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

Sabitu Babatunde Olasupo; Adamu Uzairu; Balarabe Sarki Sagagi

doi:doi:10.11648/j.ijbc.20170203.15

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

Sabitu Babatunde Olasupo, Adamu Uzairu, Balarabe Sarki Sagagi

Published in International Journal of Bioorganic Chemistry (Volume 2, Issue 3)

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 (R²_Adj) = 0.9228 and external prediction (R²_pred) =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), 2024. Published by Science Publishing Group

Keywords

QSAR, Dioxins, PCBs, QSTR, Polychlorinated Biphenyls

References

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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

Sabitu Babatunde Olasupo

Department of Chemistry, Kano University of Science and Technology, Wudil, Nigeria
Adamu Uzairu

Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
Balarabe Sarki Sagagi

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

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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}
}

Copy | Download

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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