The partition coefficient (n-octanol water) still represents one of the most informative physicochemical parameters available to medicinal chemists [#!CurrMedChem1998-5-5-353!#].
For noncomplexing substances, the partition coefficient values result from two volume-dependent entropic contributions reflecting (a) the difference in the exchange entropy between the solute and solvent molecules in the n-octanol and water phases, and (b) the characteristic difference between the two H- bonded solvents to induce a hydrophobic effect toward the solute [#!JPharmSci1998-87-8-1015!#]. The use of quantum chemical descritors has been tried by Glen and co. [#!JMolModel1997-3-3-142!#] to model the partition coefficient . A back-propagation artificial neural net has been trained to estimate logP values of a large range of organic molecules from the results of AM1 and PM3 semiempirical MO calculations. The input descriptors include molecular properties such as electrostatic potentials, total dipole moments, mean polarizabilities, surfaces, volumes and charges derived from semiempirically calculated gas phase geometries. These properties can be related to the molecule's solubility in hydrophilic or lipophilic media.