Graph Clustering-Guided Multi-View Neighborhood-Enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction

Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections. MneGCL first performs semantic clustering of drugs and targets by identifying strongly correlated nodes in the semantic similarity network to construct semantic contrastive prototypes, while simultaneously establishing phenotypic prototypes based on the Gaussian interaction profile kernel similarity. These complementary views are then combined through neighborhood-enhanced contrastive learning to effectively capture latent homogeneous features and enhance representation learning for sparse nodes in heterogeneous graphs, with final predictions generated through a graph autoencoders framework. Comparative experimental results demonstrate that MneGCL achieves superior performance across three benchmark datasets, with particularly notable improvements on the highly sparse DrugBank dataset, showing an average $2.5 \%$ increase to baseline models. Additional experiments further validate the effectiveness of MneGCL in enriching feature representations for sparsely connected nodes.