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首页> 美国卫生研究院文献>Molecules >Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach
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Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach

机译：QSAR方法估算不同取代的芳族化合物对水生纤毛四膜虫的毒性。

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Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.
机译：如今，由于其结构简单，易于实施且危害较小，已广泛使用定量构效关系（QSAR）方法来预测化合物对生物体的毒性。在这项研究中，为了评估取代的芳香族化合物对梨形四膜虫的毒性，通过多元线性回归（MLR）和径向基函数神经网络（RBFNN）建立了QSAR模型。与其他QSAR研究不同，根据功能组（-NO2，-X）的不同，整个数据集分为三组，分别进行了建模。该模型的统计特性如下：MLR：n = 36，R 2 = 0.829，RMS（均方根）= 0.192，RBFNN：n = 36，R 2 = 0.843，第1组的RMS = 0.167； MLR：n = 60，R 2 = 0.803，RMS = 0.222，RBFNN：n = 60，R 2 = 0.821，第二组的RMS = 0.193； MLR：n = 31 R 2 = 0.852，RMS = 0.192； RBFNN：对于第3组，n = 31，R 2 = 0.885，RMS = 0.163。结果在可接受的范围内，并且发现该模型在统计学上具有较高的外部预测性，具有较强的鲁棒性。此外，这些模型还对那些影响毒性最大的结构特征提供了一些见识。

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