README.md

metadata

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

@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},
}