SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.

ABSTRACT: Designing drug-like molecules that satisfy multiple objectives-such as high binding affinity, synthesizability, and drug-likeness-poses a complex global optimization problem over an astronomically large chemical space. Existing deep learning-based molecular generative models often treat this task as distribution modeling, relying on atom-level autoregressive actions with less consideration of explicit optimization feedback. Consequently, they frequently generate invalid structures, converge to local optima, or produce synthetically infeasible candidates. Here, we introduce Synthesizable Hierarchical Action-space Reinforcement learning for Pareto optimization (SHARP), a molecular generator that addresses these limitations via a fragment-based hierarchical action space and reinforcement lear

SUBMITTER: Kim J 

PROVIDER: S-EPMC12606630 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature