Show simple item record

dc.contributor.authorAli, Jogoth
dc.contributor.authorCamilleri, Patrick
dc.contributor.authorBrown, Marc
dc.contributor.authorHutt, Andrew John
dc.contributor.authorKirton, Stewart Brian
dc.date.accessioned2013-01-11T13:29:36Z
dc.date.available2013-01-11T13:29:36Z
dc.date.issued2012-10-28
dc.identifier.citationAli , J , Camilleri , P , Brown , M , Hutt , A J & Kirton , S B 2012 , ' In Silico Prediction of Aqueous Solubility Using Simple QSPR Models : The Importance of Phenol and Phenol-like Moieties ' , Journal of Chemical Information and Modeling , vol. 52 , no. 11 , pp. 2950-2957 . https://doi.org/10.1021/ci300447c
dc.identifier.issn1549-9596
dc.identifier.otherPURE: 1321874
dc.identifier.otherPURE UUID: b3a90d8d-5740-492a-97f7-086560d5ed52
dc.identifier.otherScopus: 84869994124
dc.identifier.urihttp://hdl.handle.net/2299/9579
dc.description.abstractRecently the authors published a robust QSPR model of aqueous solubility which exploited the computationally derived molecular descriptor topographical polar surface area (TPSA) alongside experimentally determined melting point and logP. This model (the “TPSA model”) is able to accurately predict to within ± one log unit the aqueous solubility of 87% of the compounds in a chemically diverse data set of 1265 molecules. This is comparable to results achieved for established models of aqueous solubility e.g. ESOL (79%) and the General Solubility Equation (81%). Hierarchical clustering of this data set according to chemical similarity shows that a significant number of molecules with phenolic and/or phenol-like moieties are poorly predicted by these equations. Modification of the TPSA model to additionally incorporate a descriptor pertaining to a simple count of phenol and phenol-like moieties improves the predictive ability within ± one log unit to 89% for the full data set (1265 compounds −8.48 < logS < 1.58) and 82% for a reduced data set (1160 compounds 6.00 < logS < 0.00) which excludes compounds at the sparsely populated extremities of the data range. This improvement can be rationalized as the additional descriptor in the model acting as a correction factor which acknowledges the effect of phenolic substituents on the electronic characteristics of aromatic molecules i.e. the generally positive contribution to aqueous solubility made by phenolic moietiesen
dc.format.extent7
dc.language.isoeng
dc.relation.ispartofJournal of Chemical Information and Modeling
dc.titleIn Silico Prediction of Aqueous Solubility Using Simple QSPR Models : The Importance of Phenol and Phenol-like Moietiesen
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionHealth & Human Sciences Research Institute
dc.contributor.institutionMedicinal and Analytical Chemistry
dc.contributor.institutionNatural Product Chemistry and Drug Design
dc.contributor.institutionCentre for Research into Topical Drug Delivery and Toxicology
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionPharmaceutics
dc.contributor.institutionSkin and Nail Group
dc.contributor.institutionAirway Group
dc.contributor.institutionBioadhesive Drug Delivery Group
dc.contributor.institutionNanopharmaceutics
dc.contributor.institutionPharmaceutical Analysis and Product Characterisation
dc.contributor.institutionToxicology
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Life and Medical Sciences
dcterms.dateAccepted2012-10-28
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1021/ci300447c
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record