An improved strategy of quantitative structure-property relationship (QSPR) studies of diverse and inhomogeneous organic datasets has been proposed. A molecular connectivity term was successively corrected for different structural features encoded in fragmental descriptors. The so-called solvation index 1chis (a weighted Randic index) was used as a "leading" variable and standardized molecular fragments were employed as "corrective" class-specific variables. Performance of the new approach was illustrated by modelling a dataset of experimental normal boiling points of 833 organic compounds belonging to 20 structural classes. Firstly, separate QSPR models were derived for each class and for eight groups of structurally similar classes. Finally, a general model formed by combining all the classes together was derived (r2=0.957, s=12.9degreesC). The strategy outlined can find application in QSPR analyses of massive, highly diverse databases of organic compounds.