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Repository of Research and Investigative Information

دانشگاه علوم پزشکی و خدمات بهداشتی درمانی زنجان

A comprehensive QSPR model for dielectric constants of binary solvent mixtures

(2016) A comprehensive QSPR model for dielectric constants of binary solvent mixtures. Sar and Qsar in Environmental Research. pp. 165-181. ISSN 1062-936X

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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 (f(1) and f(2)), individual dielectric constants of solvents (dc(1) and dc(2)), 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 f(1), f(2), dc(1) and dc(2). 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 r(2) and Q((F3))(2) parameters as criteria of model prediction ability. The r(2) values were equal to 0.925 and 0.922, and Q((F3))(2) 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.

Item Type: Article
Keywords: robust regression dielectric constants binary solvent mixtures genetic algorithm based multiple linear regression Jackknifing Quantitative structure-property relationship STRUCTURE-PROPERTY RELATIONSHIPS WATER PARTITION-COEFFICIENT SUPPORT VECTOR MACHINES NEURAL-NETWORKS VARIABLE SELECTION GENETIC ALGORITHM PHYSICOCHEMICAL PROPERTIES DESCRIPTOR SELECTION LINEAR-REGRESSION ORGANIC-SOLVENTS Chemistry, Multidisciplinary Computer Science, Interdisciplinary Applications Environmental Sciences Mathematical & Computational Biology Toxicology
Page Range: pp. 165-181
Journal or Publication Title: Sar and Qsar in Environmental Research
Abstract and Indexing: ISI, Pubmed, Scopus
Quartile : Q2
Volume: 27
Number: 3
Identification Number: https://doi.org/10.1080/1062936x.2015.1120779
ISSN: 1062-936X
Depositing User: خانم مریم زرقانی
URI: http://repository.zums.ac.ir/id/eprint/559

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