A review on machine learning methods for in silico toxicity prediction.

Journal: Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Published Date: Jan 10, 2019

Abstract

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

Authors

  • Gabriel Idakwo
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.
  • Joseph Luttrell
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.
  • Minjun Chen
    Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
  • 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.
  • Zhaoxian Zhou
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.
  • Ping Gong
    Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Chaoyang Zhang
    a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA.

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