A data-driven group retrosynthesis planning model inspired by neurosymbolic programming.

ABSTRACT: Deep generative models have garnered significant attention for their efficiency in drug discovery, yet the synthesis of proposed molecules remains a challenge. Retrosynthetic planning, a part of computer-assisted synthesis planning, addresses this challenge by recursively decomposing molecules using symbolic rules and machine-trained scoring functions. However, current methods often treat each molecule independently, missing the opportunity to utilize shared synthesis patterns and repeat pathways, which may contribute from known synthesis routes to newly emerging, similar molecules, a notable challenge with AI-generated small molecules. Our investigation reveals reusable synthesis patterns that augment the reaction template library, resulting in progressively decreasing marginal inference

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11695995 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature