Single-step retrosynthesis prediction via multitask graph representation learning.

ABSTRACT: Inferring appropriate synthesis reaction (i.e., retrosynthesis) routes for newly designed molecules is vital. Recently, computational methods have produced promising single-step retrosynthesis predictions. However, template-based methods are limited by the known synthesis templates; template-free methods are weakly interpretable; and semi template-based methods are deficient with regard to utilizing the associations between chemical entities. To address these issues, this paper leverages the intra-associations between synthons, the inter-associations between synthons and leaving groups (LGs), and the intra-associations between LGs. It develops a multitask graph representation learning model for single-step retrosynthesis prediction (Retro-MTGR) to solve reaction centre deduction and LG ide

SUBMITTER: Zhao PC 

PROVIDER: S-EPMC11742932 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature