We describe the development of a computational model for the prediction of the inhibition of K(+) flow through the hERG ion channel. Using a collection of 1075 discovery compounds with hERG inhibition measured in our standard patch-clamp electrophysiology assay, molecular features important for drug-induced inhibition were identified using a combination of statistical inference algorithms and manual hypothesis generation and testing. While many of the features used in the model reflect those referenced in the literature, several aspects of the model provide new insight into the role of physicochemical properties, electrostatics, and novel pharmacophores in hERG inhibition. Coefficients for these 10 features were then determined by least median squares regression, resulting in a model with an R(2) approximately 0.66 and RMS error (RMSe) of 0.47 log units for an external test set. Significant additional validation performed using a large collection of subsequent discovery data has been very encouraging with an R(2)=0.54 and an RMSe of 0.63 log units. The performance of the model across several different chemotypes is demonstrated and discussed.