Predicting drug-target interactions based on multivariate information fusion and graph contrast learning

Drug-target interaction (DTI) prediction is of great significant in stimulating innovation and research in the medical field. In recent years, traditional experimental methods for predicting DTIs have proven to be time-consuming and costly. As a result, machine learning methods have been extensively applied to improve the prediction of drug-target interactions. However, the sparsity of inter-node connections often results in insufficiently learned node representations. Furthermore, many methods do not take into account the topological similarity between nodes when integrating similarities. This study proposes a model that integrates multiple sources of information and utilizes Graph Contrastive Learning (GCL) to predict potential drug and target interactions (MGCLDTI). Firstly, MGCLDTI employs the DeepWalk algorithm to extract global topological representations from the heterogeneous graph which incorporates multi-view information of drugs, targets, and diseases. Subsequently, a densification strategy is implemented to alleviate the noise impact arising from the sparsity of the DTI matrix. Furthermore, a GCL model with node masking is applied to enhance local structural awareness and optimize the embeddings of drugs and targets. Finally, DTI scores are predicted using the LightGBM algorithm. Comparative results against state-of-the-art methods demonstrate that MGCLDTI achieves superior predictive performance. Besides, ablation studies reveal the effectiveness of each component. Case studies also provide compelling evidence of MGCLDTI's accuracy in identifying potential DTIs.