The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Revisiting the General Solubility Equation: In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area
Abstract
The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds. The studies presented show that the GSE is able to predict, to within 1 log unit, the experimental aqueous solubility (log S) for 81% of the compounds in a data set of 1265 diverse chemical structures (-8.48 < log S < 1.58). However, the predictive ability of the GSE is reduced to 75% when applied to a subset of the data (1160 compounds -6.00 < log S < 0.00), which discounts those compounds occupying the sparsely populated regions of data space. This highlights how sparsely populated extremities of data sets can significantly skew results for linear regression-based models. Replacing the melting point descriptor of the GSE with a descriptor which accounts for topographical polar surface area (TPSA) produces a model of comparable quality to the GSE (the solubility of 81% of compounds in the full data set predicted accurately). As such, we propose an alternative simple model for predicting aqueous solubility which replaces the melting point descriptor of the GSE with TPSA and hence can be applied to virtual compounds. In addition, incorporating TPSA into the GSE in addition to log P and melting point gives a three descriptor model that improves accurate prediction of aqueous solubility over the GSE by 5.1% for the full and 6.6% for the reduced data set, respectively.
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Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Ertl, P., Rohde, B., & Selzer, P. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000942e · Cited by 3,084
A Beginner's Guide to Partial Least Squares Analysis
Haenlein, M., & Kaplan, A. M. (2004) · Understanding Statistics · doi:10.1207/s15328031us0304_4 · Cited by 1,694
Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans
Palm, K., Stenberg, P., Luthman, K., & Artursson1, P. (1997) · Pharmaceutical Research · doi:10.1023/a:1012188625088 · Cited by 756
Clark, D. E. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js980402t · Cited by 519
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468
Estimation of the aqueous solubility I: Application to organic nonelectrolytes
Jain, N., & Yalkowsky, S. H. (2001) · Journal of Pharmaceutical Sciences · doi:10.1002/1520-6017(200102)90:2<234::aid-jps14>3.0.co;2-v · Cited by 375
The emerging importance of predictive ADME simulation in drug discovery
Selick, H. E., Beresford, A. P., & Tarbit, M. H. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(01)02100-6 · Cited by 233
Predicting aqueous solubility from structure
Delaney, J. S. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03365-3 · Cited by 191
Computational approaches to determine drug solubility
Faller, B., & Ertl, P. (2007) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2007.05.005 · Cited by 172
Estimation of aqueous solubility of organic compounds by using the general solubility equation
Ran, Y., He, Y., Yang, G., Johnson, J. L. H., & Yalkowsky, S. H. (2002) · Chemosphere · doi:10.1016/s0045-6535(02)00118-2 · Cited by 149
Recent Advances on Aqueous Solubility Prediction
Wang, J., & Hou, T. (2011) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620711795508331 · Cited by 128
Quantitative structure–property relationships in pharmaceutical research – Part 1
Grover, M., Singh, B., Bakshi, M., & Singh, S. (2000) · Pharmaceutical Science & Technology Today · doi:10.1016/s1461-5347(99)00214-x · Cited by 128
In silicoprediction of aqueous solubility
Dearden, J. C. (2006) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.1.1.31 · Cited by 114
Quantitative structure–property relationships in pharmaceutical research – Part 2
Grover, M., Singh, B., Bakshi, M., & Singh, S. (2000) · Pharmaceutical Science & Technology Today · doi:10.1016/s1461-5347(99)00215-1 · Cited by 75
Selection of data sets for qsars: Analyses of tetrahymena toxicity from aromatic compounds
Schultz, T. W., Netzeva, T. I., & Cronin, M. (2003) · SAR and QSAR in environmental research · doi:10.1080/1062936021000058782 · Cited by 54
Prediction of aqueous solubility from SCRATCH
Jain, P. K., & Yalkowsky, S. H. (2009) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2009.10.003 · Cited by 32
Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Skyner, R. E., McDonagh, J. L., Groom, C. R., van Mourik, T., & Mitchell, J. B. O. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp00288e · Cited by 531
In silico ADME/T modelling for rational drug design
Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., et al. (2015) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583515000190 · Cited by 367
1,5-Benzodiazepin-2(3H)-ones: In Vitro Evaluation as Antiparkinsonian Agents
Ortíz de Zárate, A., Pérez-Torralba, M., Bonet Isidro, I., López, C., Claramunt, R. M., Martínez-Casanova, D., Sánchez-Vera, I., Jiménez-González, J., & Lavandera, J. L. (2021) · Antioxidants · doi:10.3390/antiox10101584 · Cited by 11
Metadata
- Authors
- Ali, J., Camilleri, P., Brown, M. B., Hutt, A. J., & Kirton, S. B.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-01-13
- Volume
- 52
- Issue
- 2
- Pages
- 420-428
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2144877918
- Cited by
- 300
- References
- 17
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- closed
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