Background: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human\nether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening\ncardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could\nfacilitate drug discovery by filtering out toxic drug candidates.\nResult: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which\nwere carried out under constant conditions. Based on our dataset, we developed a computational hERG-related\ncardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic\ncurve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321,\nand a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten\ndrug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and\na specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.\nConclusion: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high\naccuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not\ncause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.
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