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