Heterogeneous Graph Contrastive Learning with Graph Diffusion for Drug Repositioning.

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains challenging. We present HGCL-DR, a novel heterogeneous graph contrastive learning framework for drug repositioning that effectively integrates global and local feature representations through three key components. First, we introduce an improved heterogeneous graph contrastive learning approach to model drug-disease relationships. Second, for local feature extraction, we employ a bidirectional graph convolutional network with a subgraph generation strategy in the bipartite drug-disease association graph, while utilizing a graph diffusion process to capture long-range dependencies in drug-drug and disease-disease relation graphs. Third, for global feature extraction, we leverage contrastive learning in the heterogeneous graph to enhance embedding consistency across different feature spaces. Extensive experiments on four benchmark data sets using 10-fold cross-validation demonstrate that HGCL-DR consistently outperforms state-of-the-art baselines in both AUPR, AUROC, and F1-score metrics. Ablation studies confirm the significance of each proposed component, while case studies on Alzheimer's disease and breast neoplasms validate HGCL-DR's practical utility in identifying novel drug candidates. These results establish HGCL-DR as an effective approach for computational drug repositioning.