Generative Active Learning for Molecular Design with REINVENT: Balancing Binding Affinity and Synthetic Accessibility
TL;DR
The results show that integrating synthesizability prediction into physics-based GAL workflows enables the efficient design of compounds that are at once chemically diverse as well as predicted to be strong binders and synthetically tractable, demonstrating efficient and practical computational drug design.
Abstract
We present a generative active learning (GAL) framework for molecular design that integrates the generative AI platform REINVENT with physics-based free-energy estimation via ESMACS, specifically addressing synthetic tractability. In a previous study we have shown that iterative generation and optimization of molecules for protein binding affinity can be effectively achieved. For drug discovery, however, molecules need to be synthesized for experimental validation. Molecules with unclear synthetic routes will be de-prioritized regardless of their predicted binding affinity. Here, we address this by incorporating additional synthesizability constraints into the computational design process, framing the problem as a multi-objective optimization task. We show that it is possible to simultaneously optimize candidate molecules for both binding affinity and synthetic accessibility. Our results show that integrating synthesizability prediction into physics-based GAL workflows enables the efficient design of compounds that are at once chemically diverse as well as predicted to be strong binders and synthetically tractable, demonstrating efficient and practical computational drug design.