Enabling Drug-Drug Interaction Event Prediction with Multi-view-enhanced Chemical Structural Information

Predicting drug-drug interaction (DDI) events is critical for ensuring patient safety, optimizing therapeutic efficacy, and advancing drug discovery. Deep learning-based models have recently attracted considerable attention in this domain and achieved promising results. However, most existing approaches insufficiently account for both the chemical structural information of drugs and the multiplicity of interaction types, thereby limiting predictive accuracy. In this work, we present ChemDDI, a deep learning framework for DDI event prediction enabled by multi-view-enhanced chemical structural information. Specifically, ChemDDI employs multi-view graph- and image-based encoders to extract chemical structural information from three-dimensional conformations. Building on these chemically informed representations, ChemDDI incorporates multi-relational interaction information through Transformer-based graph neural networks and relational graph embeddings, while contrastive learning further aligns interaction features to enable robust DDI event prediction. Extensive experiments demonstrate that ChemDDI consistently outperforms state-of-the-art baselines, achieving substantial gains on rare interaction events. ChemDDI is available at https://github.com/gjin-DLOU/ChemDDI .