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| - Prediction of Aqueous Solubility of Organic Compounds by the General SolubilityEquation (GSE)
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| - The revised general solubility equation (GSE) is used along with four different methods including Huuskonen'sartificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueoussolubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selectedtest sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE hasthe advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are theCelsius melting point (MP) and the octanol water partition coefficient (Kow). No fitted parameters and notraining data are used in the GSE, whereas other methods utilize a large number of parameters and requirea training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied byHuuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors isonly 0.1 log units larger than that of the ANN, which requires many parameters and a large training set.The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen'sANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predictaqueous solubilities of organic compounds.
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