Comments on prediction of the aqueous solubility using the general solubility equation (GSE) versus a genetic algorithm and a support vector machine model

被引:12
作者
Alantary, Doaa [1 ,2 ]
Yalkowsky, Samuel [1 ,2 ]
机构
[1] Univ Arizona, Dept Pharmaceut, Coll Pharm, Tucson, AZ USA
[2] Univ Arizona, Coll Pharm, Dept Pharmaceut, Tucson, AZ 85721 USA
关键词
Aqueous solubility; support vector machine; quantitative structure-property relationship; Genetic algorithm; OCTANOL SOLUBILITY; WATER;
D O I
10.1080/10837450.2017.1321663
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The general solubility equation (GSE) is the state-of-the-art method for estimating the aqueous solubilities of organic compounds. It is an extremely simple equation that expresses aqueous solubility as a function of only two inputs: the octanol-water partition coefficient calculated by readily available softwares like clogP and ACD/logP, and the commonly known melting point of the solute. Recently, Bahadori etal. proposed that their genetic algorithm support vector machine is a better predictor. This paper compares the use of the of Bahadori etal. model for the prediction of aqueous solubility to the existing GSE model.
引用
收藏
页码:739 / 740
页数:2
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