Wang, HongmeiGuo, FangDu, MengyanWang, GuishenCao, Chen2022-11-062022-11-062022-11-03BMC Bioinformatics. 2022 Nov 03;23(1):459http://hdl.handle.net/1880/115421https://doi.org/10.11575/PRISM/44750Abstract Background Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target. Results We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction. Conclusions This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions.Journal Article2022-11-06enThe Author(s)https://doi.org/10.1186/s12859-022-04812-w