Prediction of corneal permeability using artificial neural networks

The purpose of this study was to develop a simple model for prediction of corneal permeability of structurally different drugs as a function of calculated molecular descriptors using artificial neural networks. A set of 45 compounds with experimentally derived values of corneal permeability (log C) was used to develop, test and validate a predictive model. Each compound was encoded with 1194 calculated molecular structure descriptors. A genetic algorithm was used to select a subset of descriptors that best describe corneal permeability coefficient log C and a supervised network with radial basis transfer function (RBF) was used to correlate calculated molecular descriptors with experimentally derived measures of corneal permeability. The best model, with 4 input descriptors and 12 hidden neurones was chosen, and the significance of the selected descriptors to corneal permeability was examined. Strong correlation of predicted with experimentally derived log C values (correlation coefficient greater than 0.87 and 0.83 respectively) was obtained for the training and testing data sets. The developed model could be useful for the rapid prediction of the corneal permeability of candidate drugs based on molecular structure alone as it does not require experimentally derived data.