Using general regression and probabilistic neural networks to predict human intestinal absorption with topological...

The objective of this study was to develop rapid and reliable methods to predict the percent human intestinal absorption (%HIA) of compounds based on their 2D descriptors. The analyzed data set included 86 drug and drug-like molecules and was the same as that studied by Wessel and co-workers. Instead of using three-dimensional descriptors such as polar surface area, which require lengthy computations, we employed only two-dimensional topological descriptors derived from information about the two-dimensional structure of molecules. The %HIA values were modeled using a general regression neural network (GRNN) and a probabilistic neural network (PNN), variants of normalized radial basis function networks. Both networks performed well to model the %HIA values. The root-mean square (rms) error was 22.8 %HIA unit for the external prediction set for a GRNN model, and 80% of the external prediction set was correctly classified for a PNN model, indicating the potential of our approach to estimate the %HIA values for a large set of compounds as virtual libraries.