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Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches

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Abstract

The present work aimed at developing in silico models allowing for a reliable prediction of bioaccumulative compounds and non-bioaccumulative compounds based on the definition of Bioconcentration Factor (BCF) using a diverse data set of 238 organic molecules. The partial least squares analysis (PLS), C4.5, support vector machine (SVM), and random forest (RF) algorithms were applied, and their performance classifying these compounds in terms of their quantitative structure-activity relationships (QSAR) was evaluated and verified with 5-fold cross-validation and an independent evaluation data set. The obtained results show that the overall prediction accuracies (Q) of the optimal PLS, C4.5, SVM and RF models are 84.5–87.7% for the internal cross-validation, with prediction accuracy (CO) of 86.3–91.1% in the external test sets, and C4.5 is slightly better than the three other methods which presents a Q of 87.7%, and a CO of 91.1% for the test sets. All these results prove the reliabilities of the in silico models, which should be valuable for the environmental risk assessment of the substances.

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Authors and Affiliations

  1. Key Lab of Mariculture and Biotechnology, Ministry of Agriculture, Dalian Fisheries University, Dalian, China

    Xiuli Sun, Xianjie Liu, Jun Ding, Yonghua Wang, Hui Shen & Yaqing Chang

  2. School of Chemical Engineering, Dalian University of Technology, Dalian, China

    Yan Li

Authors
  1. Xiuli Sun
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  2. Yan Li
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  3. Xianjie Liu
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  4. Jun Ding
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  5. Yonghua Wang
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  6. Hui Shen
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  7. Yaqing Chang
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Correspondence to Yonghua Wang.

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Sun, X., Li, Y., Liu, X. et al. Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches. Mol Divers 12, 157–169 (2008). https://doi.org/10.1007/s11030-008-9092-x

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