Prediction of the Blood–Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods

The
ability of chemicals to enter the blood–brain barrier
(BBB) is a key factor for central nervous system (CNS) drug development.
Although many models for BBB permeability prediction have been developed,
they have insufficient accuracy (ACC) and sensitivity (SEN). To improve
performance, ensemble models were built to predict the BBB permeability
of compounds. In this study, in silico ensemble-learning models were
developed using 3 machine-learning algorithms and 9 molecular fingerprints
from 1757 chemicals (integrated from 2 published data sets) to predict
BBB permeability. The best prediction performance of the base classifier
models was achieved by a prediction model based on an random forest
(RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area
under the receiver-operating characteristic (ROC) curve (AUC) of 0.957,
a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation.
The prediction performance of the ensemble models is better than that
of most of the base classifiers. The final ensemble model has also
demonstrated good accuracy for an external validation and can be used
for the early screening of CNS drugs.