Rich-Neighborhood Contrastive Learning Framework for Drug Repositioning via Structural and Semantic Neighbor Fusion
Yuhua Yao, Hao Zhang, Xianfang Tang, Jincan Li, Junlin Xu, Jialiang Yang, Yajie Meng · IEEE Journal of Biomedical and Health Informatics · 2026
Drug repositioning accelerates therapeutic discovery by identifying new indications for approved drugs, substantially reducing the time and cost associated with drug development. However, graph collaborative filtering (GCF)-based methods for predicting drug-disease associations are limited by data sparsity and structural noise, impeding the modeling of latent high-order and semantic relationships. We hypothesize that jointly leveraging complementary information from structural and semantic neighborhoods can alleviate data sparsity and improve predictive performance. To this end, we propose a unified framework, Rich-Neighborhood Contrastive Learning for Drug Repositioning (RCL-DR), which integrates both structural and semantic neighborhood modeling into a LightGCN-based collaborative filtering backbone and optimizes semantic prototypes via an Expectation-Maximization (EM) algorithm. Experiments on three public datasets using 10 × 10- fold cross-validation demonstrate that RCL-DR outperforms representative baselines, achieving an area under the receiver operating characteristic curve (AUROC) of 0.9419 and an area under the precision-recall curve (AUPR) of 0.5126, representing absolute improvements of 0.0345 and 0.0138, respectively. Furthermore, RCL-DR identifies promising drug candidates for Alzheimer's disease (e.g., buspirone) and Parkinson's disease (e.g., trihexyphenidyl) by predicting previously unknown drug-disease associations on the Fdataset and validating them against authoritative databases. In summary, RCL-DR provides a unified contrastive learning framework for robust drug repositioning and precision pharmacology.