Automatic collaborative learning for drug repositioning

Abstract

Drug repositioning seeks to identify new therapeutic uses for existing drugs, accelerating development and reducing costs. While traditional wet lab experiments are costly, computational methods offer a low-cost, efficient alternative. Despite their potential, most research in this field has uncritically employed the standard message-passing mechanism of Graph Neural Network (GNN), limiting the assessment of collaborative effects on prediction accuracy. In this paper, we introduce a novel model, an automatic collaborative learning framework for drug repositioning. Initially, we propose a metric to measure the interaction levels among neighbors and integrate it with the intrinsic message-passing mechanism of GNN, thereby enhancing the impact of various collaborative effects on prediction accuracy. Furthermore, we introduce an advanced contrastive learning technique to align feature consistency between the disease–drug association space and the customized neighbor space. This approach leverages the inherent regularities across different feature dimensions to minimize feature redundancy. Extensive experiments conducted on three benchmark datasets demonstrate substantial improvements of this novel model over various state-of-the-art methods. Case studies further highlight the practical utility of this model.