Deep learning for predicting toxicity of chemicals: a mini review.

Journal: Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Published Date: Mar 1, 2019

Abstract

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Authors

  • Weihao Tang
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
  • Jingwen Chen
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China. Electronic address: jwchen@dlut.edu.cn.
  • Zhongyu Wang
    a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China.
  • Hongbin Xie
    a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China.
  • Huixiao Hong
    National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Electronic address: Huixiao.Hong@fda.hhs.gov.

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