A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.
Center for Systems Biology, Soochow University, Suzhou 215006, China.
Daqing
D
Zhang
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Jianfeng
J
Xiao
School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Nannan
N
Zhou
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Mingyue
M
Zheng
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Xiaomin
X
Luo
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China ; School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Hualiang
H
Jiang
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Kaixian
K
Chen
D000465:Algorithms; D001693:Biological Transport, Active; D001812:Blood-Brain Barrier / Q000187:drug effects* / Q000502:physiology*; D016208:Databases, Factual; D015195:Drug Design; D006801:Humans; D006860:Hydrogen Bonding; D006863:Hydrogen-Ion Concentration; D008954:Models, Biological; D010539:Permeability; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine