Relation-aware Heterogeneous Graph Transformer based drug repurposing

Drug repurposing refers to discovery of new medical instructions for existing chemical drugs, which has great pharmaceutical significance. Recently, large-scale biological datasets are increasingly available, and many graph neural network (GNN) based methods for drug repurposing have been developed. These methods often deem drug repurposing as a link prediction problem, which mines features of biological data to identify drug–disease associations (i.e., drug–disease links). Due to heterogeneity of data, we need to deeply explore heterogeneous information of biological network for drug repurposing. In this paper, we propose a Relation-aware Heterogeneous Graph Transformer (RHGT) model to capture heterogeneous information for drug repurposing. We first construct a drug–gene–disease interactive network-based on biological data, and then propose a three-level network embedding model, which learns network embeddings at fine-grained subtype-level, node-level and coarse-grained edge-level, respectively. The output of subtype-level is the input of node-level and edge-level, and the output of node-level is the input of edge level. We get edge embeddings at edge-level, which integrates edge type embeddings and node embeddings. We deem that in this way, characteristics of drug–gene–disease interactive network can be captured more comprehensively. Finally, we identify drug–disease associations (i.e., drug–disease links) based on the relationship between drug–gene edge embeddings and gene–disease edge embeddings. Experimental results show that our model performs better than other state-of-the-art graph neural network methods, which validates effectiveness of the proposed model. •A novel neural model, called RHGT, is proposed for drug repurposing.•RHGT characterizes the heterogeneity of the network at node level and edge level.•A fine-grained method is developed to learn edge type embedding.•RHGT achieves state-of-art performance in CTD and TTD datasets.