The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure–activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear featuremapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by thismodel are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q2 = 0.97 and SPRESS=0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds.
