HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction.

BACKGROUND: Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings. RESULTS: In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating multi-omics information. HNF-DDA also proposes the concept of subgraph contrastive learning to capture the local structure of drug-disease subgraphs, learning the high-order semantic information of nodes. Experimental results on two benchmark datasets demonstrate that HNF-DDA outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability to generalize to novel drug and disease categories. Case studies for breast cancer and prostate cancer reveal that 9 out of the top 10 predicted candidate drugs for breast cancer and 8 out of the top 10 for prostate cancer have documented therapeutic effects. CONCLUSIONS: HNF-DDA incorporates all-pairs message passing and subgraph capture strategies into heterogeneous network embedding, enabling effective learning of drug and disease representations enriched with heterogeneous information, while also demonstrating significant potential for applications in drug repositioning.