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Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors

Received: 25 February 2017     Accepted: 22 March 2017     Published: 7 April 2017
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Abstract

The Density functional theory (DFT) at B3LYP of 6-31G* basis set was employed to optimize 30 polychlorinated Biphenyls (PCBs) involved in this study by using Genetic function appropriation algorithm (GFA) approach to develop regression models in order to predict the toxicity of the compounds. The optimum model which has squared correlation coefficient (R2) = 0.9382, cross validated correlation coefficient (R2cv) = 0.9056, adjusted squared correlation coefficient (R2Adj) = 0.9228 and external prediction (R2pred) =0.7238 was selected. The robustness of the model was confirmed by method of Y- randomization and the accuracy of the proposed model was also illustrated by using cross-Validation, validation through an external test set and applicability domain techniques. This QSTR model proved to be a useful tool in the prediction of toxicity of the congeneric compounds and a guide in the identification of structural features that could be responsible for toxicity of other polychlorinated aromatic compounds.

Published in International Journal of Bioorganic Chemistry (Volume 2, Issue 3)
DOI 10.11648/j.ijbc.20170203.15
Page(s) 107-117
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

QSAR, Dioxins, PCBs, QSTR, Polychlorinated Biphenyls

References
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Cite This Article
  • APA Style

    Sabitu Babatunde Olasupo, Adamu Uzairu, Balarabe Sarki Sagagi. (2017). Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors. International Journal of Bioorganic Chemistry, 2(3), 107-117. https://doi.org/10.11648/j.ijbc.20170203.15

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    ACS Style

    Sabitu Babatunde Olasupo; Adamu Uzairu; Balarabe Sarki Sagagi. Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors. Int. J. Bioorg. Chem. 2017, 2(3), 107-117. doi: 10.11648/j.ijbc.20170203.15

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    AMA Style

    Sabitu Babatunde Olasupo, Adamu Uzairu, Balarabe Sarki Sagagi. Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors. Int J Bioorg Chem. 2017;2(3):107-117. doi: 10.11648/j.ijbc.20170203.15

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  • @article{10.11648/j.ijbc.20170203.15,
      author = {Sabitu Babatunde Olasupo and Adamu Uzairu and Balarabe Sarki Sagagi},
      title = {Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors},
      journal = {International Journal of Bioorganic Chemistry},
      volume = {2},
      number = {3},
      pages = {107-117},
      doi = {10.11648/j.ijbc.20170203.15},
      url = {https://doi.org/10.11648/j.ijbc.20170203.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbc.20170203.15},
      abstract = {The Density functional theory (DFT) at B3LYP of 6-31G* basis set was employed to optimize 30 polychlorinated Biphenyls (PCBs) involved in this study by using Genetic function appropriation algorithm (GFA) approach to develop regression models in order to predict the toxicity of the compounds. The optimum model which has squared correlation coefficient (R2) = 0.9382, cross validated correlation coefficient (R2cv) = 0.9056, adjusted squared correlation coefficient (R2Adj) = 0.9228 and external prediction (R2pred) =0.7238 was selected. The robustness of the model was confirmed by method of Y- randomization and the accuracy of the proposed model was also illustrated by using cross-Validation, validation through an external test set and applicability domain techniques. This QSTR model proved to be a useful tool in the prediction of toxicity of the congeneric compounds and a guide in the identification of structural features that could be responsible for toxicity of other polychlorinated aromatic compounds.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Quantitative Structure Toxicity Relationship (QSTR) Models for Predicting Toxicity of Polychlorinated Biphenyls (PCBs) Using Quantum Chemical Descriptors
    AU  - Sabitu Babatunde Olasupo
    AU  - Adamu Uzairu
    AU  - Balarabe Sarki Sagagi
    Y1  - 2017/04/07
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijbc.20170203.15
    DO  - 10.11648/j.ijbc.20170203.15
    T2  - International Journal of Bioorganic Chemistry
    JF  - International Journal of Bioorganic Chemistry
    JO  - International Journal of Bioorganic Chemistry
    SP  - 107
    EP  - 117
    PB  - Science Publishing Group
    SN  - 2578-9392
    UR  - https://doi.org/10.11648/j.ijbc.20170203.15
    AB  - The Density functional theory (DFT) at B3LYP of 6-31G* basis set was employed to optimize 30 polychlorinated Biphenyls (PCBs) involved in this study by using Genetic function appropriation algorithm (GFA) approach to develop regression models in order to predict the toxicity of the compounds. The optimum model which has squared correlation coefficient (R2) = 0.9382, cross validated correlation coefficient (R2cv) = 0.9056, adjusted squared correlation coefficient (R2Adj) = 0.9228 and external prediction (R2pred) =0.7238 was selected. The robustness of the model was confirmed by method of Y- randomization and the accuracy of the proposed model was also illustrated by using cross-Validation, validation through an external test set and applicability domain techniques. This QSTR model proved to be a useful tool in the prediction of toxicity of the congeneric compounds and a guide in the identification of structural features that could be responsible for toxicity of other polychlorinated aromatic compounds.
    VL  - 2
    IS  - 3
    ER  - 

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Author Information
  • Department of Chemistry, Kano University of Science and Technology, Wudil, Nigeria

  • Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria

  • Department of Chemistry, Kano University of Science and Technology, Wudil, Nigeria

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