Quantitative modeling for prediction of thermodynamic properties of some pyridine derivatives using molecular descriptors and genetic algorithm‐multiple linear regressions

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 (ΔH f kJ/mol), and Gibbs free energy (ΔG 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. K E Y W O R D S QSPR, pyridine derivatives, leave-one-out cross-validation, genetic algorithm-multiple linear regression

Superparamagnetic core‐shell metal–organic framework Fe3O4@Ni‐MOF as efficient catalyst for oxidation of 1,4‐dihydropyridines using hydrogen peroxide

“…[36][37][38] 2 | RESULTS AND DISCUSSION Recently, we have been using Nickel (II) MOF (Ni-MOF) as a new and effective catalyst for the preparation of 2-aryl benzimidazole and benzothiazole derivatives. [39] In continuation of our research to develop efficient and green methods for the synthesis of heterocyclic compounds, [40][41][42] here, we describe a facile and convenient procedure for the oxidation of 3,5-diacyl or 3,5-diester 1,4-dihydropyridines using H 2 O 2 in the presence of Fe 3 O 4 @Ni-MOF as a catalyst (Scheme 1).…”

QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

“…Quantitative modeling for prediction of thermodynamic properties, such as the thermal energy ( E th kJ/mol), heat capacity (Cv J/molK), entropy ( S J/mol K), enthalpy of formation (Δ H f kJ/mol), and Gibbs free energy (Δ G kJ/mol) of 171pyridine derivatives have been studied using molecular descriptors and genetic algorithm‐multiple linear regression(GA‐MLR) [40].…”

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

“…The geometry of chemical structures can be characterized by a molecular graph [6][7][8][9][10][11][12][13]. Scientific researches in the past have found a considerable correlation between the molecular structure of pharmaceuticals and their characteristics [14][15][16][17][18]. The pharmaceutical qualities of drugs can be determined by applying chemical graph theory methods on the molecular structure of drugs.…”