Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods

Aims: Prediction of oral acute toxicity of organophosphates using QSAR methods. Background: Prediction of oral acute toxicity of organophosphates (including some pesticides and insecticides) using GA-MLR and BPANN methods. Objective: 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. Methods: The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BPANN) methods were proposed. The prediction experiment showed that the BPANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BPANN models could well characterize the molecular structure of each compound. Results: It was indicated that among molecular descriptors to predict the LD50 of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BPANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R² = 0.9999) and absolute average deviation (AAD=0.001675045) gave the best outcome, and the model predictions were in good agreement with experimental data. Conclusion: The proposed model may be useful for predicting LD50 of new compounds of similar class.