Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.

ABSTRACT: Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure-substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundament

SUBMITTER: Yang Z 

PROVIDER: S-EPMC9337739 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature