Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.
TL;DR
This work introduces a novel approach to transform the concept of "how to design chemical analogs like a medicinal chemist" into a primary training objective for generative models by focusing on matched molecular pair transformations (MMPTs) as the fundamental unit of chemical modifications.
Abstract
Chemical analog design in the hit-to-lead and lead optimization stages of drug discovery relies on systematic structural modifications, often guided by medicinal chemistry intuition. Although a matched molecular pair (MMP) provides an interpretable framework to capture intuition, models trained on individual MMP instances face significant limitations, such as bias toward frequent transformations in historical data. This work introduces a novel approach to transform the concept of "how to design chemical analogs like a medicinal chemist" into a primary training objective for generative models by focusing on matched molecular pair transformations (MMPTs) as the fundamental unit of chemical modifications. This allows for a more generalizable and context-independent representation of medicinal chemistry intuition, enabling the application of the same transformation priors across different projects, regardless of the target or indication. Using a consistently curated ChEMBL-derived data set, we compared a transformation-centric foundation model (MMPT-FM) with multiple MMP-based generative formulations trained on the same underlying data. Furthermore, performance is assessed through challenging within-patent and cross-patent real-world test cases derived from drug discovery patents. The MMPT-FM model achieves comparable or improved recall metrics across all test cases, demonstrating particularly strong performance for low-frequency and previously unseen transformations. This work not only shifts the paradigm of learning and utilizes medicinal chemistry intuition in an efficient and scalable manner in the AI for drug discovery era but also establishes a significant competitive advantage by enabling the drug discovery industry to encode decades of collective medicinal chemistry knowledge─both public and proprietary─into a scalable foundation generative model that could help researchers design chemical analogs.