Regression model (regression) QMRF
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 125 | 0.831 | 0.747 |
Validation set | external validation | 125 | 0.793 | 0.785 |
When using this QDB archive, please cite (see details) it together with the original article:
Kahn, I. Data for: Screening and Ranking of POPs for Global Half-Life: QSAR Approaches for Prioritization Based on Molecular Structure. QsarDB repository, QDB.113. 2014. https://doi.org/10.15152/QDB.113
Gramatica, P.; Papa, E. Screening and Ranking of POPs for Global Half-Life: QSAR Approaches for Prioritization Based on Molecular Structure. Environ. Sci. Technol. 2007, 41, 2833–2839. https://doi.org/10.1021/es061773b
Title: | Gramatica, P.; Papa, E. Screening and Ranking of POPs for Global Half-Life: QSAR Approaches for Prioritization Based on Molecular Structure. Environ. Sci. Technol. 2007, 41, 8, 2833–2839. |
Abstract: | Persistence in the environment is an important criterion in prioritizing hazardous chemicals and in identifying new persistent organic pollutants (POPs). Degradation half-life in various compartments is among the more commonly used criteria for studying environmental persistence, but the limited availability of experimental data or reliable estimates is a serious problem. Available half-life data for degradation in air, water, sediment, and soil, for a set of 250 organic POP-type chemicals, were combined in a multivariate approach by principal component analysis to obtain a ranking of the studied organic pollutants according to their relative overall half-life. A global half-life index (GHLI) applicable for POP screening purposes is proposed. The reliability of this index was verified in comparison with multimedia model results. This global index was then modeled as a cumulative end-point using a QSAR approach based on few theoretical molecular descriptors, and a simple and robust regression model externally validated for its predictive ability was derived. The application of this model could allow a fast preliminary identification and prioritization of not yet known POPs, just from the knowledge of their molecular structure. This model can be applied a priori also in the chemical design of safer and alternative non-POP compounds. |
URI: | http://hdl.handle.net/10967/113
http://dx.doi.org/10.15152/QDB.113 |
Date: | 2014-04-07 |
Name | Description | Format | Size | View |
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2007EST2833.qdb.zip | Validated multilinear model | application/zip | 182.1Kb | View/ |
Q13-22b-0015.pdf | QMRF | 37.78Kb | View/ |