The aim of this work was to develop mathematical models relating the hydrophobicity and dissociation constant of an analyte with its structure, which would be useful in predicting analyte retention times in reversed-phase liquid chromatography. For that purpose a large and diverse group of 115 drugs was used to build three QSRR models combining retention-related parameters (logkw—chromatographic measure of hydrophobicity, S—slope factor from Snyder-Soczewinski equation, and pKa) with structural descriptors calculated by means of molecular modeling for both dissociated and nondissociated forms of analytes. Lasso, Stepwise and PLS regressions were used to build statistical models. Moreover a simple QSRR equations based on lipophilicity and dissociation constant parameters calculated in the ACD/Labs software were proposed and compared with quantum chemistry-based QSRR equations. The obtained relationships were further used to predict chromatographic retention times. The predictive performances of the obtained models were assessed using 10-fold cross-validation and external validation. The QSRR equations developed were simple and were characterized by satisfactory predictive performance. Application of quantum chemistry-based and ACD-based descriptors leads to similar accuracy of retention times' prediction.