A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability

A novel approach by using a panel of plausible pharmacophore hypothesis candidates to constitute the pharmacophore ensemble (PhE) and subject them to regression by support vector machine (SVM) has been developed for predicting the liability of human ether-a-go-go-related gene (hERG). This PhE/SVM scheme takes into account the protein conformational flexibility while interacting with structurally diverse ligands, which is crucial yet often neglected by most of the analogue-based modeling methods. Thirty-nine molecules were carefully selected and cross-examined from the literature data for this study, of which 26 and 13 molecules were deliberately treated as the training set and the test set to generate the model and to validate the generated model, respectively. The final PhE/SVM model gave rise to an r(2) value of 0.97 for observed vs predicted pIC(50) values for the training set, a q(2) value of 0.89 by the 10-fold cross-validation and an r(2) value of 0.94 for the test set. Thus, this PhE/SVM model provides a fast and accurate tool for predicting liability of hERG and can be utilized to guide medicinal chemistry to avoid molecules with an inhibition potential of this potassium channel.