Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach

被引：1

作者：

GOLMOHAMMADI Hassan ^{[1
]}

DASHTBOZORGI Zahra ^{[2
]}

KHOOSHECHIN Sajad ^{[2
]}

机构：

[1] Young Researchers and Elite Club, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University

[2] Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad

来源：

物理化学学报 | 2017年 / 06期

关键词：

Quantitative structure-activity relationship; Blood-to-brain barrier partitioning; Drug; Enhanced replacement method; Support vector machine;

D O I：

暂无

中图分类号：

R96 [药理学]; TP18 [人工智能理论];

学科分类号：

100602 ; 100706 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The purpose of this study was to develop a quantitative structure–property relationship（QSPR） model based on the enhanced replacement method（ERM） and support vector machine（SVM） to predict the blood-to-brain barrier partitioning behavior（log BB） of various drugs and organic compounds. Different molecular descriptors were calculated using a dragon package to represent the molecular structures of the compounds studied. The enhanced replacement method（ERM） was used to select the variables and construct the SVM model. The correlation coefficient, R2, between experimental results and predicted log BB was 0.878 and 0.986, respectively. The results obtained demonstrated that, for all compounds, the log BB values estimated by SVM agreed with the experimental data, demonstrating that SVM is an effective method for model development, and can be used as a powerful chemometric tool in QSPR studies.

引用

页码：1160 / 1170

页数：11