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Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing
Author(s) -
Sun Hongmao,
Huang Ruili,
Xia Menghang,
Shahane Sampada,
Southall Noel,
Wang Yuhong
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600126
Subject(s) - herg , bootstrapping (finance) , computer science , support vector machine , drug discovery , cross validation , artificial intelligence , machine learning , potassium channel , mathematics , econometrics , bioinformatics , medicine , biology
Drug‐induced QT prolongation leads to life‐threatening cardiotoxicity, mostly through blockage of the human ether‐à‐go‐go‐related gene (hERG) encoded potassium ion (K + ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC‐ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under‐sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC‐ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.