Deep reinforcement learning as an interaction agent to steer fragment-based 3D molecular generation for protein pockets.

ABSTRACT: Designing high-affinity molecules for protein targets (especially novel protein families) is a crucial yet challenging task in drug discovery. Recently, there has been tremendous progress in structure-based 3D molecular generative models that incorporate structural information of protein pockets. However, the capacity for molecular representation learning and the generalization for capturing interaction patterns need substantial further developments. Here, we propose AMG, a framework that leverages deep reinforcement learning as a pocket-ligand interaction agent (IA) to gradually steer fragment-based 3D molecular generation targeting protein pockets. AMG is trained using a two-stage strategy to capture interaction features and explicitly optimize the IA. The framework also introduces a pai

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12718310 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature