Key result
Machine learning methods combined with recursive feature elimination achieved an overall prediction accuracy of 72.3–80.9% for identifying hERG inhibitors in an independent validation set.
Machine learning methods combined with feature selection can effectively predict potential hERG potassium channel inhibitors in early drug discovery to prevent drug-induced QT prolongation.
May aid preclinical hERG screening in drug discovery; leaves open clinical translation and prospective validation.
The human Ether‐a‐Go‐Go‐Related Gene (hERG) potassium ion channel plays a crucial role in cardiac repolarization. The inhibition of this channel by marketed drug may prolong the length of time between the start of Q wave and end of the T wave on an electrocardiogram (QT interval) and then possibly leads to fatal cardiac arrhythmia. Therefore, it is vital to predict the potential hERG inhibitors in early stages of drug discovery. This work explored the identifying of hERG inhibitors by different machine learning methods including C4.5 Decision Tree (C4.5 DT), multilayer perceptron, Radial Basis Function Network (RBFNetwork), and Support Vector Machine (SVM). Recursive Feature Elimination (RFE), a feature selection method, was used to select molecular descriptors appropriate for distinguishing hERG inhibitors and noninhibitors. For Data_91 classification system with 91 compounds, the prediction accuracies of those methods are 72.1–92.3% for hERG inhibitors and 81.7–98.1% for noninhibitors in a five‐fold crossvalidation calculation. For an independent validation set of 47 compounds of Data_91, these methods gave an overall accuracy of 72.3–80.9% using the selected descriptors. This suggests that the combination of the machine learning methods and the feature selection method can provide an effective way to filter the hERG inhibitors in the drug discovery process.
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Wang et al. (2008) studied hERG potassium channel inhibition (drug discovery) (n=138). Machine learning methods (C4.5 DT, MLP, RBFNetwork, SVM) with Recursive Feature Elimination was evaluated on Prediction accuracy for hERG inhibitors and noninhibitors. Machine learning methods combined with recursive feature elimination achieved an overall prediction accuracy of 72.3–80.9% for identifying hERG inhibitors in an independent validation set.
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