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.
Hengpeng Zhao, Xiaoli Lin, Jun Pang et al.· IEEE journal of biomedical a...· 0 citations
Drug–drug interactions (DDIs) are modulated not only by structural relations between drugs but are also profoundly influenced by underlying biological mechanisms. Most existing methods fail to adequately consider biologically interpretable signals at multiple biological hierarchies, limiting their ability to model the mechanistic complexity of drug–drug interactions. In this paper, we propose a heterogeneous semantic-aware framework (HSAF-DDI) that integrates molecular motif, protein sequence, and knowledge graph representations for DDI prediction. This framework learns local functional semantic representations of molecular motifs, fine-grained target protein semantics that capture deep biological characteristics, and higher-order associations encoded in knowledge graphs. We design a hierarchical adaptive fusion module that facilitates robust fusion and adaptive representation learning over multisource heterogeneous information. In addition, we introduce a contrastive learning mechanism with adversarial negatives and perturbations to improve the discrimination of DDI types and enhance the discriminability and robustness of learned representations. Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Xiaoli Lin, Siyuan Zhang, Bo Li et al.· Journal of Chemical Informat...· 0 citations