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Modarresi, H.; Modarress, H.; Dearden, J.C. Henry’s law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy. SAR QSAR Environ. Res. 2005, 16, 461–482.

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Modarresi, H.; Modarress, H.; Dearden, J.C. Henry’s law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy. SAR QSAR Environ. Res. 2005, 16, 461–482.

QDB archive DOI: 10.15152/QDB.150   DOWNLOAD

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Property lnH: Henry's law constant as lnH

Compounds: 189 | Models: 4 | Predictions: 5

Eq16: TSA model for hydrocarbons i

Regression model (regression)

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Name Type n

R2

σ

Training set training 189 0.678 0.399
Eq18: CHSA model for hydrocarbons i

Regression model (regression)

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Name Type n

R2

σ

Training set training 189 0.722 0.371
Eq19: COSA model for hydrocarbons i

Regression model (regression)

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Name Type n

R2

σ

Training set training 189 0.903 0.220
ANN: Neural network model for hydrocarbons

Neural network (regression)

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Name Type n

R2

σ

Training set training 145 0.936 0.187
Validation set i external validation 44 0.946 0.125

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Title: Modarresi, H.; Modarress, H.; Dearden, J.C. Henry’s law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy. SAR QSAR Environ. Res. 2005, 16, 461–482.
Abstract: In this study, a quantitative structure-property relationship (QSPR) model for the prediction of Henry's law constants of aliphatic hydrocarbons in air-water system has been developed, based on a data-set of 189 compounds. The well-known linear thermodynamic relation between the logarithm of Henry's law constant and solvation free energy has been used for developing the model. It is emphasised that the solvent-accessible surface area (SASA) descriptor is not adequate for predicting the solvation free energy of a wide range of aliphatic hydrocarbons; there are many compounds that have the same solvent-accessible surface area with different solvation free energy. Therefore, we have introduced cavity ovality as a good descriptor of molecular cavity shape factor. The root mean square error (RMSE) of the QSPR regression model based on SASA improves from 0.40 to 0.22 by introducing the cavity ovality descriptor. The QSPR linear ovality model has good statistical parameters (r2 = 0.90). To emphasise the significant effect of the new descriptor, a non-linear neural network model with only two nodes in the hidden layer was developed, and also yielded a RMSE of 0.22.
URI: http://hdl.handle.net/10967/150
http://dx.doi.org/10.15152/QDB.150
Date: 2015-04-01


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