Quantitative modeling for prediction of boiling points of phenolic compounds
Soumaya Kherouf, Nabil Bouarra, Djelloul Messadi · International Journal of Chemistry and Technology · 2019
This work aims to reveal the correlation of the boiling point values of phenolic compounds with their molecular structures using a quantitative structure-property relationship (QSPR) approach. A large number of molecular descriptors have been calculated from molecular structures by the DRAGON software. In this study, all 56 phenolic compounds were divided into two subsets: one for the model formation and the other for external validation, by using the Kennard and Stone algorithm. A four-descriptor model was constructed by applying a multiple linear regression based on the ordinary least squares regression method and genetic algorithm/variables subsets selection. The good of fit and predictive power of the proposed model were evaluated by different approaches, including single or multiple output cross-validations, the Y-scrambling test, and external validation through prediction set. Also, the applicability domain of the developed model was examined using Williams plot. The model shows R² = 0.876, Q²LOO = 0.841, Q²LMO = 0.831 and Q²EXT = 0.848. The results obtained demonstrate that the model is reliable with good predictive accuracy.