A comprehensive QSPR model for dielectric constants of binary solvent mixtures

Abstract

The dielectric constant is a key physicochemical property in solubility, chemical equilibrium and the synthesis of compounds in pharmaceutical/chemical sciences. In this context, a quantitative structure–property relationship (QSPR) model was designed from 3207 binary solvent mixtures by using 23 calculated experimental–theoretical descriptors including solvent fractions (f₁ and f₂), individual dielectric constants of solvents (dc₁ and dc₂), temperature, and Abraham and Hansen solvation parameters. The QSPR model was developed using a genetic algorithm based multiple linear regression (GA-MLR) and robust regression. Jackknifing was implemented for internal–external validation of the selected descriptors by GA containing f₁, f₂, dc₁ and dc₂. Implementation of jackknifing on the selected descriptors revealed that p values were close to zero. Consequently, the significance of selected descriptors was confirmed through the sign change point of view and their validity was verified. The model was evaluated using the r² and Q²_(F3) parameters as criteria of model prediction ability. The r² values were equal to 0.925 and 0.922, and Q²_(F3) were reported as 0.873 and 0.862 for the cross-validation and prediction steps, respectively. Finally, model performance was clearly acceptable to anticipate the modelling of dielectric constants for a wide range of binary solvent mixtures.

Keywords:

• Quantitative structure–property relationship
• dielectric constants
• binary solvent mixtures
• genetic algorithm based multiple linear regression
• robust regression
• Jackknifing

Acknowledgements

The authors would like to express sincere gratitude to the Institute for Advanced Studies in Basic Sciences (IASBS) (Zanjan, Iran) and the Faculty of Pharmacy, Zanjan University of Medical Sciences (Zanjan, Iran) for financial support and funding.