Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction.

ABSTRACT: Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn mo

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC10070395 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature