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The quantitative structure–retention relationship (QSRR) was employed to predict the retention time (min) (RT) of pesticides using five molecular descriptors selected by genetic algorithm (GA) as a feature selection technique. Then the data set was randomly divided into training and prediction sets. The selected descriptors were used as inputs of multi-linear regression (MLR), multilayer perceptron neural network (MLP-NN) and generalized regression neural network (GR-NN) modeling techniques to build QSRR models. Both linear and nonlinear models show good predictive ability, of which the GR-NN model demonstrated a better performance than that of the MLR and MLP-NN models. The root mean square error of cross validation of the training and the prediction set for the GR-NN model was 1.245 and 2.210, and the correlation coefficients (R) were 0.975 and 0.937 respectively, while the square correlation coefficient of the cross validation (Q2 LOO) on the GR-NN model was 0.951, revealing the reliability of this model. The obtained results indicated that GR-NN could be used as predictive tools for prediction of RT (min) values for understudy pesticides.