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Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

机译:有机化学对伪高压Chneriella Subcapitata的定量结构毒性关系

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摘要

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC(10) of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a WA set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R-2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R-2 of 0.75 and MAE of 0.61 for the WA set. Compared with other models reported in the literature, our SVM model for the toxicity pEC(10) shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the WA set. A QSTR model for pEC(50) of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.
机译:通过化学计量方法预测有机毒物对水生寿命的毒性是具有挑战性的地区。在本文中,通过将支持向量机(SVM)与遗传算法一起使用支持向量机(SVM),成功开发了六个描述符的定量结构 - 活性/毒性关系(QSAR / QSTR)模型。 。将由334种有机化学品组成的足够大的数据集随随机分为训练组(167化合物)和WA组(167化合物),其比例为1:1。最佳SVM模型具有0.76的测定系数R-2,训练集的训练集0.60的平均误差(MAE)和0.75和Mae为0.61的R-2为0.61。与文献中报道的其他模型相比,我们的毒性PEC(10)的SVM模型显示出显着的统计质量和令人满意的预测能力,尽管它具有较少的分子描述符和WA集中的更多样品。还通过相同的子集和分子描述符开发了针对伪高科基氏菌菌的有机化学品的PEC(50)的QSTR模型。

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