J Chem Inf Model - Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area.

Tópicos

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Resumo

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.

Resumo Limpo

general solubl equat gse qspr model base melt point log p chemic substanc use predict aqueous solubl nonioniz chemic compound howev relianc experiment deriv descriptor particular melt point limit applic virtual compound studi present show gse abl predict within log unit experiment aqueous solubl log s compound data set divers chemic structur log s howev predict abil gse reduc appli subset data compound log s discount compound occupi spars popul region data space highlight spars popul extrem data set can signific skew result linear regressionbas model replac melt point descriptor gse descriptor account topograph polar surfac area tpsa produc model compar qualiti gse solubl compound full data set predict accur propos altern simpl model predict aqueous solubl replac melt point descriptor gse tpsa henc can appli virtual compound addit incorpor tpsa gse addit log p melt point give three descriptor model improv accur predict aqueous solubl gse full reduc data set respect

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