Prediction of blood-brain partitioning: a model based on ab initio calculated quantum chemical descriptors

Abstract

A new model for the prediction of log BB, a penetration measure through the blood-brain barrier, based on a molecular set of 82 diverse molecules is developed. The majority of the descriptors are derived from quantum chemical ab initio calculations, augmented with a number of classical descriptors. The quantum chemical information enables one to compute fundamental properties of the molecules. The best set of descriptors was selected by sequential selection and multiple linear regression was used to develop the QSAR model. The predictive capability of the model was tested using internal and external test procedures and the domain of applicability was determined to identify reliable predictions. The selected set of descriptors shows a significant correlation with the experimental log BB. The proposed model could reproduce the data with an error approaching the experimental uncertainty and satisfies the available validation procedures. The obtained results indicate that the use of quantum chemical information in describing molecules improves the behavior of the model.