Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction

Comprehensive evaluations demonstrate that MMDDI with hit@4 of 0.86 outperforms the compared baselines, with ablation studies validating the critical contributions of multi-view contrastive and mechanism disentanglement. MMDDI continues to demonstrate excellent performance in cold-start scenarios, achieving accuracy of 0.94 and recall of 0.95. Clinically, MMDDI enables interpretable causal analysis of drug interaction pathways through its mechanism-aware representations, providing operability for optimizing therapeutic regimens.