Prediction techniques solvation energy models

The present chapter thus provides an overview of the current status of continuum models of solvation. We review available continuum models and computational techniques implementing such models for both electrostatic and non-electrostatic components of the free energy of solvation. We then consider a number of case studies, with particular focus on the prediction of heterocyclic tautomeric equilibria. In the discussion of the latter we center attention on the subtleties of actual chemical systems and some of the dangers of applying continuum models uncritically. We hope the reader will emerge with a balanced appreciation of the power and limitations of these methods. 

A modest data base for aqueous systems has beSen obtained by the use of these techniques. The data are reasonably reliable for systems with y values less then a couple thousand and not measured by the liquid-liquid chromatography technique. A reliable data base is required in the development of predictive techniques for y. Several predictive techniques are currently available the MOSCED (45) model has not yet been extended to aqueous systems. UNIFAC (46-48), which is really an outgrowth of ASOG (21,49) does include water, but with mixed results at best. Linear solvation energy relationships (LSER s) have been used to correlate ratios of y values for aqueous systems (50) and may be capable of some prediction. Nonetheless, a more extensive and accurate data base is what is really needed for correlation development... 

Liquid/liquid partition constants within pharmaceutical chemistry have been of primary interest because of tlieir correlation with liquid/membrane partitioning behavior. A sufficiently fluid membrane may, in some sense, be regarded as a solvent. With such an outlook, tlie partitioning phenomenon may again be regarded as a liquid/liquid example, amenable to treatment with standard continuum techniques. Of course, accurate continuum solvation models typically rely on the availabihty of solvation free energies or bulk solvent properties in order to develop useful parameterizations, and such data may be sparse or non-existent for membranes. Some success, however, has been demonstrated for predicting such data either by intuitive or statistical analysis (see, for example. Chambers etal. 1999). 

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