Quantitative structure property relationships (QSPR) were developed for the first time predicting of solubility in supercritical carbon dioxide, ethane and trifluoromethane over a wide range of pressures (5.1-36.2 MPa) and temperatures (308-343 K). A large number of descriptors were calculated and a subset of calculated descriptors was selected by genetic algorithm-multiple linear regression (GA-MLR). Four molecular descriptors and three experimental descriptors such as pressure, temperature and melting point were selected as the most feasible descriptors for prediction of solubility in three supercritical fluids. The data set consisted of 14 molecules in various temperatures and pressures, which form 586 solubility data. Modeling of the relationship between selected descriptors and solubility data was achieved by linear (multiple linear regression; MLR) and nonlinear (artificial neural network; ANN) methods. The artificial neural network architectures and their parameters were optimized simultaneously. The root mean squares error (RMSE) for supercritical carbon dioxide, ethane and trifluoromethane were 0.56, 0.68 and 0.72, respectively. The performance of the ANN models was also compared with multiple linear regression models and the results showed the superiority of the ANN over MLR model. (C) 2014 Elsevier B.V. All rights reserved.