MRLF-DDI: A Multi-view Representation Learning Framework for Drug-Drug Interaction Event Prediction.

Accurately predicting drug-drug interaction events (DDIEs) is critical for improving medication safety and guiding clinical decision-making. However, existing graph neural network (GNN)-based methods often struggle to effectively integrate multi-view features and generalize to novel or understudied drugs. To address these limitations, we propose MRLF-DDI, a multi-view representation learning framework that jointly models information from individual drug features, local interaction contexts, and global interaction patterns. MRLF-DDI introduces the use of atomlevel structural features enriched with bond angle information-marking the first incorporation of this geometryaware feature in DDIE prediction. It further employs a multigranularity GNN and a gated knowledge transfer strategy to enhance feature learning and cold-start generalization. Extensive experiments on benchmark datasets demonstrate that MRLF-DDI achieves superior performance in both warm-start and cold-start scenarios. Case studies and visualization analyses further highlight its practical utility in identifying clinically relevant DDIEs. The code for MRLFDDI is available at https://github.com/jianzhong123/MRLFDDI.