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Saliner, A. G.; Netzeva, T. I.; Worth, A. P. Prediction of estrogenicity: validation of a classification model. SAR QSAR Environ. Res. 2006, 17, 195–223.

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Saliner, A. G.; Netzeva, T. I.; Worth, A. P. Prediction of estrogenicity: validation of a classification model. SAR QSAR Environ. Res. 2006, 17, 195–223.

QDB archive DOI: 10.15152/QDB.213   DOWNLOAD

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Property ER_activity: Estrogenic activity

Compounds: 460 | Models: 1 | Predictions: 2

Figure.1: Classification model for estrogenic activity

Decision tree (classification)

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Name Type n Accuracy
Training set training 117 0.906
Validation set external validation 343 0.714

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dc.date.accessioned 2020-01-10T08:46:28Z
dc.date.available 2020-01-10T08:46:28Z
dc.date.issued 2020-01-10
dc.identifier.uri http://hdl.handle.net/10967/213
dc.identifier.uri http://dx.doi.org/10.15152/QDB.213
dc.description.abstract (Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.
dc.publisher Geven Piir
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.title Saliner, A. G.; Netzeva, T. I.; Worth, A. P. Prediction of estrogenicity: validation of a classification model. SAR QSAR Environ. Res. 2006, 17, 195–223.
qdb.property.endpoint 4. Human health effects 4.18. Endocrine Activity en_US
qdb.descriptor.application TSAR 3.3 en_US
qdb.prediction.application TSAR 3.3 en_US
qdb.prediction.application KOWWIN 1.67 en_US
bibtex.entry article en_US
bibtex.entry.author Saliner, A. G.
bibtex.entry.author Netzeva, T. I.
bibtex.entry.author Worth, A. P.
bibtex.entry.doi 10.1080/10659360600636022 en_US
bibtex.entry.journal SAR QSAR Environ. Res. en_US
bibtex.entry.month Apr
bibtex.entry.number 2 en_US
bibtex.entry.pages 195–223 en_US
bibtex.entry.title Prediction of estrogenicity: validation of a classification model en_US
bibtex.entry.volume 17 en_US
bibtex.entry.year 2006
qdb.model.type Decision tree (classification) en_US


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  • Miscellaneous
    University of Tartu (Estonia), Institute of Chemistry, Molecular Technology

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