Prediction of blood-brain barrier permeation using quantum chemically derived information

A model for the prediction of the blood-brain distribution (logBB) is obtained by multiple regression analysis of molecular descriptors for a training set of 90 compounds. The majority of the descriptors are derived from quantum chemical information using semi-empirical AM1 calculations to compute fundamental properties of the molecules investigated. The polar surface area of the compounds can be described appropriately by six descriptors derived from the molecular electrostatic potential. This set shows a strong correlation with the observed logBB. Additional quantum chemically computed properties that contribute to the final model comprise the ionization potential and the covalent hydrogen-bond basicity. Complementary descriptors account for the presence of certain chemical groups, the number of hydrogen-bond donors, and the number of rotatable bonds of the compounds. The quality of the fit is further improved by including variables derived from principal component analysis of the molecular geometry.