MMDDI-GDSS: Multi-Modal Feature Fusion Drug-Drug Interaction Event Prediction Model

Combination drug therapy represents a cornerstone of modern medicine, offering enhanced therapeutic efficacy and reduced drug resistance. Predicting adverse drug-drug inter-action (DDI) events is crucial, and while multi-modal models show significant promise, the effective integration of diverse data sources remains an open challenge. In this work, we introduce MMDDI-GDSS, a novel multi-modal framework for DDI event prediction. Our framework leverages a multi-head attention mechanism to fuse diverse data modalities-including SMILES, protein targets, enzymes, and pharmacological information-into a unified, enhanced molecular attribute graph. To learn robust and expressive drug representations from this complex graph, MMDDI-GDSS employs a graph diffusion process over static subgraphs, generating a powerful and coherent representation for each drug. We benchmark MMDDI-GDSS against several state-of-the-art methods on two widely used DDI event prediction datasets. Experimental results demonstrate that our model consistently outperforms all baselines. Furthermore, comprehensive ablation studies validate the effectiveness of our key components, highlighting the distinct contributions of the attention-based feature integration and the graph diffusion mechanism.