Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted remarkable attention, which can narrow down repositioning candidates. Recently, graph neural networks (GNNs) have been widely used and achieved promising results in drug repositioning. However, the existing GNNs based methods usually focus on modeling the complex drug-disease association graph, but ignore the semantic information on the graph, which may lead to a lack of consistency of global topology information and local semantic information for the learned features. To alleviate the above challenge, we propose a novel drug repositioning model based on graph contrastive learning, termed DRGCL. First, we treat the known drug-disease associations as the topology graph. Second, we select the top- similar neighbor from drug/disease similarity information to construct the semantic graph rather than use the traditional data augmentation strategy, thereby maximally retaining rich semantic information. Finally, we pull closer to embedding consistency of the different embedding spaces by graph contrastive learning to enhance the topology and semantic feature on the graph. We have evaluated DRGCL on four benchmark datasets and the experiment results show that the proposed DRGCL is superior to the state-of-the-art methods. Especially, the average result of DRGCL is 11.92% higher than that of the second-best method in terms of AUPRC. The case studies further demonstrate the reliability of DRGCL.

MOTIVATION: Drug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs. RESULTS: In this study, we propose an adaptive GCNs approach, termed AdaDR, for drug repositioning by deeply integrating node features and topological structures. Distinct from conventional graph convolution networks, AdaDR models interactive information between them with adaptive graph convolution operation, which enhances the expression of model. Concretely, AdaDR simultaneously extracts embeddings from node features and topological structures and then uses the attention mechanism to learn adaptive importance weights of the embeddings. Experimental results show that AdaDR achieves better performance than multiple baselines for drug repositioning. Moreover, in the case study, exploratory analyses are offered for finding novel drug-disease associations. AVAILABILITY AND IMPLEMENTATION: The soure code of AdaDR is available at: https://github.com/xinliangSun/AdaDR.