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
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机构:
Univ Arizona, Dept Pharmaceut, Coll Pharm, Tucson, AZ USA
Univ Arizona, Coll Pharm, Dept Pharmaceut, Tucson, AZ 85721 USAUniv Arizona, Dept Pharmaceut, Coll Pharm, Tucson, AZ USA
Alantary, Doaa
[1
,2
]
Yalkowsky, Samuel
论文数: 0引用数: 0
h-index: 0
机构:
Univ Arizona, Dept Pharmaceut, Coll Pharm, Tucson, AZ USA
Univ Arizona, Coll Pharm, Dept Pharmaceut, Tucson, AZ 85721 USAUniv Arizona, Dept Pharmaceut, Coll Pharm, Tucson, AZ USA
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
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.