Quantitative structure–activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air

Quantitative structure–activity relationship for the partition coefficient of hydrophobic... The silicon oil-air partition coefficients (K SiO/A) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure–activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K SiO/A) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK SiO/A, the number of non-hydrogen atoms (#nonHatoms) and energy gap of E LUMO and E HOMO (E LUMO–E HOMO) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R 2 of the model is 0.922, and the internal and external validation coefficient, Q 2 LOO and Q 2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Science and Pollution Research Springer Journals

Quantitative structure–activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Environment; Environment, general; Environmental Chemistry; Ecotoxicology; Environmental Health; Atmospheric Protection/Air Quality Control/Air Pollution; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
ISSN
0944-1344
eISSN
1614-7499
D.O.I.
10.1007/s11356-018-1705-z
Publisher site
See Article on Publisher Site

Abstract

The silicon oil-air partition coefficients (K SiO/A) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure–activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K SiO/A) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK SiO/A, the number of non-hydrogen atoms (#nonHatoms) and energy gap of E LUMO and E HOMO (E LUMO–E HOMO) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R 2 of the model is 0.922, and the internal and external validation coefficient, Q 2 LOO and Q 2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.

Journal

Environmental Science and Pollution Research – Springer Journals

Published: Mar 25, 2018

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