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Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure–activity relationship models

Authors :
Lukman K. Akinola
Adamu Uzairu
Gideon A. Shallangwa
Stephen E. Abechi
Abdullahi B. Umar
Source :
Current Research in Toxicology, Vol 6, Iss , Pp 100158- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

Details

Language :
English
ISSN :
2666027X
Volume :
6
Issue :
100158-
Database :
Directory of Open Access Journals
Journal :
Current Research in Toxicology
Publication Type :
Academic Journal
Accession number :
edsdoj.969984ea76ff44ed8ef6d1c676e2ee6f
Document Type :
article
Full Text :
https://doi.org/10.1016/j.crtox.2024.100158