Quantitative Structure- Property Relationship(QSPR) Study of 2-Phenylindole derivatives as Anticancer Drugs Using Molecular Descriptors
محورهای موضوعی : Journal of Physical & Theoretical Chemistry
samira Bahrami
1
,
fatemeh shafiei
2
,
Azam Marjani
3
,
Tahereh Momeni Isfahani
4
1 - Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
2 - Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
3 - Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
4 - Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
کلید واژه: structure -property relationship, 2-Phenylindole derivatives, genetic algorithm -multiple linear regressions (GA-MLR), Entropy, Heat capacity,
چکیده مقاله :
A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the DurbinWatson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives.
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