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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.

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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

Compounds: 151 | Models: 1 | Predictions: 2

Tab2: Model for diverse chemicals

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 128 0.823 0.752
External validation set external validation 23 0.694 0.730

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dc.date.accessioned 2014-11-08T17:54:55Z
dc.date.available 2014-11-08T17:54:55Z
dc.date.issued 2014-11-08 *
dc.identifier.uri http://hdl.handle.net/10967/124
dc.identifier.uri http://dx.doi.org/10.15152/QDB.124
dc.description.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.
dc.publisher Iiris Kahn
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.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.
qdb.property.endpoint 4. Human health effects 4.18. Endocrine Activity en_US
qdb.property.species Rattus norvegicus (Rat) en_US
qdb.descriptor.application DRAGON 5.4 en_US
qdb.prediction.application MOBY DIGS 1.2 en_US
bibtex.entry article en_US
bibtex.entry.author Liu, H.
bibtex.entry.author Papa, E.
bibtex.entry.author Gramatica, P.
bibtex.entry.doi 10.1021/tx0601509 en_US
bibtex.entry.journal Chem. Res. Toxicol. en_US
bibtex.entry.number 11 en_US
bibtex.entry.pages 1540–1548 en_US
bibtex.entry.title QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles en_US
bibtex.entry.volume 19 en_US
bibtex.entry.year 2006
qdb.model.type Regression model (regression) en_US


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

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