README.md

metadata

license: mit

1. Model Overview

• Model Name: MMPT-FM & its MMP variants
• Summary: MMPT-FM (Matched Molecular Pair Transformation Foundation Model) and its MMP (Matched Molecular Pair) variants – MMP-M2M (molecule-to-molecule), MMP-M2T (molecule-to-transformation), MMP-C2V (constant-to-variable) – are generative foundation model designed to support medicinal chemistry analog design. The model learns from matched molecular pair transformations (MMPTs) or MMPs, i.e., context-independent variable-to-variable chemical modifications or matched molecular pairs 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 for each model.
• 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 & SMILES (chemical substructure representation)
• Pipeline Tag: text2text-generation for MMP transformation
• Library: Transformers, PyTorch

2. Intended Use

• Direct Use:
  • MMPT-FM:
    • Generation of chemically valid matched molecular pair transformations (MMPTs)
    • Analog design at a user-specified edit site.
  • MMP-M2M:
    • Generation of chemically valid matched molecular pairs (MMPs)
  • MMP-M2T:
    • Generation of chemically valid matched molecular pair transformations
    • Analog design at a user-specified edit site
  • MMP-C2V:
    • Analog design at a user-specified edit site
• Downstream Use:
  • MMPT-FM:
    • Integration into analog enumeration pipelines
    • Integration into high-throughput virtual screening pipelines
    • Serve as the base model for retrieval-augmented generation (MMPT-RAG).
  • MMP-M2M:
    • Integration into analog enumeration pipelines
    • Integration into high-throughput virtual screening pipelines
  • MMP-M2T:
    • Integration into analog enumeration pipelines
    • Integration into high-throughput virtual screening pipelines
  • MMP-C2V:
    • Integration into analog enumeration pipelines
    • Integration into high-throughput virtual screening pipelines

3. Bias, Risks, and Limitations

• Known Limitations: The models rely 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.
• Recommendations: None

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
  • Cross-entropy loss
  • Batch size: 64
  • Learning rate: 5 × 10⁻⁴
  • Hardware:
    • MMPT-FM: 4 × NVIDIA A6000 GPUs
    • MMP variants: 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.
• GitHub Links: https://github.com/MSDLLCpapers/MMPTTransformer

7. Citation

BibTeX:

@article{pang2026scalable,
  title={Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations},
  author={Pang, Hao-Wei and Zhang, Peter Zhiping and Pan, Bo and Zhao, Liang and Yu, Xiang and Zhang, Liying},
  journal={ChemRxiv},
  doi={10.26434/chemrxiv.15001722},
  year={2026}
}

@article{pan2026retrieval,
  title={Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition},
  author={Pan, Bo and Zhang, Peter Zhiping and Pang, Hao-Wei and Zhu, Alex and Yu, Xiang and Zhang, Liying and Zhao, Liang},
  journal={arXiv preprint arXiv:2602.16684},
  year={2026}
}

@article{pan2026transformer,
  title={Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds},
  author={Pan, Bo and Zhang, Zhiping and Spiekermann, Kevin and Chen, Tianchi and Yu, Xiang and Zhang, Liying and Zhao, Liang},
  journal={arXiv preprint arXiv:2601.07930},
  year={2026}
}