Computational determination of hERG-related cardiotoxicity of drug candidates
DC Field | Value | Language |
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dc.contributor.author | Lee, Hyang-Mi | - |
dc.contributor.author | Yu, Myeong-Sang | - |
dc.contributor.author | Kazmi, Sayada Reemsha | - |
dc.contributor.author | Oh, Seong Yun | - |
dc.contributor.author | Rhee, Ki-Hyeong | - |
dc.contributor.author | Bae, Myung-Ae | - |
dc.contributor.author | Lee, Byung Ho | - |
dc.contributor.author | Shin, Dae-Seop | - |
dc.contributor.author | Oh, Kwang-Seok | - |
dc.contributor.author | Ceong, Hyithaek | - |
dc.contributor.author | Lee, Donghyun | - |
dc.contributor.author | Na, Dokyun | - |
dc.date.available | 2019-08-09T07:59:33Z | - |
dc.date.issued | 2019-05 | - |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/32767 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | BMC | - |
dc.title | Computational determination of hERG-related cardiotoxicity of drug candidates | - |
dc.type | Article | - |
dc.identifier.doi | 10.1186/s12859-019-2814-5 | - |
dc.identifier.bibliographicCitation | BMC BIOINFORMATICS, v.20 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000469321900006 | - |
dc.identifier.scopusid | 2-s2.0-85066311631 | - |
dc.citation.title | BMC BIOINFORMATICS | - |
dc.citation.volume | 20 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | In silico model | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | hERG-related cardiotoxicity | - |
dc.subject.keywordAuthor | Drug discovery | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PHARMACOPHORE | - |
dc.subject.keywordPlus | INHIBITION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | METHODOLOGY | - |
dc.subject.keywordPlus | CHANNELS | - |
dc.subject.keywordPlus | POTENCY | - |
dc.subject.keywordPlus | BIOLOGY | - |
dc.subject.keywordPlus | 3D-QSAR | - |
dc.subject.keywordPlus | MODELS | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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