The DFT‐B3LYP functional method on the 6‐31G* basis set was employed to optimize and calculate molecular descriptors of 76 organo (phosphates and thiophosphate) derivatives. The molecular descriptors were used to establish the quantitative structure‐toxicity relationship (QSTR) for the acute oral toxicity of studied compounds by multiple linear regression (MLR) and artificial neural network (ANN) methods. The best results were obtained with an ANN model trained with the back‐propagation (BP‐ANN) algorithm. The prediction accuracy for the external test set was estimated by the root mean square (RMS) error, square correlation coefficient (R ²), and absolute average deviation (AAD) which were equal to 0.0248797, 0.9091, and 15.12187, respectively. It was specified that 90.91% of external test set was correctly predicted and the present model proved to be superior to the MLR model. Accordingly, the model developed in this study can be used to predict the oral acute toxicity of organo (phosphates and thiophosphate) derivatives, particularly for those that have not been experienced as well as new compounds. The analysis of the statistical parameters of models published in the literature showed that our model was more efficient.

中文翻译：

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遗传算法-多元线性回归与反向传播-人工神经网络方法预测有机（磷酸盐和硫代磷酸盐）化合物的LD50的比较

使用DFT-B3LYP功能方法（6-31G *基集）来优化和计算76种有机（磷酸盐和硫代磷酸盐）衍生物的分子描述子。通过多元线性回归（MLR）和人工神经网络（ANN）方法，使用分子描述符建立被研究化合物的急性口服毒性的定量结构-毒性关系（QSTR）。使用经过反向传播（BP-ANN）算法训练的ANN模型可获得最佳结果。外部测试集的预测准确性由均方根（RMS）误差，平方相关系数（R ²）和绝对平均偏差（AAD）分别等于0.0248797、0.9991和15.12187。据指出，正确预测了90.91％的外部测试集，并且该模型证明优于MLR模型。因此，在这项研究中开发的模型可用于预测有机（磷酸盐和硫代磷酸盐）衍生物的口服急性毒性，特别是对于那些尚未经历过的化合物以及新化合物。对文献中发表的模型的统计参数的分析表明，我们的模型更为有效。