Quantitative structure-property relationship (QSPR) solvent model has been developed for the McReynolds constants (prototypical solutes) on 36 gas-liquid chromatographic stationary phases. PM6 semiempirical quantum chemical calculations combined with conductor-like screening model (COSMO) has been utilized. From 276 descriptors considered, forward stepwise variable selection, followed by best subset selection, yielded linear regression models containing six purely quantum chemical and two hybrid, topologically based descriptors. Internal (leave-one-out and bootstrap) as well as external validation methods confirmed the predictive power of these structure-driven models across all 10 McReynolds constants, with 40 Kováts-index units overall root-mean-square prediction error estimate.