M²DDI: A Unified Framework for Dynamic Multimodal Fusion in Drug-Drug Interaction Prediction
TL;DR
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Abstract
Drug-drug interaction (DDI) event prediction is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing computational approaches are limited by their inability to jointly model the heterogeneous mechanisms underlying DDIs, which span molecular structure, pharmacodynamic function, and network-mediated relations. To address this limitation, we introduce M2DDI, a unified framework for dynamic multimodal fusion in DDI prediction. M2DDI utilizes a Mixture-of-Experts architecture, with each expert dedicated to a distinct pharmacological modality. A novel prior-enhanced dual-path gating strategy adaptively selects relevant experts for each drug pair by integrating mechanism-matched feature queries and ATC-based biomedical priors, thereby aligning expert selection with underlying pharmacological mechanisms and addressing the challenge of data incompleteness. Empirical evaluation on benchmark datasets demonstrates that M2DDI achieves state-of-the-art performance, particularly in new drug scenarios. Additional robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions. Analysis of expert selection patterns further confirms alignment with established pharmacological mechanisms. These results establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction. The code is available at: https://github.com/RunqingXuCn/M2DDI.