IAMV-DDI: Interaction-Aware Multi-View Molecular Representation Learning for Drug-Drug Interaction Event Prediction

Drug-drug interaction (DDI) prediction is an important task in computational pharmacology because unidentified interactions may reduce therapeutic efficacy or induce severe adverse effects. Although recent deep learning methods have achieved promising performance, most existing approaches primarily rely on either two-dimensional (2D) molecular topology or coarse multimodal fusion strategies, while insufficiently modeling fine-grained interaction dependencies between drug pairs. To address these limitations, we propose IAMV-DDI, an interaction-aware multi-view molecular representation learning framework for DDI prediction and DDI event classification. The proposed framework jointly integrates 2D molecular topology, 3D spatial geometry, and token-level interdrug interaction modeling. Specifically, a SimSGT-based masked graph encoder is employed to learn informative 2D molecular representations, while an E(n) Equivariant Graph Neural Network (EGNN) encoder with contrastive conformer pretraining captures geometry-aware 3D structural features. The learned 2D and 3D token representations are integrated through a gated cross-modal fusion module, followed by a bidirectional cross-attention mechanism to explicitly model interaction-aware dependencies between drug pairs. Experiments conducted on the benchmark DrugBank and ZhangDDI data sets demonstrate that IAMV-DDI achieves strong performance compared with representative network-based, chemical-structure-based, and hybrid baseline methods. In binary DDI prediction, IAMV-DDI achieves highly competitive performance on DrugBank and ZhangDDI, with closely matched results to the strongest baseline on ZhangDDI. In DrugBank multiclass DDI event classification, IAMV-DDI achieves an Accuracy of 0.9650, Macro-Precision of 0.9439, Macro-Recall of 0.9347, and Macro-F1 of 0.9361, substantially outperforming the strongest baseline. Ablation studies further confirm the effectiveness of the multiview molecular fusion strategy and the interaction-aware cross-attention mechanism. These results demonstrate that jointly modeling molecular topology, spatial geometry, and fine-grained interdrug dependencies can produce highly discriminative representations for accurate DDI prediction.