QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR)

Authors

  • Hamid Rafiei Department of Chemistry, Dashtestan Branch, Islamic Azad University, Dashtestan, Iran
  • Marziyeh Khanzadeh Department of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran
  • Shahla Mozaffari Department of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran
  • Mohammad Hassan Bostanifar Department of Chemistry, Dashtestan Branch, Islamic Azad University, Dashtestan, Iran
  • Zhila Mohajeri Avval Department of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran
  • Reza Aalizadeh Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
  • Eslam Pourbasheer Department of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran

DOI:

https://doi.org/10.17179/excli2015-731

Keywords:

QSAR, genetic algorithms, multiple linear regression, HCV

Abstract

Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors. A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r2, concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.

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Published

2016-01-18

How to Cite

Rafiei, H., Khanzadeh, M., Mozaffari, S., Bostanifar, M. H., Avval, Z. M., Aalizadeh, R., & Pourbasheer, E. (2016). QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR). EXCLI Journal, 15, 38–53. https://doi.org/10.17179/excli2015-731

Issue

Vol. 15 (2016)

Section

Original articles

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