: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BP-ANN) methods were proposed. The prediction experiment showed that the BP-ANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BP-ANN models could well characterize the molecular structure of each compound. It was indicated that among molecular descriptors to predict the LD50 (mgkg-1) of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BP-ANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2= 0.9999) and absolute average deviation (AAD=0.6981631) gave the best outcome, and the model predictions were in good agreement with experimental data. The proposed model may be useful for predicting LD50 (mgkg-1) of new compounds of similar class.