J Chem Inf Model - In silico prediction of aqueous solubility using simple QSPR models: the importance of phenol and phenol-like moieties.

Tópicos

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Resumo

Recently 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 moieties.

Resumo Limpo

recent author publish robust qspr model aqueous solubl exploit comput deriv molecular descriptor topograph polar surfac area tpsa alongsid experiment determin melt point logp model tpsa model abl accur predict within one log unit aqueous solubl compound chemic divers data set molecul compar result achiev establish model aqueous solubl eg esol general solubl equat hierarch cluster data set accord chemic similar show signific number molecul phenol andor phenollik moieti poor predict equat modif tpsa model addit incorpor descriptor pertain simpl count phenol phenollik moieti improv predict abil within one log unit full data set compound log reduc data set compound log exclud compound spars popul extrem data rang improv can ration addit descriptor model act correct factor acknowledg effect phenol substitu electron characterist aromat molecul ie general posit contribut aqueous solubl made phenol moieti

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