TL;DR
Results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.
A novel multiview feature fusion-based graph representation model (MFF-GRM) for predicting DDI that integrates drug molecular graphs, SMILES sequences, DDI information networks, and drug biological features to learn drug features more comprehensively.
IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.
Predicting drug–target interactions is critical for drug discovery, yet many deep learning methods overlook atom–residue–level relationships, so PHGDTI is proposed, a multimodal framework that integrates sequence and structural cues for binding prediction.
Results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.
Results reveal that MAGNETIC consistently out performs baselines on both the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), indicating improved identification and ranking of true interactions under class imbalance.
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.
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