Rich-Neighborhood Contrastive Learning Framework for Drug Repositioning via Structural and Semantic Neighbor Fusion.

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.