In this work quantitative structure-retention relationship models were developed to predict the solute retention factors in supercritical fluid chromatography (SFC) in various organic modifiers. Data set contains the retention data of 35 various organic compounds in 0, 2, 4 and 6% of methanol in mobile phase. The obtained 140 data points were divided into training, internal and external test sets which have 93, 23 and 24 retention data. The diversity validation test was performed on data the set to ensure that the structure of the training and/or test sets can represent those of the whole ones. Descriptors which were selected by stepwise multiple linear regression (MLR) methods are: the percent of organic modifier in mobile phase, salvation connectivity index chi-2, salvation connectivity index chi-5, H attached to heteroatom, the 2nd structural information content and polarity parameter. These descriptors were used as features in generation of linear and non-linear models using MLR and artificial neural network (ANN) methods, respectively. The root mean square error of MLR model are 0.116, 0.138 and 0.260 for training, internal and external test sets, respectively, while these values are 0.036, 0.097 and 0.244 for ANN model, respectively. Comparison between these values and other statistical parameters obtained from these two models reveals the credibility of ANN in prediction of solute retention factors in SFC.
