MGRL-DDI: Multiview Graph Representation Learning for Accurate Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) present a significant challenge in clinical practice, as they may lead to adverse reactions, diminished therapeutic efficacy, and serious risks to patient safety. However, most existing methods depend on single-view representations of drug molecules or substructures, which limits their capacity to capture the diverse and complex nature of drug properties. To overcome this limitation, we propose MGRL-DDI, a multiview graph representation learning framework that comprehensively models drug structures from three complementary perspectives: Three-dimensional (3D) molecular graphs, motif graphs, and molecular graphs. Specifically, the 3D graph captures the spatial and topological configuration of drug molecules, the motif graph encodes biologically meaningful substructures and their interactions, and the molecular graph reflects local atomic connectivity. To effectively integrate information across these structural dimensions, we introduce a multiview fusion module. Extensive experiments conducted on multiple real-world data sets demonstrate that MGRL-DDI consistently outperforms most advanced methods in both warm-start and cold-start scenarios, underscoring the advantages of multiview structural modeling for DDI prediction.