Quantitative modeling for prediction of boiling points of phenolic compounds

Abstract

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, Q² LOO = 0.841, Q² LMO = 0.831 and Q² EXT = 0.848. The results obtained demonstrate that the model is reliable with good predictive accuracy.