Original Research

Medicinal Chemistry Research

, Volume 23, Issue 5, pp 2264-2276

First online:

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

  • Eslam PourbasheerAffiliated withYoung Researchers and Elite Club, Ardabil Branch, Islamic Azad University Email author 
  • , Reza AalizadehAffiliated withYoung Researchers and Elite Club, Ardabil Branch, Islamic Azad University
  • , Mohammad Reza GanjaliAffiliated withCenter of Excellence in Electrochemistry, Faculty of Chemistry, University of TehranBiosensor Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences
  • , Parviz NorouziAffiliated withCenter of Excellence in Electrochemistry, Faculty of Chemistry, University of TehranBiosensor Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences
  • , Javad ShadmaneshAffiliated withDepartment of Chemistry, University of Athens
  • , Constantinos MethenitisAffiliated withDepartment of Chemistry, University of Athens

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Abstract

In this work, a quantitative structure–activity relationship study was developed to predict the NaV1.7 antagonist activities. A data set consisted of 36 compounds with known NaV1.7 antagonist activities was split into two subsets of training set and test set using hierarchical clustering technique. To select the most respective descriptors among the pool of descriptors, genetic algorithm was applied. The model based on selected descriptors through genetic algorithm (GA) was built by employing multiple linear regression (MLR) method. The squared correlation coefficient (\( R_{\text{train}}^{2} \)) of 0.813, squared cross-validated correlation coefficient for leave-one-out (\( Q_{\text{LOO}}^{2} \)) of 0.699 and root mean square error of 0.214 were calculated for the training set compounds by GA–MLR model. The proposed model performed good predictive ability when it was verified by internal and external validation tests. The results of predictive model can lead to design better compounds with high NaV1.7 antagonist activities.

Keywords

QSAR Multiple linear regressions Genetic algorithm Hierarchical clustering NaV1.7 antagonists