MNGCCL: Multi-neighborhood graph collaborative contrastive learning for drug-disease association prediction

Exploring new therapeutic applications for existing drugs can effectively reduce drug development costs. However, current drug-disease association (DDA) prediction methods often fail to effectively integrate multi-domain information. The lack of multi-domain information integration causes these methods to heavily rely on prior knowledge, thereby limiting their generalization ability. To address this issue, we developed a Multi-Domain Graph Collaborative Contrastive Learning (MNGCCL) model for DDA prediction. In the MNGCCL framework, a feature extraction module is designed to effectively extract both single-domain and multi-domain features. The single-domain and multi-domain feature extraction components in this module run in parallel, extracting key features of drugs and diseases from different latent spaces (e.g., homogeneous and heterogeneous networks). MNGCCL employs graph collaborative contrastive learning to integrate these features and enhances information interaction by designing new node scoring for negative sample sampling. This significantly enriches the semantic features of drugs and diseases. In DDA prediction, MNGCCL outperforms other state-of-the-art models across various datasets and partitioning methods. Notably, MNGCCL excels in drug repositioning for Parkinson’s disease and Alzheimer’s disease, as well as handling data sparsity. These findings highlight its tremendous potential for drug repositioning and DDA prediction, especially in the context of sparse omics data.