Multivariate analysis of hydrophobic descriptors

Multivariate approaches like principal component analysis (PCA) are powerful tools to investigate hydrophobic descriptors and to discriminate between intrinsic hydrophobicity and polar contributions as hydrogen bonds and other electronic effects. PCA of log P values measured for 37 solutes in eight solvent-water systems and of hydrophobic octanol-water substituent constants p for 25 meta- and para-substituents from seven phenyl series were performed (re-analysis of previous work). In both cases, the descriptors are repro­duced within experimental errors by two principal components, an intrinsic hydrophobic component and a second component accounting for differences between the systems due to electronic interactions. Underlying effects were identified by multiple linear regression analysis. Log P values depend on the water solubility of the solvents and hydrogen bonding capabilities of both the solute and the solvents. Results indicate different impacts of hydrogen bonds in nonpolar and polar solvent-water systems on log P and their dependence on isotropic and hydrated surface areas. In case of the p-values, the second component (loadings and scores) correlates with electronic substituent constants. More detailed analysis of the data as p-values of disubstituted benzenes XPhY has led to extended symmetric bilinear Hammett-type models relating interaction increments to cross products pX sY, pY sX and sX sY which are mainly due to mutual effects on hydrogen-bonds with octanol.