Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
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Training set | training | 30 | 0.018 | 0.470 |
Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
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Training set | training | 26 | 0.427 | 0.212 |
Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
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Training set | training | 26 | 0.596 | 0.178 |
Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
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Training set | training | 25 | 0.794 | 0.128 |
When using this QDB archive, please cite (see details) it together with the original article:
Piir, G. Data for: Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. QsarDB repository, QDB.112. 2014. https://doi.org/10.15152/QDB.112
Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 767-774. https://doi.org/10.1021/tx4000655
Title: | Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 5, 767-774. |
Abstract: | This study outlines the development of a series of quantitative mechanistic models enabling skin sensitization potency in the LLNA to be predicted for direct acting Michael acceptors. These models utilized several computational descriptors based on knowledge of the Michael addition reaction mechanism. The key descriptor was calculated using density functional theory and modeled the stability of the reaction intermediate. A second descriptor relating to the available surface area at the site of the reaction was also found to be important. Several poorly predicted compounds were identified, and in all cases, these could be rationalized mechanistically. The analysis of these compounds allowed a well-defined mechanistically driven applicability domain to be developed. The study showed thatin silico quantitative mechanistic models, with a well-defined applicability domain, can be used to predict skin sensitization potency in the LLNA. The approach presented has the potential to be of use as part of a weight of evidence approach for predicting skin sensitization without the use of animals in risk assessment. |
URI: | http://hdl.handle.net/10967/112
http://dx.doi.org/10.15152/QDB.112 |
Date: | 2014-03-27 |
Name | Description | Format | Size | View |
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2013CRT767.qdb.zip | Linear model for Michael acceptors | application/zip | 29.04Kb | View/ |