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Prediction of Biodegradability for Polycyclic Aromatic Hydrocarbons Using Various In Silico Modeling Methods

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Abstract

Polycyclic aromatic hydrocarbons (PAHs) have attracted great concern as global environmental pollutants. In this work, the quantitative structure–biodegradability relationship (QSBR) study has been done to predict the biodegradability of PAHs and develop the correlation between the biodegradability and the molecular structures. The structural chemistry and quantum chemistry descriptors were used to represent molecular structures. Three in silico modeling methods, i.e., multiple linear regression (MLR), radial basis function neural network, and back-propagation artificial neural network (BPANN), are utilized to construct the linear and nonlinear prediction models and provide some insights into the structural characteristics affecting the biodegradability of PAHs. The stability of these QSBR models was tested by leave-one-out cross-validation, and the cross-validated correlation coefficients (q2) were 0.6109, 0.6887, and 0.6586, respectively. The correlation coefficients (R2) of the three models for the training set were 0.7811, 0.8883, and 0.9667, respectively. The comparison of the three models showed that the BPANN model produced a statistically more significant model than the other two models. On the basis of molecular structure, the dominant molecular structure descriptor affecting biodegradability of PAHs were analyzed and discussed.

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Acknowledgements

This work was supported by Major Science and Technology Program for Water Pollution Control and Treatment (2012ZX07206-002), Knowledge Innovation Program of Shenzhen (JCYJ20160526162154729), and Shanghai Pujiang Program (17PJ1400900).

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Authors and Affiliations

  1. Shenzhen Academy of Environmental Sciences, Shenzhen, 518001, China

    Gong Cheng & Liming Sun

  2. Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China

    Jie Fu

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  1. Gong Cheng
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  2. Liming Sun
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  3. Jie Fu
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Correspondence to Jie Fu.

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Cheng, G., Sun, L. & Fu, J. Prediction of Biodegradability for Polycyclic Aromatic Hydrocarbons Using Various In Silico Modeling Methods. Arch Environ Contam Toxicol 75, 607–615 (2018). https://doi.org/10.1007/s00244-018-0556-4

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  • DOI: https://doi.org/10.1007/s00244-018-0556-4

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