Affiliation:
1. Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida , Gainesville, Florida 32610, USA
2. Center for Environmental and Human Toxicology, University of Florida , Gainesville, Florida 32608, USA
Abstract
Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
Funder
United States Department of Agriculture (USDA) National Institute of Food and Agriculture
Food Animal Residue Avoidance Databank (FARAD) Program
United States National Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Grant Program
New Faculty Start-up Funds from the University of Florida
NIH
Publisher
Oxford University Press (OUP)
Cited by
28 articles.
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