Targeted molecular generation with latent reinforcement learning.

ABSTRACT: Computational methods for generating molecules with specific physiochemical properties or biological activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. We introduce a novel approach that utilizes a Reinforcement Learning paradigm, called proximal policy optimization, for optimizing molecules in the latent space of a pretrained generative model. Working in the latent space of a generative model lets us bypass the need for explicitly defining chemical rules when computationally designing molecules. The generation of molecules is achieved through navigating the latent space for identifying regions that correspond to molecules with desired properties. Proximal policy optimization is a state-of-the-art policy

SUBMITTER: Haddad R 

PROVIDER: S-EPMC12043925 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature