Group contribution approaches aqueous solubility

Klopman G, S Wang and D M Balthasar 1992. Estimation of Aqueous Solubility of Organic Molecule by the Group Contribution Approach. Application to the Study of Biodegradation. Journal c Chemical Information and Computer Science 32 474-482. 

D. M., Estimation of aqueous solubility of organic molecules by the group contribution approach. Application... 

Klopman, G., Wang, S., Balthasar, D. M. (1992) Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation. J. Chem. Inf. Comput. Sci. 32, 474-482. 

Duffythe group contribution approach of Klopman and the neural network model of Huuskonen. " The PCCHEM program used at the US Environmental Protection Agency (EPA) incorporates three different equations. All of them are similar to GSE but have different coefficients to predict aqueous solubility depending on the range of log P values. " Meylan and Howard used a database of 817 (RMSE = 0.62) compounds to derive a heuristic equation ... 

Aqueous solubility prediction continues to be an active area of research, with a wide variety of approaches being applied to this important and challenging area. To date, group contribution approaches as well as correlation with physicochemical properties (partition coefficient) appear to be the most promising (29, 32). It is important to keep in mind that correlations that are developed from structurally related analogs would consistently yield more accurate predictions. 

An extensive series of studies for the prediction of aqueous solubility has been reported in the literature, as summarized by Lipinski et al. [15] and jorgensen and Duffy [16]. These methods can be categorized into three types 1 correlation of solubility with experimentally determined physicochemical properties such as melting point and molecular volume 2) estimation of solubility by group contribution methods and 3) correlation of solubility with descriptors derived from the molecular structure by computational methods. The third approach has been proven to be particularly successful for the prediction of solubility because it does not need experimental descriptors and can therefore be applied to collections of virtual compounds also. 

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