Computational determination of hERG-related cardiotoxicity of drug candidates
- Authors
- Lee, Hyang-Mi; Yu, Myeong-Sang; Kazmi, Sayada Reemsha; Oh, Seong Yun; Rhee, Ki-Hyeong; Bae, Myung-Ae; Lee, Byung Ho; Shin, Dae-Seop; Oh, Kwang-Seok; Ceong, Hyithaek; Lee, Donghyun; Na, Dokyun
- Issue Date
- May-2019
- Publisher
- BMC
- Keywords
- In silico model; Machine learning; hERG-related cardiotoxicity; Drug discovery
- Citation
- BMC BIOINFORMATICS, v.20
- Journal Title
- BMC BIOINFORMATICS
- Volume
- 20
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/32767
- DOI
- 10.1186/s12859-019-2814-5
- ISSN
- 1471-2105
- Abstract
- BackgroundDrug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.ResultIn this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.ConclusionThe neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.
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Collections - College of ICT Engineering > School of Integrative Engineering > 1. Journal Articles
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