Abstract

A supervised artificial neural network (ANN) model has been developed for the accurate prediction of the Blood-Brain Barrier (BBB) partition (in Log BB scale) of chemical compounds. A structural diverse set of 108 compounds of known experimental Log BB value was chosen for this study. The molecules were defined by means of a non-supervised neural network using our CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively the information of its chemical structure from its Simplified Molecular Input Line System (SMILES) code. The model obtained averages 83% of accuracy in the training set and of 73% in the external prediction set. The model is able to predict correctly the behavior of a very heterogeneous series of compounds in terms of the BBB permeation. The results indicate that this approach may represent a useful tool for the prediction of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties. CODES (c) is available free of charge for academic institutions.