TL;DR
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.
Results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.
Drug combination therapy plays an increasingly important role in the clinical treatment of complex diseases, such as cancer, as rational drug combinations can enhance therapeutic efficacy and reduce toxic side effects. However, existing methods still exhibit limitations in the granularity of drug molecular representation, drug interaction modeling, and cell line context awareness, which restrict further improvements in predictive performance. To address these issues, we propose FragSyn, a deep graph learning framework for predicting synergistic drug combinations based on molecular fragmentations. FragSyn first decomposes drug molecules into chemically meaningful fragments according to breaks of retrosynthetically interesting chemical substructure rules and learns fragment-level molecular representations through a graph isomorphism network with edge features. It then captures nonlinear relationships between drug pairs from multiple perspectives while introducing a gating modulation mechanism conditioned on cell line features, enabling drug representations to adapt dynamically to the cell line context. Finally, multisource features are fused to perform binary classification of synergy versus antagonism. FragSyn achieves AUC, AUPR, and ACC of 0.944, 0.942, and 0.872, respectively, outperforming eight baseline models, and demonstrates optimal generalization performance in both leave-one-out cross-validation and external validation. Ablation studies and interpretability analyses further validate the rationality of FragSyn and its ability to identify key fragments. These 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.
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.
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.