CMAF-DDI: A Knowledge-Enhanced Cross-Modal Fusion Method Leveraging Protein Representation for Multi-Class Drug-Drug Interactions.
TL;DR
Experimental results show that CMAF-DDI improves multi-class DDI prediction compared with representative graph-based and multi-source fusion baselines and ablation, hyperparameter sensitivity, controlled protein perturbation, representation, and case-level analyses to examine the contribution and behavior of protein-enhanced cross-modal fusion.
Abstract
Accurate prediction of drug-drug interactions (DDIs) is crucial for medication safety and personalized treatment. Most existing methods primarily exploit molecular graphs or biomedical knowledge graphs, while target protein sequence information is often underused. This paper proposes CMAF-DDI, a multi-class DDI prediction framework that integrates protein sequence features, molecular graph features, and knowledge graph features. CMAF-DDI contains a bi-level cross-modal fusion module: an Attention Fusion (AF) level that models global dependencies among modalities using multi-head attention, and a Triple-feature Product Fusion (TPF) level that captures high-order cross-modal co-activation after projecting all modalities into a shared latent space. Experimental results on DrugBank and DRKG show that CMAF-DDI improves multi-class DDI prediction compared with representative graph-based and multi-source fusion baselines. We further provide ablation, hyperparameter sensitivity, controlled protein perturbation, representation, and case-level analyses to examine the contribution and behavior of protein-enhanced cross-modal fusion.