Palmer, D. S.; Llinas, A.; Morao, I.; Day, G. M.; Goodman, J. M.; Glen, R. C.; Mitchell, J. B. O. Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle. Mol. Pharmaceutics 2008, 5, 266-279.

QsarDB Repository

Palmer, D. S.; Llinas, A.; Morao, I.; Day, G. M.; Goodman, J. M.; Glen, R. C.; Mitchell, J. B. O. Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle. Mol. Pharmaceutics 2008, 5, 266-279.

QDB archive DOI: 10.15152/QDB.148   DOWNLOAD

QsarDB content

Property logS0: Intrinsic aqueous solubility as logS0 [mol/L]

Eq8: Model for drugs or drug precursors

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining340.8310.635
External validation setexternal validation260.7680.708

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Piir, G. Data for: Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle. QsarDB repository, QDB.148. 2015. https://doi.org/10.15152/QDB.148

  • Palmer, D. S.; Llinas, A.; Morao, I.; Day, G. M.; Goodman, J. M.; Glen, R. C.; Mitchell, J. B. O. Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle. Mol. Pharmaceutics 2008, 5, 266-279. https://doi.org/10.1021/mp7000878

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Title: Palmer, D. S.; Llinas, A.; Morao, I.; Day, G. M.; Goodman, J. M.; Glen, R. C.; Mitchell, J. B. O. Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle. Mol. Pharmaceutics 2008, 5, 266-279.
Abstract:We report methods to predict the intrinsic aqueous solubility of crystalline organic molecules from two different thermodynamic cycles. We find that direct computation of solubility, via ab initio calculation of thermodynamic quantities at an affordable level of theory, cannot deliver the required accuracy. Therefore, we have turned to,a mixture of direct computation and informatics, using the calculated thermodynamic properties, along with a few other key descriptors, in regression models. The prediction of log intrinsic solubility (referred to mol/L) by a three-variable linear regression equation gave r(2) = 0.77 and RMSE = 0.71 for an external test set comprising drug molecules. The model includes a calculated crystal lattice energy which provides a computational method to account for the interactions in the solid state. We suggest that it is not necessary to know. the polymorphic form prior to prediction. Furthermore, the method developed here may be applicable to other solid-state systems such as salts or cocrystals.
URI:http://hdl.handle.net/10967/148
http://dx.doi.org/10.15152/QDB.148
Date:2015-04-01


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