In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model

This paper has two objectives:  first to develop an in silico model for the prediction of blood brain barrier
permeability of new chemical entities and second to find the role of active transport specific to the
P-glycoprotein (P-gp) substrate probability in blood brain barrier permeability. An Artificial Neural Network
(ANN) model has been developed to predict the ratios of the steady-state concentrations of drugs in the
brain to those in the blood (logBB) from their molecular structural parameters. Seven descriptors including
P-gp substrate probability have been used for model development. The developed model is able to capture
a relationship between P-gp and logBB. The predictive ability of the ANN model has also been compared
with earlier computational models.