In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods.

Journal: Toxicology in vitro : an international journal published in association with BIBRA
PMID: 33444712

Abstract

In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essential and necessary. In silico tools, such as QSAR models can play an important role in predicting physicochemical and biological properties of chemicals. In this study, a total of 54 classification models were developed by combination of 6 machine learning methods along with 9 kinds of molecular fingerprints based on the experimental honey bees acute contact toxicity data (LD) of 676 structurally diverse pesticides. The best model proposed was SVM algorithm combined with CDK extended fingerprint. The analysis of the applicability domain of the model successfully excluded some extreme molecules. Additionally, 9 structural alerts about honey bees acute contact toxicity were identified by information gain and substructure frequency analysis.

Authors

  • Xuan Xu
    1DATA Consortium, USA; Institute of Computational Comparative Medicine, Kansas State University, Manhattan, KS, USA; Department of Anatomy and Physiology, Kansas State University, Manhattan, KS, USA; Department of Mathematics, Kansas State University, Manhattan, KS, USA.
  • Piaopiao Zhao
    Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
  • Zhiyuan Wang
    Department of Systems and Information Engineering, University of Virginia, USA.
  • Xiaoxiao Zhang
    Key Laboratory of Drug Quality Control&Pharmacovigilance (China Pharmaceutical University), Ministry of Education, Nanjing, China.
  • Zengrui Wu
    Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
  • Weihua Li
    State Key Laboratory of Molecular Engineering of Polymers, Key Laboratory of Computational Physical Sciences, Department of Macromolecular Science, Fudan University, Shanghai 200438, China.
  • Yun Tang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Guixia Liu
    Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China . Email: gxliu@ecust.edu.cn ; Email: ytang234@ecust.edu.cn ; ; Tel: +86-21-64250811.

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