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AI-based toxicity prediction models using ToxCast data: Current status and future directions for explainable models.

Donghyeon Kim1, Jinhee Choi1

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|July 11, 2025
PubMed
Summary

Artificial intelligence (AI) models using ToxCast data are advancing chemical toxicity prediction. These AI approaches analyze vast toxicological data to improve environmental chemical screening and risk assessment.

Keywords:
Artificial intelligenceNext generation risk assessmentToxCastToxicity prediction

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Area of Science:

  • Environmental toxicology and computational chemistry.
  • Application of artificial intelligence in chemical safety assessment.

Background:

  • The U.S. Environmental Protection Agency's (EPA) ToxCast program offers extensive toxicological data, crucial for developing AI-driven toxicity prediction models.
  • ToxCast data is widely utilized for building predictive models to screen environmental chemicals.

Purpose of the Study:

  • To review and analyze artificial intelligence (AI) models developed using ToxCast data since 2015.
  • To provide an overview of the current landscape of ToxCast data-based AI models, including database structure, target endpoints, molecular representations, and learning algorithms.

Main Methods:

  • Systematic review of 93 peer-reviewed papers published since 2015.
  • Analysis of AI model components: database structure, target endpoints, molecular representations (e.g., fingerprints, descriptors, graphs, images, text), and machine learning algorithms (supervised, semi-supervised, unsupervised).

Main Results:

  • Most AI models focus on data-rich endpoints and organ-specific toxicity, such as endocrine disruption and hepatotoxicity.
  • Recent models increasingly use advanced molecular representations (graphs, images, text) and deep learning, moving beyond traditional fingerprints.
  • There is a growing trend towards semi- and unsupervised learning to address data sparsity, complementing traditional supervised methods.

Conclusions:

  • ToxCast data-based AI models show significant promise for accelerating chemical toxicity prediction and environmental risk assessment.
  • Future directions include refining AI models and integrating them into next-generation risk assessment (NGRA) frameworks.
  • Continued development in AI algorithms and data representation is key to overcoming current limitations.