiles = np.linspace(0, 100, n_bins + 1)\n",
+ " bin_edges = np.percentile(vals, quantiles)\n",
+ " vals_binned = np.digitize(vals, bin_edges) - 1\n",
+ " \n",
+ " scatter = plt.scatter(emb_2d[:,0], emb_2d[:,1], \n",
+ " c=vals_binned, \n",
+ " cmap='tab10' if n_bins <= 10 else 'viridis', \n",
+ " alpha=0.75, \n",
+ " s=30)\n",
+ " \n",
+ " cbar = plt.colorbar(scatter, shrink=0.8)\n",
+ " cbar.set_label(f'{label} (Decile)', fontsize=12)\n",
+ " cbar.set_ticks(range(n_bins))\n",
+ " cbar.set_ticklabels([f'Q{i+1}' for i in range(n_bins)])\n",
+ " \n",
+ " plt.title(f'{title} (Quantile Binned)', fontsize=14, fontweight='bold')\n",
+ " plt.xlabel('Component 1', fontsize=12)\n",
+ " plt.ylabel('Component 2', fontsize=12)\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "embeddings_2d_pca = pca.fit_transform(embeddings)\n",
+ "\n",
+ "tsne = TSNE(n_components=2, random_state=42, perplexity=30)\n",
+ "embeddings_2d_tsne = tsne.fit_transform(embeddings)\n",
+ "\n",
+ "umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
+ "embeddings_2d_umap = umap_reducer.fit_transform(embeddings)\n",
+ "\n",
+ "# Method 1: Percentile-based scaling (recommended for most cases)\n",
+ "plot_embedding_2d_improved(embeddings_2d_pca, y_true_co2, 'PCA Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_tsne, y_true_co2, 't-SNE Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_umap, y_true_co2, 'UMAP Embedding (CO₂ permeability)', 'CO₂ permeability')\n",
+ "\n",
+ "# Method 2: Log scale (if values span orders of magnitude)\n",
+ "plot_embedding_2d_improved(embeddings_2d_pca, y_true_ch4, 'PCA Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_tsne, y_true_ch4, 't-SNE Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "#plot_embedding_2d_improved(embeddings_2d_umap, y_true_ch4, 'UMAP Embedding (CH₄ permeability)', 'CH₄ permeability')\n",
+ "\n",
+ "# Method 3: Quantile binning (for categorical-like visualization)\n",
+ "# plot_embedding_2d_quantile(embeddings_2d_umap, y_true_co2, 'PCA Embedding (CO₂ permeability)', 'CO₂ permeability', n_bins=8)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e727d25f-70fa-4cfb-ac85-8bd69eff8bef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Correlation between PC1 and CO2 permeability: 0.519\n",
+ "Correlation between PC1 and CH4 permeability: 0.853\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute the first principal component of the embeddings\n",
+ "pc1 = pca.components_[0]\n",
+ "projected = embeddings @ pc1 # Project embeddings onto PC1\n",
+ "\n",
+ "from scipy.stats import pearsonr\n",
+ "corr_co2 = pearsonr(projected, y_true_co2)[0]\n",
+ "corr_ch4 = pearsonr(projected, y_true_ch4)[0]\n",
+ "print(f'Correlation between PC1 and CO2 permeability: {corr_co2:.3f}')\n",
+ "print(f'Correlation between PC1 and CH4 permeability: {corr_ch4:.3f}')\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+version https://git-lfs.github.com/spec/v1
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diff --git a/simson_modeling/simson_ddp_train.py b/simson_modeling/simson_ddp_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dacb8247ad825daecc40d24b3a131eb9e4f79d3
--- /dev/null
+++ b/simson_modeling/simson_ddp_train.py
@@ -0,0 +1,545 @@
+# ==============================================================================
+# 1. IMPORTS
+# ==============================================================================
+import os
+import warnings
+import wandb
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+import numpy as np
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset, load_from_disk
+from transformers import AutoTokenizer, BertModel, BertConfig
+import pandas as pd
+
+# ==============================================================================
+# 2. INITIAL SETUP
+# ==============================================================================
+# Suppress RDKit console output
+RDLogger.DisableLog('rdApp.*')
+# Ignore warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# ==============================================================================
+# 3. MODEL AND LOSS FUNCTION
+# ==============================================================================
+def global_average_pooling(x):
+ """Global Average Pooling: from [B, max_len, hid_dim] to [B, hid_dim]"""
+ return torch.mean(x, dim=1)
+
+class SimSonEncoder(nn.Module):
+ """The main encoder model based on BERT."""
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.bert.config.pad_token_id)
+
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled_output = global_average_pooling(hidden_states)
+ return self.linear(pooled_output)
+
+class ContrastiveLoss(nn.Module):
+ """Calculates the contrastive loss for the SimSon model."""
+ def __init__(self, temperature=0.2):
+ super(ContrastiveLoss, self).__init__()
+ self.temperature = temperature
+ self.similarity_fn = F.cosine_similarity
+
+ def forward(self, proj_1, proj_2):
+ batch_size = proj_1.shape[0]
+ device = proj_1.device
+
+ # Normalize projections
+ z_i = F.normalize(proj_1, p=2, dim=1)
+ z_j = F.normalize(proj_2, p=2, dim=1)
+
+ # Concatenate for similarity matrix calculation
+ representations = torch.cat([z_i, z_j], dim=0)
+
+ # Calculate cosine similarity between all pairs
+ similarity_matrix = self.similarity_fn(representations.unsqueeze(1), representations.unsqueeze(0), dim=2)
+
+ # Identify positive pairs (original and its augmentation)
+ sim_ij = torch.diag(similarity_matrix, batch_size)
+ sim_ji = torch.diag(similarity_matrix, -batch_size)
+ positives = torch.cat([sim_ij, sim_ji], dim=0)
+
+ # Create a mask to exclude self-comparisons
+ nominator = torch.exp(positives / self.temperature)
+ mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool, device=device)).float()
+ denominator = mask * torch.exp(similarity_matrix / self.temperature)
+
+ # Calculate the final loss
+ loss = -torch.log(nominator / torch.sum(denominator, dim=1))
+ return torch.sum(loss) / (2 * batch_size)
+
+# ==============================================================================
+# 4. DATA HANDLING (Keeping your existing classes unchanged)
+# ==============================================================================
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+class ContrastiveSmilesDataset(Dataset):
+ """Dataset for creating pairs of augmented SMILES for contrastive learning."""
+ def __init__(self, smiles_list, tokenizer, max_length=512):
+ self.smiles_list = smiles_list
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.enumerator = SmilesEnumerator()
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ original_smiles = self.smiles_list[idx]
+
+ # Create two different augmentations of the same SMILES
+ smiles_1 = self.enumerator.randomize_smiles(original_smiles)
+ smiles_2 = self.enumerator.randomize_smiles(original_smiles)
+
+ # Tokenize and do pad. Padding will be handled by the collate_fn.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PrecomputedContrastiveSmilesDataset(Dataset):
+ """
+ A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.
+ This is significantly faster as it offloads the expensive SMILES randomization
+ to a one-time preprocessing step.
+ """
+ def __init__(self, tokenizer, file_path: str, max_length: int = 512):
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ # Load the entire dataset from the Parquet file into memory.
+ # This is fast and efficient for subsequent access.
+ print(f"Loading pre-computed data from {file_path}...")
+ self.data = pd.read_parquet(file_path)
+ print("Data loaded successfully.")
+
+ def __len__(self):
+ """Returns the total number of pairs in the dataset."""
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ """
+ Retrieves a pre-augmented pair, tokenizes it, and returns it
+ in the format expected by the DataCollator.
+ """
+ # Retrieve the pre-augmented pair from the DataFrame
+ row = self.data.iloc[idx]
+ smiles_1 = row['smiles_1']
+ smiles_2 = row['smiles_2']
+
+ # Tokenize the pair. This operation is fast and remains in the data loader.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PreTokenizedSmilesDataset(Dataset):
+ """
+ A Dataset that loads a pre-tokenized and pre-padded dataset created
+ by the preprocessing script. It uses memory-mapping for instant loads
+ and high efficiency.
+ """
+ def __init__(self, dataset_path: str):
+ # Load the dataset from disk. This is very fast due to memory-mapping.
+ self.dataset = load_from_disk(dataset_path)
+ # Set the format to PyTorch tensors for direct use in the model
+ self.dataset.set_format(type='torch', columns=[
+ 'input_ids_1', 'attention_mask_1', 'input_ids_2', 'attention_mask_2'
+ ])
+ print(f"Successfully loaded pre-tokenized dataset from {dataset_path}.")
+
+ def __len__(self):
+ """Returns the total number of items in the dataset."""
+ return len(self.dataset)
+
+ def __getitem__(self, idx):
+ """Retrieves a single pre-processed item."""
+ return self.dataset[idx]
+
+class DataCollatorWithPadding:
+ """
+ A collate function that dynamically pads inputs to the longest sequence
+ across both augmented views in the batch, ensuring consistent tensor shapes.
+ """
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def __call__(self, features):
+ # Create a combined list of features for both views to find the global max length
+ combined_features = []
+ for feature in features:
+ combined_features.append({'input_ids': feature['input_ids_1'], 'attention_mask': feature['attention_mask_1']})
+ combined_features.append({'input_ids': feature['input_ids_2'], 'attention_mask': feature['attention_mask_2']})
+
+ # Pad the combined batch. This ensures all sequences are padded to the same length.
+ padded_combined = self.tokenizer.pad(combined_features, padding='longest', return_tensors='pt')
+
+ # Split the padded tensors back into two views
+ batch_size = len(features)
+ input_ids_1, input_ids_2 = torch.split(padded_combined['input_ids'], batch_size, dim=0)
+ attention_mask_1, attention_mask_2 = torch.split(padded_combined['attention_mask'], batch_size, dim=0)
+
+ return {
+ 'input_ids_1': input_ids_1,
+ 'attention_mask_1': attention_mask_1,
+ 'input_ids_2': input_ids_2,
+ 'attention_mask_2': attention_mask_2,
+ }
+
+# ==============================================================================
+# 5. CHECKPOINT UTILITIES
+# ==============================================================================
+def save_checkpoint(model, optimizer, scheduler, global_step, save_path):
+ """Save complete checkpoint with model, optimizer, scheduler states and step count."""
+ checkpoint = {
+ 'model_state_dict': model.state_dict(),
+ 'optimizer_state_dict': optimizer.state_dict(),
+ 'scheduler_state_dict': scheduler.state_dict(),
+ 'global_step': global_step,
+ }
+ torch.save(checkpoint, save_path)
+ print(f"Full checkpoint saved at step {global_step}")
+
+def load_checkpoint(checkpoint_path, model, optimizer, scheduler):
+ """Load checkpoint and return the global step to resume from."""
+ checkpoint = torch.load(checkpoint_path, map_location='cpu')
+ model.load_state_dict(checkpoint['model_state_dict'])
+ optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+ scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+ global_step = checkpoint['global_step']
+ print(f"Checkpoint loaded from step {global_step}")
+ return global_step
+
+# ==============================================================================
+# 6. TRAINING AND EVALUATION LOOPS - MODIFIED
+# ==============================================================================
+def evaluation_step(model, batch, criterion, device):
+ """Performs a single evaluation step on a batch of data."""
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ with torch.no_grad():
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+ return proj_1, proj_2, loss
+
+def train_with_step_based_validation(model, train_loader, val_loader, optimizer, criterion, device,
+ scheduler, checkpoint_path, save_steps, validation_steps,
+ start_step=0, max_steps=None):
+ """
+ Modified training function with step-based validation and checkpointing.
+ """
+ model.train()
+ global_step = start_step
+ best_val_loss = float('inf')
+
+ # Calculate total steps if max_steps is not provided
+ if max_steps is None:
+ max_steps = len(train_loader)
+
+ progress_bar = tqdm(total=max_steps - start_step, desc="Training Steps", initial=start_step)
+
+ # Create iterator that can be resumed from any point
+ train_iterator = iter(train_loader)
+
+ # Skip batches if resuming from checkpoint
+ if start_step > 0:
+ batches_to_skip = start_step % len(train_loader)
+ for _ in range(batches_to_skip):
+ try:
+ next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+
+ while global_step < max_steps:
+ try:
+ batch = next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+ batch = next(train_iterator)
+
+ # Training step
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ optimizer.zero_grad()
+ with torch.autocast(dtype=torch.float16, device_type="cuda"):
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+
+ loss.backward()
+ optimizer.step()
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+ scheduler.step()
+
+ global_step += 1
+
+ progress_bar.update(1)
+ progress_bar.set_postfix(loss=f"{loss.item():.4f}", step=global_step)
+
+ wandb.log({
+ "train_batch_loss": loss.item(),
+ "learning_rate": scheduler.get_last_lr()[0],
+ "global_step": global_step
+ })
+
+ # Step-based validation
+ if global_step % validation_steps == 0:
+ val_loss = validate_epoch(model, val_loader, criterion, device)
+ wandb.log({
+ "val_loss": val_loss,
+ "global_step": global_step
+ })
+
+ # Save best model (model state only for best checkpoint)
+ if val_loss < best_val_loss:
+ best_val_loss = val_loss
+ model_save_path = checkpoint_path.replace('.pt', '_best_model.bin')
+ torch.save(model.state_dict(), model_save_path)
+ progress_bar.write(f"Step {global_step}: New best model saved with val loss {val_loss:.4f}")
+
+ model.train() # Resume training mode after validation
+
+ # Step-based checkpointing (full checkpoint)
+ if global_step % save_steps == 0:
+ save_checkpoint(model, optimizer, scheduler, global_step, checkpoint_path)
+
+ progress_bar.close()
+ return global_step
+
+def validate_epoch(model, val_loader, criterion, device):
+ """Validation function - unchanged from original."""
+ model.eval()
+ total_loss = 0
+ progress_bar = tqdm(val_loader, desc="Validating", leave=False)
+
+ for batch in progress_bar:
+ _, _, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+
+ avg_loss = total_loss / len(val_loader)
+ print(f'Validation loss: {avg_loss:.4f}')
+ return avg_loss
+
+def test_model(model, test_loader, criterion, device):
+ """Test function - unchanged from original."""
+ model.eval()
+ total_loss = 0
+ all_similarities = []
+ progress_bar = tqdm(test_loader, desc="Testing", leave=False)
+
+ for batch in progress_bar:
+ proj_1, proj_2, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+
+ proj_1_norm = F.normalize(proj_1, p=2, dim=1)
+ proj_2_norm = F.normalize(proj_2, p=2, dim=1)
+ batch_similarities = F.cosine_similarity(proj_1_norm, proj_2_norm, dim=1)
+ all_similarities.extend(batch_similarities.cpu().numpy())
+
+ avg_loss = total_loss / len(test_loader)
+ avg_sim = np.mean(all_similarities)
+ std_sim = np.std(all_similarities)
+
+ return avg_loss, avg_sim, std_sim
+
+# ==============================================================================
+# 7. MODIFIED SINGLE-GPU TRAINING
+# ==============================================================================
+def run_training(model_config, hparams, data_splits):
+ """The main function to run the training and evaluation process with step-based validation."""
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {device}")
+
+ wandb_key = os.getenv("WANDB_API_KEY")
+ if wandb_key:
+ wandb.login(key=wandb_key)
+ wandb.init(
+ #project="simson-contrastive-learning-single-gpu",
+ #name=f"run-{wandb.util.generate_id()}",
+ #config=hparams
+ )
+
+ train_smiles, val_smiles, test_smiles = data_splits
+
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+
+ precomputed_train_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/train.parquet'
+ precomputed_test_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/test.parquet'
+ precomputed_val_path = '/home/jovyan/simson_training_bolgov/data/pubchem_119m_splits/validation.parquet'
+
+ train_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_train_path, max_length=hparams['max_length'])
+ test_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_test_path, max_length=hparams['max_length'])
+ val_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_val_path, max_length=hparams['max_length'])
+
+ train_loader = DataLoader(train_dataset, batch_size=hparams['batch_size'], shuffle=True, num_workers=8, prefetch_factor=128, pin_memory=True)
+ val_loader = DataLoader(val_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+ test_loader = DataLoader(test_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+
+ print('Initialized all data. Compiling the model...')
+ model = SimSonEncoder(config=model_config, max_len=hparams['max_embeddings']).to(device)
+ model = torch.compile(model)
+ model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints/checkpoint_best_model.bin'))
+ print(model)
+
+ total_params = sum(p.numel() for p in model.parameters())
+
+ print(f"Total number of parameters: {total_params // 1_000_000} M")
+ wandb.config.update({"total_params_M": total_params // 1_000_000})
+
+ criterion = ContrastiveLoss(temperature=hparams['temperature']).to(device)
+ optimizer = optim.AdamW(model.parameters(), lr=hparams['lr'], weight_decay=1e-5, fused=True)
+
+ total_steps = hparams['epochs'] * len(train_loader)
+ scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_mult=1, T_0=total_steps)
+
+ print("Starting training...")
+ wandb.watch(model, log='all', log_freq=5000)
+
+ start_step = 0
+ checkpoint_path = hparams['checkpoint_path']
+
+ # Resume from checkpoint if provided
+ if hparams.get('resume_checkpoint') and os.path.exists(hparams['resume_checkpoint']):
+ print(f"Resuming from checkpoint: {hparams['resume_checkpoint']}")
+ start_step = load_checkpoint(hparams['resume_checkpoint'], model, optimizer, scheduler)
+
+ # Train with step-based validation
+ final_step = train_with_step_based_validation(
+ model, train_loader, val_loader, optimizer, criterion, device,
+ scheduler, checkpoint_path, hparams['save_steps'], hparams['validation_steps'],
+ start_step=start_step, max_steps=total_steps
+ )
+
+ print("Training complete. Starting final testing...")
+
+ # Load the best model for testing (model state only)
+ best_model_path = checkpoint_path.replace('.pt', '_best_model.bin')
+ if os.path.exists(best_model_path):
+ model.load_state_dict(torch.load(best_model_path))
+ print("Loaded best model for testing")
+
+ test_loss, avg_sim, std_sim = test_model(model, test_loader, criterion, device)
+
+ print("\n--- Test Results ---")
+ print(f"Test Loss: {test_loss:.4f}")
+ print(f"Average Cosine Similarity: {avg_sim:.4f} ± {std_sim:.4f}")
+ print("--------------------")
+
+ wandb.log({
+ "test_loss": test_loss,
+ "avg_cosine_similarity": avg_sim,
+ "std_cosine_similarity": std_sim
+ })
+
+ # Save final model state only
+ final_model_path = hparams['save_path']
+ torch.save(model.state_dict(), final_model_path)
+ print(f"Final model saved to {final_model_path}")
+
+ wandb.finish()
+
+# ==============================================================================
+# 8. MAIN EXECUTION
+# ==============================================================================
+def main():
+ """Main function to configure and run the training process."""
+ hparams = {
+ 'epochs': 1,
+ 'lr': 6e-6,
+ 'temperature': 0.05,
+ 'batch_size': 128,
+ 'max_length': 256,
+ 'save_path': "simson_checkpoints_more_epochs/simson_model_single_gpu.bin",
+ 'checkpoint_path': "simson_checkpoints_more_epochs/checkpoint.pt", # Full checkpoint
+ 'save_steps': 50000, # Save checkpoint every 10k steps
+ 'validation_steps': 5000, # Validate every 5k steps
+ 'max_embeddings': 512,
+ 'resume_checkpoint': None, # Set to checkpoint path to resume
+ }
+
+ dataset = load_dataset('HoangHa/SMILES-250M')['train']
+ smiles_column_name = 'SMILES'
+
+ total_size = len(dataset)
+ test_size = int(0.1 * total_size)
+ val_size = int(0.1 * (total_size - test_size))
+
+ test_smiles = dataset.select(range(test_size))[smiles_column_name]
+ val_smiles = dataset.select(range(test_size, test_size + val_size))[smiles_column_name]
+ train_smiles = dataset.select(range(test_size + val_size, total_size))[smiles_column_name]
+ data_splits = (train_smiles, val_smiles, test_smiles)
+
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ model_config = BertConfig(
+ vocab_size=tokenizer.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ # Create directories
+ save_dir = os.path.dirname(hparams['save_path'])
+ checkpoint_dir = os.path.dirname(hparams['checkpoint_path'])
+ for directory in [save_dir, checkpoint_dir]:
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+ # Directly call the training function for a single-GPU run
+ run_training(model_config, hparams, data_splits)
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/simson_repo/simson_base/.gitattributes b/simson_modeling/simson_repo/simson_base/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/simson_modeling/simson_repo/simson_base/README.md b/simson_modeling/simson_repo/simson_base/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..abc960732ceac552bcaedf4a350bb8bc7fbbf7a9
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/README.md
@@ -0,0 +1,4 @@
+---
+license: apache-2.0
+---
+SimSon model, pre-trained on 100M SMILES from Zinc, Enamine and PubChem, and 1M polymers from P1M dataset (seperate checkpoint). 4 hidden layers, 12 attention heads.
\ No newline at end of file
diff --git a/simson_modeling/simson_repo/simson_base/moleculenet_eval/eval.py b/simson_modeling/simson_repo/simson_base/moleculenet_eval/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b4eb25054ae7710bbbdc4ce12f907246371b81
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/moleculenet_eval/eval.py
@@ -0,0 +1,457 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertConfig, BertModel, AutoTokenizer
+from rdkit import Chem, RDLogger
+from rdkit.Chem.Scaffolds import MurckoScaffold
+import copy
+from tqdm import tqdm
+import os
+from sklearn.metrics import roc_auc_score, root_mean_squared_error, mean_absolute_error
+from itertools import compress
+from collections import defaultdict
+from sklearn.metrics.pairwise import cosine_similarity
+RDLogger.DisableLog('rdApp.*')
+
+
+torch.set_float32_matmul_precision('high')
+
+# --- 0. Smiles enumeration
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+
+def compute_embedding_similarity(encoder, smiles_list, tokenizer, device, max_len=256):
+ encoder.eval()
+ enumerator = SmilesEnumerator()
+
+ embeddings_orig = []
+ embeddings_aug = []
+
+ with torch.no_grad():
+ for smi in smiles_list:
+ # Original SMILES encoding
+ encoding_orig = tokenizer(
+ smi,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+ # Augmented SMILES encoding
+ smi_aug = enumerator.randomize_smiles(smi)
+ encoding_aug = tokenizer(
+ smi_aug,
+ truncation=True,
+ padding='max_length',
+ max_length=max_len,
+ return_tensors='pt'
+ )
+
+ input_ids_orig = encoding_orig.input_ids.to(device)
+ attention_mask_orig = encoding_orig.attention_mask.to(device)
+ input_ids_aug = encoding_aug.input_ids.to(device)
+ attention_mask_aug = encoding_aug.attention_mask.to(device)
+
+ emb_orig = encoder(input_ids_orig, attention_mask_orig).cpu().numpy().flatten()
+ emb_aug = encoder(input_ids_aug, attention_mask_aug).cpu().numpy().flatten()
+
+ embeddings_orig.append(emb_orig)
+ embeddings_aug.append(emb_aug)
+
+ embeddings_orig = np.array(embeddings_orig)
+ embeddings_aug = np.array(embeddings_aug)
+
+ # Cosine similarity between each original and its augmented version
+ similarities = np.array([cosine_similarity([embeddings_orig[i]], [embeddings_aug[i]])[0][0] for i in range(len(embeddings_orig))])
+ return similarities
+
+# --- 1. Data Loading ---
+def load_lists_from_url(data):
+ if data == 'bbbp':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')
+ smiles, labels = df.smiles, df.p_np
+ elif data == 'clintox':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'hiv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')
+ smiles, labels = df.smiles, df.HIV_active
+ elif data == 'sider':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')
+ smiles = df.smiles
+ labels = df.drop(['smiles'], axis=1)
+ elif data == 'esol':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')
+ smiles = df.smiles
+ labels = df['ESOL predicted log solubility in mols per litre']
+ elif data == 'freesolv':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')
+ smiles = df.smiles
+ labels = df.calc
+ elif data == 'lipophicility':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')
+ smiles, labels = df.smiles, df['exp']
+ elif data == 'tox21':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['mol_id', 'smiles'], axis=1)
+ elif data == 'bace':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')
+ smiles, labels = df.mol, df.Class
+ elif data == 'qm8':
+ df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')
+ df = df.dropna(axis=0, how='any').reset_index(drop=True)
+ smiles = df.smiles
+ labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)
+ return smiles, labels
+
+# --- 2. Scaffold Splitting ---
+class ScaffoldSplitter:
+ def __init__(self, data, seed, train_frac=0.8, val_frac=0.1, test_frac=0.1, include_chirality=True):
+ self.data = data
+ self.seed = seed
+ self.include_chirality = include_chirality
+ self.train_frac = train_frac
+ self.val_frac = val_frac
+ self.test_frac = test_frac
+
+ def generate_scaffold(self, smiles):
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol, includeChirality=self.include_chirality)
+ return scaffold
+
+ def scaffold_split(self):
+ smiles, labels = load_lists_from_url(self.data)
+ non_null = np.ones(len(smiles)) == 0
+
+ if self.data in {'tox21', 'sider', 'clintox'}:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]) and labels.loc[i].isnull().sum() == 0:
+ non_null[i] = 1
+ else:
+ for i in range(len(smiles)):
+ if Chem.MolFromSmiles(smiles[i]):
+ non_null[i] = 1
+
+ smiles_list = list(compress(enumerate(smiles), non_null))
+ rng = np.random.RandomState(self.seed)
+
+ scaffolds = defaultdict(list)
+ for i, sms in smiles_list:
+ scaffold = self.generate_scaffold(sms)
+ scaffolds[scaffold].append(i)
+
+ scaffold_sets = list(scaffolds.values())
+ rng.shuffle(scaffold_sets)
+ n_total_val = int(np.floor(self.val_frac * len(smiles_list)))
+ n_total_test = int(np.floor(self.test_frac * len(smiles_list)))
+ train_idx, val_idx, test_idx = [], [], []
+
+ for scaffold_set in scaffold_sets:
+ if len(val_idx) + len(scaffold_set) <= n_total_val:
+ val_idx.extend(scaffold_set)
+ elif len(test_idx) + len(scaffold_set) <= n_total_test:
+ test_idx.extend(scaffold_set)
+ else:
+ train_idx.extend(scaffold_set)
+ return train_idx, val_idx, test_idx
+
+# --- 2a. Normal Random Split ---
+def random_split_indices(n, seed=42, train_frac=0.8, val_frac=0.1, test_frac=0.1):
+ np.random.seed(seed)
+ indices = np.random.permutation(n)
+ n_train = int(n * train_frac)
+ n_val = int(n * val_frac)
+ train_idx = indices[:n_train]
+ val_idx = indices[n_train:n_train+n_val]
+ test_idx = indices[n_train+n_val:]
+ return train_idx.tolist(), val_idx.tolist(), test_idx.tolist()
+
+# --- 3. PyTorch Dataset ---
+class MoleculeDataset(Dataset):
+ def __init__(self, smiles_list, labels, tokenizer, max_len=512):
+ self.smiles_list = smiles_list
+ self.labels = labels
+ self.tokenizer = tokenizer
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ smiles = self.smiles_list[idx]
+ label = self.labels.iloc[idx]
+
+ encoding = self.tokenizer(
+ smiles,
+ truncation=True,
+ padding='max_length',
+ max_length=self.max_len,
+ return_tensors='pt'
+ )
+ item = {key: val.squeeze(0) for key, val in encoding.items()}
+ if isinstance(label, pd.Series):
+ label_values = label.values.astype(np.float32)
+ else:
+ label_values = np.array([label], dtype=np.float32)
+ item['labels'] = torch.tensor(label_values, dtype=torch.float)
+ return item
+
+# --- 4. Model Architecture ---
+def global_ap(x):
+ return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)
+
+class SimSonEncoder(nn.Module):
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.config = config
+ self.max_len = max_len
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.config.pad_token_id)
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled = global_ap(hidden_states)
+ return self.linear(pooled)
+
+class SimSonClassifier(nn.Module):
+ def __init__(self, encoder: SimSonEncoder, num_labels: int, dropout=0.1):
+ super(SimSonClassifier, self).__init__()
+ self.encoder = encoder
+ self.clf = nn.Linear(encoder.max_len, num_labels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+ def forward(self, input_ids, attention_mask=None):
+ x = self.encoder(input_ids, attention_mask)
+ x = self.relu(self.dropout(x))
+ logits = self.clf(x)
+ return logits
+
+ def load_encoder_params(self, state_dict_path):
+ self.encoder.load_state_dict(torch.load(state_dict_path))
+ print("Pretrained encoder parameters loaded.")
+
+# --- 5. Training, Validation, and Testing Loops ---
+def get_criterion(task_type, num_labels):
+ if task_type == 'classification':
+ return nn.BCEWithLogitsLoss()
+ elif task_type == 'regression':
+ return nn.MSELoss()
+ else:
+ raise ValueError(f"Unknown task type: {task_type}")
+
+def train_epoch(model, dataloader, optimizer, scheduler, criterion, device):
+ model.train()
+ total_loss = 0
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ optimizer.zero_grad()
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ #scheduler.step()
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def eval_epoch(model, dataloader, criterion, device):
+ model.eval()
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ return total_loss / len(dataloader)
+
+def test_model(model, dataloader, device):
+ model.eval()
+ all_preds, all_labels = [], []
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels']
+ outputs = model(**inputs)
+ preds = torch.sigmoid(outputs)
+ all_preds.append(preds.cpu().numpy())
+ all_labels.append(labels.numpy())
+ return np.concatenate(all_preds), np.concatenate(all_labels)
+
+def calc_val_metrics(model, dataloader, criterion, device, task_type):
+ model.eval()
+ all_labels, all_preds = [], []
+ total_loss = 0
+ with torch.no_grad():
+ for batch in dataloader:
+ inputs = {k: v.to(device) for k, v in batch.items() if k != 'labels'}
+ labels = batch['labels'].to(device)
+ outputs = model(**inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+ if task_type == 'classification':
+ pred_probs = torch.sigmoid(outputs).cpu().numpy()
+ all_preds.append(pred_probs)
+ all_labels.append(labels.cpu().numpy())
+ else:
+ # Regression
+ preds = outputs.cpu().numpy()
+ all_preds.append(preds)
+ all_labels.append(labels.cpu().numpy())
+ avg_loss = total_loss / len(dataloader)
+ if task_type == 'classification':
+ y_true = np.concatenate(all_labels)
+ y_pred = np.concatenate(all_preds)
+ try:
+ score = roc_auc_score(y_true, y_pred, average='macro')
+ except Exception:
+ score = 0.0
+ return avg_loss, score
+ else:
+ return avg_loss, None
+
+# --- 6. Main Execution Block ---
+def main():
+ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {DEVICE}")
+
+ DATASETS_TO_RUN = {
+ # 'esol': {'task_type': 'regression', 'num_labels': 1, 'split': 'random'},
+ #'tox21': {'task_type': 'classification', 'num_labels': 12, 'split': 'random'},
+ #'hiv': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'},
+ # Add more datasets here, e.g. 'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ #'sider': {'task_type': 'classification', 'num_labels': 27, 'split': 'random'},
+ #'bace': {'task_type': 'classification', 'num_labels': 1, 'split': 'random'},
+ 'clintox': {'task_type': 'classification', 'num_labels': 2, 'split': 'random'},
+ #'bbbp': {'task_type': 'classification', 'num_labels': 1, 'split': 'scaffold'}
+ }
+ PATIENCE = 15
+ EPOCHS = 50
+ LEARNING_RATE = 1e-4
+ BATCH_SIZE = 16
+ MAX_LEN = 512
+
+ TOKENIZER = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ ENCODER_CONFIG = BertConfig(
+ vocab_size=TOKENIZER.vocab_size,
+ hidden_size=768,
+ num_hidden_layers=4,
+ num_attention_heads=12,
+ intermediate_size=2048,
+ max_position_embeddings=512
+ )
+
+ aggregated_results = {}
+
+ for name, info in DATASETS_TO_RUN.items():
+ print(f"\n{'='*20} Processing Dataset: {name.upper()} ({info['split']} split) {'='*20}")
+ smiles, labels = load_lists_from_url(name)
+
+ # Split selection
+ if info.get('split', 'scaffold') == 'scaffold':
+ splitter = ScaffoldSplitter(data=name, seed=42)
+ train_idx, val_idx, test_idx = splitter.scaffold_split()
+ elif info['split'] == 'random':
+ train_idx, val_idx, test_idx = random_split_indices(len(smiles), seed=42)
+ else:
+ raise ValueError(f"Unknown split type for {name}: {info['split']}")
+
+ train_smiles = smiles.iloc[train_idx].reset_index(drop=True)
+ train_labels = labels.iloc[train_idx].reset_index(drop=True)
+ val_smiles = smiles.iloc[val_idx].reset_index(drop=True)
+ val_labels = labels.iloc[val_idx].reset_index(drop=True)
+ test_smiles = smiles.iloc[test_idx].reset_index(drop=True)
+ test_labels = labels.iloc[test_idx].reset_index(drop=True)
+ print(f"Data split - Train: {len(train_smiles)}, Val: {len(val_smiles)}, Test: {len(test_smiles)}")
+
+ train_dataset = MoleculeDataset(train_smiles, train_labels, TOKENIZER, MAX_LEN)
+ val_dataset = MoleculeDataset(val_smiles, val_labels, TOKENIZER, MAX_LEN)
+ test_dataset = MoleculeDataset(test_smiles, test_labels, TOKENIZER, MAX_LEN)
+
+ train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+ val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
+ test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
+
+ encoder = SimSonEncoder(ENCODER_CONFIG, 512)
+ encoder = torch.compile(encoder)
+ model = SimSonClassifier(encoder, num_labels=info['num_labels']).to(DEVICE)
+ model.load_encoder_params('../simson_checkpoints/checkpoint_best_model.bin')
+ criterion = get_criterion(info['task_type'], info['num_labels'])
+ optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=0.0024)
+ scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.59298)
+
+ best_val_loss = float('-inf')
+ best_model_state = None
+ current_patience = 0
+ for epoch in range(EPOCHS):
+ train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, DEVICE)
+ val_loss, val_metric = calc_val_metrics(model, val_loader, criterion, 'cuda', info['task_type'])
+ print(f"Epoch {epoch+1}/{EPOCHS} | Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | ROC AUC: {val_metric:.4f}")
+
+ if val_metric <= val_loss:
+ best_val_loss = val_loss
+ best_model_state = copy.deepcopy(model.state_dict())
+ print(f" -> New best model saved with validation loss: {best_val_loss:.4f}")
+ current_patience = 0
+ else:
+ current_patience += 1
+ if current_patience >= PATIENCE:
+ print(f'Early stopping at {PATIENCE} epochs')
+ break
+
+ print("\nTesting with the best model...")
+ if not best_model_state is None:
+ model.load_state_dict(best_model_state)
+ test_loss = eval_epoch(model, test_loader, criterion, DEVICE)
+ print(f'Test loss: {test_loss}')
+ test_preds, test_true = test_model(model, test_loader, DEVICE)
+
+ aggregated_results[name] = {
+ 'best_val_loss': best_val_loss,
+ 'test_predictions': test_preds,
+ 'test_labels': test_true
+ }
+ print(f"Finished testing for {name}.")
+ test_smiles_list = list(test_smiles)
+ similarities = compute_embedding_similarity(
+ model.encoder, test_smiles_list, TOKENIZER, DEVICE, MAX_LEN
+ )
+ print(f"Similarity score: {similarities.mean():.4f}")
+ if name == 'do_not_save':
+ torch.save(model.encoder.state_dict(), 'moleculenet_clintox_encoder.bin')
+
+
+
+ print(f"\n{'='*20} AGGREGATED RESULTS {'='*20}")
+ for name, result in aggregated_results.items():
+ if name in ['bbbp', 'tox21', 'sider', 'clintox', 'hiv', 'bace']:
+ auc = roc_auc_score(result['test_labels'], result['test_predictions'], average='macro')
+ print(f'{name} ROC AUC: {auc}')
+
+ if name in ['lipophicility', 'esol', 'qm8']:
+ rmse = root_mean_squared_error(result['test_labels'], result['test_predictions'])
+ mae = mean_absolute_error(result['test_labels'], result['test_predictions'])
+ print(f'{name} MAE: {mae}')
+ print(f'{name} RMSE: {rmse}')
+
+ print("\nScript finished.")
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/simson_repo/simson_base/moleculenet_eval/showcase.ipynb b/simson_modeling/simson_repo/simson_base/moleculenet_eval/showcase.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..45a6d679d784a954d193f3cb241c9e388b88176c
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/moleculenet_eval/showcase.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7678e528-e2d6-4ef4-bd11-8c745bbce7c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap.umap_ as umap\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ad8d5a02-6162-41f7-a730-e31e353ed9b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PrecomputedContrastiveSmilesDataset(Dataset):\n",
+ " \"\"\"\n",
+ " A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.\n",
+ " This is significantly faster as it offloads the expensive SMILES randomization\n",
+ " to a one-time preprocessing step.\n",
+ " \"\"\"\n",
+ " def __init__(self, tokenizer, file_path: str, max_length: int = 512):\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ " \n",
+ " # Load the entire dataset from the Parquet file into memory.\n",
+ " # This is fast and efficient for subsequent access.\n",
+ " print(f\"Loading pre-computed data from {file_path}...\")\n",
+ " self.data = pd.read_parquet(file_path)\n",
+ " print(\"Data loaded successfully.\")\n",
+ "\n",
+ " def __len__(self):\n",
+ " \"\"\"Returns the total number of pairs in the dataset.\"\"\"\n",
+ " return len(self.data)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " \"\"\"\n",
+ " Retrieves a pre-augmented pair, tokenizes it, and returns it\n",
+ " in the format expected by the DataCollator.\n",
+ " \"\"\"\n",
+ " # Retrieve the pre-augmented pair from the DataFrame\n",
+ " row = self.data.iloc[idx]\n",
+ " smiles_1 = row['smiles_1']\n",
+ " smiles_2 = row['smiles_2']\n",
+ " \n",
+ " # Tokenize the pair. This operation is fast and remains in the data loader.\n",
+ " tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')\n",
+ " \n",
+ " return {\n",
+ " 'input_ids_1': torch.tensor(tokens_1['input_ids']),\n",
+ " 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),\n",
+ " 'input_ids_2': torch.tensor(tokens_2['input_ids']),\n",
+ " 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),\n",
+ " }\n",
+ "\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "476226dd-54f3-4d3e-adb4-cc30e922fd96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "OptimizedModule(\n",
+ " (_orig_mod): SimSonEncoder(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(591, 768, padding_idx=0)\n",
+ " (position_embeddings): Embedding(512, 768)\n",
+ " (token_type_embeddings): Embedding(2, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): BertEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-3): 4 x BertLayer(\n",
+ " (attention): BertAttention(\n",
+ " (self): BertSdpaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): BertSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): BertIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=2048, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): BertOutput(\n",
+ " (dense): Linear(in_features=2048, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (linear): Linear(in_features=768, out_features=512, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "model_config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ ")\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "model = SimSonEncoder(config=model_config, max_len=512).to(device)\n",
+ "model = torch.compile(model)\n",
+ "model.load_state_dict(torch.load('/home/jovyan/simson_training_bolgov/simson_checkpoints_polymer_1M/simson_model_single_gpu.bin'))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "df28c332-c20b-4804-b4ca-97de9d652445",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test dataset shape: (5000, 2)\n",
+ "Columns: ['smiles_1', 'smiles_2']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " smiles_1 | \n",
+ " smiles_2 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... | \n",
+ " c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... | \n",
+ " CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* | \n",
+ " O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ " *CCCCOC(=O)CSCCCC[PH](C)(=O)O* | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC | \n",
+ " C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " smiles_1 \\\n",
+ "0 c1c(ccc(c1)OC(=O)CCCS(CCCc1ccc(C(O*)=O)cc1)(=O... \n",
+ "1 C(CC(=O)NCCC[Si](O*)(C)C)CCCCC(OCCCCCOC(Cc1cc(... \n",
+ "2 C(SCCNC(OCC*)=O)CSCCCOCCNC(=O)O* \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(O*)COCCOCCN(C(=O)OCCC*)C(=O)OC \n",
+ "\n",
+ " smiles_2 \n",
+ "0 c1cc(ccc1OC(CCCS(CCCc1ccc(C(=O)O*)cc1)(=O)=O)=... \n",
+ "1 CN(*)Cc1cccc(c1)CC(OCCCCCOC(CCCCCCC(=O)NCCC[Si... \n",
+ "2 O=C(OCC*)NCCSCCSCCCOCCNC(O*)=O \n",
+ "3 *CCCCOC(=O)CSCCCC[PH](C)(=O)O* \n",
+ "4 C(CC*)OC(=O)N(C(=O)OC)CCOCCOCCO* "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data = pd.read_parquet('/home/jovyan/simson_training_bolgov/data/polymer_splits/test.parquet')\n",
+ "print(f\"Test dataset shape: {test_data.shape}\")\n",
+ "print(f\"Columns: {test_data.columns.tolist()}\")\n",
+ "test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a7f2e2bc-12f5-443b-9d80-899542e07370",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating embeddings for original SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Generating embeddings for augmented SMILES...\n",
+ "Processed 2560 / 5000 SMILES\n",
+ "Processed 5000 / 5000 SMILES\n",
+ "Original embeddings shape: (5000, 512)\n",
+ "Augmented embeddings shape: (5000, 512)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def generate_embeddings(model, tokenizer, smiles_list, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings for a list of SMILES strings\"\"\"\n",
+ " model.eval()\n",
+ " embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " return np.vstack(embeddings)\n",
+ "\n",
+ "# Generate embeddings for original and augmented SMILES\n",
+ "print(\"Generating embeddings for original SMILES...\")\n",
+ "original_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_1'].tolist())\n",
+ "\n",
+ "print(\"Generating embeddings for augmented SMILES...\")\n",
+ "augmented_embeddings = generate_embeddings(model, tokenizer, test_data['smiles_2'].tolist())\n",
+ "\n",
+ "print(f\"Original embeddings shape: {original_embeddings.shape}\")\n",
+ "print(f\"Augmented embeddings shape: {augmented_embeddings.shape}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c770d7f4-fa37-4519-bda2-9b084d2a4b32",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average cosine similarity between original and augmented SMILES: 0.9874\n",
+ "Standard deviation: 0.0197\n",
+ "Min similarity: 0.7691\n",
+ "Max similarity: 1.0000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def calculate_pairwise_similarities(embeddings1, embeddings2):\n",
+ " \"\"\"Calculate cosine similarities between corresponding pairs\"\"\"\n",
+ " similarities = []\n",
+ " for i in range(len(embeddings1)):\n",
+ " sim = cosine_similarity([embeddings1[i]], [embeddings2[i]])[0][0]\n",
+ " similarities.append(sim)\n",
+ " return np.array(similarities)\n",
+ "\n",
+ "# Calculate cosine similarities\n",
+ "pairwise_similarities = calculate_pairwise_similarities(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "print(f\"Average cosine similarity between original and augmented SMILES: {np.mean(pairwise_similarities):.4f}\")\n",
+ "print(f\"Standard deviation: {np.std(pairwise_similarities):.4f}\")\n",
+ "print(f\"Min similarity: {np.min(pairwise_similarities):.4f}\")\n",
+ "print(f\"Max similarity: {np.max(pairwise_similarities):.4f}\")\n",
+ "\n",
+ "# Plot similarity distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.hist(pairwise_similarities, bins=50, alpha=0.7, edgecolor='black')\n",
+ "plt.axvline(np.mean(pairwise_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(pairwise_similarities):.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Distribution of Cosine Similarities Between Original and Augmented SMILES')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "d7fa6ed4-b546-4544-bb00-4d90a99678da",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== Embedding Space Analysis ===\n",
+ "Embedding dimensionality: 512\n",
+ "Average L2 norm (original): 11.9105\n",
+ "Average L2 norm (augmented): 11.9072\n",
+ "Average intra-class similarity (original): 0.0047\n",
+ "Average intra-class similarity (augmented): 0.0048\n",
+ "Average inter-class similarity: 0.0049\n",
+ "\n",
+ "=== Nearest Neighbor Analysis (k=5) ===\n",
+ "Augmented SMILES in top-5 neighbors: 5000/5000 (100.0%)\n",
+ "Augmented SMILES as top-1 neighbor: 4872/5000 (97.4%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1. Embedding Space Statistics\n",
+ "def analyze_embedding_space(original_emb, augmented_emb):\n",
+ " \"\"\"Analyze the embedding space properties\"\"\"\n",
+ " print(\"=== Embedding Space Analysis ===\")\n",
+ " \n",
+ " # Dimensionality and norms\n",
+ " print(f\"Embedding dimensionality: {original_emb.shape[1]}\")\n",
+ " print(f\"Average L2 norm (original): {np.mean(np.linalg.norm(original_emb, axis=1)):.4f}\")\n",
+ " print(f\"Average L2 norm (augmented): {np.mean(np.linalg.norm(augmented_emb, axis=1)):.4f}\")\n",
+ " \n",
+ " # Intra-class similarities\n",
+ " orig_similarities = cosine_similarity(original_emb)\n",
+ " aug_similarities = cosine_similarity(augmented_emb)\n",
+ " \n",
+ " # Remove diagonal (self-similarity)\n",
+ " orig_similarities_off_diag = orig_similarities[np.triu_indices_from(orig_similarities, k=1)]\n",
+ " aug_similarities_off_diag = aug_similarities[np.triu_indices_from(aug_similarities, k=1)]\n",
+ " \n",
+ " print(f\"Average intra-class similarity (original): {np.mean(orig_similarities_off_diag):.4f}\")\n",
+ " print(f\"Average intra-class similarity (augmented): {np.mean(aug_similarities_off_diag):.4f}\")\n",
+ " \n",
+ " # Inter-class similarities\n",
+ " inter_similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " print(f\"Average inter-class similarity: {np.mean(inter_similarities):.4f}\")\n",
+ "\n",
+ "analyze_embedding_space(original_embeddings, augmented_embeddings)\n",
+ "\n",
+ "# 2. Nearest Neighbor Analysis\n",
+ "def nearest_neighbor_analysis(original_emb, augmented_emb, k=5):\n",
+ " \"\"\"Analyze nearest neighbors between original and augmented embeddings\"\"\"\n",
+ " print(f\"\\n=== Nearest Neighbor Analysis (k={k}) ===\")\n",
+ " \n",
+ " # For each original embedding, find its k nearest neighbors in augmented set\n",
+ " similarities = cosine_similarity(original_emb, augmented_emb)\n",
+ " \n",
+ " # Find cases where augmented version is among top-k neighbors\n",
+ " correct_matches = 0\n",
+ " top1_matches = 0\n",
+ " \n",
+ " for i in range(len(original_emb)):\n",
+ " # Get similarity scores for i-th original embedding\n",
+ " sim_scores = similarities[i]\n",
+ " top_k_indices = np.argsort(sim_scores)[-k:][::-1]\n",
+ " \n",
+ " if i in top_k_indices:\n",
+ " correct_matches += 1\n",
+ " if np.argmax(sim_scores) == i:\n",
+ " top1_matches += 1\n",
+ " \n",
+ " print(f\"Augmented SMILES in top-{k} neighbors: {correct_matches}/{len(original_emb)} ({100*correct_matches/len(original_emb):.1f}%)\")\n",
+ " print(f\"Augmented SMILES as top-1 neighbor: {top1_matches}/{len(original_emb)} ({100*top1_matches/len(original_emb):.1f}%)\")\n",
+ "\n",
+ "nearest_neighbor_analysis(original_embeddings, augmented_embeddings)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f136743a-c742-469b-a9b8-9a4da8eb4c08",
+ "metadata": {},
+ "source": [
+ "Объяснение\n",
+ "\n",
+ "* Embedding Space Analysis. Показывает, что различные величины (L2 norm - длина вектора, близости) практически идентичны для оригинальных, и для аугментированных молекул (показывая, что они отображаются практически одними и теми же для модели)\n",
+ "* Nearest Neighbor Analysis (k=5). Показывает, что топ 5 ближайших векторов к любому из векторов - всегда его аугментация (кроме его самого, естественно). В 97.4 % случаях вектор, соответствующий аугментации, является ближайшим."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133fb52e-bea8-4159-ab06-386998b71ed0",
+ "metadata": {},
+ "source": [
+ "С разными SMILES - ровно обратная картина, как и должно быть (различные smiles являются очень разными)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "cc48f04e-b8bf-415b-89f0-b51d98f7e6b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "num_molecules = original_embeddings.shape[0]\n",
+ "\n",
+ "# Shuffle indices for unrelated molecules\n",
+ "unrelated_indices = np.random.permutation(num_molecules)\n",
+ "unrelated_embeddings = augmented_embeddings[unrelated_indices]\n",
+ "\n",
+ "# Compute pairwise cosine similarity between original and unrelated\n",
+ "pairwise_unrelated_similarities = np.array([\n",
+ " cosine_similarity([original_embeddings[i]], [unrelated_embeddings[i]])[0][0]\n",
+ " for i in range(num_molecules)\n",
+ "])\n",
+ "\n",
+ "\n",
+ "plt.figure(figsize=(10,6))\n",
+ "plt.hist(pairwise_unrelated_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ "mean_sim = pairwise_unrelated_similarities.mean()\n",
+ "plt.axvline(mean_sim, color='red', linestyle='--', label=f'Mean = {mean_sim:.4f}')\n",
+ "plt.xlabel('Cosine Similarity')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Cosine Similarity Distribution of Different Molecules (Unrelated SMILES)')\n",
+ "plt.legend()\n",
+ "plt.grid(True, alpha=0.3)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "881eca62-7bf5-4b84-b0df-659942930a73",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean cosine similarity: 0.0048\n",
+ "Std deviation: 0.1357\n",
+ "Range: -0.4617 to 0.6631\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Mean cosine similarity: {pairwise_unrelated_similarities.mean():.4f}\")\n",
+ "print(f\"Std deviation: {pairwise_unrelated_similarities.std():.4f}\")\n",
+ "print(f\"Range: {pairwise_unrelated_similarities.min():.4f} to {pairwise_unrelated_similarities.max():.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "051f4149-d3eb-472b-b6d0-7060662efb6a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unrelated in top-5 neighbors: 4/5000\n",
+ "Unrelated as top-1 neighbor: 0/5000\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "k = 5\n",
+ "similarities = cosine_similarity(original_embeddings, unrelated_embeddings)\n",
+ "correct_matches = sum(i in np.argsort(similarities[i])[-k:] for i in range(num_molecules))\n",
+ "top1_matches = sum(np.argmax(similarities[i]) == i for i in range(num_molecules))\n",
+ "\n",
+ "print(f\"Unrelated in top-{k} neighbors: {correct_matches}/{num_molecules}\")\n",
+ "print(f\"Unrelated as top-1 neighbor: {top1_matches}/{num_molecules}\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/simson_repo/simson_base/moleculenet_eval/visualizations.ipynb b/simson_modeling/simson_repo/simson_base/moleculenet_eval/visualizations.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b6a3ab30e103378cecb86b7c9b9475fa814f5d50
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/moleculenet_eval/visualizations.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "90d8362c-618c-42c9-8f2a-e73d2eb34abc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import BertModel, BertConfig, AutoTokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.cluster import KMeans\n",
+ "import umap\n",
+ "from rdkit import Chem, RDLogger\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "RDLogger.DisableLog('rdApp.*')\n",
+ "\n",
+ "# Set style for better plots\n",
+ "plt.style.use('seaborn-v0_8')\n",
+ "sns.set_palette(\"husl\")\n",
+ "\n",
+ "# === HELPER FUNCTIONS AND CLASSES ===\n",
+ "\n",
+ "def load_lists_from_url(data):\n",
+ " if data == 'bbbp':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv')\n",
+ " smiles, labels = df.smiles, df.p_np\n",
+ " elif data == 'clintox':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'hiv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv')\n",
+ " smiles, labels = df.smiles, df.HIV_active\n",
+ " elif data == 'sider':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz', compression='gzip')\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles'], axis=1)\n",
+ " elif data == 'esol':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df['ESOL predicted log solubility in mols per litre']\n",
+ " elif data == 'freesolv':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/SAMPL.csv')\n",
+ " smiles = df.smiles\n",
+ " labels = df.calc\n",
+ " elif data == 'lipophicility':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv')\n",
+ " smiles, labels = df.smiles, df['exp']\n",
+ " elif data == 'tox21':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz', compression='gzip')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['mol_id', 'smiles'], axis=1)\n",
+ " elif data == 'bace':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv')\n",
+ " smiles, labels = df.mol, df.Class\n",
+ " elif data == 'qm8':\n",
+ " df = pd.read_csv('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv')\n",
+ " df = df.dropna(axis=0, how='any').reset_index(drop=True)\n",
+ " smiles = df.smiles\n",
+ " labels = df.drop(['smiles', 'E2-PBE0.1', 'E1-PBE0.1', 'f1-PBE0.1', 'f2-PBE0.1'], axis=1)\n",
+ " return smiles, labels\n",
+ "\n",
+ "class SmilesEnumerator:\n",
+ " \"\"\"Generates randomized SMILES strings for data augmentation.\"\"\"\n",
+ " def randomize_smiles(self, smiles):\n",
+ " try:\n",
+ " mol = Chem.MolFromSmiles(smiles)\n",
+ " return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles\n",
+ " except:\n",
+ " return smiles\n",
+ "\n",
+ "class MoleculeDataset(Dataset):\n",
+ " def __init__(self, smiles_list, labels, tokenizer, max_len=512):\n",
+ " self.smiles_list = smiles_list\n",
+ " self.labels = labels\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.smiles_list)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " smiles = self.smiles_list[idx]\n",
+ " label = self.labels.iloc[idx]\n",
+ "\n",
+ " encoding = self.tokenizer(\n",
+ " smiles,\n",
+ " truncation=True,\n",
+ " padding='max_length',\n",
+ " max_length=self.max_len,\n",
+ " return_tensors='pt'\n",
+ " )\n",
+ " item = {key: val.squeeze(0) for key, val in encoding.items()}\n",
+ " if isinstance(label, pd.Series):\n",
+ " label_values = label.values.astype(np.float32)\n",
+ " else:\n",
+ " label_values = np.array([label], dtype=np.float32)\n",
+ " item['labels'] = torch.tensor(label_values, dtype=torch.float)\n",
+ " return item\n",
+ "\n",
+ "# Your model classes (assuming these exist from previous conversation)\n",
+ "def global_ap(x):\n",
+ " return torch.mean(x.view(x.size(0), x.size(1), -1), dim=1)\n",
+ "\n",
+ "class SimSonEncoder(nn.Module):\n",
+ " def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):\n",
+ " super(SimSonEncoder, self).__init__()\n",
+ " self.config = config\n",
+ " self.max_len = max_len\n",
+ " self.bert = BertModel(config, add_pooling_layer=False)\n",
+ " self.linear = nn.Linear(config.hidden_size, max_len)\n",
+ " self.dropout = nn.Dropout(dropout)\n",
+ " \n",
+ " def forward(self, input_ids, attention_mask=None):\n",
+ " if attention_mask is None:\n",
+ " attention_mask = input_ids.ne(self.config.pad_token_id)\n",
+ " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
+ " hidden_states = self.dropout(outputs.last_hidden_state)\n",
+ " pooled = global_ap(hidden_states)\n",
+ " return self.linear(pooled)\n",
+ "\n",
+ "# === MAIN ANALYSIS FUNCTIONS ===\n",
+ "\n",
+ "def prepare_dataset_with_augmentation(dataset_name, sample_size=1000):\n",
+ " \"\"\"\n",
+ " Load dataset, create augmented SMILES, and prepare for embedding generation\n",
+ " \"\"\"\n",
+ " print(f\"Loading {dataset_name} dataset...\")\n",
+ " smiles, labels = load_lists_from_url(dataset_name)\n",
+ " \n",
+ " # Handle different label structures\n",
+ " if dataset_name == 'bbbp':\n",
+ " # Binary classification - convert to binary labels\n",
+ " label_values = labels.values\n",
+ " label_names = ['Non-permeable', 'Permeable']\n",
+ " elif dataset_name == 'clintox':\n",
+ " # Multi-task - use first task for visualization\n",
+ " label_values = labels.iloc[:, 0].values # Use first toxicity endpoint\n",
+ " label_names = ['Non-toxic', 'Toxic']\n",
+ " \n",
+ " # Remove NaN labels and sample for visualization\n",
+ " valid_mask = ~pd.isna(label_values)\n",
+ " smiles_clean = smiles[valid_mask]\n",
+ " labels_clean = label_values[valid_mask]\n",
+ " \n",
+ " # Sample for manageable visualization\n",
+ " if len(smiles_clean) > sample_size:\n",
+ " indices = np.random.choice(len(smiles_clean), sample_size, replace=False)\n",
+ " smiles_clean = smiles_clean.iloc[indices]\n",
+ " labels_clean = labels_clean[indices]\n",
+ " \n",
+ " # Create augmented SMILES\n",
+ " enumerator = SmilesEnumerator()\n",
+ " print(\"Generating augmented SMILES...\")\n",
+ " augmented_smiles = []\n",
+ " \n",
+ " for smiles_str in smiles_clean:\n",
+ " aug_smiles = enumerator.randomize_smiles(smiles_str)\n",
+ " augmented_smiles.append(aug_smiles)\n",
+ " \n",
+ " print(f\"Dataset: {dataset_name}\")\n",
+ " print(f\"Total molecules: {len(smiles_clean)}\")\n",
+ " print(f\"Label distribution: {np.bincount(labels_clean.astype(int))}\")\n",
+ " \n",
+ " return smiles_clean.tolist(), augmented_smiles, labels_clean, label_names\n",
+ "\n",
+ "def generate_embeddings_for_classes(model, tokenizer, smiles_list, labels, batch_size=256, max_length=512):\n",
+ " \"\"\"Generate embeddings and organize by class labels\"\"\"\n",
+ " model.eval()\n",
+ " device = next(model.parameters()).device\n",
+ " \n",
+ " all_embeddings = []\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for i in range(0, len(smiles_list), batch_size):\n",
+ " batch_smiles = smiles_list[i:i+batch_size]\n",
+ " \n",
+ " # Tokenize batch\n",
+ " tokens = tokenizer(batch_smiles, \n",
+ " max_length=max_length, \n",
+ " truncation=True, \n",
+ " padding='max_length', \n",
+ " return_tensors='pt')\n",
+ " \n",
+ " # Move to device\n",
+ " input_ids = tokens['input_ids'].to(device)\n",
+ " attention_mask = tokens['attention_mask'].to(device)\n",
+ " \n",
+ " # Generate embeddings\n",
+ " batch_embeddings = model(input_ids, attention_mask)\n",
+ " all_embeddings.append(batch_embeddings.cpu().numpy())\n",
+ " \n",
+ " if (i // batch_size + 1) % 10 == 0:\n",
+ " print(f\"Processed {i + len(batch_smiles)} / {len(smiles_list)} SMILES\")\n",
+ " \n",
+ " embeddings = np.vstack(all_embeddings)\n",
+ " \n",
+ " # Organize by class\n",
+ " class_embeddings = {}\n",
+ " unique_labels = np.unique(labels)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels == label\n",
+ " class_embeddings[label] = embeddings[mask]\n",
+ " \n",
+ " return embeddings, class_embeddings\n",
+ "\n",
+ "def analyze_class_separation(class_embeddings, label_names):\n",
+ " \"\"\"Analyze how well different classes are separated in embedding space\"\"\"\n",
+ " print(\"=== Class Separation Analysis ===\")\n",
+ " \n",
+ " class_keys = list(class_embeddings.keys())\n",
+ " \n",
+ " if len(class_keys) == 2:\n",
+ " # Binary classification analysis\n",
+ " emb1 = class_embeddings[class_keys[0]]\n",
+ " emb2 = class_embeddings[class_keys[1]]\n",
+ " \n",
+ " # Inter-class similarity (between different classes)\n",
+ " inter_sim = cosine_similarity(emb1, emb2)\n",
+ " mean_inter_sim = np.mean(inter_sim)\n",
+ " \n",
+ " # Intra-class similarity (within same class)\n",
+ " intra_sim1 = cosine_similarity(emb1)\n",
+ " intra_sim2 = cosine_similarity(emb2)\n",
+ " \n",
+ " # Remove diagonal for intra-class\n",
+ " intra_sim1_off_diag = intra_sim1[np.triu_indices_from(intra_sim1, k=1)]\n",
+ " intra_sim2_off_diag = intra_sim2[np.triu_indices_from(intra_sim2, k=1)]\n",
+ " \n",
+ " mean_intra_sim1 = np.mean(intra_sim1_off_diag)\n",
+ " mean_intra_sim2 = np.mean(intra_sim2_off_diag)\n",
+ " \n",
+ " print(f\"Class 0 ({label_names[0]}) size: {len(emb1)}\")\n",
+ " print(f\"Class 1 ({label_names[1]}) size: {len(emb2)}\")\n",
+ " print(f\"Mean inter-class similarity: {mean_inter_sim:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 0): {mean_intra_sim1:.4f}\")\n",
+ " print(f\"Mean intra-class similarity (Class 1): {mean_intra_sim2:.4f}\")\n",
+ " print(f\"Separation ratio: {(mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim):.4f}\")\n",
+ " \n",
+ " return {\n",
+ " 'inter_class_sim': mean_inter_sim,\n",
+ " 'intra_class_sim': [mean_intra_sim1, mean_intra_sim2],\n",
+ " 'separation_ratio': (mean_intra_sim1 + mean_intra_sim2)/(2 * mean_inter_sim)\n",
+ " }\n",
+ "\n",
+ "def visualize_class_embeddings(embeddings, labels, label_names, dataset_name, sample_size=2000):\n",
+ " \"\"\"Create comprehensive visualizations of class-separated embeddings\"\"\"\n",
+ " \n",
+ " # Sample for visualization if needed\n",
+ " if len(embeddings) > sample_size:\n",
+ " indices = np.random.choice(len(embeddings), sample_size, replace=False)\n",
+ " embeddings_viz = embeddings[indices]\n",
+ " labels_viz = labels[indices]\n",
+ " print(f\"Sampling {sample_size} points for visualization\")\n",
+ " else:\n",
+ " embeddings_viz = embeddings\n",
+ " labels_viz = labels\n",
+ " \n",
+ " # Create color mapping\n",
+ " unique_labels = np.unique(labels_viz)\n",
+ " colors = plt.cm.Set1(np.linspace(0, 1, len(unique_labels)))\n",
+ " color_map = {label: colors[i] for i, label in enumerate(unique_labels)}\n",
+ " \n",
+ " # Create subplots\n",
+ " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+ " fig.suptitle(f'{dataset_name.upper()} Dataset - Class Separation Visualization', fontsize=16, fontweight='bold')\n",
+ " \n",
+ " # PCA\n",
+ " print(\"Computing PCA...\")\n",
+ " pca = PCA(n_components=2, random_state=42)\n",
+ " pca_embeddings = pca.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 0].scatter(pca_embeddings[mask, 0], pca_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 0].set_title('PCA Visualization')\n",
+ " axes[0, 0].set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)')\n",
+ " axes[0, 0].set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)')\n",
+ " axes[0, 0].legend()\n",
+ " axes[0, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # t-SNE\n",
+ " print(\"Computing t-SNE...\")\n",
+ " tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(embeddings_viz)//4))\n",
+ " tsne_embeddings = tsne.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 1].scatter(tsne_embeddings[mask, 0], tsne_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 1].set_title('t-SNE Visualization')\n",
+ " axes[0, 1].set_xlabel('t-SNE 1')\n",
+ " axes[0, 1].set_ylabel('t-SNE 2')\n",
+ " axes[0, 1].legend()\n",
+ " axes[0, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # UMAP\n",
+ " print(\"Computing UMAP...\")\n",
+ " umap_reducer = umap.UMAP(n_components=2, random_state=42, n_neighbors=min(15, len(embeddings_viz)//4))\n",
+ " umap_embeddings = umap_reducer.fit_transform(embeddings_viz)\n",
+ " \n",
+ " for label in unique_labels:\n",
+ " mask = labels_viz == label\n",
+ " axes[0, 2].scatter(umap_embeddings[mask, 0], umap_embeddings[mask, 1], \n",
+ " c=[color_map[label]], label=f'{label_names[int(label)]} (n={np.sum(mask)})', \n",
+ " alpha=0.7, s=30)\n",
+ " \n",
+ " axes[0, 2].set_title('UMAP Visualization')\n",
+ " axes[0, 2].set_xlabel('UMAP 1')\n",
+ " axes[0, 2].set_ylabel('UMAP 2')\n",
+ " axes[0, 2].legend()\n",
+ " axes[0, 2].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Inter-class distance distribution\n",
+ " if len(unique_labels) == 2:\n",
+ " class_0_emb = embeddings_viz[labels_viz == unique_labels[0]]\n",
+ " class_1_emb = embeddings_viz[labels_viz == unique_labels[1]]\n",
+ " \n",
+ " # Sample for distance calculation if too large\n",
+ " if len(class_0_emb) > 500:\n",
+ " class_0_sample = class_0_emb[np.random.choice(len(class_0_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_0_sample = class_0_emb\n",
+ " \n",
+ " if len(class_1_emb) > 500:\n",
+ " class_1_sample = class_1_emb[np.random.choice(len(class_1_emb), 500, replace=False)]\n",
+ " else:\n",
+ " class_1_sample = class_1_emb\n",
+ " \n",
+ " inter_similarities = cosine_similarity(class_0_sample, class_1_sample).flatten()\n",
+ " \n",
+ " axes[1, 0].hist(inter_similarities, bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
+ " axes[1, 0].axvline(np.mean(inter_similarities), color='red', linestyle='--', \n",
+ " label=f'Mean: {np.mean(inter_similarities):.4f}')\n",
+ " axes[1, 0].set_xlabel('Cosine Similarity')\n",
+ " axes[1, 0].set_ylabel('Frequency')\n",
+ " axes[1, 0].set_title(f'Inter-Class Similarity Distribution')\n",
+ " axes[1, 0].legend()\n",
+ " axes[1, 0].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Embedding norms by class\n",
+ " for i, label in enumerate(unique_labels):\n",
+ " mask = labels_viz == label\n",
+ " norms = np.linalg.norm(embeddings_viz[mask], axis=1)\n",
+ " axes[1, 1].hist(norms, bins=30, alpha=0.6, \n",
+ " color=color_map[label], label=f'{label_names[int(label)]}')\n",
+ " \n",
+ " axes[1, 1].set_xlabel('L2 Norm')\n",
+ " axes[1, 1].set_ylabel('Frequency')\n",
+ " axes[1, 1].set_title('Embedding Norm Distribution by Class')\n",
+ " axes[1, 1].legend()\n",
+ " axes[1, 1].grid(True, alpha=0.3)\n",
+ " \n",
+ " # Class statistics summary\n",
+ " axes[1, 2].axis('off')\n",
+ " stats_text = f\"\"\"\n",
+ " {dataset_name.upper()} Dataset Statistics:\n",
+ " \n",
+ " • Total samples: {len(embeddings_viz):,}\n",
+ " • Embedding dimension: {embeddings_viz.shape[1]}\n",
+ " • Classes: {len(unique_labels)}\n",
+ " \"\"\"\n",
+ " \n",
+ " for i, label in enumerate(unique_labels):\n",
+ " count = np.sum(labels_viz == label)\n",
+ " percentage = 100 * count / len(labels_viz)\n",
+ " stats_text += f\"\\n - {label_names[int(label)]}: {count} ({percentage:.1f}%)\"\n",
+ " \n",
+ " axes[1, 2].text(0.1, 0.9, stats_text, transform=axes[1, 2].transAxes, fontsize=12, \n",
+ " verticalalignment='top', fontfamily='monospace')\n",
+ " \n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ " \n",
+ " return pca_embeddings, tsne_embeddings, umap_embeddings\n",
+ "\n",
+ "def clustering_analysis_for_classes(embeddings, labels, label_names, n_clusters=None):\n",
+ " \"\"\"Perform clustering analysis to evaluate class separation\"\"\"\n",
+ " if n_clusters is None:\n",
+ " n_clusters = len(np.unique(labels))\n",
+ " \n",
+ " print(f\"\\n=== Clustering Analysis (k={n_clusters}) ===\")\n",
+ " \n",
+ " # K-means clustering\n",
+ " kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
+ " cluster_labels = kmeans.fit_predict(embeddings)\n",
+ " \n",
+ " # Calculate cluster purity\n",
+ " cluster_purities = []\n",
+ " for cluster_id in range(n_clusters):\n",
+ " cluster_mask = cluster_labels == cluster_id\n",
+ " cluster_true_labels = labels[cluster_mask]\n",
+ " \n",
+ " if len(cluster_true_labels) > 0:\n",
+ " unique, counts = np.unique(cluster_true_labels, return_counts=True)\n",
+ " purity = np.max(counts) / len(cluster_true_labels)\n",
+ " cluster_purities.append(purity)\n",
+ " print(f\"Cluster {cluster_id}: {len(cluster_true_labels)} samples, purity: {purity:.3f}\")\n",
+ " \n",
+ " avg_purity = np.mean(cluster_purities)\n",
+ " print(f\"Average cluster purity: {avg_purity:.4f}\")\n",
+ " \n",
+ " \n",
+ " return avg_purity\n",
+ "\n",
+ "# === MAIN EXECUTION ===\n",
+ "\n",
+ "def main_class_visualization():\n",
+ " \"\"\"Main function to run the complete class-based visualization analysis\"\"\"\n",
+ " \n",
+ " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ " print(f\"Using device: {device}\")\n",
+ " tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')\n",
+ "\n",
+ " config = BertConfig(\n",
+ " vocab_size=tokenizer.vocab_size,\n",
+ " hidden_size=768,\n",
+ " num_hidden_layers=4,\n",
+ " num_attention_heads=12,\n",
+ " intermediate_size=2048,\n",
+ " max_position_embeddings=512\n",
+ " )\n",
+ " model = SimSonEncoder(config, max_len=512) # Adjust max_len as needed\n",
+ " model = torch.compile(model)\n",
+ " \n",
+ " model.to(device)\n",
+ " model.eval()\n",
+ " ds_name_to_model_path = {'bbbp': '/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_bbp_encoder.bin', 'clintox': '/home/jovyan/simson_training_bolgov/moleculenet_eval/moleculenet_clintox_encoder.bin'}\n",
+ " \n",
+ " \n",
+ " # Analyze both datasets\n",
+ " datasets = ['bbbp', 'clintox']\n",
+ " \n",
+ " for dataset_name in datasets:\n",
+ " model.load_state_dict(torch.load(ds_name_to_model_path[dataset_name], map_location=device))\n",
+ " print(f\"\\n{'='*50}\")\n",
+ " print(f\"ANALYZING {dataset_name.upper()} DATASET\")\n",
+ " print(f\"{'='*50}\")\n",
+ " \n",
+ " # Prepare dataset\n",
+ " smiles_list, augmented_smiles, labels, label_names = prepare_dataset_with_augmentation(\n",
+ " dataset_name, sample_size=1000\n",
+ " )\n",
+ " \n",
+ " # Generate embeddings for original SMILES\n",
+ " print(\"Generating embeddings...\")\n",
+ " embeddings, class_embeddings = generate_embeddings_for_classes(\n",
+ " model, tokenizer, smiles_list, labels\n",
+ " )\n",
+ " \n",
+ " # Analyze class separation\n",
+ " separation_stats = analyze_class_separation(class_embeddings, label_names)\n",
+ " \n",
+ " # Create visualizations\n",
+ " pca_emb, tsne_emb, umap_emb = visualize_class_embeddings(\n",
+ " embeddings, labels, label_names, dataset_name\n",
+ " )\n",
+ " \n",
+ " # Clustering analysis\n",
+ " clustering_analysis_for_classes(embeddings, labels, label_names)\n",
+ " \n",
+ " print(f\"\\nCompleted analysis for {dataset_name.upper()} dataset\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "17ada2a4-e44e-4ff1-8b07-cd1f1b53298d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using device: cuda\n",
+ "\n",
+ "==================================================\n",
+ "ANALYZING BBBP DATASET\n",
+ "==================================================\n",
+ "Loading bbbp dataset...\n",
+ "Generating augmented SMILES...\n",
+ "Dataset: bbbp\n",
+ "Total molecules: 1000\n",
+ "Label distribution: [239 761]\n",
+ "Generating embeddings...\n",
+ "=== Class Separation Analysis ===\n",
+ "Class 0 (Non-permeable) size: 239\n",
+ "Class 1 (Permeable) size: 761\n",
+ "Mean inter-class similarity: -0.1155\n",
+ "Mean intra-class similarity (Class 0): 0.3215\n",
+ "Mean intra-class similarity (Class 1): 0.5806\n",
+ "Separation ratio: -3.9042\n",
+ "Computing PCA...\n",
+ "Computing t-SNE...\n",
+ "Computing UMAP...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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f1uTJk9WxY0c999xzxk03LXX3Zp4lSpTQxIkTc9wUs2rVqpo/f36O7bZo0ULr1q3LU24AAAC2Zsv7rOxcuHBB//nPf7Rjxw7dunVLDzzwgKZMmaIGDRpIktLT0/Xxxx/r66+/1rVr19SkSRO98847zEgEAAAACrmhQ4dq6NCh9k4DAGBlFhX+HBwc1KZNG7Vp00aXL1/W+vXrtXbtWq1atUo1atRQ165d1blzZ+OeewBso9yli2r00fuSpL/+1Vs3q1S1c0YAgLzKz/usq1evqlu3bmrRooXmz58vd3d3/f3333JzczPGzJ8/X4sXL1ZYWJh8fHz00UcfqW/fvoqIiFCJEiXynAMAAAAAAACsx6KlPu/k7u6u3r17a9q0aXrooYd06tQpTZ8+Xa1bt9Zbb72lhIQEa+QJIBuuCZdUf+4s1Z87S6Uuxtg7HQCAldn6Pmv+/Pny8vLS1KlT1bBhQ1WrVk3BwcGqXr26pIzZfl9++aUGDRqk9u3bq06dOpo+fbouXryo77//3hpdBAAAAAAAgBXlqfB3/fp1LVu2TM8995xCQkKUmJioCRMmaMeOHXrnnXe0f/9+vfbaa9bKFQAAoNjIj/usbdu2qX79+ho2bJhatmypLl26aOXKlcbzUVFRiouLU1BQkPFY2bJl1ahRIx06dChX1zIY8v7HWu0U5T+MEePD+DA+RW18CjPeP0XzdQVwb2vWrFFAQICioqJyjHvjjTcUEBBg/LN37958yrBoioiIUPPmzZWYmGjvVGwmOTlZrVu31tKlS+2dClAoWLTU5549e7Rq1Spt3bpVjo6Oevrpp/Xee++pfv36xpjnn39eVapU0cCBA62WLAAAQFGXn/dZ586d0/Lly9WnTx8NHDhQv/76qyZPnixnZ2eFhIQoLi5OkrIsK+rh4aFLly6ZfR0XF8c85SllfEjo6Ogog0FKT89zc0USY5QzxidnjE/OGJ+c2XJ8HBwKb5XI2Tnjv3+8f4Dia82aNRozZozxcenSpeXj46POnTvr5ZdflouLix2zs5/u3burVatWOn36tObMmWPvdLJo166doqOjsz0XFBSkRYsWGR+np6drxYoVWrZsmf7++2+VKVNGjRo10syZM1WmTBlJGUWrefPm6cCBAzp8+LBu3LihL7/8Ui1atMhzrqmpqQoPD1f37t3l6upq036YKzf9TUpKkrOzswz3+VaIs7Oz+vTpozlz5uj5559n2wngPiwq/PXp00eNGjXS+PHj9fTTT6tUqVLZxvn6+uqZZ57JU4IAiqbU1BRFRp6/b1z16r5ycrLonyoAKJTy8z4rPT1d9evX18iRIyVJdevW1V9//aUVK1YoJCQkT23fKSkpNc/f7s/8sDQlJZUPTe+BMcoZ45MzxidnjE/ObDk+aWmFd8CTk1Ml8f4BIA0bNkw+Pj5KTEzUpk2b9J///Ee//fabPvjgA3unZhdNmjRRkyZNtHfv3gJZ+Bs7dqxu3Lhhciw6OlofffSRyWookjRr1izNmzdPHTp0UM+ePXXz5k0dOHBAt2/fNhbMbt26pY8//lg+Pj6qXbt2rldPycn27dt15swZhYaG2rwf5jKnv0lJSRo/frwiIiJUsmRJDR06VL169cqx3a5du2rGjBnasGGDnn/++VzlBBQ3Fn2a/s0336h27dr3jatataqmTp1qySUAFHEJsecVm5ykSNeUe8ZciolSF0k1atTMt7wAwN7y8z6rYsWK8vf3NzlWo0YNbdq0yXhekuLj41WpUiVjTHx8vOrUqZOra1nrg870dGZL3A9jlDPGJ2eMT84Yn5wxPqbuHgvGByi+Hn30UTVo0ECS1K1bN73wwguKiIjQ6NGjVblyZTtnh7u1b98+y7HZs2fLYDCYfPny1KlT+uyzzzRw4ECTbRh69+5t8twyZcrohx9+UJUqVbRx40arFv5Wr16txo0bq0qVKjbvh7nM6e/ChQv1yy+/KCwsTJcuXdKMGTPUuHFjNWrU6J7tli1bVo888ojWrl1L4Q+4D4v2+PP29tbFixezPXfx4sUs3yQAgOxU8KoqL1//e/7xrOJj7xQBIN/l531WkyZNdObMGZNjZ8+eVdWqVSVJPj4+qlixovbs2WM8n5iYqCNHjigwMNBqeQAAAADFhYODg5o3by5JJsswJiQk6O2339YjjzyiBg0aKCQkRD/++GO2bQQEBCg8PFzff/+9OnXqpAYNGqh9+/b68ccfFRUVpYCAAM2aNUstWrRQx44ddfDgQXXu3FktWrTQsmXLTNoy57pXrlzRtGnT1KlTJwUGBqpJkybq3bu3Dh48mG1+p0+fVrdu3dSwYUM99dRT2rx5s8XjlZtxsaUNGzaoadOmJgW2b7/9Vk5OTurfv78k3fP/1RwdHbMtzOXV7du3tWPHDrVs2dLs5+SlH+Yyp7+HDh1Snz599Mwzz6h3795q3bq1Dhw4cN+2g4KCdODAAV25ciVPOQJFnUWFv/Hjx+ujjz7K9lx4eLgmTJiQp6QAAACKq/y8z+rVq5eOHDmiOXPm6O+//9aGDRu0cuVKde/eXZJkMBj08ssv69NPP9XWrVt14sQJjRo1SpUqVcr226MAAAAA7u/cuXOSpPLly0vK+HJd9+7dtXnzZr344osaPXq0ypUrp0GDBmnv3r3ZtnH06FGNGTNGjz76qMaOHaugoCBFRUUZz+/atUuDBg1SdHS0evXqpccee0yBgYGaPn26kpOTc3Xdc+fOadWqVWrevLnGjh2rIUOGKDY2Vr1799apU6ey5DZq1CjVqFFDb775pkqXLq0RI0aYVdS5myXjEhAQoICAgFxfKydHjhzR2bNns2y1cOTIEdWuXVvbtm1Ty5Yt1aRJE7Vu3Vr/93//Z9Xr38uxY8eUnJxssh98TgpSP3x9fRUREaFTp05p3759+uWXX+Tn52c8f/ToUV24cCHL8+rVq6f09HSrzpoEiiKLlvrcv3+/Jk6cmO251q1b6913381TUgAgsQ8ggOIpP++zGjZsqNmzZ2vWrFn673//Kx8fH40dO1bPPvusMaZfv366deuWJkyYoGvXrqlp06ZasGABm6kDAAAAZkpMTFRCQoISExO1ceNGff/996pdu7Zq1KghSVqwYIGioqK0Zs0a47L/3bp1U+fOnTV79my1aNEiS5s7d+7U6tWrVbduXeOx1NRUxcTESMpYprFTp07asWOHoqKiNGzYMB05ckTbt29XZGSk/P39zb6un5+ftm/fLldXV+O1OnTooHbt2mn16tUaNWqUSW5t27bV+++/L0kKCQlRmzZtNG/ePM2dOzdX42bJuNjC+vXr5ezsrA4dOpgcz1yRZeLEiXr11Vfl7e2tZcuW6Y033pCfn5/q1atn07xOnz4tSapWrZpZ8QWpHwMGDFCfPn3UsWNHSVKXLl1Ut25dzZ8/X+vXr5e7u7tGjRqVZSnc6tWrS5JOnjyptm3bWj0voKiw6JPyq1ev3nNTz1KlSjHVFsgn1z0q6vCwsZKkm17eds7G+tgHEEBxlN/3WW3bts3xf5gMBoOGDx+u4cOHW/W6AAAAQHFx915pQUFBeuedd4yPt2zZovr168vT01MJCQnG44GBgVqzZo1SU1Pl6OiYpY07i36STGI8PDwkZcwqvHnzpiTJzc1NknTt2rVcXffOgl9KSoquX7+ukiVLyt3d3WSWYabMYo4kubq66tFHH9UPP/xwz/G5F0vG5c5ZY9aQnJysiIgIPfLII3J3dzc5d+vWLUVHR2vChAnq0aOHpIwva7Zq1UqfffaZZs2aZdVc7pb5/4aZr2tOClo/KlSooNWrV+vo0aP6+eefdfDgQfXu3VtPP/20/vvf/+qBBx7I9nmZfb18+bLVcwKKEosKf9WqVdPu3bsVFBSU5dyePXuM+8IAsK3rHhX128DX7Z2GTWXuAwgAxQX3WQAAAID9pKammhSZpIwixd0FptyYMGGC/Pz8VKZMGVWtWlWenp4m5yMjI5WUlHTPvdoSExOzFHcyZwveS2a+zs7OxlWSMv/OXOrT3OumpaVp8eLFWrp0qaKiopSammqMSUpKyvI8Ly8vk8eVK1fWjRs3lJiYaFJEvB9LxmXjxo1mt2+On376SZcvX86yPKaUMbaS9MQTTxiPlSlTRoGBgTpx4oRV88hJenr6fWMKWj/i4uIUHh6unTt3Kjg4WK+++qr+/vtvTZkyRfPnz1fPnj31xhtvZHleZl8NBoNN8gKKCosKfy+88IJmzpwpNzc3de3aVRUqVFBCQoLWrFmjzz//XCNHjrR2ngAAAMUC91kAAACA/cTExOixxx4zObZ161b5+PhY3GbDhg3VoEGDe543GAwKDg5W3759sz1funTpLMfKlStncT53Fk/Mue78+fM1a9YsderUSSNGjDDuTThy5Eizik6Z13JxcclVnpaMi7WtX79epUuXzvKekDIKwqdPn1aFChVMjpcvX16//fabzXPLfB2uXr2apdh6t4LWj5IlS+rhhx/WuHHjVKJECZ0+fVovv/yyhg0bpooVKyosLEx16tTJUqi8evWqMTcA92ZR4a93796KjIzUrFmzNGvWLDk6Ohq/6fHiiy/q3//+t1WTBAAAKC64zwIAAADsp2LFilq0aFGWY7ZUrVo13b59O9tVPwrCdSMiItSsWTPNmDHDeCw5OVnXr1/PNj42Nla1atUyPr5w4YI8PT2zFP4yZ5rdOYPQkvxs5fr16/rhhx/0+OOPZ1tk9Pf31/79+3Xp0iWTvegSEhJUqVIlm+eXOevz3LlzCggIuGdcQexH2bJlTZaE3bVrlx5++GH1799fUsaMwC1btmQp/J07d86YM4B7c7DkSQaDQRMnTtR3332nCRMmaMiQIcbHEydOtHaOAO7BPTZaLUcPUsvRg+Qaecbe6QAArID7LAAAAMB+SpQooaCgIJM/JUqUsOk127dvr/379+vgwYNZzsXExNj9uo6OjsZlQjN9/fXXSklJybbdiIgI48+JiYn66aef9PDDD2eJy5y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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Clustering Analysis (k=2) ===\n",
+ "Cluster 0: 353 samples, purity: 0.654\n",
+ "Cluster 1: 647 samples, purity: 0.988\n",
+ "Average cluster purity: 0.8210\n",
+ "\n",
+ "Completed analysis for BBBP dataset\n",
+ "\n",
+ "==================================================\n",
+ "ANALYZING CLINTOX DATASET\n",
+ "==================================================\n",
+ "Loading clintox dataset...\n",
+ "Generating augmented SMILES...\n",
+ "Dataset: clintox\n",
+ "Total molecules: 1000\n",
+ "Label distribution: [ 64 936]\n",
+ "Generating embeddings...\n",
+ "=== Class Separation Analysis ===\n",
+ "Class 0 (Non-toxic) size: 64\n",
+ "Class 1 (Toxic) size: 936\n",
+ "Mean inter-class similarity: -0.0687\n",
+ "Mean intra-class similarity (Class 0): 0.7217\n",
+ "Mean intra-class similarity (Class 1): 0.9002\n",
+ "Separation ratio: -11.8095\n",
+ "Computing PCA...\n",
+ "Computing t-SNE...\n",
+ "Computing UMAP...\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== Clustering Analysis (k=2) ===\n",
+ "Cluster 0: 921 samples, purity: 0.996\n",
+ "Cluster 1: 79 samples, purity: 0.759\n",
+ "Average cluster purity: 0.8776\n",
+ "\n",
+ "Completed analysis for CLINTOX dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "if __name__ == \"__main__\":\n",
+ " main_class_visualization()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
+ "language": "python",
+ "name": "conda-env-.mlspace-bolgov_simson_training-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simson_modeling/simson_repo/simson_base/polymer_1M_weights.bin b/simson_modeling/simson_repo/simson_base/polymer_1M_weights.bin
new file mode 100644
index 0000000000000000000000000000000000000000..11398a176dd227bda2fee07dbe4f77463a6ba4d0
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/polymer_1M_weights.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fcfbfd2153cd530eee32bab48b294d5b833d5ad26a6f500d9c3be6636446678
+size 133
diff --git a/simson_modeling/simson_repo/simson_base/simson_state.bin b/simson_modeling/simson_repo/simson_base/simson_state.bin
new file mode 100644
index 0000000000000000000000000000000000000000..4e0b83b4adc3b4bcfb313fdbe0e3c0b925e23f28
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/simson_state.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e772229455ad8bac53a4eada24f6149e8a70ba46f2f1b589a4f79f9caaa1f666
+size 133
diff --git a/simson_modeling/simson_repo/simson_base/train.py b/simson_modeling/simson_repo/simson_base/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e7eb1acb56b27fc075df8f48668e9a7ec28e6f7
--- /dev/null
+++ b/simson_modeling/simson_repo/simson_base/train.py
@@ -0,0 +1,450 @@
+# ==============================================================================
+# 1. IMPORTS
+# ==============================================================================
+import os
+import warnings
+import wandb
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+import numpy as np
+from tqdm import tqdm
+from rdkit import Chem, RDLogger
+from datasets import load_dataset, load_from_disk
+from transformers import AutoTokenizer, BertModel, BertConfig
+import pandas as pd
+
+# ==============================================================================
+# 2. INITIAL SETUP
+# ==============================================================================
+# Suppress RDKit console output
+RDLogger.DisableLog('rdApp.*')
+# Ignore warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# ==============================================================================
+# 3. MODEL AND LOSS FUNCTION
+# ==============================================================================
+def global_average_pooling(x):
+ """Global Average Pooling: from [B, max_len, hid_dim] to [B, hid_dim]"""
+ return torch.mean(x, dim=1)
+
+class SimSonEncoder(nn.Module):
+ """The main encoder model based on BERT."""
+ def __init__(self, config: BertConfig, max_len: int, dropout: float = 0.1):
+ super(SimSonEncoder, self).__init__()
+ self.bert = BertModel(config, add_pooling_layer=False)
+ self.linear = nn.Linear(config.hidden_size, max_len)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, input_ids, attention_mask=None):
+ if attention_mask is None:
+ attention_mask = input_ids.ne(self.bert.config.pad_token_id)
+
+ outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+ hidden_states = self.dropout(outputs.last_hidden_state)
+ pooled_output = global_average_pooling(hidden_states)
+ return self.linear(pooled_output)
+
+class ContrastiveLoss(nn.Module):
+ """Calculates the contrastive loss for the SimSon model."""
+ def __init__(self, temperature=0.2):
+ super(ContrastiveLoss, self).__init__()
+ self.temperature = temperature
+ self.similarity_fn = F.cosine_similarity
+
+ def forward(self, proj_1, proj_2):
+ batch_size = proj_1.shape[0]
+ device = proj_1.device
+
+ # Normalize projections
+ z_i = F.normalize(proj_1, p=2, dim=1)
+ z_j = F.normalize(proj_2, p=2, dim=1)
+
+ # Concatenate for similarity matrix calculation
+ representations = torch.cat([z_i, z_j], dim=0)
+
+ # Calculate cosine similarity between all pairs
+ similarity_matrix = self.similarity_fn(representations.unsqueeze(1), representations.unsqueeze(0), dim=2)
+
+ # Identify positive pairs (original and its augmentation)
+ sim_ij = torch.diag(similarity_matrix, batch_size)
+ sim_ji = torch.diag(similarity_matrix, -batch_size)
+ positives = torch.cat([sim_ij, sim_ji], dim=0)
+
+ # Create a mask to exclude self-comparisons
+ nominator = torch.exp(positives / self.temperature)
+ mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool, device=device)).float()
+ denominator = mask * torch.exp(similarity_matrix / self.temperature)
+
+ # Calculate the final loss
+ loss = -torch.log(nominator / torch.sum(denominator, dim=1))
+ return torch.sum(loss) / (2 * batch_size)
+
+# ==============================================================================
+# 4. DATA HANDLING
+# ==============================================================================
+class SmilesEnumerator:
+ """Generates randomized SMILES strings for data augmentation."""
+ def randomize_smiles(self, smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ return Chem.MolToSmiles(mol, doRandom=True, canonical=False) if mol else smiles
+ except:
+ return smiles
+
+class ContrastiveSmilesDataset(Dataset):
+ """Dataset for creating pairs of augmented SMILES for contrastive learning."""
+ def __init__(self, smiles_list, tokenizer, max_length=512):
+ self.smiles_list = smiles_list
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.enumerator = SmilesEnumerator()
+
+ def __len__(self):
+ return len(self.smiles_list)
+
+ def __getitem__(self, idx):
+ original_smiles = self.smiles_list[idx]
+
+ # Create two different augmentations of the same SMILES
+ smiles_1 = self.enumerator.randomize_smiles(original_smiles)
+ smiles_2 = self.enumerator.randomize_smiles(original_smiles)
+
+ # Tokenize and do pad. Padding will be handled by the collate_fn.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PrecomputedContrastiveSmilesDataset(Dataset):
+ """
+ A Dataset class that reads pre-augmented SMILES pairs from a Parquet file.
+ This is significantly faster as it offloads the expensive SMILES randomization
+ to a one-time preprocessing step.
+ """
+ def __init__(self, tokenizer, file_path: str, max_length: int = 512):
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ # Load the entire dataset from the Parquet file into memory.
+ # This is fast and efficient for subsequent access.
+ print(f"Loading pre-computed data from {file_path}...")
+ self.data = pd.read_parquet(file_path)
+ print("Data loaded successfully.")
+
+ def __len__(self):
+ """Returns the total number of pairs in the dataset."""
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ """
+ Retrieves a pre-augmented pair, tokenizes it, and returns it
+ in the format expected by the DataCollator.
+ """
+ # Retrieve the pre-augmented pair from the DataFrame
+ row = self.data.iloc[idx]
+ smiles_1 = row['smiles_1']
+ smiles_2 = row['smiles_2']
+
+ # Tokenize the pair. This operation is fast and remains in the data loader.
+ tokens_1 = self.tokenizer(smiles_1, max_length=self.max_length, truncation=True, padding='max_length')
+ tokens_2 = self.tokenizer(smiles_2, max_length=self.max_length, truncation=True, padding='max_length')
+
+ return {
+ 'input_ids_1': torch.tensor(tokens_1['input_ids']),
+ 'attention_mask_1': torch.tensor(tokens_1['attention_mask']),
+ 'input_ids_2': torch.tensor(tokens_2['input_ids']),
+ 'attention_mask_2': torch.tensor(tokens_2['attention_mask']),
+ }
+
+class PreTokenizedSmilesDataset(Dataset):
+ """
+ A Dataset that loads a pre-tokenized and pre-padded dataset created
+ by the preprocessing script. It uses memory-mapping for instant loads
+ and high efficiency.
+ """
+ def __init__(self, dataset_path: str):
+ # Load the dataset from disk. This is very fast due to memory-mapping.
+ self.dataset = load_from_disk(dataset_path)
+ # Set the format to PyTorch tensors for direct use in the model
+ self.dataset.set_format(type='torch', columns=[
+ 'input_ids_1', 'attention_mask_1', 'input_ids_2', 'attention_mask_2'
+ ])
+ print(f"Successfully loaded pre-tokenized dataset from {dataset_path}.")
+
+ def __len__(self):
+ """Returns the total number of items in the dataset."""
+ return len(self.dataset)
+
+ def __getitem__(self, idx):
+ """Retrieves a single pre-processed item."""
+ return self.dataset[idx]
+
+
+class DataCollatorWithPadding:
+ """
+ A collate function that dynamically pads inputs to the longest sequence
+ across both augmented views in the batch, ensuring consistent tensor shapes.
+ """
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def __call__(self, features):
+ # Create a combined list of features for both views to find the global max length
+ combined_features = []
+ for feature in features:
+ combined_features.append({'input_ids': feature['input_ids_1'], 'attention_mask': feature['attention_mask_1']})
+ combined_features.append({'input_ids': feature['input_ids_2'], 'attention_mask': feature['attention_mask_2']})
+
+ # Pad the combined batch. This ensures all sequences are padded to the same length.
+ padded_combined = self.tokenizer.pad(combined_features, padding='longest', return_tensors='pt')
+
+ # Split the padded tensors back into two views
+ batch_size = len(features)
+ input_ids_1, input_ids_2 = torch.split(padded_combined['input_ids'], batch_size, dim=0)
+ attention_mask_1, attention_mask_2 = torch.split(padded_combined['attention_mask'], batch_size, dim=0)
+
+ return {
+ 'input_ids_1': input_ids_1,
+ 'attention_mask_1': attention_mask_1,
+ 'input_ids_2': input_ids_2,
+ 'attention_mask_2': attention_mask_2,
+ }
+
+# ==============================================================================
+# 5. TRAINING AND EVALUATION LOOPS
+# ==============================================================================
+def evaluation_step(model, batch, criterion, device):
+ """Performs a single evaluation step on a batch of data."""
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ with torch.no_grad():
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+ return proj_1, proj_2, loss
+
+def train_epoch(model, train_loader, optimizer, criterion, device, scheduler, save_path, save_steps):
+ model.train()
+ total_loss = 0
+ progress_bar = tqdm(train_loader, desc="Training Batch", leave=False)
+
+ for step, batch in enumerate(progress_bar, 1):
+ input_ids_1 = batch['input_ids_1'].to(device)
+ attention_mask_1 = batch['attention_mask_1'].to(device)
+ input_ids_2 = batch['input_ids_2'].to(device)
+ attention_mask_2 = batch['attention_mask_2'].to(device)
+
+ optimizer.zero_grad()
+ with torch.autocast(dtype=torch.float16, device_type="cuda"):
+ combined_input_ids = torch.cat([input_ids_1, input_ids_2], dim=0)
+ combined_attention_mask = torch.cat([attention_mask_1, attention_mask_2], dim=0)
+
+ combined_proj = model(combined_input_ids, combined_attention_mask)
+
+ batch_size = input_ids_1.size(0)
+ proj_1, proj_2 = torch.split(combined_proj, batch_size, dim=0)
+
+ loss = criterion(proj_1, proj_2)
+
+ loss.backward()
+
+ optimizer.step()
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+ scheduler.step()
+
+ total_loss += loss.item()
+
+ progress_bar.set_postfix(loss=f"{loss.item():.4f}")
+ wandb.log({
+ "train_batch_loss": loss.item(),
+ "learning_rate": scheduler.get_last_lr()[0]
+ })
+ if save_path and step % save_steps == 0:
+ torch.save(model.state_dict(), save_path)
+ progress_bar.write(f"Checkpoint saved at step {step}")
+
+ return total_loss / len(train_loader)
+
+def validate_epoch(model, val_loader, criterion, device):
+ model.eval()
+ total_loss = 0
+ progress_bar = tqdm(val_loader, desc="Validating", leave=False)
+
+ for batch in progress_bar:
+ _, _, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+ print(f'Validation loss: {total_loss / len(val_loader)}')
+ return total_loss / len(val_loader)
+
+def test_model(model, test_loader, criterion, device):
+ model.eval()
+ total_loss = 0
+ all_similarities = []
+ progress_bar = tqdm(test_loader, desc="Testing", leave=False)
+
+ for batch in progress_bar:
+ proj_1, proj_2, loss = evaluation_step(model, batch, criterion, device)
+ total_loss += loss.item()
+
+ proj_1_norm = F.normalize(proj_1, p=2, dim=1)
+ proj_2_norm = F.normalize(proj_2, p=2, dim=1)
+ batch_similarities = F.cosine_similarity(proj_1_norm, proj_2_norm, dim=1)
+ all_similarities.extend(batch_similarities.cpu().numpy())
+
+ avg_loss = total_loss / len(test_loader)
+ avg_sim = np.mean(all_similarities)
+ std_sim = np.std(all_similarities)
+
+ return avg_loss, avg_sim, std_sim
+
+# ==============================================================================
+# 6. SINGLE-GPU TRAINING
+# ==============================================================================
+def run_training(model_config, hparams, data_splits):
+ """The main function to run the training and evaluation process."""
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ print(f"Using device: {device}")
+
+ wandb_key = os.getenv("WANDB_API_KEY")
+ if wandb_key:
+ wandb.login(key=wandb_key)
+ wandb.init(
+ project="simson-contrastive-learning-single-gpu",
+ name=f"run-{wandb.util.generate_id()}",
+ config=hparams
+ )
+ train_smiles, val_smiles, test_smiles = data_splits
+
+
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+
+ precomputed_train_path = 'data/splits/train.parquet'
+ precomputed_test_path = 'data/splits/test.parquet'
+ precomputed_val_path = 'data/splits/validation.parquet'
+
+ train_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_train_path, max_length=hparams['max_length'])
+ test_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_test_path, max_length=hparams['max_length'])
+ val_dataset = PrecomputedContrastiveSmilesDataset(tokenizer, file_path=precomputed_val_path, max_length=hparams['max_length'])
+
+ train_loader = DataLoader(train_dataset, batch_size=hparams['batch_size'], shuffle=True, num_workers=16, prefetch_factor=128, pin_memory=True)
+ val_loader = DataLoader(val_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+ test_loader = DataLoader(test_dataset, batch_size=hparams['batch_size'], shuffle=False, num_workers=2, pin_memory=True)
+ print('Initialized all data. Compiling the model...')
+ model = SimSonEncoder(config=model_config, max_len=hparams['max_embeddings']).to(device)
+ model = torch.compile(model)
+ print(model)
+ total_params = sum(p.numel() for p in model.parameters())
+
+ print(f"Total number of parameters: {total_params // 1_000_000} M")
+ wandb.config.update({"total_params_M": total_params // 1_000_000})
+
+ criterion = ContrastiveLoss(temperature=hparams['temperature']).to(device)
+ optimizer = optim.AdamW(model.parameters(), lr=hparams['lr'], weight_decay=1e-5, fused=True)
+ print(f"Len of dataloader is {len(train_loader)}, with bs: {len(train_loader) // hparams['batch_size']}")
+ scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_mult=1, T_0=int(hparams['epochs'] * len(train_loader)))
+ print("Starting training...")
+ wandb.watch(model, log='all', log_freq=5000)
+
+ best_val_loss = float('inf')
+ epoch_iterator = tqdm(range(hparams['epochs']), desc="Epochs")
+ model.load_state_dict(torch.load(hparams['save_path']))
+ val_loss = validate_epoch(model, val_loader, criterion, device)
+
+ for epoch in epoch_iterator:
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ val_loss = validate_epoch(model, val_loader, criterion, device)
+ epoch_iterator.set_postfix(train_loss=f"{train_loss:.4f}", val_loss=f"{val_loss:.4f}")
+ wandb.log({
+ "epoch": epoch + 1,
+ "train_epoch_loss": train_loss,
+ "val_epoch_loss": val_loss,
+ })
+
+ if val_loss < best_val_loss:
+ best_val_loss = val_loss
+ torch.save(model.state_dict(), hparams['save_path'])
+ epoch_iterator.write(f"Epoch {epoch + 1}: New best model saved with val loss {val_loss:.4f}")
+
+ epoch_iterator.write("Training complete. Starting final testing...")
+ # Load the best model for testing
+ model.load_state_dict(torch.load(hparams['save_path']))
+
+ test_loss, avg_sim, std_sim = test_model(model, test_loader, criterion, device)
+
+ print("\n--- Test Results ---")
+ print(f"Test Loss: {test_loss:.4f}")
+ print(f"Average Cosine Similarity: {avg_sim:.4f} \u00B1 {std_sim:.4f}")
+ print("--------------------")
+
+ wandb.log({
+ "test_loss": test_loss,
+ "avg_cosine_similarity": avg_sim,
+ "std_cosine_similarity": std_sim
+ })
+
+ wandb.finish()
+
+# ==============================================================================
+# 7. MAIN EXECUTION
+# ==============================================================================
+def main():
+ """Main function to configure and run the training process."""
+ hparams = {
+ 'epochs': 1,
+ 'lr': 1e-5,
+ 'temperature': 0.05,
+ 'batch_size': 64,
+ 'max_length': 128,
+ 'save_path': "simson_checkpoints/simson_model_single_gpu.bin",
+ 'save_steps': 100_000,
+ 'max_embeddings': 512,
+ }
+
+ dataset = load_dataset('HoangHa/SMILES-250M')['train']
+ smiles_column_name = 'SMILES'
+
+ total_size = len(dataset)
+ test_size = int(0.1 * total_size)
+ val_size = int(0.1 * (total_size - test_size))
+
+ test_smiles = dataset.select(range(test_size))[smiles_column_name]
+ val_smiles = dataset.select(range(test_size, test_size + val_size))[smiles_column_name]
+ train_smiles = dataset.select(range(test_size + val_size, total_size))[smiles_column_name]
+ data_splits = (train_smiles, val_smiles, test_smiles)
+ tokenizer = AutoTokenizer.from_pretrained('DeepChem/ChemBERTa-77M-MTR')
+ model_config = BertConfig(
+ vocab_size=tokenizer.vocab_size, # Keep your optimal SMILES vocabulary
+ hidden_size=768, # 2x increase (768 → 1536)
+ num_hidden_layers=12, # ~1.67x increase (12 → 20)
+ num_attention_heads=12, # 2x increase (12 → 24)
+ intermediate_size=2048, # Traditional size (2048 → 4096)
+ max_position_embeddings=512
+ )
+ save_dir = os.path.dirname(hparams['save_path'])
+ if not os.path.exists(save_dir):
+ os.makedirs(save_dir)
+
+ # Directly call the training function for a single-GPU run
+ run_training(model_config, hparams, data_splits)
+
+if __name__ == '__main__':
+ main()
diff --git a/simson_modeling/upload_state_to_hf.py b/simson_modeling/upload_state_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb3b4950595fe7fdce983fc7e61a07622a406734
--- /dev/null
+++ b/simson_modeling/upload_state_to_hf.py
@@ -0,0 +1,23 @@
+from huggingface_hub import HfApi
+
+state_path = '/home/jovyan/simson_training_bolgov/simson_checkpoints_1M/checkpoint_best_model.bin'
+
+from huggingface_hub import HfApi
+api = HfApi()
+upload_folder = True
+if not upload_folder:
+
+ api.upload_file(
+ path_or_fileobj=state_path,
+ path_in_repo="polymer_1M_weights.bin",
+ repo_id="Defetya/simson_base",
+ repo_type="model",
+ )
+else:
+
+ api.upload_folder(
+ folder_path="/home/jovyan/simson_training_bolgov",
+ repo_id="Defetya/simson_base",
+ path_in_repo="simson_modeling",
+ repo_type="model",
+ )
\ No newline at end of file
diff --git a/simson_modeling/wandb/debug-cli.jovyan.log b/simson_modeling/wandb/debug-cli.jovyan.log
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/simson_modeling/wandb/debug-internal.log b/simson_modeling/wandb/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..b7659237ed755863e9db0cb5c82b0ee0a8cf7485
--- /dev/null
+++ b/simson_modeling/wandb/debug-internal.log
@@ -0,0 +1,8 @@
+{"time":"2025-08-07T16:59:40.510350375+03:00","level":"INFO","msg":"stream: starting","core version":"0.21.0"}
+{"time":"2025-08-07T16:59:40.65577314+03:00","level":"WARN","msg":"GraphQL client is nil, skipping feature loading"}
+{"time":"2025-08-07T16:59:40.657566864+03:00","level":"INFO","msg":"stream: created new stream","id":"vwaul17m"}
+{"time":"2025-08-07T16:59:40.657588242+03:00","level":"INFO","msg":"stream: started","id":"vwaul17m"}
+{"time":"2025-08-07T16:59:40.657610013+03:00","level":"INFO","msg":"writer: Do: started","stream_id":"vwaul17m"}
+{"time":"2025-08-07T16:59:40.657725995+03:00","level":"INFO","msg":"sender: started","stream_id":"vwaul17m"}
+{"time":"2025-08-07T16:59:40.657738214+03:00","level":"INFO","msg":"handler: started","stream_id":"vwaul17m"}
+{"time":"2025-08-07T16:59:40.658490076+03:00","level":"WARN","msg":"runupserter: server does not expand metric globs but the x_server_side_expand_glob_metrics setting is set; ignoring"}
diff --git a/simson_modeling/wandb/debug.log b/simson_modeling/wandb/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..4033ce3d81ef36475ab935afc9ed5c7b0048dfbb
--- /dev/null
+++ b/simson_modeling/wandb/debug.log
@@ -0,0 +1,23 @@
+2025-08-07 16:59:40,279 INFO MainThread:374223 [wandb_setup.py:_flush():80] Current SDK version is 0.21.0
+2025-08-07 16:59:40,279 INFO MainThread:374223 [wandb_setup.py:_flush():80] Configure stats pid to 374223
+2025-08-07 16:59:40,279 INFO MainThread:374223 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/.config/wandb/settings
+2025-08-07 16:59:40,279 INFO MainThread:374223 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/simson_training_bolgov/wandb/settings
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_setup.py:_flush():80] Loading settings from environment variables
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_init.py:setup_run_log_directory():703] Logging user logs to /home/jovyan/simson_training_bolgov/wandb/offline-run-20250807_165940-vwaul17m/logs/debug.log
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_init.py:setup_run_log_directory():704] Logging internal logs to /home/jovyan/simson_training_bolgov/wandb/offline-run-20250807_165940-vwaul17m/logs/debug-internal.log
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_init.py:init():830] calling init triggers
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
+config: {'_wandb': {}}
+2025-08-07 16:59:40,280 INFO MainThread:374223 [wandb_init.py:init():871] starting backend
+2025-08-07 16:59:40,497 INFO MainThread:374223 [wandb_init.py:init():874] sending inform_init request
+2025-08-07 16:59:40,504 INFO MainThread:374223 [wandb_init.py:init():882] backend started and connected
+2025-08-07 16:59:40,506 INFO MainThread:374223 [wandb_init.py:init():953] updated telemetry
+2025-08-07 16:59:40,507 INFO MainThread:374223 [wandb_init.py:init():977] communicating run to backend with 90.0 second timeout
+2025-08-07 16:59:40,659 INFO MainThread:374223 [wandb_init.py:init():1029] starting run threads in backend
+2025-08-07 16:59:40,949 INFO MainThread:374223 [wandb_run.py:_console_start():2458] atexit reg
+2025-08-07 16:59:40,949 INFO MainThread:374223 [wandb_run.py:_redirect():2306] redirect: wrap_raw
+2025-08-07 16:59:40,949 INFO MainThread:374223 [wandb_run.py:_redirect():2375] Wrapping output streams.
+2025-08-07 16:59:40,949 INFO MainThread:374223 [wandb_run.py:_redirect():2398] Redirects installed.
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diff --git a/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug-core.log b/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug-core.log
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diff --git a/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug-internal.log b/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug-internal.log
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diff --git a/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug.log b/simson_modeling/wandb/offline-run-20250807_164332-cd5u0onl/logs/debug.log
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diff --git a/simson_modeling/wandb/run-20250717_191323-m73t2s2o/files/output.log b/simson_modeling/wandb/run-20250717_191323-m73t2s2o/files/output.log
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+ ],
+ "cudaVersion": "12.2",
+ "writerId": "3uyzdob6btbafv1e2usuktdoq8vrzi05"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/.ipynb_checkpoints/wandb-summary-checkpoint.json b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/.ipynb_checkpoints/wandb-summary-checkpoint.json
new file mode 100644
index 0000000000000000000000000000000000000000..e952fba7dc9cf9f8506aa4a70d904e84fe0555cd
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/.ipynb_checkpoints/wandb-summary-checkpoint.json
@@ -0,0 +1 @@
+{"_runtime":116,"_step":25,"train_batch_loss":2.8073110580444336,"learning_rate":9.999999985363156e-05,"_timestamp":1.7527694946982894e+09,"_wandb":{"runtime":116}}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/config.yaml b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f4ce0134195a0fc0bc6e10c63c9e3dc9f4300163
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/config.yaml
@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ 3uyzdob6btbafv1e2usuktdoq8vrzi05:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136822915072"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T16:23:03.511455Z"
+ writerId: 3uyzdob6btbafv1e2usuktdoq8vrzi05
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "3":
+ - 1
+ - 13
+ - 16
+ "4": 3.12.11
+ "5": 0.21.0
+ "6": 4.53.2
+ "12": 0.21.0
+ "13": linux-x86_64
+batch_size:
+ value: 64
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 512
+save_path:
+ value: simson_checkpoints/simson_model.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/output.log b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..c546387cc1209e59b74263663bd458c5285dcea3
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/output.log
@@ -0,0 +1,22 @@
+Total number of parameters: 23 M
+Starting training on 1 GPUs...
+Epochs: 0%| | 0/1 [01:51, ?it/s]
+Exception ignored in:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1604, in __del__
+ self._shutdown_workers()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1568, in _shutdown_workers
+ w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 149, in join
+ res = self._popen.wait(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 40, in wait
+ if not wait([self.sentinel], timeout):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 1136, in wait
+ ready = selector.select(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/selectors.py", line 415, in select
+ fd_event_list = self._selector.poll(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt:
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/requirements.txt b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/requirements.txt
@@ -0,0 +1,87 @@
+Brotli==1.1.0
+certifi==2025.7.14
+charset-normalizer==3.4.2
+filelock==3.18.0
+gmpy2==2.2.1
+hpack==4.1.0
+hyperframe==6.1.0
+idna==3.10
+MarkupSafe==3.0.2
+mpmath==1.3.0
+networkx==3.5
+numpy==2.3.1
+pillow==11.3.0
+pycparser==2.22
+PySocks==1.7.1
+PyYAML==6.0.2
+setuptools==80.9.0
+typing_extensions==4.14.1
+wheel==0.45.1
+cffi==1.17.1
+h2==4.2.0
+Jinja2==3.1.6
+pip==25.1.1
+sympy==1.14.0
+zstandard==0.23.0
+urllib3==2.5.0
+requests==2.32.4
+torch==2.5.1
+torchaudio==2.5.1
+triton==3.1.0
+torchvision==0.20.1
+typing-inspection==0.4.1
+smmap==5.0.2
+sentry-sdk==2.33.0
+pydantic_core==2.33.2
+protobuf==6.31.1
+platformdirs==4.3.8
+packaging==25.0
+click==8.2.1
+annotated-types==0.7.0
+pydantic==2.11.7
+gitdb==4.0.12
+GitPython==3.1.44
+wandb==0.21.0
+tqdm==4.67.1
+safetensors==0.5.3
+regex==2024.11.6
+hf-xet==1.1.5
+huggingface-hub==0.33.4
+tokenizers==0.21.2
+transformers==4.53.2
+pytz==2025.2
+xxhash==3.5.0
+tzdata==2025.2
+six==1.17.0
+rdkit==2025.3.3
+pyarrow==20.0.0
+propcache==0.3.2
+multidict==6.6.3
+frozenlist==1.7.0
+fsspec==2025.3.0
+dill==0.3.8
+attrs==25.3.0
+aiohappyeyeballs==2.6.1
+yarl==1.20.1
+python-dateutil==2.9.0.post0
+multiprocess==0.70.16
+aiosignal==1.4.0
+pandas==2.3.1
+aiohttp==3.12.14
+datasets==4.0.0
+autocommand==2.2.2
+backports.tarfile==1.2.0
+importlib_metadata==8.0.0
+inflect==7.3.1
+jaraco.collections==5.1.0
+jaraco.context==5.3.0
+jaraco.functools==4.0.1
+jaraco.text==3.12.1
+more-itertools==10.3.0
+packaging==24.2
+platformdirs==4.2.2
+tomli==2.0.1
+typeguard==4.3.0
+typing_extensions==4.12.2
+wheel==0.45.1
+zipp==3.19.2
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a630a28fabb5a6a8c03f9652c4e69dbfc27fd3f8
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-metadata.json
@@ -0,0 +1,34 @@
+{
+ "os": "Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35",
+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T16:23:03.511455Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136822915072"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "3uyzdob6btbafv1e2usuktdoq8vrzi05"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e952fba7dc9cf9f8506aa4a70d904e84fe0555cd
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_runtime":116,"_step":25,"train_batch_loss":2.8073110580444336,"learning_rate":9.999999985363156e-05,"_timestamp":1.7527694946982894e+09,"_wandb":{"runtime":116}}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-core.log b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..6cac8a01df5442c0ce4c146f92b39199e2bf0f72
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-core.log
@@ -0,0 +1,14 @@
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+{"time":"2025-07-17T19:25:03.019731483+03:00","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
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diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..3a297f5fe217fd36e81bbdd0bd994b0a456287c0
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug-internal.log
@@ -0,0 +1,12 @@
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diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug.log b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..7576216534b5693e2875555ca3ef6c4f61756722
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/logs/debug.log
@@ -0,0 +1,24 @@
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_setup.py:_flush():80] Current SDK version is 0.21.0
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_setup.py:_flush():80] Configure stats pid to 1124
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/.config/wandb/settings
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/simson_training_bolgov/wandb/settings
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_setup.py:_flush():80] Loading settings from environment variables
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_init.py:setup_run_log_directory():703] Logging user logs to /home/jovyan/simson_training_bolgov/wandb/run-20250717_192303-kqui3lpi/logs/debug.log
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_init.py:setup_run_log_directory():704] Logging internal logs to /home/jovyan/simson_training_bolgov/wandb/run-20250717_192303-kqui3lpi/logs/debug-internal.log
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_init.py:init():830] calling init triggers
+2025-07-17 19:23:03,531 INFO MainThread:1124 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
+config: {'epochs': 1, 'lr': 0.0001, 'temperature': 0.2, 'batch_size': 64, 'max_length': 512, 'save_path': 'simson_checkpoints/simson_model.bin', 'save_steps': 2000, '_wandb': {}}
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diff --git a/simson_modeling/wandb/run-20250717_192303-kqui3lpi/run-kqui3lpi.wandb b/simson_modeling/wandb/run-20250717_192303-kqui3lpi/run-kqui3lpi.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..1f70fabf37ed94d2942e5a931e2a85ac41c9377f
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diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/config.yaml b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b2a8c567174697f37597da021e9072182e7b27a1
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/config.yaml
@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ zabpo5lelznex83zbe77nv4xz7zr8e0n:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136813342720"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T16:26:17.383723Z"
+ writerId: zabpo5lelznex83zbe77nv4xz7zr8e0n
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "3":
+ - 1
+ - 13
+ - 16
+ "4": 3.12.11
+ "5": 0.21.0
+ "6": 4.53.2
+ "12": 0.21.0
+ "13": linux-x86_64
+batch_size:
+ value: 64
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 512
+save_path:
+ value: simson_checkpoints/simson_model.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/output.log b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..0066f833ed3d30fe703c9ba222306360c955a941
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@@ -0,0 +1,4 @@
+Total number of parameters: 23 M
+Starting training on 1 GPUs...
+Epochs: 0%| | 0/1 [00:17, ?it/s]
+
diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/requirements.txt b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
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diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..4d137a9eb245100cbdc7338698f6df2b6b8fa298
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+++ b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/wandb-metadata.json
@@ -0,0 +1,34 @@
+{
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+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T16:26:17.383723Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136813342720"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "zabpo5lelznex83zbe77nv4xz7zr8e0n"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_192617-483ujs6r/files/wandb-summary.json
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diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_192617-483ujs6r/logs/debug-internal.log
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diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/logs/debug.log b/simson_modeling/wandb/run-20250717_192617-483ujs6r/logs/debug.log
new file mode 100644
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@@ -0,0 +1,24 @@
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+config: {'epochs': 1, 'lr': 0.0001, 'temperature': 0.2, 'batch_size': 64, 'max_length': 512, 'save_path': 'simson_checkpoints/simson_model.bin', 'save_steps': 2000, '_wandb': {}}
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diff --git a/simson_modeling/wandb/run-20250717_192617-483ujs6r/run-483ujs6r.wandb b/simson_modeling/wandb/run-20250717_192617-483ujs6r/run-483ujs6r.wandb
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diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/config.yaml b/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/config.yaml
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@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ g767130z37y3ffozxoggy8btxv0k7sq1:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136823709696"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T16:34:04.573972Z"
+ writerId: g767130z37y3ffozxoggy8btxv0k7sq1
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
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+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
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+batch_size:
+ value: 64
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 512
+save_path:
+ value: simson_checkpoints/simson_model.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/output.log b/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..6f7a5dfc5de626f4f81e18b0a6d523104d62d467
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@@ -0,0 +1,25 @@
+Total number of parameters: 23 M
+Starting training on 1 GPUs...
+Epochs: 0%| | 0/1 [00:00, ?it/s]Exception ignored in:
+Traceback (most recent call last): | 0/1067504 [00:00, ?it/s]
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1604, in __del__
+ self._shutdown_workers()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1568, in _shutdown_workers
+ w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 149, in join
+ res = self._popen.wait(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 40, in wait
+ if not wait([self.sentinel], timeout):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 1136, in wait
+ ready = selector.select(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/selectors.py", line 415, in select
+ fd_event_list = self._selector.poll(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/_utils/signal_handling.py", line 73, in handler
+ _error_if_any_worker_fails()
+RuntimeError: DataLoader worker (pid 1921) is killed by signal: Aborted.
+Epochs: 0%| | 0/1 [00:18, ?it/s]
+
diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/requirements.txt b/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/requirements.txt
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+platformdirs==4.2.2
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+zipp==3.19.2
diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..771b25c214a335d02056733ba35c986706d67814
--- /dev/null
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@@ -0,0 +1,34 @@
+{
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+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T16:34:04.573972Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136823709696"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "g767130z37y3ffozxoggy8btxv0k7sq1"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_193404-akhw42y4/files/wandb-summary.json
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug-core.log b/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug-core.log
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diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug-internal.log
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug.log b/simson_modeling/wandb/run-20250717_193404-akhw42y4/logs/debug.log
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193404-akhw42y4/run-akhw42y4.wandb b/simson_modeling/wandb/run-20250717_193404-akhw42y4/run-akhw42y4.wandb
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/config.yaml b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..03ad7f82b97b041bfccd9240d7d3257316b0bc9f
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/config.yaml
@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ 1l1kziel8578inzhvc2qcx4ykc77spd9:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136824913920"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T16:38:58.323180Z"
+ writerId: 1l1kziel8578inzhvc2qcx4ykc77spd9
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
+ - 11
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+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
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+ value: 64
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+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 512
+save_path:
+ value: simson_checkpoints/simson_model.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
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+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/output.log b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..96da0b06955631375ca9a6a504fca11e5994da42
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/output.log
@@ -0,0 +1,22 @@
+Total number of parameters: 23 M
+Starting training on 1 GPUs...
+Epochs: 0%| | 0/1 [02:05, ?it/s]
+Exception ignored in:
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1604, in __del__
+ self._shutdown_workers()
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1568, in _shutdown_workers
+ w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/process.py", line 149, in join
+ res = self._popen.wait(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/popen_fork.py", line 40, in wait
+ if not wait([self.sentinel], timeout):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/multiprocessing/connection.py", line 1136, in wait
+ ready = selector.select(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/selectors.py", line 415, in select
+ fd_event_list = self._selector.poll(timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt:
diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/requirements.txt b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..3d61107b17ef77eb7401b97175efc89ef1187e6a
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/wandb-metadata.json
@@ -0,0 +1,34 @@
+{
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+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T16:38:58.323180Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136824913920"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "1l1kziel8578inzhvc2qcx4ykc77spd9"
+}
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/files/wandb-summary.json
new file mode 100644
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@@ -0,0 +1 @@
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug-core.log b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug-core.log
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug-internal.log
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug.log b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/logs/debug.log
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_193858-cqv6aghf/run-cqv6aghf.wandb b/simson_modeling/wandb/run-20250717_193858-cqv6aghf/run-cqv6aghf.wandb
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/config.yaml b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/config.yaml
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@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ 9nkrnr7idk76ftj050sh8v2jbhzfczw6:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136773324800"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T20:26:26.020573Z"
+ writerId: 9nkrnr7idk76ftj050sh8v2jbhzfczw6
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
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+ - 51
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+batch_size:
+ value: 128
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 128
+save_path:
+ value: simson_checkpoints/simson_model_single_gpu.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/output.log b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..88948393948d637a39792cac2af36bbe1d5c67eb
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/output.log
@@ -0,0 +1,37 @@
+Total number of parameters: 23 M
+Starting training...
+Epochs: 0%| | 0/1 [05:40, ?it/s]
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 379, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 376, in main
+ run_training(model_config, hparams, data_splits)
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 300, in run_training
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 185, in train_epoch
+ for step, batch in enumerate(progress_bar, 1):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
+ data = self._next_data()
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1448, in _next_data
+ idx, data = self._get_data()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1402, in _get_data
+ success, data = self._try_get_data()
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1243, in _try_get_data
+ data = self._data_queue.get(timeout=timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/queue.py", line 180, in get
+ self.not_empty.wait(remaining)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 359, in wait
+ gotit = waiter.acquire(True, timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/requirements.txt b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
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@@ -0,0 +1,34 @@
+{
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+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T20:26:26.020573Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
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+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
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+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
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+}
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diff --git a/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/files/wandb-summary.json
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diff --git a/simson_modeling/wandb/run-20250717_232626-2o1r3b46/run-2o1r3b46.wandb b/simson_modeling/wandb/run-20250717_232626-2o1r3b46/run-2o1r3b46.wandb
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diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/config.yaml b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/config.yaml
new file mode 100644
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+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ 75fmi0pxqocultywke13rf3dqru5r62d:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136667938816"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T20:34:37.816008Z"
+ writerId: 75fmi0pxqocultywke13rf3dqru5r62d
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
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+ - 49
+ - 51
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+ - 1
+ - 11
+ - 41
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+ "13": linux-x86_64
+batch_size:
+ value: 128
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 128
+save_path:
+ value: simson_checkpoints/simson_model_single_gpu.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/output.log b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..27b5615cfba963875543d545735154d7bdbb9940
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/output.log
@@ -0,0 +1,37 @@
+Total number of parameters: 23 M
+Starting training...
+Epochs: 0%| | 0/1 [09:15, ?it/s]
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 380, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 377, in main
+ run_training(model_config, hparams, data_splits)
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 301, in run_training
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 185, in train_epoch
+ for step, batch in enumerate(progress_bar, 1):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
+ data = self._next_data()
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1448, in _next_data
+ idx, data = self._get_data()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1402, in _get_data
+ success, data = self._try_get_data()
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1243, in _try_get_data
+ data = self._data_queue.get(timeout=timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/queue.py", line 180, in get
+ self.not_empty.wait(remaining)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 359, in wait
+ gotit = waiter.acquire(True, timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/requirements.txt b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
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+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/requirements.txt
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+pydantic==2.11.7
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+aiohttp==3.12.14
+datasets==4.0.0
+autocommand==2.2.2
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+jaraco.text==3.12.1
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+tomli==2.0.1
+typeguard==4.3.0
+typing_extensions==4.12.2
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+zipp==3.19.2
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e8e52fe6ed289436211ebd7897eb921ce0c0e337
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@@ -0,0 +1,34 @@
+{
+ "os": "Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35",
+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T20:34:37.816008Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136667938816"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "75fmi0pxqocultywke13rf3dqru5r62d"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/wandb-summary.json
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index 0000000000000000000000000000000000000000..6746c2881779ef0ca15a15c947ab3c7d56036b3f
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+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/files/wandb-summary.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-core.log b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..7f56b08e54b920bf271332a8f2cc81c14fea1a46
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-core.log
@@ -0,0 +1,12 @@
+{"time":"2025-07-17T23:34:37.880236992+03:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmphhdhteyb/port-3066.txt","pid":3066,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2025-07-17T23:34:37.880812549+03:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":3066}
+{"time":"2025-07-17T23:34:37.880814977+03:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-3066-3089-3986368931/socket","Net":"unix"}}
+{"time":"2025-07-17T23:34:38.060483817+03:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2025-07-17T23:34:38.069237426+03:00","level":"INFO","msg":"handleInformInit: received","streamId":"v8x24r2k","id":"1(@)"}
+{"time":"2025-07-17T23:34:38.512434006+03:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"v8x24r2k","id":"1(@)"}
+{"time":"2025-07-17T23:43:57.5733725+03:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2025-07-17T23:43:57.5736861+03:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2025-07-17T23:43:57.573824688+03:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-3066-3089-3986368931/socket","Net":"unix"}}
+{"time":"2025-07-17T23:43:57.573671892+03:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2025-07-17T23:43:57.573881189+03:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2025-07-17T23:43:58.778487538+03:00","level":"INFO","msg":"server: parent process exited, terminating service process"}
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..d779235cb890fd66e57e35ac97e23f2c93a22820
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug-internal.log
@@ -0,0 +1,7 @@
+{"time":"2025-07-17T23:34:38.070307957+03:00","level":"INFO","msg":"stream: starting","core version":"0.21.0"}
+{"time":"2025-07-17T23:34:38.51238291+03:00","level":"INFO","msg":"stream: created new stream","id":"v8x24r2k"}
+{"time":"2025-07-17T23:34:38.512427691+03:00","level":"INFO","msg":"stream: started","id":"v8x24r2k"}
+{"time":"2025-07-17T23:34:38.512439114+03:00","level":"INFO","msg":"writer: Do: started","stream_id":"v8x24r2k"}
+{"time":"2025-07-17T23:34:38.512451388+03:00","level":"INFO","msg":"handler: started","stream_id":"v8x24r2k"}
+{"time":"2025-07-17T23:34:38.512503592+03:00","level":"INFO","msg":"sender: started","stream_id":"v8x24r2k"}
+{"time":"2025-07-17T23:43:57.573725541+03:00","level":"INFO","msg":"stream: closing","id":"v8x24r2k"}
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug.log b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..2bdcf5d5cf2c2f86f20be07889f11ac798060a21
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/logs/debug.log
@@ -0,0 +1,3868 @@
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_setup.py:_flush():80] Current SDK version is 0.21.0
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_setup.py:_flush():80] Configure stats pid to 3066
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/.config/wandb/settings
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/simson_training_bolgov/wandb/settings
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_setup.py:_flush():80] Loading settings from environment variables
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_init.py:setup_run_log_directory():703] Logging user logs to /home/jovyan/simson_training_bolgov/wandb/run-20250717_233437-v8x24r2k/logs/debug.log
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_init.py:setup_run_log_directory():704] Logging internal logs to /home/jovyan/simson_training_bolgov/wandb/run-20250717_233437-v8x24r2k/logs/debug-internal.log
+2025-07-17 23:34:37,841 INFO MainThread:3066 [wandb_init.py:init():830] calling init triggers
+2025-07-17 23:34:37,842 INFO MainThread:3066 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
+config: {'epochs': 1, 'lr': 0.0001, 'temperature': 0.2, 'batch_size': 128, 'max_length': 128, 'save_path': 'simson_checkpoints/simson_model_single_gpu.bin', 'save_steps': 2000, '_wandb': {}}
+2025-07-17 23:34:37,842 INFO MainThread:3066 [wandb_init.py:init():871] starting backend
+2025-07-17 23:34:38,060 INFO MainThread:3066 [wandb_init.py:init():874] sending inform_init request
+2025-07-17 23:34:38,067 INFO MainThread:3066 [wandb_init.py:init():882] backend started and connected
+2025-07-17 23:34:38,070 INFO MainThread:3066 [wandb_init.py:init():953] updated telemetry
+2025-07-17 23:34:38,070 INFO MainThread:3066 [wandb_init.py:init():977] communicating run to backend with 90.0 second timeout
+2025-07-17 23:34:38,864 INFO MainThread:3066 [wandb_init.py:init():1029] starting run threads in backend
+2025-07-17 23:34:39,030 INFO MainThread:3066 [wandb_run.py:_console_start():2458] atexit reg
+2025-07-17 23:34:39,030 INFO MainThread:3066 [wandb_run.py:_redirect():2306] redirect: wrap_raw
+2025-07-17 23:34:39,031 INFO MainThread:3066 [wandb_run.py:_redirect():2375] Wrapping output streams.
+2025-07-17 23:34:39,031 INFO MainThread:3066 [wandb_run.py:_redirect():2398] Redirects installed.
+2025-07-17 23:34:39,035 INFO MainThread:3066 [wandb_init.py:init():1075] run started, returning control to user process
+2025-07-17 23:34:41,011 INFO MainThread:3066 [wandb_run.py:_config_callback():1363] config_cb None None {'total_params_M': 23}
+2025-07-17 23:34:41,012 INFO MainThread:3066 [wandb_watch.py:_watch():70] Watching
+2025-07-17 23:43:57,572 INFO MsgRouterThr:3066 [mailbox.py:close():129] [no run ID] Closing mailbox, abandoning 1 handles.
+2025-07-17 23:43:58,080 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,088 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,089 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,089 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,090 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,091 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,092 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,093 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,094 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,094 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,095 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,096 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,097 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,097 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,098 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,099 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,100 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,100 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,101 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,101 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,102 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,103 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,104 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,104 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,105 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,105 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,106 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,107 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,107 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,108 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,108 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,109 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,109 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,110 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,110 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,111 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,112 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,112 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,113 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,113 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,114 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,114 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,115 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,117 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,118 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,118 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,119 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,119 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,120 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,121 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,121 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,122 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,123 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,123 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,124 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,124 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,125 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,125 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,126 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,127 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,127 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,128 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,128 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,129 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,129 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,130 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,130 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,131 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,131 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,132 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,132 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,133 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,133 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,134 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,134 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,163 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,164 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,164 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,165 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,168 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,169 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,170 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,170 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,171 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,171 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,172 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,172 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,173 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,174 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,174 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,175 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,176 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,176 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,177 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,177 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,178 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,178 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,179 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,179 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,180 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,180 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,181 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,181 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,182 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,182 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,183 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,183 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,184 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,185 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,185 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,186 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,186 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,187 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,187 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,188 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,188 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,189 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,189 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,190 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,190 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,191 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,191 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,193 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-17 23:43:58,194 ERROR MainThread:3066 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
diff --git a/simson_modeling/wandb/run-20250717_233437-v8x24r2k/run-v8x24r2k.wandb b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/run-v8x24r2k.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..06b109e14482f76d5a84fa3dcdea7b0db786529e
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_233437-v8x24r2k/run-v8x24r2k.wandb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bd8e85a4f016e88f11e2cc000d0859a64077fa15677b4f2011c95e9b426cbea
+size 3407872
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/config.yaml b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0d024941b3afd8062afdb4ecf0dc2d616ed85c81
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/config.yaml
@@ -0,0 +1,71 @@
+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ ijis7un3h44u6ngef1ym7hjlpjd4yygf:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136794472448"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T20:50:06.066176Z"
+ writerId: ijis7un3h44u6ngef1ym7hjlpjd4yygf
+ m: []
+ python_version: 3.12.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ "3":
+ - 1
+ - 13
+ - 16
+ "4": 3.12.11
+ "5": 0.21.0
+ "6": 4.53.2
+ "12": 0.21.0
+ "13": linux-x86_64
+batch_size:
+ value: 128
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 128
+save_path:
+ value: simson_checkpoints/simson_model_single_gpu.bin
+save_steps:
+ value: 2000
+temperature:
+ value: 0.2
+total_params_M:
+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/output.log b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..fe2af7b188ac890cfddfdb97b1f90f11b798bee3
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/output.log
@@ -0,0 +1,37 @@
+Total number of parameters: 23 M
+Starting training...
+Epochs: 0%| | 0/1 [02:15, ?it/s]
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 381, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 378, in main
+ run_training(model_config, hparams, data_splits)
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 302, in run_training
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 185, in train_epoch
+ for step, batch in enumerate(progress_bar, 1):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
+ data = self._next_data()
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1448, in _next_data
+ idx, data = self._get_data()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1402, in _get_data
+ success, data = self._try_get_data()
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1243, in _try_get_data
+ data = self._data_queue.get(timeout=timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/queue.py", line 180, in get
+ self.not_empty.wait(remaining)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 359, in wait
+ gotit = waiter.acquire(True, timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/requirements.txt b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/requirements.txt
@@ -0,0 +1,87 @@
+Brotli==1.1.0
+certifi==2025.7.14
+charset-normalizer==3.4.2
+filelock==3.18.0
+gmpy2==2.2.1
+hpack==4.1.0
+hyperframe==6.1.0
+idna==3.10
+MarkupSafe==3.0.2
+mpmath==1.3.0
+networkx==3.5
+numpy==2.3.1
+pillow==11.3.0
+pycparser==2.22
+PySocks==1.7.1
+PyYAML==6.0.2
+setuptools==80.9.0
+typing_extensions==4.14.1
+wheel==0.45.1
+cffi==1.17.1
+h2==4.2.0
+Jinja2==3.1.6
+pip==25.1.1
+sympy==1.14.0
+zstandard==0.23.0
+urllib3==2.5.0
+requests==2.32.4
+torch==2.5.1
+torchaudio==2.5.1
+triton==3.1.0
+torchvision==0.20.1
+typing-inspection==0.4.1
+smmap==5.0.2
+sentry-sdk==2.33.0
+pydantic_core==2.33.2
+protobuf==6.31.1
+platformdirs==4.3.8
+packaging==25.0
+click==8.2.1
+annotated-types==0.7.0
+pydantic==2.11.7
+gitdb==4.0.12
+GitPython==3.1.44
+wandb==0.21.0
+tqdm==4.67.1
+safetensors==0.5.3
+regex==2024.11.6
+hf-xet==1.1.5
+huggingface-hub==0.33.4
+tokenizers==0.21.2
+transformers==4.53.2
+pytz==2025.2
+xxhash==3.5.0
+tzdata==2025.2
+six==1.17.0
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+frozenlist==1.7.0
+fsspec==2025.3.0
+dill==0.3.8
+attrs==25.3.0
+aiohappyeyeballs==2.6.1
+yarl==1.20.1
+python-dateutil==2.9.0.post0
+multiprocess==0.70.16
+aiosignal==1.4.0
+pandas==2.3.1
+aiohttp==3.12.14
+datasets==4.0.0
+autocommand==2.2.2
+backports.tarfile==1.2.0
+importlib_metadata==8.0.0
+inflect==7.3.1
+jaraco.collections==5.1.0
+jaraco.context==5.3.0
+jaraco.functools==4.0.1
+jaraco.text==3.12.1
+more-itertools==10.3.0
+packaging==24.2
+platformdirs==4.2.2
+tomli==2.0.1
+typeguard==4.3.0
+typing_extensions==4.12.2
+wheel==0.45.1
+zipp==3.19.2
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a034aa207e147be1aaa3e2faf583ef4d081e3d2d
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/wandb-metadata.json
@@ -0,0 +1,34 @@
+{
+ "os": "Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35",
+ "python": "CPython 3.12.11",
+ "startedAt": "2025-07-17T20:50:06.066176Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
+ "cpu_count": 64,
+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
+ "total": "3148559777792",
+ "used": "136794472448"
+ }
+ },
+ "memory": {
+ "total": "2164135477248"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "ijis7un3h44u6ngef1ym7hjlpjd4yygf"
+}
\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/wandb-summary.json b/simson_modeling/wandb/run-20250717_235006-x4au33ay/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c8744d33db439aee9b5390964b7081b49138530
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\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-core.log b/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..d54930af40900ed8f3818763100243d7fb5e17b2
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-core.log
@@ -0,0 +1,14 @@
+{"time":"2025-07-17T23:50:06.125828962+03:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmptiwaai81/port-8802.txt","pid":8802,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2025-07-17T23:50:06.126352548+03:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":8802}
+{"time":"2025-07-17T23:50:06.126340888+03:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-8802-8825-153448745/socket","Net":"unix"}}
+{"time":"2025-07-17T23:50:06.30747654+03:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2025-07-17T23:50:06.316567657+03:00","level":"INFO","msg":"handleInformInit: received","streamId":"x4au33ay","id":"1(@)"}
+{"time":"2025-07-17T23:50:06.751559569+03:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"x4au33ay","id":"1(@)"}
+{"time":"2025-07-17T23:52:24.521549508+03:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2025-07-17T23:52:24.521897578+03:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2025-07-17T23:52:24.522005742+03:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-8802-8825-153448745/socket","Net":"unix"}}
+{"time":"2025-07-17T23:52:24.521886743+03:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2025-07-17T23:52:24.522046699+03:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2025-07-17T23:52:27.287523645+03:00","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
+{"time":"2025-07-17T23:52:27.287536986+03:00","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"}
+{"time":"2025-07-17T23:52:27.287546285+03:00","level":"INFO","msg":"server is closed"}
diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..1703c2809dacbcda61cb00b7deba079eb0768fd4
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235006-x4au33ay/logs/debug-internal.log
@@ -0,0 +1,12 @@
+{"time":"2025-07-17T23:50:06.317549684+03:00","level":"INFO","msg":"stream: starting","core version":"0.21.0"}
+{"time":"2025-07-17T23:50:06.7515183+03:00","level":"INFO","msg":"stream: created new stream","id":"x4au33ay"}
+{"time":"2025-07-17T23:50:06.751553507+03:00","level":"INFO","msg":"stream: started","id":"x4au33ay"}
+{"time":"2025-07-17T23:50:06.751566618+03:00","level":"INFO","msg":"writer: Do: started","stream_id":"x4au33ay"}
+{"time":"2025-07-17T23:50:06.751614331+03:00","level":"INFO","msg":"sender: started","stream_id":"x4au33ay"}
+{"time":"2025-07-17T23:50:06.751574681+03:00","level":"INFO","msg":"handler: started","stream_id":"x4au33ay"}
+{"time":"2025-07-17T23:52:24.521848389+03:00","level":"INFO","msg":"stream: closing","id":"x4au33ay"}
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+2025-07-17 23:50:06,090 INFO MainThread:8802 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
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diff --git a/simson_modeling/wandb/run-20250717_235006-x4au33ay/run-x4au33ay.wandb b/simson_modeling/wandb/run-20250717_235006-x4au33ay/run-x4au33ay.wandb
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+_wandb:
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+ e:
+ w7qyutziq4pu56qztqk6e24oc5y9xo1c:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136784891904"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T20:53:46.630759Z"
+ writerId: w7qyutziq4pu56qztqk6e24oc5y9xo1c
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+ value: 128
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+ value: 128
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+ value: 2000
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+ value: 23
diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/output.log b/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..3a2d156b62a8e4c0bd330b8a769b29e34363ee3c
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+Total number of parameters: 23 M
+Starting training...
+Epochs: 0%| | 0/1 [06:47, ?it/s]
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 381, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 378, in main
+ run_training(model_config, hparams, data_splits)
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 302, in run_training
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 185, in train_epoch
+ for step, batch in enumerate(progress_bar, 1):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
+ data = self._next_data()
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1448, in _next_data
+ idx, data = self._get_data()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1402, in _get_data
+ success, data = self._try_get_data()
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1243, in _try_get_data
+ data = self._data_queue.get(timeout=timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/queue.py", line 180, in get
+ self.not_empty.wait(remaining)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 359, in wait
+ gotit = waiter.acquire(True, timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/requirements.txt b/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/requirements.txt
new file mode 100644
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diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/wandb-metadata.json b/simson_modeling/wandb/run-20250717_235346-1bpueejc/files/wandb-metadata.json
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+ "startedAt": "2025-07-17T20:53:46.630759Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
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+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
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+ }
+ },
+ "memory": {
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+ },
+ "gpu_nvidia": [
+ {
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+ }
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+}
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\ No newline at end of file
diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-core.log b/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..0f7db19c6351aee8b2a4fe91253ad628d8525dd8
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-core.log
@@ -0,0 +1,14 @@
+{"time":"2025-07-17T23:53:46.683529754+03:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmps1k74m18/port-9304.txt","pid":9304,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2025-07-17T23:53:46.684091171+03:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":9304}
+{"time":"2025-07-17T23:53:46.684094496+03:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-9304-9327-3088094566/socket","Net":"unix"}}
+{"time":"2025-07-17T23:53:46.865447445+03:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2025-07-17T23:53:46.874510183+03:00","level":"INFO","msg":"handleInformInit: received","streamId":"1bpueejc","id":"1(@)"}
+{"time":"2025-07-17T23:53:47.302436593+03:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"1bpueejc","id":"1(@)"}
+{"time":"2025-07-18T00:00:37.086174304+03:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2025-07-18T00:00:37.086588559+03:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2025-07-18T00:00:37.086685588+03:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2025-07-18T00:00:37.086694153+03:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2025-07-18T00:00:37.086842806+03:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-9304-9327-3088094566/socket","Net":"unix"}}
+{"time":"2025-07-18T00:00:38.883564736+03:00","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
+{"time":"2025-07-18T00:00:38.883583028+03:00","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"}
+{"time":"2025-07-18T00:00:38.883597588+03:00","level":"INFO","msg":"server is closed"}
diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-internal.log b/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..4d5e4ea7c8ce787bc1fc82475a1769a77240af9a
--- /dev/null
+++ b/simson_modeling/wandb/run-20250717_235346-1bpueejc/logs/debug-internal.log
@@ -0,0 +1,12 @@
+{"time":"2025-07-17T23:53:46.875448441+03:00","level":"INFO","msg":"stream: starting","core version":"0.21.0"}
+{"time":"2025-07-17T23:53:47.302399607+03:00","level":"INFO","msg":"stream: created new stream","id":"1bpueejc"}
+{"time":"2025-07-17T23:53:47.302430738+03:00","level":"INFO","msg":"stream: started","id":"1bpueejc"}
+{"time":"2025-07-17T23:53:47.302442952+03:00","level":"INFO","msg":"writer: Do: started","stream_id":"1bpueejc"}
+{"time":"2025-07-17T23:53:47.302450431+03:00","level":"INFO","msg":"sender: started","stream_id":"1bpueejc"}
+{"time":"2025-07-17T23:53:47.302499554+03:00","level":"INFO","msg":"handler: started","stream_id":"1bpueejc"}
+{"time":"2025-07-18T00:00:37.086563651+03:00","level":"INFO","msg":"stream: closing","id":"1bpueejc"}
+{"time":"2025-07-18T00:00:38.302034627+03:00","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
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+{"time":"2025-07-18T00:00:38.88251493+03:00","level":"INFO","msg":"writer: Close: closed","stream_id":"1bpueejc"}
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+2025-07-17 23:53:46,649 INFO MainThread:9304 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
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diff --git a/simson_modeling/wandb/run-20250717_235346-1bpueejc/run-1bpueejc.wandb b/simson_modeling/wandb/run-20250717_235346-1bpueejc/run-1bpueejc.wandb
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diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/config.yaml b/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/config.yaml
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+_wandb:
+ value:
+ cli_version: 0.21.0
+ e:
+ 4ob1ui8botljirp0leu7nkcwavph7zo3:
+ cpu_count: 64
+ cpu_count_logical: 128
+ cudaVersion: "12.2"
+ disk:
+ /:
+ total: "3148559777792"
+ used: "136687747072"
+ email: bossprocool@gmail.com
+ executable: /home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python
+ gpu: NVIDIA A100-SXM4-80GB
+ gpu_count: 1
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "85899345920"
+ name: NVIDIA A100-SXM4-80GB
+ uuid: GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947
+ host: korotkov-research-polymers-1gpu-0
+ memory:
+ total: "2164135477248"
+ os: Linux-5.15.0-1044-nvidia-x86_64-with-glibc2.35
+ program: -m simson_ddp_train
+ python: CPython 3.12.11
+ root: /home/jovyan/simson_training_bolgov
+ startedAt: "2025-07-17T21:01:37.928552Z"
+ writerId: 4ob1ui8botljirp0leu7nkcwavph7zo3
+ m: []
+ python_version: 3.12.11
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+batch_size:
+ value: 8
+epochs:
+ value: 1
+lr:
+ value: 0.0001
+max_length:
+ value: 128
+save_path:
+ value: simson_checkpoints/simson_model_single_gpu.bin
+save_steps:
+ value: 2000
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+ value: 0.2
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+ value: 23
diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/output.log b/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..68899c1ade8b1e3a0db7cac514bd95b5df751061
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+Total number of parameters: 23 M
+Starting training...
+Epochs: 0%| | 0/1 [00:53, ?it/s]
+Traceback (most recent call last):
+ File "", line 198, in _run_module_as_main
+ File "", line 88, in _run_code
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 381, in
+ main()
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 378, in main
+ run_training(model_config, hparams, data_splits)
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 302, in run_training
+ train_loss = train_epoch(model, train_loader, optimizer, criterion, device, scheduler, hparams['save_path'], hparams['save_steps'])
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/simson_training_bolgov/simson_ddp_train.py", line 185, in train_epoch
+ for step, batch in enumerate(progress_bar, 1):
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/tqdm/std.py", line 1181, in __iter__
+ for obj in iterable:
+ ^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
+ data = self._next_data()
+ ^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1448, in _next_data
+ idx, data = self._get_data()
+ ^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1402, in _get_data
+ success, data = self._try_get_data()
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1243, in _try_get_data
+ data = self._data_queue.get(timeout=timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/queue.py", line 180, in get
+ self.not_empty.wait(remaining)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/threading.py", line 359, in wait
+ gotit = waiter.acquire(True, timeout)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/requirements.txt b/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8686d3b566643e5072febd4723a60e8607642c46
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diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/wandb-metadata.json b/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/wandb-metadata.json
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@@ -0,0 +1,34 @@
+{
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+ "startedAt": "2025-07-17T21:01:37.928552Z",
+ "program": "-m simson_ddp_train",
+ "email": "bossprocool@gmail.com",
+ "root": "/home/jovyan/simson_training_bolgov",
+ "host": "korotkov-research-polymers-1gpu-0",
+ "executable": "/home/jovyan/.mlspace/envs/bolgov_simson_training/bin/python",
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+ "cpu_count_logical": 128,
+ "gpu": "NVIDIA A100-SXM4-80GB",
+ "gpu_count": 1,
+ "disk": {
+ "/": {
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+ "used": "136687747072"
+ }
+ },
+ "memory": {
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+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-80GB",
+ "memoryTotal": "85899345920",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-b9407cb6-2e4b-5860-db5b-42a7982d8947"
+ }
+ ],
+ "cudaVersion": "12.2",
+ "writerId": "4ob1ui8botljirp0leu7nkcwavph7zo3"
+}
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diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/wandb-summary.json b/simson_modeling/wandb/run-20250718_000137-paiph8s9/files/wandb-summary.json
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diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-core.log b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc2194e63cda8ffa7bfbd29eff079d36d22f1f38
--- /dev/null
+++ b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-core.log
@@ -0,0 +1,14 @@
+{"time":"2025-07-18T00:01:37.976281479+03:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpudpgtwrx/port-9806.txt","pid":9806,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2025-07-18T00:01:37.976852394+03:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":9806}
+{"time":"2025-07-18T00:01:37.976841472+03:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-9806-9829-1657144359/socket","Net":"unix"}}
+{"time":"2025-07-18T00:01:38.156890935+03:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2025-07-18T00:01:38.166256962+03:00","level":"INFO","msg":"handleInformInit: received","streamId":"paiph8s9","id":"1(@)"}
+{"time":"2025-07-18T00:01:38.605225651+03:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"paiph8s9","id":"1(@)"}
+{"time":"2025-07-18T00:02:34.166601917+03:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2025-07-18T00:02:34.166926413+03:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2025-07-18T00:02:34.166944686+03:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2025-07-18T00:02:34.16700097+03:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2025-07-18T00:02:34.167077506+03:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-9806-9829-1657144359/socket","Net":"unix"}}
+{"time":"2025-07-18T00:02:36.062228915+03:00","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
+{"time":"2025-07-18T00:02:36.062247742+03:00","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"}
+{"time":"2025-07-18T00:02:36.062261052+03:00","level":"INFO","msg":"server is closed"}
diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-internal.log b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..df827c2b46c1061ac82708bcc5aa99957c110821
--- /dev/null
+++ b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug-internal.log
@@ -0,0 +1,12 @@
+{"time":"2025-07-18T00:01:38.167078564+03:00","level":"INFO","msg":"stream: starting","core version":"0.21.0"}
+{"time":"2025-07-18T00:01:38.605189815+03:00","level":"INFO","msg":"stream: created new stream","id":"paiph8s9"}
+{"time":"2025-07-18T00:01:38.605219316+03:00","level":"INFO","msg":"stream: started","id":"paiph8s9"}
+{"time":"2025-07-18T00:01:38.605350006+03:00","level":"INFO","msg":"writer: Do: started","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:01:38.605355082+03:00","level":"INFO","msg":"sender: started","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:01:38.605363832+03:00","level":"INFO","msg":"handler: started","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:02:34.166967585+03:00","level":"INFO","msg":"stream: closing","id":"paiph8s9"}
+{"time":"2025-07-18T00:02:35.219441751+03:00","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
+{"time":"2025-07-18T00:02:36.058133014+03:00","level":"INFO","msg":"handler: closed","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:02:36.058174506+03:00","level":"INFO","msg":"writer: Close: closed","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:02:36.058208426+03:00","level":"INFO","msg":"sender: closed","stream_id":"paiph8s9"}
+{"time":"2025-07-18T00:02:36.061873999+03:00","level":"INFO","msg":"stream: closed","id":"paiph8s9"}
diff --git a/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug.log b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..6246173577dc54fad4d2b1021d51a1a8e5e6dc27
--- /dev/null
+++ b/simson_modeling/wandb/run-20250718_000137-paiph8s9/logs/debug.log
@@ -0,0 +1,6782 @@
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_setup.py:_flush():80] Current SDK version is 0.21.0
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_setup.py:_flush():80] Configure stats pid to 9806
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/.config/wandb/settings
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_setup.py:_flush():80] Loading settings from /home/jovyan/simson_training_bolgov/wandb/settings
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_setup.py:_flush():80] Loading settings from environment variables
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_init.py:setup_run_log_directory():703] Logging user logs to /home/jovyan/simson_training_bolgov/wandb/run-20250718_000137-paiph8s9/logs/debug.log
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_init.py:setup_run_log_directory():704] Logging internal logs to /home/jovyan/simson_training_bolgov/wandb/run-20250718_000137-paiph8s9/logs/debug-internal.log
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_init.py:init():830] calling init triggers
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_init.py:init():835] wandb.init called with sweep_config: {}
+config: {'epochs': 1, 'lr': 0.0001, 'temperature': 0.2, 'batch_size': 8, 'max_length': 128, 'save_path': 'simson_checkpoints/simson_model_single_gpu.bin', 'save_steps': 2000, '_wandb': {}}
+2025-07-18 00:01:37,939 INFO MainThread:9806 [wandb_init.py:init():871] starting backend
+2025-07-18 00:01:38,157 INFO MainThread:9806 [wandb_init.py:init():874] sending inform_init request
+2025-07-18 00:01:38,163 INFO MainThread:9806 [wandb_init.py:init():882] backend started and connected
+2025-07-18 00:01:38,166 INFO MainThread:9806 [wandb_init.py:init():953] updated telemetry
+2025-07-18 00:01:38,166 INFO MainThread:9806 [wandb_init.py:init():977] communicating run to backend with 90.0 second timeout
+2025-07-18 00:01:38,926 INFO MainThread:9806 [wandb_init.py:init():1029] starting run threads in backend
+2025-07-18 00:01:39,093 INFO MainThread:9806 [wandb_run.py:_console_start():2458] atexit reg
+2025-07-18 00:01:39,093 INFO MainThread:9806 [wandb_run.py:_redirect():2306] redirect: wrap_raw
+2025-07-18 00:01:39,094 INFO MainThread:9806 [wandb_run.py:_redirect():2375] Wrapping output streams.
+2025-07-18 00:01:39,094 INFO MainThread:9806 [wandb_run.py:_redirect():2398] Redirects installed.
+2025-07-18 00:01:39,098 INFO MainThread:9806 [wandb_init.py:init():1075] run started, returning control to user process
+2025-07-18 00:01:40,055 INFO MainThread:9806 [wandb_run.py:_config_callback():1363] config_cb None None {'total_params_M': 23}
+2025-07-18 00:01:40,056 INFO MainThread:9806 [wandb_watch.py:_watch():70] Watching
+2025-07-18 00:02:34,165 INFO MsgRouterThr:9806 [mailbox.py:close():129] [no run ID] Closing mailbox, abandoning 1 handles.
+2025-07-18 00:02:35,187 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,194 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,194 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,195 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,196 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,196 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,198 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,198 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,199 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,200 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,201 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,201 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,202 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,203 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,203 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,204 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,204 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,205 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,205 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,206 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,206 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,207 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,207 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,208 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,208 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,209 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,209 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,210 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,210 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,211 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,211 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,212 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,212 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,213 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,214 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,214 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,215 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,215 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,216 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,216 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,217 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,217 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,218 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,220 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,220 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,221 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,221 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,222 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,222 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,223 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,224 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,224 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,225 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,225 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,226 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,226 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,227 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,227 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,228 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,228 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,229 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,229 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,230 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,230 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,231 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,231 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,232 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,232 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,233 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,233 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,234 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,234 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,235 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,235 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in
+ lambda data: self._console_raw_callback("stderr", data),
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 398, in wrapper
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 456, in wrapper_fn
+ return func(self, *args, **kwargs)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 1566, in _console_raw_callback
+ self._backend.interface.publish_output_raw(name, data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface.py", line 771, in publish_output_raw
+ self._publish_output_raw(o)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_shared.py", line 38, in _publish_output_raw
+ self._publish(rec)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 39, in _publish
+ self._sock_client.send_record_publish(record)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 170, in send_record_publish
+ self.send_server_request(server_req)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_server_request
+ self._send_message(msg)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 147, in _send_message
+ self._sendall_with_error_handle(header + data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/sock_client.py", line 126, in _sendall_with_error_handle
+ sent = self._sock.send(data)
+ ^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+2025-07-18 00:02:35,236 ERROR MainThread:9806 [redirect.py:_on_write():664] [all runs] error in stderr callback
+Traceback (most recent call last):
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 662, in _on_write
+ cb(written_data)
+ File "/home/jovyan/.mlspace/envs/bolgov_simson_training/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2385, in