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用于预测鱼类生物富集因子的分子电负性距离矢量模型

Molecular electronegativity distance vector model for the prediction of bioconcentration factors in fish.

作者信息

Liu Shu-Shen, Qin Li-Tang, Liu Hai-Ling, Yin Da-Qiang

机构信息

Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, People's Republic of China.

出版信息

J Mol Model. 2008 Feb;14(2):83-92. doi: 10.1007/s00894-007-0255-y. Epub 2007 Dec 13.

Abstract

Molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was used to describe the structures of 122 nonionic organic compounds (NOCs) and a quantitative relationship between the MEDV descriptors and the bioconcentration factors (BCF) of NOCs in fish was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the BCFs of NOCs are the substructures expressed by four atomic types of nos. 2, 3, 5, and 13, i.e., atom groups -CH(2)- or =CH-, -CH< or =C<, -NH(2), and -Cl or -Br where the former two groups exist in the molecular skeleton of NOC and the latter three groups are related closely to the substituting groups on a benzene ring. The best 5-variable model, with the correlation coefficient (r(2)) of 0.9500 and the leave-one-out cross-validation correlation coefficient (q(2)) of 0.9428, was built by multiple linear regressions, which shows a good estimation ability and stability. A predictive power for the external samples was tested by the model from the training set of 80 NOCs and the predictive correlation coefficient (u(2)) for the 42 external samples in the test set was 0.9028.

摘要

直接从分子拓扑结构推导得到的分子电负性距离矢量(MEDV)用于描述122种非离子有机化合物(NOCs)的结构,并基于预测的变量选择和建模(VSMP)建立了MEDV描述符与NOCs在鱼类中的生物富集因子(BCF)之间的定量关系。结果发现,影响NOCs生物富集因子的一些主要结构因素是由编号为2、3、5和13的四种原子类型所表示的子结构,即原子团-CH(2)-或=CH-、-CH<或=C<、-NH(2)以及-Cl或-Br,其中前两个基团存在于NOC的分子骨架中,而后三个基团与苯环上的取代基团密切相关。通过多元线性回归建立了最佳的五变量模型,其相关系数(r(2))为0.9500,留一法交叉验证相关系数(q(2))为0.9428,显示出良好的估计能力和稳定性。利用来自80种NOCs训练集的模型对外部样本进行预测能力测试,测试集中42个外部样本的预测相关系数(u(2))为0.9028。

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