Scaffold Hopping with Generative Reinforcement Learning.

ABSTRACT: Scaffold hopping-the design of novel scaffolds for existing lead candidates-is a multifaceted and nontrivial task, for medicinal chemists and computational approaches alike. Generative reinforcement learning can iteratively optimize desirable properties of de novo designs, thereby offering opportunities to accelerate scaffold hopping. Current approaches confine the generation to a predefined molecular substructure (e.g., a linker or scaffold) for scaffold hopping. This confined generation may limit the exploration of the chemical space and require intricate molecule (dis)assembly rules. In this work, we aim to advance reinforcement learning for scaffold hopping, by allowing "unconstrained", full-molecule generation. This is achieved via the RuSH (Reinforcement Learning

SUBMITTER: Rossen L 

PROVIDER: S-EPMC12264953 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature