OBJECTIVE To provide predictive cosolvency models, the Abraham solvation parameters of solutes and the solvent coefficients were combined with the Jouyban-Acree and the log-linear models. These models require two and one solubility data points to predict the solubility of drugs in water-cosolvent mixtures. Ab initio prediction methods also were employed and the results were discussed. METHODS The Jouyban-Acree model constants were correlated with variables derived from the Abraham solvation parameters of solutes and the solvent coefficients to present quantitative structure property relationship (QSPR) models. The calculated model constants using the QSPR models were used to predict the solubility in water-cosolvent mixtures. The mean percentage deviation (MPD), average absolute error (AAE) and root mean square error (RMSE) criteria were calculated to show the accuracy of the predictions. RESULTS The overall MPD (+/-SD) of the proposed method employing solubility data in mono-solvents, i.e. two data points for each set, was 18.5+/-12.0 which indicates an acceptable prediction error from the practical point of view. The best cosolvency model employing aqueous solubility data was produced overall MPD of 75.2+/-72.6. The overall MPD of the proposed ab initio method was 74.9+/-19.3%. The models produced the same accuracy pattern considering MPD, AAE and RMSE criteria. CONCLUSIONS The proposed model employing two solubility data points for each set produced acceptable prediction error (>>18%) and could be recommended for practical applications in pharmaceutical industry. MPD, AAE and RMSE criteria produced similar results considering various models. However, MPD criterion was preferred since its numerical values could be compared with experimental relative standard deviations for repeated experiments.