Adaptive Topology-Semantic Fusion Contrastive Learning for Drug Repositioning

Résumé fourni par la source

Abstract Drug repositioning offers an efficient alternative to de novo drug development; however, accurately identifying potential drug–disease associations remains challenging. Existing methods do not fully exploit higher-order collaborative information at the drug–drug and disease–disease levels, and their feature fusion strategies are often static, lacking adaptability to node-specific differences. To address these limitations, we propose ATSFCL-DR, an adaptive topology–semantic fusion contrastive learning framework for drug repositioning. The method constructs a topology graph and a semantic graph, and introduces an adaptive fusion strategy to integrate node representations learned from these two views, thereby enabling more effective use of higher-order information. In addition, the framework designs a multiview fusion contrastive learning mechanism to enhance the consistency and discriminative ability of cross-view representations. To mitigate the noise introduced during higher-order neighborhood aggregation, the framework further incorporates a layer-level contrastive learning objective to improve the robustness of representation learning. Experiments using 10 repetitions of 10-fold cross-validation on three public benchmark data sets show that ATSFCL-DR outperforms representative baseline methods, achieving an average AUROC of 0.9093 and an average AUPR of 0.5362. Furthermore, by predicting previously unknown drug–disease associations on the Gdata set and validating them against authoritative databases, ATSFCL-DR identifies pramipexole as a promising candidate drug for Alzheimer’s disease. In summary, ATSFCL-DR provides an effective approach for drug–disease association prediction and candidate drug screening by adaptively integrating topological and semantic information within a contrastive learning framework.

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