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

Doaa Alantary, Samuel H Yalkowsky

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

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 et al. proposed that their genetic algorithm support vector machine is a “better” predictor. This paper compares the use of the of Bahadori et al. model for the prediction of aqueous solubility to the existing GSE model.

Original languageEnglish (US)
Pages (from-to)1-2
Number of pages2
JournalPharmaceutical Development and Technology
DOIs
StateAccepted/In press - May 5 2017

Keywords

  • Aqueous solubility
  • Genetic algorithm
  • quantitative structure–property relationship
  • support vector machine

ASJC Scopus subject areas

  • Pharmaceutical Science

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