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.
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.
Accurate prediction of drug-target interactions is pivotal for accelerating drug discovery and drug repurposing. However, existing advanced methods often fail to effectively characterize the many-to-many interactions between drugs and targets. Furthermore, they struggle to fully mine the structural features of drugs and proteins. To address these limitations, we propose IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization. First, we construct drug, protein, and drug−protein pair hypergraphs, where shared-drug and shared-target relationships are used to characterize multi-target activity and shared-target regulation. We then optimize hyperedge weights using a Laplacian matrix to enhance biologically meaningful high-order associations and suppress potential noise. Second, we use simplified graph convolution and graph convolutional network to extract global and local features, enabling efficient modeling of multi-level semantic information for drugs and targets. Next, we introduce a cross-attention mechanism and a dynamic gating module to perform fine-grained fusion of multi-channel features, improving the representation of cross-modal information interactions. Finally, we jointly train the model with contrastive learning and cross-entropy loss to enhance the consistency and discriminability of the learned representations. IHLO-DTI achieves mean AUROC and AUPR values of 0.9777 and 0.9716, respectively, on two public datasets. IHLO-DTI can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness. It provides a more reliable computational tool for clinical drug screening, repurposing, and precision medicine research.
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.
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.