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首页> 外文期刊>Environmental toxicology and chemistry >PREDICTING TOXIC EQUIVALENCE FACTORS FROM ~(13)C NUCLEAR MAGNETIC RESONANCE SPECTRA FOR DIOXINS, FURANS, AND POLYCHLORINATED BIPHENYLS USING LINEAR AND NONLINEAR PATTERN RECOGNITION METHODS
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PREDICTING TOXIC EQUIVALENCE FACTORS FROM ~(13)C NUCLEAR MAGNETIC RESONANCE SPECTRA FOR DIOXINS, FURANS, AND POLYCHLORINATED BIPHENYLS USING LINEAR AND NONLINEAR PATTERN RECOGNITION METHODS

机译：使用线性和非线性模式识别方法从〜（13）C核磁共振谱预测二恶英，呋喃和多氯联苯的毒性当量因子

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Two quantitative spectrometric data-activity relationships (QSDAR) models have been developed relating 29 dioxin or dioxin-like molecules to their toxic equivalence factors (TEFs). These models were based on patterns in simulated ~(13)C nuclear magnetic resonance (NMR) data with the patterns defined by comparative spectral analysis (CoSA). Two versions of CoSA multiple linear regression (MLR) models using 7 or 10 spectral bins had, respectively, explained variances (r~2) of 0.88 and 0.95, and leave-one-out (LOO) cross-validated variances (q~2) of 0.78 and 0.88. A third, artificial neural network model―using a feed forward, back propagating, three-layer neural network―produced an r~2 of 0.99, a LOO q~2 of 0.82, and a leave-three-out q~2 of 0.81. A postulated reason that the results of these QSDAR models are better than traditional quantitative structure-activity relationship (QSAR) models is based on the difference in descriptors rather than on any differences in pattern recognition approach. Results suggest that the ~(13)C NMR spectral data contain molecular quantum mechanical information more reflective of each molecule's biochemical properties than do the calculated electrostatic potentials and molecular alignment assumptions used in developing QSAR models. The QSDAR models provide a rapid, simple way to model the toxicity of dioxin and dioxin-like compounds.
机译：已经建立了两个定量光谱数据-活性关系（QSDAR）模型，将29种二恶英或类二恶英分子与它们的毒性当量因子（TEF）相关联。这些模型基于模拟〜（13）C核磁共振（NMR）数据中的模式，并具有通过比较光谱分析（CoSA）定义的模式。使用7个或10个光谱仓的两个版本的CoSA多元线性回归（MLR）模型的解释方差（r〜2）分别为0.88和0.95，而留一法则（LOO）交叉验证方差（q〜2 ）分别为0.78和0.88。第三个人工神经网络模型-使用前馈，反向传播的三层神经网络-产生的r〜2为0.99，LOO q〜2为0.82，离开三分q〜2为0.81 。这些QSDAR模型的结果优于传统的定量结构-活性关系（QSAR）模型的一个推测原因是基于描述符的差异，而不是基于模式识别方法的任何差异。结果表明，〜（13）C NMR光谱数据包含的分子量子力学信息比开发QSAR模型中使用的计算出的静电势和分子排列假设更能反映每个分子的生化特性。 QSDAR模型提供了一种快速，简单的方法来模拟二恶英和类二恶英化合物的毒性。

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