Update README.md

README.md

@@ -12,37 +12,32 @@ tags:
 - molecular-generation
 pretty_name: Curated Chemical Structures & Properties
 size_categories:
-- 1K<n<10K
+- 1M<n<10M
 ---
 
-🧪 Dataset Summary
+⚗️ **Dataset Summary**
 
-The Curated Chemical Structures & Properties dataset is a 5,000-compound resource for cheminformatics and AI-driven drug discovery. It provides standardized molecular representations (Canonical SMILES, Isometric SMILES, InChI, InChIKey) alongside computed physicochemical properties and hierarchical ClassyFire taxonomic classifications.
+A large-scale chemical structure database with 1 million compounds, providing standardized cheminformatics identifiers and taxonomic classification. Designed for structure-based drug discovery, similarity search, and chemical space analysis.
 
-Ideal for training molecular generative models, predicting ADMET properties, or building structure-activity relationship (SAR) models.
+**🚀 Key Features**
 
-🚀 Key Features
-
-•
-Standardized Representations: Includes canonical_smiles, isometric_smiles, and inchi_key for robust molecular matching.
-
-•
-Rich Physicochemical Features: Pre-computed properties including Molecular Weight (mwt), LogP (xlogp3), Hydrogen Bond Donors (hbd), and Acceptors (hba).
-
-•
-Deep Taxonomic Classification: Hierarchical ClassyFire annotations (Kingdom, Superclass, Class, Direct Parent) for chemical space analysis.
-
-💻 Quick Start & MCP Integration
-
-Integrate chemical structure search into your LLM workflows using our Chemical Structure MCP Tool on GitHub.
+- **Standard Identifiers:** Every record includes InChI, InChIKey, and molecular formula; isomeric SMILES present for 99.8% of records.
+- **Multiple Name Forms:** JChem-generated names (97%), IUPAC names (22%), and compound names where available.
+- **ClassyFire Taxonomy:** Hierarchical chemical classification for ~11% of records, enabling class-level filtering.
+- **Patent Linkage:** `patent_count` connects compounds to their IP landscape.
 
+**💻 Quick Start**
 
 ```python
 from datasets import load_dataset
 
 dataset = load_dataset("your-org/chemical-structures", split="train")
-print(dataset[0]["canonical_smiles"])
-# Output: 'CC1(C2=C(C=CC3=CC=CC=C32)C4=C1C(=NC=C4)C5=CC=CC=[C-]5)C'
+print(dataset[0]["inchi_key"])
+# Output: 'AAACIFUGYABHIU-UHFFFAOYSA-M'
+print(dataset[0]["isometric_smiles"])
+# Output: '[O-][Cl](=O)(=O)=O.[I-][Pt++]12[NH2]CC[NH]1CC[NH2]2'
+print(dataset[0]["mol_formula"])
+# Output: 'C4H13ClIN3O4Pt'
 ```
 
 ## 🔗 Resources