Author:
Wang Hongmei,Guo Fang,Du Mengyan,Wang Guishen,Cao Chen
Abstract
AbstractBackgroundDrug-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.ResultsWe 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.ConclusionsThis 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.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference45 articles.
1. Kim I-W, Jang H, Kim JH, Kim MG, Kim S, Oh JM. Computational drug repositioning for gastric cancer using reversal gene expression profiles. Sci Rep. 2019;9(1):1–10.
2. Ganotra GK, Wade RC. Prediction of drug-target binding kinetics by comparative binding energy analysis. ACS Med Chem Lett. 2018;9(11):1134–9.
3. Ding Y, Tang J, Guo F. Identification of drug-target interactions via fuzzy bipartite local model. Neural Comput Appl. 2020;32(14):10303–19.
4. Pliakos K, Vens C. Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinform. 2020;21(1):1–11.
5. Ye Y, Wen Y, Zhang Z, He S, Bo X. Drug-target interaction prediction based on adversarial Bayesian personalized ranking. BioMed Res Int. 2021;2021:6690154.
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