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

Received Date: 5 February 2017
Revised Date: 2 March 2017

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.
- Quantitative structure-activity relationship,
- Blood-to-brain barrier partitioning,
- Drug,
- Enhanced replacement method,
- Support vector machine
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1.
沈阳化工大学材料科学与工程学院沈阳 110142