Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 594 | 0.750 | 0.604 |
Validation set | external validation | 107 | 0.811 | 0.634 |
Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 594 | 0.785 | 0.546 |
Validation set | external validation | 107 | 0.809 | 0.625 |
Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
---|---|---|---|---|
Training set | training | 594 | 0.826 | 0.497 |
Validation set | external validation | 107 | 0.809 | 0.625 |
When using this QDB archive, please cite (see details) it together with the original article:
Ahte, P. Data for: Linear and non-linear relationships between soil sorption and hydrophobicity: Model, validation and influencing factors. QsarDB repository, QDB.159. 2015. https://doi.org/10.15152/QDB.159
Wen, Y.; Su, L. M.; Qin, W. C.; Fu, L.; He, J.; Zhao, Y. H. Linear and non-linear relationships between soil sorption and hydrophobicity: Model, validation and influencing factors. Chemosphere 2012, 86, 634–640. https://doi.org/10.1016/j.chemosphere.2011.11.001
Title: | Wen, Y.; Su, L. M.; Qin, W. C.; Fu, L.; He, J.; Zhao, Y. H. Linear and non-linear relationships between soil sorption and hydrophobicity: Model, validation and influencing factors. Chemosphere 2012, 86, 634–640. |
Abstract: | The hydrophobic parameter represented by the octanol/water partition coefficient (logP) is commonly used to predict the soil sorption coefficient (Koc). However, a simple non-linear relationship between logKoc and logP has not been reported in the literature. In the present paper, soil sorption data for 701 compounds was investigated. The results show that logKoc is linearly related to logP for compounds with logP in the range of 0.5–7.5 and non-linearly related to logP for the compounds in a wide range of logP. A non- linear model has been developed between logKoc and log P for a wide range of compounds in the training set. This model was validated in terms of average error (AE), average absolute error (AAE) and root-mean squared error (RMSE) by using an external test set with 107 compounds. Nearly the same predictive capacity was observed in comparison with existing models. However, this non-linear model is simple, and uses only one parameter. The best model developed in this paper is a non-linear model with six cor- rection factors for six specific classes of compounds. This model can well predict logKoc for 701 diverse compounds with AAE = 0.37. The reasons for systemic deviations in these groups may be attributed to the difference of sorption mechanism for hydrophilic/polar compounds, low solubility for highly hydrophobic compounds, hydrolysis of esters in solution, volatilization for volatile compounds and highly experimental errors for compounds with extremely high or low sorption coefficients. |
URI: | http://hdl.handle.net/10967/159
http://dx.doi.org/10.15152/QDB.159 |
Date: | 2015-05-28 |
Name | Description | Format | Size | View |
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2011C634.qdb.zip | Linear and non-linear regression models for logKoc | application/zip | 304.6Kb | View/ |