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Acta Phys. -Chim. Sin.  2017, Vol. 33 Issue (6): 1160-1170    DOI: 10.3866/PKU.WHXB201704051
Article     
Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach
GOLMOHAMMADI Hassan1, DASHTBOZORGI Zahra2, KHOOSHECHIN Sajad2
1 Young Researchers and Elite Club, Yadegar-e-Imam Khomeini(RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran;
2 Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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Abstract  

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 (logBB) 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 logBB was 0.878 and 0.986, respectively. The results obtained demonstrated that, for all compounds, the logBB 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.



Key wordsQuantitative structure-activity relationship      Blood-to-brain barrier partitioning      Drug      Enhanced replacement method      Support vector machine     
Received: 05 February 2017      Published: 05 April 2017
Corresponding Authors: GOLMOHAMMADI Hassan     E-mail: Hassan.gol@gmail.com
Cite this article:

GOLMOHAMMADI Hassan, DASHTBOZORGI Zahra, KHOOSHECHIN Sajad. Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach. Acta Phys. -Chim. Sin., 2017, 33(6): 1160-1170.

URL:

http://www.whxb.pku.edu.cn/Jwk_wk/wlhx/10.3866/PKU.WHXB201704051     OR     http://www.whxb.pku.edu.cn/Jwk_wk/wlhx/Y2017/V33/I6/1160

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