Subgraph Neural Networks Enhanced by Global Similarity for Drug Repositioning - Chengyan Zhou; Xinliang Sun; Xiang Du; Min Zeng; Min Li (Interdisciplinary Sciences: Computational Life Sciences, 2026)
Drug repositioning is a promising strategy for accelerating drug development and reducing costs by identifying potential indications for existing drugs. Recently, technological advancements have enabled the development of numerous graph convolutional network (GCN)-based methods for drug repositioning. However, many existing methods overlook the distinct roles of nodes within drug-disease association graphs, limiting their ability to learn effective representations. To address this limitation, we propose a subgraph neural network enhanced by global similarity for drug repositioning, termed GSESNN. Specifically, GSESNN first extracts the subgraph of each drug-disease pair from the entire drug-disease graph. Then, GCN and a sort pooling strategy are utilized to learn the subgraph representation. In addition, to distinguish between different drug-disease pairs with the identical subgraph topology, GSESNN utilizes GCN to learn the similarity information of drugs and diseases, fusing it with the subgraph representation to produce the final representation. Finally, we regard the drug-disease association prediction as a graph classification task. Experimental results show that GSESNN outperforms the baseline model in drug repositioning tasks. Case studies on Alzheimer’s disease and Gastric Cancer further demonstrate that our model successfully identifies more accurate drug-disease associations, highlighting its potential for practical applications in drug discovery.