Liu, H.; Papa, E.; Gramatica, P. QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles. Chem. Res. Toxicol. 2006, 19, 11, 1540–1548.

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Liu, H.; Papa, E.; Gramatica, P. QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles. Chem. Res. Toxicol. 2006, 19, 11, 1540–1548.

QDB archive DOI: 10.15152/QDB.124   DOWNLOAD

QsarDB content

Property LogRBA: Estrogen receptor relative binding affinity

Tab2: Model for diverse chemicals

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1280.8230.752
External validation setexternal validation230.6940.730

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When using this QDB archive, please cite (see details) it together with the original article:

  • Kahn, I. Data for: QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles. QsarDB repository, QDB.124. 2014. http://dx.doi.org/10.15152/QDB.124

  • Liu, H.; Papa, E.; Gramatica, P. QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles. Chem. Res. Toxicol. 2006, 19, 1540–1548. http://dx.doi.org/10.1021/tx0601509

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Title: Liu, H.; Papa, E.; Gramatica, P. QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles. Chem. Res. Toxicol. 2006, 19, 11, 1540–1548.
Abstract:A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure-activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
URI:http://hdl.handle.net/10967/124
http://dx.doi.org/10.15152/QDB.124
Date:2014-11-08


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    Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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