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Machine Learning and Artificial Intelligence in Toxicological Sciences.

Zhoumeng Lin1,2, Wei-Chun Chou1,2

  • 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida 32610, USA.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|July 21, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning and artificial intelligence are transforming toxicology by enabling efficient chemical modeling and accurate toxicity prediction. These advanced computational approaches accelerate the discovery of toxicity mechanisms from complex datasets.

Keywords:
artificial intelligencecomputational toxicologymachine learningphysiologically based pharmacokinetic (PBPK) modelingquantitative structure-activity relationship (QSAR)

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Area of Science:

  • Toxicological Sciences
  • Computational Toxicology
  • Bioinformatics

Background:

  • Machine learning (ML) and artificial intelligence (AI) have significantly impacted various scientific fields.
  • Toxicology is increasingly leveraging ML/AI for advanced data analysis and predictive modeling.
  • Traditional methods face limitations in handling large, complex toxicological datasets.

Purpose of the Study:

  • To review recent applications of ML/AI in diverse areas of toxicology.
  • To highlight the advancements in PBPK modeling, toxicity prediction, and mechanism elucidation.
  • To identify key challenges and future directions for ML/AI in toxicology.

Main Methods:

  • Literature review of representative recent applications of ML/AI in toxicology.
  • Summarization of ML/AI use in physiologically based pharmacokinetic (PBPK) modeling.
  • Analysis of ML/AI in quantitative structure-activity relationship (QSAR) modeling, adverse outcome pathway (AOP) analysis, high-throughput screening (HTS), toxicogenomics, big data, and toxicological databases.

Main Results:

  • ML/AI enables efficient development of PBPK models for numerous chemicals.
  • In silico toxicity prediction models achieve accuracy comparable to in vivo animal experiments.
  • Rapid analysis of diverse data types (toxicogenomics, imaging) yields novel insights into toxicity mechanisms.

Conclusions:

  • ML/AI approaches offer powerful tools for advancing toxicological sciences.
  • Future work requires careful selection of ML/AI methods, prediction of effect intensity, robust big data management, and user-friendly interface development.
  • Continued integration of ML/AI is crucial for efficient and accurate toxicological assessments.