Upload README.md

README.md

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+---
+license: mit
+---
+
+## 1. Model Overview
+- **Model Name:** MMPT-FM (Matched Molecular Pair Transformation Foundation Model)
+- **Summary:** MMPT-FM is a transformation-centric generative foundation model designed to support medicinal chemistry analog design. The model learns from matched molecular pair transformations (MMPTs), i.e., context-independent variable-to-variable chemical modifications derived from large-scale matched molecular pair data. This formulation enables scalable, interpretable, and generalizable encoding of medicinal chemistry intuition across diverse chemical series.
+- **Model Specification:** Encoder–decoder Transformer. 220M parameters.
+- **Developed by:** Merck & Co., Inc. (Rahway, NJ, USA) and Emory University.
+- **License:** MIT license.
+- **Base Model:** ChemT5 (chemistry-domain pretrained T5).
+- **Model Type:** Transformer
+- **Languages:** SMARTS (chemical substructure representation)
+- **Pipeline Tag:** text2text-generation for MMP transformation
+- **Library:** Transformers, PyTorch
+
+## 2. Intended Use
+- **Direct Use:**
+  - Generation of chemically valid **matched molecular pair transformations (MMPTs)**.
+  - Analog design at a **user-specified edit site** (R-group substitution or core hopping)
+- **Downstream Use:**
+  - Integration into analog enumeration pipelines
+  - Retrieval-augmented generation (MMPT-RAG) to bias suggestions toward project- or series-specific chemistry
+
+## 3. Bias, Risks, and Limitations
+- **Known Limitations:** The model relies on the availability and coverage of large historical transformation datasets, and its performance may vary in underrepresented chemical domains.
+- **Biases:** Inherits biases from ChEMBL-derived medicinal chemistry literature.
+- **Risk Areas:** Our framework is intended for research use, and does not introduce specific ethical concerns.
+
+## 4. Training Details
+- **Training Data:** Raw data is downloaded from ChEMBL database and available at https://chembl.gitbook.io/chembl-interface-documentation/downloads.
+- **Training Data Preprocessing:**
+  - Drug-likeness filtering using *rule_of_druglike_soft*
+  - Molecular weight ≥ 200 Da
+  - Removal of structural alerts using the curated Walters alert list
+  - Data is processed with MMPDB that is available at https://github.com/rdkit/mmpdb.
+- **Pre-Training:** Base model ChemT5 is available at https://github.com/GT4SD/multitask_text_and_chemistry_t5.
+- **Training Procedure:**
+  - Supervised sequence-to-sequence learning with teacher forcing
+  - Cross-entropy loss
+  - Batch size: 64
+  - Learning rate: 5 × 10⁻⁴
+  - Hardware:
+    - MMPT-FM: 4 × NVIDIA A6000 GPUs
+    - MMP-based baselines: 4 × NVIDIA H100 GPUs
+
+## 5. Evaluation
+- **Metrics:**
+  - Validity
+  - Novelty (Novel/valid, Novel/all)
+  - Recall (overall, in-training, out-of-training)
+- **Benchmarks:**
+  - Held-out ChEMBL MMPT test set (in-distribution)
+  - Within-patent analog generation (PMV17)
+  - Cross-patent analog generation (PMV17 → PMV21)
+- **Testing Data:** Patent-derived datasets from PMV Pharmaceuticals (2017, 2021)
+
+## 6. Usage
+- **Sample Inference Code:** Described conceptually in the publications ; code corresponds to variable-to-variable generation with beam search can be found at our GitHub repository: https://github.com/MSDLLCpapers/MMPTTransformer.
+
+## 7. Citation
+```bibtex
+@misc{pan2026retrievalaugmentedfoundationmodelsmatched,
+      title={Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition}, 
+      author={Bo Pan and Peter Zhiping Zhang and Hao-Wei Pang and Alex Zhu and Xiang Yu and Liying Zhang and Liang Zhao},
+      year={2026},
+      eprint={2602.16684},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.16684}, 
+}
+@article{
+doi:10.26434/chemrxiv.15001722/v1,
+author = {Hao-Wei Pang  and Peter Zhiping Zhang  and Bo Pan  and Liang Zhao  and Xiang Yu  and Liying Zhang },
+title = {Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations},
+journal = {ChemRxiv},
+volume = {2026},
+number = {0407},
+pages = {},
+year = {2026},
+doi = {10.26434/chemrxiv.15001722/v1},
+URL = {https://chemrxiv.org/doi/abs/10.26434/chemrxiv.15001722/v1},
+eprint = {https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15001722/v1},
+}