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----
-license: mit
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-## 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},
-}