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

引用本文: GOLMOHAMMADI Hassan, DASHTBOZORGI Zahra, KHOOSHECHIN Sajad. Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach[J]. 物理化学学报, 2017, 33(6): 1160-1170. doi: 10.3866/PKU.WHXB201704051 shu

Citation: GOLMOHAMMADI Hassan, DASHTBOZORGI Zahra, KHOOSHECHIN Sajad. Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach[J]. Acta Physico-Chimica Sinica, 2017, 33(6): 1160-1170. doi: 10.3866/PKU.WHXB201704051 shu

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

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

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 (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.

关键词:

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

English

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

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
Received Date: 05 February 2017
Revised Date: 02 March 2017
Available Online: 05 April 2017

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 words:

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

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沈阳化工大学材料科学与工程学院沈阳 110142

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

English

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

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

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

English

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

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

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CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

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