Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

Journal: Journal of hazardous materials
PMID: 29567407

Abstract

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S) and charge distribution area (S) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot.

Authors

  • Lingdi Cao
    Forschungszentrum Jülich GmbH, Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (IEK-11), Egerlandstr. 3, 91058, Erlangen, Germany.
  • Peng Zhu
    Center for Drug Safety Evaluation and Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Yongsheng Zhao
    Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Chemical Engineering, University of California, Santa Barbara, CA, 93106-5080, USA. Electronic address: yzhao01@ucsb.edu.
  • Jihong Zhao
    Collaborative Innovation Center of Environmental Pollution Control and Ecological Restoration, Zhengzhou, Henan, 450001, China; Xuchang University, Xuchang, Henan, 461001, China. Electronic address: zjh@zzuli.edu.cn.

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