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非离子有机化合物生物富集因子的定量结构-性质关系建模

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds.

作者信息

Deeb Omar, Khadikar Padmakar V, Goodarzi Mohammad

机构信息

Faculty of Pharmacy, Al-Quds University, P.O. Box 20002 Jerusalem, Palestine.

出版信息

Environ Health Insights. 2010 Jul 6;4:33-47. doi: 10.4137/ehi.s5168.

Abstract

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds.

摘要

生物累积和生物浓缩这两个术语指的是可能在生物体中发生的化学物质的摄取和积累。生物浓缩的实验测量既耗时又昂贵,对于大量具有潜在监管关注的化学物质来说并不可行。一种基于定量结构-性质关系(QSPR)的高效工具可用于描述由生物浓缩因子的对数(log BCF)这一重要生态毒理学参数所表示的化学物质在生物体内富集的趋势,该工具使用分子描述符来描述大量非离子有机化合物。使用多元线性回归、偏最小二乘法和神经网络分析开发了QSPR模型。针对相关描述符开发了用于预测化合物log BCF的线性和非线性QSPR模型。所获得的结果提供了具有良好预测能力的良好回归模型。这些模型中使用的描述符取决于体积、连接性、摩尔折射度、表面张力以及接受氢键的原子的存在情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5fb/2918358/f93ebf971522/ehi-2010-033f1.jpg

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