Predicting blood-brain barrier penetration of drugs using an artificial neural network.
An artificial neural network model is developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (log BB) from their molecular structural parameters. These molecular structural parameters are the molecular volume (V), the sum of the absolute values of the net atomic charges of oxygen and nitrogen atoms which are hydrogen-bond acceptors (Q(O, N)), and the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)). For a training set of 56 compounds and a test set of 5 compounds, root mean squared errors (RMSE) between experimental log BB values and calculated/predicted log BB values were 0.236 and 0.258, respectively. These molecular structural parameters can be obtained easily from quantum chemical calculations. The model is suitable for the rapid prediction of the blood-brain barrier penetration of drugs.
Fu
X C
XC
Department of Pharmacy, Zhejiang University City College, Hangzhou, PR China. Fuxc@zucc.edu.cn
Wang
G P
GP
Liang
W Q
WQ
Yu
Q S
QS
D000465:Algorithms; D001812:Blood-Brain Barrier; D004563:Electrochemistry; D006860:Hydrogen Bonding; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010539:Permeability; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations