In Silico Classification of hERG Channel Blockers: a Knowledge‐Based Strategy

The blockage of the hERG potassium channel by a wide number of diverse compounds has become a major pharmacological safety concern as it can lead to sudden cardiac death. In silico models can be potent tools to screen out potential hERG blockers as early as possible during the drug‐discovery process. In this study, predictive models developed using the recursive partitioning method and created using diverse datasets from 203 molecules tested on the hERG channel are described. The first model was built with hERG compounds grouped into two classes, with a separation limit set at an IC50 value of 1 μm, and reaches an overall accuracy of 81 %. The misclassification of molecules having a range of activity between 1 and 10 μM led to the generation of a tri‐class model able to correctly classify high, moderate, and weak hERG blockers with an overall accuracy of 90 %. Another model, constructed with the high and weak hERG‐blocker categories, successfully increases the accuracy to 96 %. The results reported herein indicate that a combination of precise, knowledge management resources and powerful modeling tools are invaluable to assessing potential cardiotoxic side effects related to hERG blockage.