Development and Evaluation of an in Silico Model for hERG Binding, J. Chem. Inf. Model. 2006, 46(1), 392-400.
A diverse set of 90 compounds with hERG IC50 inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r2 of 0.912 and 0.848 for the training and testing data sets, respectively. The following URL can access the datasets at the QSAR world pages: http://www.qsarworld.com/qsar-datasets-song.php