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.

目的：使用 QSAR 方法预测有机磷的口服急性毒性。背景：使用 GA-MLR 和 BPANN 方法预测有机磷（包括一些农药和杀虫剂）的口服急性毒性。

目的：本研究的目的是开发定量构效关系 (QSAR) 模型，基于分子描述符来预测有机磷化合物的口服急性毒性 (LD ₅₀ )。

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.

结果：表明在预测有机磷LD50的分子描述符中，ALOGP2、RDF030u、RDF065p和GATS5m描述符比其他描述符更重要。BPANN 方法的均方根误差 (RMSE= 0.00168)、平方相关系数 (R ² = 0.9999) 和绝对平均偏差 (AAD=0.001675045) 给出了最好的结果，并且模型预测与实验非常吻合数据。

结论：所提出的模型可能有助于预测类似类别的新化合物的 LD50。