Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism  
[PERSON]  
1 Department of Information Engineering, Dalian University, Dalian 116622, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China; yaokohia.edu.cn  
[PERSON]  
2 Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China; yaokohia.edu.cn  
[PERSON]  
3 Department of Software Engineering, Dalian University, Dalian 116622, China; [EMAIL_ADDRESS] (Z.C. ); [EMAIL_ADDRESS] (F.K.) [PERSON]  
3 Correspondence: [EMAIL_ADDRESS]; Tel: +86-0411-8740-20463  
several stages of clinical trials or are already on the market, their risks are lower compared with strategies such as developing from scratch and obtaining patent licenses. In addition, compared with obtaining patent licenses and restructuring strategies, it has the advantages of a short time to market and a greater possibility of discovering differences in drug effects, so higher returns are expected. Therefore, drug repositioning is one of the strategies with the best risk/benefit ratio among currently known drug development strategies. As many drugs treat disease by acting on related targets, most drug repositioning research studies predict new drug-disease interaction (DDI) by discovering new drug-target interaction (DTI). However, for a new disease, because the corresponding target of the disease has not been fully discovered, if drug repositioning is achieved only through the discovery of new DTI, the results may be limited. In addition, the existing data of known drugs and related information, such as proteins and genes, are huge, so it is particularly important to use appropriate databases and preliminary screening of data when redirecting drugs to an emerging disease. Wearable devices and internet medical service platforms generate a large number of high-dimensional medical data. Traditional machine learning methods cannot be used to process high-dimensional medical data from different sources. Deep learning methods are widely used in feature extraction [3] and disease prediction [4] for medical data. In the drug redirection problem, the drug and target naturally form a graph structure, and the most used deep learning model is the Graph Convolutional Network (GCN). In order to find drug candidates against COVID-19, we construct a knowledge graph (KG) for COVID-19 and propose a new model called Graph Convolutional Network with Attentional mechanism for Drug-Disease Interaction (Att-GCN-DDI) to predict potential therapeutic drugs for COVID-19. We first collect information about COVID-19 in the latest published literature and add the gene targets of COVID-19 to our drug KG. Then, we screen the nodes and relationships associated with COVID-19 to build the KG. Borrowing the idea from Neo-DTI [5] and DTI-NET [6], the Att-GCN-DDI model first employs GCN with attentional mechanism to extract features from KG and then performs matrix factorization for DDI prediction. The tests on two scenarios in DDI prediction have demonstrated that Att-GCN-DDI can significantly outperform several baseline prediction methods. Att-GCN-DDI also has good performance on the DDI prediction against COVID-19. Five drug candidates predicted by Att-GCN-DDI have proved effective in the clinical treatment. The main contributions of our work lie in:  
1. We gather and add the target of COVID-19 to our KG. Then, we select the related knowledge to construct a KG for COVID-19, which is applied to find the potential therapeutic drugs against COVID-19. 2. We propose a GCN-based model for drug repositioning on KG. The model can learn the topology around the disease effectively, which is utilized to predict new drugs for the disease. 3. We evaluate our method by predicting drugs for both ordinary diseases and COVID-19.