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dc.contributor.authorAli, Jogoth
dc.contributor.authorCamilleri, Patrick
dc.contributor.authorBrown, Marc
dc.contributor.authorHutt, Andrew J.
dc.contributor.authorKirton, Stewart B.
dc.identifier.citationAli , J , Camilleri , P , Brown , M , Hutt , A J & Kirton , S B 2012 , ' Revisiting the General Solubility Equation : In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area ' , Journal of Chemical Information and Modeling , vol. 52 , no. 2 , pp. 420-428 .
dc.identifier.otherPURE: 933807
dc.identifier.otherPURE UUID: e6fd0eb8-6c52-4927-91fa-b92be6deef8c
dc.identifier.otherWOS: 000300650400016
dc.identifier.otherScopus: 84863418861
dc.description.abstractThe 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 (logs) 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.en
dc.relation.ispartofJournal of Chemical Information and Modeling
dc.titleRevisiting the General Solubility Equation : In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Areaen
dc.contributor.institutionHealth & Human Sciences Research Institute
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionCentre for Research into Topical Drug Delivery and Toxicology
dc.contributor.institutionSkin and Nail Group
dc.contributor.institutionAirway Group
dc.contributor.institutionBioadhesive Drug Delivery Group
dc.contributor.institutionPharmaceutical Analysis and Product Characterisation
dc.contributor.institutionMedicinal and Analytical Chemistry
dc.contributor.institutionNatural Product Chemistry and Drug Design
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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