MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design
TL;DR
Findings indicate that MMP-derived chemical knowledge can improve the focus and reproducibility of ligand-based molecular generation under limited-data conditions and provides a practical strategy for computational candidate generation and prioritization in early-stage drug discovery.
Abstract
Abstract Designing target-relevant molecules is particularly difficult when only limited bioactivity data are available. We present MMP2Mol, a ligand-based generative framework that combines matched molecular pair (MMP) analysis with a pretrained chemical language model (CLM). MMP2Mol extracts target-specific structure–activity transformations from available ligands, applies the prioritized transformations to construct a virtual focused library, and fine-tunes the CLM toward target-relevant chemical space. The framework was evaluated across ten therapeutic targets and compared with direct CLM fine-tuning, Seq2Seq, and Reinvent 4. In repeated experiments on F2, BRD4, and PARP1, MMP2Mol achieved 92.8%–95.3% validity and 99.5%–99.7% uniqueness. When both methods were evaluated against the same original active-compound reference, molecular novelty increased from 57.8%–64.0% for the CLM baseline to 79.8%–82.0% for MMP2Mol, while internal diversity remained broadly comparable. The gains were most evident for F2 and BRD4, whereas performance varied across the smaller target datasets. These findings indicate that MMP-derived chemical knowledge can improve the focus and reproducibility of ligand-based molecular generation under limited-data conditions. MMP2Mol therefore provides a practical strategy for computational candidate generation and prioritization in early-stage drug discovery.