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

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Abstract

The silicon oil-air partition coefficients (KSiO/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 KSiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the KSiO/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 (KSiO/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 logKSiO/A, the number of non-hydrogen atoms (#nonHatoms) and energy gap of ELUMO and EHOMO (ELUMOEHOMO) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R2 of the model is 0.922, and the internal and external validation coefficient, Q2 LOO and Q2 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 logKSiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.

Keywords

Hydrophobic compounds Silicone oil-air partition coefficients (KSiO/AQuantitative structure–activity relationship (QSAR) Density functional theory (DFT) 

Notes

Acknowledgements

This research has been supported by the National Natural Science Foundation of China (No. 31570568 and No. 31670585), State Key Laboratory of Pulp and Paper Engineering (No. 201535), Science and Technology Planning Project of Guangzhou City, China (No. 201607010079 and No. 201607020007). The authors are grateful to all the anonymous reviewers for their insightful comments and suggestions.

Supplementary material

11356_2018_1705_MOESM1_ESM.doc (598 kb)
ESM 1 (DOC 598 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yanfei Qu
    • 1
  • Yongwen Ma
    • 1
    • 2
    • 3
  • Jinquan Wan
    • 1
    • 2
    • 3
  • Yan Wang
    • 1
    • 2
  1. 1.College of Environment and EnergySouth China University of TechnologyGuangzhouChina
  2. 2.The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  3. 3.State Key Laboratory of Pulp and Paper EngineeringSouth China University of TechnologyGuangzhouChina

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