In this paper, genetic algorithm (GA) and stepwise multiple regression variable selection methods were used as a featureselection tools and neural network was employed for feature mapping. To provide an extended test of these hybrid methods, a data set consists of the bioconcentration factors (BCF) for 53 molecules were selected. Suitable set of molecular descriptors were calculated and the important descriptors were selected by genetic algorithm and stepwise multiple regression methods. These variables serve as inputs to generated neural networks. After optimization and training of the networks, they were used for the calculation of bioconcentration factors for the prediction set. Comparison between results obtained showed the superiority of genetic algorithm over stepwise multiple regression method in feature-selection. For network that used the genetic algorithm for feature-selection methods the average relative error between predicted and experimental values of bioconcentration factor for training and prediction set are 0.07 and 0.13, respectively. Also the standard error of calibration and standard error of prediction are 0.26 and 0.40%, respectively, for this model. An analysis of the descriptor selected by genetic algorithm showed that they are essential to implies the steric and electrostatic attributes of studied molecules to obtain a satisfactory quantitative structure–activity relationship (QSAR) model.
