A  substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.

Identifying drug-drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects. Current substructure-based prediction methods still have some limitations: (i) The process of substructure extraction does not fully exploit the graph structure information of drugs, as it only evaluates the importance of different radius substructures from a single perspective. (ii) The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations. In this work, we propose a substructure-aware graph neural network incorporating relation features (RFSA-DDI) for DDI prediction, which introduces a directed message passing neural network with substructure attention mechanism based on graph self-adaptive pooling (GSP-DMPNN) and a substructure-aware interaction module incorporating relation features (RSAM). GSP-DMPNN utilizes graph self-adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures. RSAM interacts drug features with relation representations to enhance their respective features individually, highlighting substructures that significantly impact predictions. RFSA-DDI is evaluated on two real-world datasets. Compared to existing methods, RFSA-DDI demonstrates certain advantages in both transductive and inductive settings, effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability. The experimental results show that RFSA-DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction, and provide more reliable assistance for potential DDIs detection in drug development and treatment stages.