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.