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Structure–property relationships of aliphatic esters using topological descriptors and backward ‐ multiple linear regression method
Author(s) -
Rajabi Mehdi,
Shafiei Fatemeh
Publication year - 2020
Publication title -
journal of the chinese chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 45
eISSN - 2192-6549
pISSN - 0009-4536
DOI - 10.1002/jccs.201900528
Subject(s) - quantitative structure–activity relationship , multicollinearity , chemistry , linear regression , molecular descriptor , stepwise regression , variance inflation factor , boiling point , regression analysis , enthalpy of vaporization , enthalpy , thermodynamics , biological system , statistics , stereochemistry , mathematics , organic chemistry , physics , biology
In the present investigation, quantitative structure–property relationship (QSPR) modeling was carried out on 48 aliphatic esters to develop a robust model for the prediction of thermodynamic properties such as the enthalpy of vaporization at standard condition (∆ H ° vap kJ mol −1 ) and normal temperature of boiling points ( T bp ° K). Multiple linear regression (MLR) and backward (BW) stepwise regression methods were used to select the descriptors derived from the Chemicalize program to give the QSPR models. These models were used to delineate the important descriptors responsible for the properties of the aliphatic esters. The multicollinearity and autocorrelation properties of the descriptors used in the models were tested by calculating the variance inflation factor, Pearson correlation coefficient, and the Durbin–Watson statistics. Leave‐one‐out cross‐validation, leave‐group (fivefold)‐out, and external validation criteria ( Q 2 F1 , Q 2 F2 , Q 2 F3, CCC, R 2 m ) were proposed to verify the predictive performance of QSPR models derived by BW‐MLR analysis. The predictive ability of the models was found to be satisfactory. Thus, QSPR models derived from this study may be helpful for modeling and designing some new aliphatic esters and predicting their properties.