Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

Mingxuan Che, Kui Yao, Chao Che, Zhangwei Cao and Fanchen Kong
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Mingxuan Che: Department of Information Engineering, Dalian University, Dalian 116622, China
Kui Yao: Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
Chao Che: Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
Zhangwei Cao: Department of Software Engineering, Dalian University, Dalian 116622, China
Fanchen Kong: Department of Software Engineering, Dalian University, Dalian 116622, China

Future Internet, 2021, vol. 13, issue 1, 1-10

Abstract: The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

Keywords: COVID-19; drug–disease interaction prediction; knowledge graph; graph convolutional network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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