Algebraic graph-assisted bidirectional transformers for molecular property prediction.

ABSTRACT: The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebr

SUBMITTER: Chen D 

PROVIDER: S-EPMC8192505 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature