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Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches

Blockade of human ether-à-go-go-related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse dataset. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naïve Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification…

Link to Full Article: Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches