A prediction model for blood-brain barrier permeation and analysis on its parameter biologically.
The objective of this paper is to build a reliable model based on the artificial neural network (ANN) for predicting the blood-brain barrier (BBB) permeability and reveal the effects of the molecular descriptor on the BBB permeability. Eight descriptors including high-affinity P-gp substrate probability and plasma protein binding ratio are selected to develop the model. The three layers feedforward neural network (8-5-1) is employed for the prediction of logBB. By analyzing the experimental results, polar surface area (PSA) seems to be the most important factor for BBB permeability. Different from traditional view, the Abraham's hydrogen-bond basicity (HBB) can make a positive contribution to logBB in rational range. The experimental results show that the ANN based model with eight selected descriptors as inputs can achieve good performance for logBB prediction, and the results of sensitivity analysis can be confirmed by the present biological and chemical research.
Chen
Yan
Y
Engineering Research Center of Bio-Process, Hefei University of Technology, Ministry of Education, Hefei 230009, China.
Zhu
Qing-Jun
QJ
Pan
Jian
J
Yang
Yi
Y
Wu
Xiao-Pei
XP
D001798:Blood Proteins / Q000502:physiology*; D001812:Blood-Brain Barrier / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008955:Models, Cardiovascular; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010539:Permeability
D001798:Blood Proteins