QSAR study of camptothecin derivatives as anticancer drugs using genetic algorithm and multiple linear regression analysis
Subject Areas : Journal of Physical & Theoretical Chemistry
fatemeh shafiei
1
,
Shahaboddin Mohebbi
2
,
Tahereh Momeni Isfahani
3
,
Mehdi Ahmadi Sabegh
4
1 - Department of Chemistry, Science Faculty, Islamic Azad University, Arak, Iran
2 - Department of Chemistry, Science Faculty, Islamic Azad University, Arak, Iran
3 - Department of Chemistry, Science Faculty, Islamic Azad University, Arak, Iran
4 - Department of Chemistry, Science Faculty, Islamic Azad University, Ahar, Iran
Keywords: GA-MLR, Molecular descriptors, Leave-One-Out Cross-Validation, Nuclear quadrupole coupling constants (QCC), Quantum parameters, Camptothecin (CPT) derivatives, QSAR,
Abstract :
A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively. QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the DurbinWatson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts descriptors contains important structural information sufficient to develop useful predictive models for the studied activities.
A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively. QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the Durbin–Watson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts descriptors contains important structural information sufficient to develop useful predictive models for the studied activities.