Key result
Computational in silico prediction models, including QSAR, pharmacophore, classification, and structure-based models, provide a rapid and economic way to screen compounds for hERG inhibition.
In silico prediction models offer a rapid and economic approach to screen compounds for hERG channel blockade during early drug discovery to prevent drug-induced QT prolongation.
In silico hERG models enable rapid QT risk screening in discovery; leaves open optimal model selection for reliable predictions.
The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure-activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).
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Jing et al. (2015) conducted a review in hERG inhibition and QT prolongation. In silico prediction models was evaluated. Computational in silico prediction models, including QSAR, pharmacophore, classification, and structure-based models, provide a rapid and economic way to screen compounds for hERG inhibition.
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