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首页> 外文期刊>Environmental toxicology and chemistry >THE USE OF CARBON THIRTEEN NUCLEAR MAGNETIC RESONANCE SPECTRA TO PREDICT DIOXIN AND FURAN BINDING AFFINITIES TO THE ARYL HYDROCARBON RECEPTOR
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THE USE OF CARBON THIRTEEN NUCLEAR MAGNETIC RESONANCE SPECTRA TO PREDICT DIOXIN AND FURAN BINDING AFFINITIES TO THE ARYL HYDROCARBON RECEPTOR

机译：利用碳三裂核磁共振谱预测芳烃受体的二恶英和呋喃结合亲和力

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摘要

Four spectroscopic data―activity relationship (SDAR) models for polychlorinated dibenzofurans (PCDFs) and diben-xodioxins (PCDDs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on simulated ~(13)C nuclear magnetic resonance (NMR) data. Models were developed using discriminant function analysis of the compounds' spectral data. An SDAR model with two classifications for 26 PCDF compounds had a leave-one-out (LOO) cross-validation accuracy of 89%. A two-classification SDAR model for 14 PCDD compounds had LOO cross-validation accuracy of 95%. A two-classification SDAR model combining 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 88%, while a four-classification SDAR model based on the same 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 92%. We used each appropriate SDAR model to classify 41 PCDD and/or 121 PCDF compounds with unknown binding affinities to the AhR. The SDAR models provide a rapid, simple, and valid way to model the PCDF and PCDD binding activity in relation to the AhR.
机译：基于模拟的〜（13）C核磁共振（NMR）数据，开发了四种与芳基烃受体（AhR）结合的多氯二苯并呋喃（PCDF）和二苯并二恶英（PCDD）的光谱数据-活性关系（SDAR）模型。 。使用化合物光谱数据的判别函数分析开发了模型。具有26种PCDF化合物两种分类的SDAR模型的留一法（LOO）交叉验证准确性为89％。针对14种PCDD化合物的两级SDAR模型的LOO交叉验证准确性为95％。结合了14种PCDD和26种PCDF化合物的两级SDAR模型的LOO交叉验证准确性为88％，而基于相同的14种PCDD和26种PCDF化合物的四分类SDAR模型的LOO交叉验证准确性为92％。我们使用每种合适的SDAR模型对与AhR的结合亲和力未知的41种PCDD和/或121种PCDF化合物进行分类。 SDAR模型提供了一种快速，简单且有效的方式来对与AhR相关的PCDF和PCDD绑定活动进行建模。

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