A k-nearest neighbor classification of hERG K(+) channel blockers

Abstract

A series of 172 molecular structures that block the hERG K(+) channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.

Keywords: Classification model; Ikr; KCNH2; Toxicity; hERG blockers; k-nearest neighbor (k-NN).

Fig. 1

Venn diagram representing the number of training set compounds correctly predicted by all three models (yellow), by any two models (magenta), by only one model (blue) and by none of the models (green). The shaded area represents compounds correctly predicted by the consensus model