Prediction of Skin Penetration using Artificial Neural Network

The artificial neural networks (ANN) technologies provide on-line capability to analyze many inputs and provide information to multiple outputs, and have the capability to learn or adapt to changing conditions. No doubt that the determination of Skin permeability is a time consuming process; which involves a quite tedious work. Material and method: Software Neurodimension was used for this study. A data set was taken from literature and used to train the network. A set of 20 compounds were used to construct the ANN models for training and 10 compounds used for prediction of skin penetration (n=30, molecular weight>500 da). Skin permeability expressed in log Kp (cm/h). Abraham descriptors of R2 (excess molar refraction), π2 H (dipolarity/polarizability), Σα2 H, Σβ2 H (the overall or effective hydrogen-bond acidity and basicity), and Vx (the McGowan characteristic volume) were obtained. Result: The correlation between the skin permeability coefficient and the Abraham descriptors were obtained from the trained neural network. The regression coefficient was 0.856 for training subset and MSE was 0.04. In addition, the predictability of the neural network model was compared to the experimental data. This paper uses artificial neural network for prediction of Skin permeability study.