Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure
Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure
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DOI：
10.1080/10629369908039170
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发表时间：
1999-01-01
影响因子：
3
通讯作者：
Jurs, PC
中科院分区：
文献类型：
--
作者：
Eldred, DV;Jurs, PC
采用定量构效关系（QSAR）研究了54种有机磷农药的急性口服毒性（LD50）。这些化合物用计算的分子结构描述符表示，这些描述符编码了它们的拓扑、电子和几何特征。特征选择使用遗传算法来找到描述符子集，这些描述符子集将支持高质量的计算神经网络（CNN）模型，将结构描述符与化合物的- log（mmol/kg）值联系起来。发现的最佳七描述符非线性CNN模型的训练集化合物的均方根误差为0.22 log单位，预测集化合物的均方根误差为0.25 log单位。
A quantitative structure-activity relationship (QSAR) investigation was done for the acute oral mammalian toxicity (LD50) of a set of 54 organophosphorus pesticide compounds. The compounds were represented with calculated molecular structure descriptors, which encoded their topological, electronic, and geometrical features. Feature selection was done with a genetic algorithm to find subsets of descriptors that would support a high quality computational neural network (CNN) model to link the structural descriptors to the - log(mmol/kg) values for the compounds. The best seven-descriptor non-linear CNN model found had an rms error of 0.22 log units for the training set compounds and 0.25 log units for the prediction set compounds.