hERG classification model based on a combination of support vector machine method and GRIND descriptors

Molecular Pharmaceutics
Qiyuan LiOlivier Taboureau

Abstract

The human Ether-a-go-go Related Gene (hERG) potassium channel is one of the major critical factors associated with QT interval prolongation and development of arrhythmia called Torsades de Pointes (TdP). It has become a growing concern of both regulatory agencies and pharmaceutical industries who invest substantial effort in the assessment of cardiac toxicity of drugs. The development of in silico tools to filter out potential hERG channel inhibitors in early stages of the drug discovery process is of considerable interest. Here, we describe binary classification models based on a large and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 microm and achieved an overall accuracy up to 94% with a Matthews coefficient correlation (MCC) of 0.86 ( F-measure of 0.90 for blockers and 0.95 for nonblockers). The model at a 40 microm threshold showed the best performance and was validated internally (MCC of 0.40 and F-measure of 0.57 for blockers and 0.81 for nonblockers, using a leave-one-out cross-validation). On an external ...Continue Reading

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