PURE: policy-guided unbiased REpresentations for structure-constrained molecular generation.

ABSTRACT: Structure-constrained molecular generation (SCMG) generates novel molecules that are structurally similar to a given molecule and have optimized properties. Deep learning solutions for SCMG are limited in that they are predisposed towards existing knowledge, and they suffer from a natural impedance mismatch problem due to the discrete nature of molecules, while deep learning methods for SCMG often operate in continuous space. Moreover, many task-specific evaluation metrics used during training often bias the model towards a particular metric -"metric-leakage". To overcome these shortcomings, we propose Policy-guided Unbiased REpresentations (PURE) for SCMG that learns within a framework simulating molecular transformations for drug synthesis. PURE combines self-supervised learning with a p

SUBMITTER: Gupta A 

PROVIDER: S-EPMC12522651 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature