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An QSAR Model for Predicting PBDEs Toxicity Established Based on Ridge Regression

机译:基于RIDGE回归建立的PBDES毒性的QSAR模型

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In this paper, the ridge regression (RR) method was employed to establish the quantitative structure-activity relationships (QSAR) model for predicting toxicity with 15 polybrominated biphenyl ethers (PBDEs) and their 27 kinds of quantum descriptors. Quantum descriptors used to establish the QSAR model were filtrated out based on correlation analysis and variables importance of project (VIP) supported by partial least squares (PLS). The multicollinearity among the descriptors was removed during the calculation of RR method in order to ensure the validation of the final regression equation. The research showed that descriptors of Δα, α_(xx), α_(xy), α_(xz), α_(yz), β_(xxy) and β_(yyy) had significant effect on toxicity. The model with the simulation efficiency coefficient of 0.916 could be used to predict the toxicity of the unchecked PBDEs and as a preliminary analysis for environmental risk of organic compounds.
机译:本文采用脊回归(RR)方法来建立用于预测15种多溴联烯基醚(PBDE)及其27种量子描述符的定量结构 - 活性关系(QSAR)模型。基于部分最小二乘(PLS)支持的项目(VIP)的相关性分析和变量重要性来过滤QSAR模型的量子描述符。在计算RR方法期间除了描述符中的多色性,以确保最终回归方程的验证。研究表明,Δα,α_(XX),α_(XY),α_(XZ),α,α,β(xxy)和β_(yyy)的描述符对毒性有显着影响。模拟效率系数为0.916的模型可用于预测未经检查的PBDES的毒性以及有机化合物环境风险的初步分析。

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