QSAR Models for Predicting Toxicity of Polychlorinated Dibenzo-p-dioxins and Dibenzofurans Using Quantum Chemical Descriptors

  • Jianxiong Diao
  • Yang Li
  • Shuqiong Shi
  • Ye Sun
  • Ying Sun
Article
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Accepted:

Abstract

By partial least square regression, simple quantitative structure–activity relationship (QSAR) models were developed for the toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Quantum chemical descriptors computed by semi-empirical PM3 method were used as predictor variables. Three optimal QSAR models are developed for 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together, respectively. The cross-validated Q cum 2 values for the three QSAR models of 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together are 0.816, 0.629 and 0.603, respectively, indicating good predictive capabilities for the biological toxicity of these PCDD/Fs. The present study suggests that quantum chemical descriptors of POPs indeed govern the binding affinity of these chemicals for aryl hydrocarbon receptors. Moreover, different models contain different molecular descriptors to define respective equation, which suggests that the relationship between molecular structure and the binding affinity of these chemicals for aryl hydrocarbon receptors is complex.

Keywords

PCDD/Fs Quantum chemical descriptors QSARs Toxicity 
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Notes

Acknowledgments

This work was supported by the National Key Technology R&D Program of China (No. 2008BAC32B05-2).

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jianxiong Diao
    • 1
  • Yang Li
    • 1
  • Shuqiong Shi
    • 2
  • Ye Sun
    • 1
  • Ying Sun
    • 1
  1. 1.Department of ChemistryChina Agricultural UniversityBeijingPeople’s Republic of China
  2. 2.State Key Laboratory of Water Environment Simulation, School of EnvironmentBeijing Normal UniversityBeijingPeople’s Republic of China

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