Prediction of hERG Potassium Channel Blockade Using kNN-QSAR and Local Lazy Regression Methods

We have collated hERG inhibition data of 165 compounds from literature and employed two regression procedures, namely, Local Lazy Regression (LLR) and k-Nearest Neighbor (kNN)-QSAR regression methods in combination with Genetic Algorithms (GAs) to select significant and independent molecular descriptors and to build robust predictive models. This methodology helped us to derive four, optimal 2D- and 3D-QSPR models, M1–M4, based on five descriptors. Extensive validation tests using leave-one-out method and 61 compounds that are not used in the model generation strongly suggest that: (i) models M1 and M2, based on LLR, are very stable and robust; (ii) the model, M2 based on 3-D descriptors, performs better than the one based on 2-D descriptors, M1; and (iii) LLR method outperforms kNN regression approach. These results strongly suggest that the combination of GA and LLR method is a promising methodology, to build multiple stable models that are useful in consensus prediction. Further, from the analysis of the physical meaning of the descriptors, used in the best 2-D and 3-D descriptor models, M1 and M2, the significant physico-chemical forces that determine the hERG inhibition profile of small organic compounds are uncovered. Finally, as the models reported herein, are based on computed properties, they appear a valuable tool in virtual screening, where selection and prioritization of candidates is required.