Multiview feature fusion-based graph representation model for drug-drug interaction prediction

Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.

Results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.

SynerMolNet: Molecular Representation and Attention Fusion for Cancer Drug Synergy Prediction

Molecular Fragment-Based Graph Isomorphism Networks for Interpretable Prediction of Synergistic Drug Combinations.

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: Drug-Target Interaction Prediction Based on Improved Hypergraph Neural Network and Laplacian Matrix Optimization

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.

A Structure-Aware Multimodal Framework for Drug–Target Interaction Prediction via Heterogeneous Graph Learning

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.

HSAF-DDI: Heterogeneous Semantic-Aware Drug-Drug Interaction Prediction with Hierarchical Adaptive Fusion.

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.