A comprehensive QSPR model for dielectric constants of binary solvent mixtures

S. Soltanpour, M. Shahbazy, N. Omidikia, M. Kompany-Zareh, M. Taghi Baharifard

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)165-181
Number of pages17
JournalSAR and QSAR in Environmental Research
Volume27
Issue number3
DOIs
Publication statusPublished - 3 Mar 2016
Externally publishedYes

Keywords

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

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