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首页> 外文期刊>Indian Journal of Marine Sciences >Study on aromatic hydrocarbons toxicity to chlorella vulgaris based on QSAR model
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Study on aromatic hydrocarbons toxicity to chlorella vulgaris based on QSAR model

机译:基于QSAR模型的芳烃对小球藻的毒性研究

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Predicting ability based on the quantitative structure activity relationships (QSAR) model of unknown aromatic hydrocarbons toxicity is one of the tasks of security precaution. To establish the QSAR model between the physical and chemical properties of aromatic hydrocarbons and the inhibited activity of Chlorella vulgaris(C. Vulgaris), the optimized geometries, based on the 96 hr-EC50 of 25 aromatic hydrocarbons with C. Vulgaris were carried out at the B3LYP/6311G** level by density functional theory (DFT) calculation. With matlab2010(a) software, multiply linear regression(MLR) and three types principal components regression (PCR) methods were used to develop the QSAR model. Three methods were introduced to select the PCs, namely k-values(K), eigenvalue ranking(EV) and correlation ranking(CR) procedures. The different resutls then were compared. After eliminated one of the collinear descriptor and constants, 6, 1 and 4 PCs were select by K, EV, CR for PCR method respectively. LOO cross-validated coefficient (R-cv(2)) of training set to MLR,K-PCR,EVPCR,CRPCR were 0.673,0.817,0.874,0.907,the R-2 of prediction were 0.569,0.678,0.519,0.79,respectively.
机译:基于未知芳香烃毒性定量结构活性关系(QSAR)模型的预测能力是安全防范的任务之一。为了建立芳香烃的理化性质与小球藻(C. Vulgaris)抑制活性之间的QSAR模型,在25℃的25种芳香烃与寻常小球藻的96小时EC50的基础上,优化了几何构型。通过密度泛函理论(DFT)计算得出B3LYP / 6311G **水平。借助matlab2010(a)软件,使用多元线性回归(MLR)和三种类型的主成分回归(PCR)方法开发了QSAR模型。介绍了三种选择PC的方法,即k值(K),特征值排序(EV)和相关性排序(CR)程序。然后比较不同的结果。消除共线描述符和常数之一后,分别通过K,EV,CR选择6、1和4个PC用于PCR方法。设置为MLR,K-PCR,EVPCR,CRPCR的训练的LOO交叉验证系数(R-cv(2))为0.673、0.817、0.874、0.907,预测的R-2为0.569、0.678、0.519、0.79,分别。

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