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

doi:10.3866/PKU.WHXB201704051

Abstract

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

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