ABSTRACT
Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs. According to the simplified molecular input line entry specification (SMILES) code of drugs, firstly, the atomic features were extracted to construct the point sets, and edge sets were constructed according to atomic bonds. Then the undirected graph with atomic features was generated by RDKit tool and the graph attention layer was used to extract the drug feature information. Five ion channel target proteins were screened from the whole SARS-CoV-2 genome sequences of NCBI database, and the protein features were extracted by convolution neural network (CNN). Using attention mechanism and graph convolutional network (GCN), the extracted drug features and target features information were connected. After two full connection layers operation, the drug-target affinity was output, and model was obtained. Kiba dataset was used to train the model and determine the model parameters. Compared with DeepDTA, WideDTA, graph attention network (GAT), GCN and graph isomorphism network (GIN) models, it was proved that the mean square error (MSE) of the proposed model was decreased by 0.055, 0.04, 0.001, 0.046, 0.013 and the consistency index (CI) was increased by 0.028, 0.016, 0.003, 0.03 and 0.01, respectively. It can predict the drug-target affinity more accurately. According to the prediction results of drug-target affinity of SARS-CoV-2 ion channel targets, seven kinds of small molecule drugs acting on five ion channel targets were obtained, namely SCH-47112, Dehydroaltenusin, alternariol 5-o-sulfate, LPA1 antagonist 1, alternariol, butin, and AT-9283.These drugs provide a reference for drug repositioning and precise treatment of COVID-19.