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• W2981538049 abstract "Abstract Pyridines are compounds with a wide range of biological activities and they are the basis of several groups of drugs. In this study, the relationship between molecular descriptors and the thermal energy ( E th kJ/mol), heat capacity ( C v J/molK), entropy (S J/mol K), enthalpy of formation (Δ H f kJ/mol), and Gibbs free energy (Δ G kJ/mol) of pyridine derivatives is studied. These properties were calculated at the Hartree–Fock level of theory and 6‐311G* G basis sets by Gaussian 09 software. The chemical structures of 171 pyridine derivatives were drawn by the Gauss View 05 program. The genetic algorithm‐multiple linear regression (GA‐MLR) and backward methods—were used to select the suitable descriptors and also for predicting the thermodynamic properties of pyridine derivatives. The correlations between the molecular descriptors in the best models were discussed by the Pearson coefficient correlation and collinearity statistics. The predictive powers of the GA‐MLR models are studied using leave‐one‐out (LOO) cross‐validation and external test set. The best quantitative structure–property relationship (QSPR) models are obtained based on the statistical parameters, such as R 2 , Q 2 LOO, and RMSE values for the data sets. The predictive ability of the GA‐MLR models with two‐three selected molecular descriptors was found to be satisfactory and could be used for modeling and predicting of the mentioned properties of nontested pyridine derivatives." @default.
• W2981538049 created "2019-11-01" @default.
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• W2981538049 date "2019-10-22" @default.
• W2981538049 modified "2023-10-01" @default.
• W2981538049 title "Quantitative modeling for prediction of thermodynamic properties of some pyridine derivatives using molecular descriptors and genetic algorithm‐multiple linear regressions" @default.
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• W2981538049 doi "https://doi.org/10.1002/jccs.201900283" @default.
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