Prediction of human skin permeability using a combination of molecular orbital calculations and artificial neural network

This study was carried out to develop a novel method for predicting the skin permeability coefficient (logK(p)) of compounds from their three-dimensional molecular structure using a combination of molecular orbital (MO) calculation and artificial neural network. Human skin permeability data on 92 structurally diverse compounds were analyzed. The molecular descriptors of each compound, such as the dipole moment, polarizability, sum of charges of nitrogen and oxygen atoms (sum(N,O)), and sum of charges of hydrogen atoms bonding to nitrogen or oxygen atoms (sum(H)) were obtained from MO calculations. The correlation between these molecular descriptors and logK(p) was examined using feed-forward back-propagation neural networks. To improve the generalization capability of a neural network, the network was trained with input patterns given 5% random noise. The neural network model with a configuration of 4-4-1 for input, hidden, and output layers was much superior to the conventional multiple linear regression model in terms of root mean square (RMS) errors (0.528 vs. 0.930). A "leave-one-out" cross-validation revealed that the neural network model could predict skin permeability with a reasonable accuracy (predictive RMS error of 0.669).