Logistic regression (classification)
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Name | Type | n | Accuracy |
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Training set | training | 88 | 0.898 |
When using this QDB archive, please cite (see details) it together with the original article:
Piir, G. Data for: Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors. QsarDB repository, QDB.168. 2015. https://doi.org/10.15152/QDB.168
Ren, S.; Schultz, T. W. Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors. Toxicol. Lett. 2002, 129, 151–160. https://doi.org/10.1016/s0378-4274(01)00550-1
Title: | Ren, S.; Schultz, T. W. Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors. Toxicol. Lett. 2002, 129, 151–160. |
Abstract: | The most successful quantitative structure–activity relationships (QSARs) have been developed by separating toxicants by their mechanisms of action (MOAs). However, since the activity of a chemical compound on an organism is dependent upon several physical, chemical and biological factors, among which interactions may also exist, the MOA of a compound is not easily determined. In this study, the use of discriminant analysis and logistic regression in distinguishing between narcotic and reactive compounds was investigated. The discriminating variables included hydrophobicity (log(Kow)) and electrophilicity descriptors (SavN, EHOMO, and ELUMO). Classification results showed that logistic regression gave a smaller total error rate compared to discriminant analysis. Since the value of the descriptors can be calculated, the classification methods can be used in predictive toxicology. |
URI: | http://hdl.handle.net/10967/168
http://dx.doi.org/10.15152/QDB.168 |
Date: | 2015-09-09 |
Name | Description | Format | Size | View |
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2002TL151.qdb.zip | Logistic regression model for separation of toxicants by their mechanisms of action | application/zip | 10.50Kb | View/ |