NCH-DDA: Neighborhood contrastive learning heterogeneous network for drug–disease association prediction

Exploring new therapeutic diseases for existing drugs plays an essential role in reducing drug development costs. However, existing methods for predicting drug–disease association (DDA) lack fusion to multi-neighborhood information, which limits their ability to generalize and forces them to rely on prior knowledge. To this end, we propose a novel DDA model called the Neighborhood Contrastive Learning Heterogeneous Networks (NCH-DDA). NCH-DDA uses both single-neighborhood and multi-neighborhood feature extraction modules to extract important features of drugs and diseases in parallel from multiple potential spaces, such as heterogeneous networks and similarity networks. NCH-DDA fuses single-neighborhood and multi-neighborhood features using contrastive learning to enhance information interaction in different neighborhood spaces, ultimately obtaining universal domain features of drugs and diseases. NCH-DDA uses a combination of predictive loss and triplet loss to reduce dependence on prior knowledge. In different partition schemes of multiple datasets, NCH-DDA achieved the best performance in predicting DDA, outperforming several current state-of-the-art methods. Moreover, NCH-DDA demonstrated better performance in experiments on data sparsity and drug repositioning for Alzheimer's disease, indicating its greater potential in DDA prediction with sparse omics data and drug repositioning applications.