Prediction of inhibition of the sodium ion-proton antiporter by benzoylguanidine derivatives from molecular structure.

Abstract

The use of quantitative structure-activity relationships to predict IC50 values of 113 potential Na+/H+ antiporter inhibitors is reported. Multiple linear regression and computational neural networks (CNNs) are used to develop models using a set of information-rich descriptors. The descriptors encode information about topology, geometry, electronics, and combination hybrids. A five-descriptor CNN model with root-mean-square (rms) errors of 0.278 log units for the training set and 0.377 log units for the prediction set was developed. Examination of data set subclasses showed that systematic structural variations were also well-encoded resulting in 100% accuracy of prediction trends. An experiment involving a committee of five CNNs was also performed to examine the effect of network output averaging. This showed improved results decreasing the training and cross-validation set rms error to 0.228 log units and the prediction set rms error to 0.296 log units.