Quantitative Structure Toxicity Relationship (QSTR) study was applied to a dataset of 35 polychlorinated dibenzofurans (PCDFs) to investigate the relationship between toxicities of the compounds and their structures by employing Density Functional Theory (DFT) (B3LYP/6-31G*) method to compute their quantum molecular descriptors. The model was built using Genetic Function Algorithm (GFA) approach. The model (N= 24, Friedman LOF = 0.361, squared correlation coefficient (R2) = 0.963, R2adj = 0.955, cross-validation correlation coefficient (Q2) = 0.889, external prediction ability (R2pred) = 0.8286, P-value of optimization at P95% < 0.05) of the best statistical significance was selected. The accuracy of the model was evaluated through Leave one out (LOOV) cross-validation, external validation using test set molecules, Y-randomization and applicability domain techniques. The results of the present study are expected to be useful to the environmental regulatory agencies locally and internationally in the area of environmental risk assessment of toxicity of Polychlorinated dibenzofurans (PCDFs) and other related Polychlorinated aromatic compounds/ pollutants that fall within the model’s applicability domain.
| Published in | International Journal of Computational and Theoretical Chemistry (Volume 5, Issue 2) |
| DOI | 10.11648/j.ijctc.20170502.11 |
| Page(s) | 14-24 |
| 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 |
GFA, PCDFs, QSTR, Toxicity, Polychlorinated Dibenzofurans
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APA Style
Sabitu Babatunde Olasupo, Adamu Uzairu, Balarabe Sarki Sagagi. (2017). Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans. International Journal of Computational and Theoretical Chemistry, 5(2), 14-24. https://doi.org/10.11648/j.ijctc.20170502.11
ACS Style
Sabitu Babatunde Olasupo; Adamu Uzairu; Balarabe Sarki Sagagi. Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans. Int. J. Comput. Theor. Chem. 2017, 5(2), 14-24. doi: 10.11648/j.ijctc.20170502.11
AMA Style
Sabitu Babatunde Olasupo, Adamu Uzairu, Balarabe Sarki Sagagi. Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans. Int J Comput Theor Chem. 2017;5(2):14-24. doi: 10.11648/j.ijctc.20170502.11
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Download@article{10.11648/j.ijctc.20170502.11,
author = {Sabitu Babatunde Olasupo and Adamu Uzairu and Balarabe Sarki Sagagi},
title = {Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans},
journal = {International Journal of Computational and Theoretical Chemistry},
volume = {5},
number = {2},
pages = {14-24},
doi = {10.11648/j.ijctc.20170502.11},
url = {https://doi.org/10.11648/j.ijctc.20170502.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijctc.20170502.11},
abstract = {Quantitative Structure Toxicity Relationship (QSTR) study was applied to a dataset of 35 polychlorinated dibenzofurans (PCDFs) to investigate the relationship between toxicities of the compounds and their structures by employing Density Functional Theory (DFT) (B3LYP/6-31G*) method to compute their quantum molecular descriptors. The model was built using Genetic Function Algorithm (GFA) approach. The model (N= 24, Friedman LOF = 0.361, squared correlation coefficient (R2) = 0.963, R2adj = 0.955, cross-validation correlation coefficient (Q2) = 0.889, external prediction ability (R2pred) = 0.8286, P-value of optimization at P95% < 0.05) of the best statistical significance was selected. The accuracy of the model was evaluated through Leave one out (LOOV) cross-validation, external validation using test set molecules, Y-randomization and applicability domain techniques. The results of the present study are expected to be useful to the environmental regulatory agencies locally and internationally in the area of environmental risk assessment of toxicity of Polychlorinated dibenzofurans (PCDFs) and other related Polychlorinated aromatic compounds/ pollutants that fall within the model’s applicability domain.},
year = {2017}
}
TY - JOUR T1 - Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans AU - Sabitu Babatunde Olasupo AU - Adamu Uzairu AU - Balarabe Sarki Sagagi Y1 - 2017/04/13 PY - 2017 N1 - https://doi.org/10.11648/j.ijctc.20170502.11 DO - 10.11648/j.ijctc.20170502.11 T2 - International Journal of Computational and Theoretical Chemistry JF - International Journal of Computational and Theoretical Chemistry JO - International Journal of Computational and Theoretical Chemistry SP - 14 EP - 24 PB - Science Publishing Group SN - 2376-7308 UR - https://doi.org/10.11648/j.ijctc.20170502.11 AB - Quantitative Structure Toxicity Relationship (QSTR) study was applied to a dataset of 35 polychlorinated dibenzofurans (PCDFs) to investigate the relationship between toxicities of the compounds and their structures by employing Density Functional Theory (DFT) (B3LYP/6-31G*) method to compute their quantum molecular descriptors. The model was built using Genetic Function Algorithm (GFA) approach. The model (N= 24, Friedman LOF = 0.361, squared correlation coefficient (R2) = 0.963, R2adj = 0.955, cross-validation correlation coefficient (Q2) = 0.889, external prediction ability (R2pred) = 0.8286, P-value of optimization at P95% < 0.05) of the best statistical significance was selected. The accuracy of the model was evaluated through Leave one out (LOOV) cross-validation, external validation using test set molecules, Y-randomization and applicability domain techniques. The results of the present study are expected to be useful to the environmental regulatory agencies locally and internationally in the area of environmental risk assessment of toxicity of Polychlorinated dibenzofurans (PCDFs) and other related Polychlorinated aromatic compounds/ pollutants that fall within the model’s applicability domain. VL - 5 IS - 2 ER -
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