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

Lukman K Akinola, Adamu Uzairu, Gideon A Shallangwa, Stephen E Abechi, Abdullahi B Umar

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Article in Current research in toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors at 1 institution in 1 country.

Lukman K AkinolaDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria.

Adamu UzairuDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria.

Gideon A ShallangwaDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria.

Stephen E AbechiDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria.

Abdullahi B UmarDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria.

Ahmadu Bello University · NG

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No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Autocorrelation descriptorBinary logistic regressionEstrogen receptorHydroxylated polychlorinated biphenylQuantitative structure–activity relationship

Identifiers

PMID38435023

DOI10.1016/j.crtox.2024.100158

PMCPMC10907392

OpenAlexW4392122619

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LicenceCC BY-NC-ND

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