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Acta Physico-Chimica Sinca  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
Hassan GOLMOHAMMADI1,*(),Zahra DASHTBOZORGI2,Sajad KHOOSHECHIN2
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: Hassan GOLMOHAMMADI     E-mail: Hassan.gol@gmail.com
Cite this article:

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

URL:

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

 
Descriptor Notation Coefficient Mean effect VIF
average valence connectivity index chi-4X4av1.9560.1271.164
balaban centric indexBAC?0.006?0.1341.202
information content index neighborhood symmetry of 1-orderIC1?0.805?1.9611.416
dCoMMA2 value/weighted by atomic van der waals volumesDISPv0.0470.3061.337
leverage-weighted autocorrelation of lag 7/weighted by atomicHats7p3.1020.3301.187
3D-morse signal 04/weighted by atomic massesMor04m0.1390.0101.182
constant1.365
 
X4avBACIC1DISPvHats7pMor04m
X4av1?0.032?0.297?0.300?0.053?0.092
BAC10.2610.1320.1010.319
IC110.3800.3320.075
DISPv10.2730.234
Hats7p1?0.042
Mor04m1
 
Training set Prediction set
R2 RMSEC QCV2 RMSECV R2 RMSEP
ERM0.8700.2620.8650.2780.8780.228
SVM0.9900.0720.9880.0730.9860.074
 
 
 
 
 
R2Rcv2kk'R2-R02R2-R'02
R2 R2
SVM0.9860.9871.0160.971?0.011?0.013
ERM0.8780.5940.8741.023?0.112?0.122
 
Model Method Dataset size Overall accuracy Reference
Kortagere et al. regression model 78-376 77 64
support vector machine 80-83
Li et al. logistic regression 415 71.0 65
linear discriminate analysis 71.2
K nearest neighbor 74.3
C4.5 decision tree 77.1
probabilistic neural network 76.5
support vector machine 83.7
Gerebtzoff and Seelig calculated molecular cross-sectional area 42-122 83.3-90.2 66
Muehlbacher et al. multiple linear regression 362 - 67
random forest 88.2
our models enhanced replacement method 310 90.4 this work
support vector machine 93.1
 
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