Knowledge graph aids comprehensive explanation of drug and chemical toxicity.

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  • Author(s): Hao Y;Hao Y; Romano JD; Romano JD; Romano JD; Moore JH; Moore JH
  • Source:
    CPT: pharmacometrics & systems pharmacology [CPT Pharmacometrics Syst Pharmacol] 2023 Aug; Vol. 12 (8), pp. 1072-1079. Date of Electronic Publication: 2023 Jul 20.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Wiley Country of Publication: United States NLM ID: 101580011 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2163-8306 (Electronic) Linking ISSN: 21638306 NLM ISO Abbreviation: CPT Pharmacometrics Syst Pharmacol Subsets: MEDLINE
    • Publication Information:
      Publication: 2015- : Hoboken, NJ : Wiley
      Original Publication: New York, NY : Nature Pub. Group
    • Subject Terms:
      Pattern Recognition, Automated*; Humans ; HEK293 Cells
    • Abstract:
      In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.
      (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
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    • Grant Information:
      P30 ES013508 United States ES NIEHS NIH HHS; R01 AG066833 United States AG NIA NIH HHS; K99 LM013646 United States LM NLM NIH HHS; R01 LM010098 United States LM NLM NIH HHS
    • Publication Date:
      Date Created: 20230721 Date Completed: 20230817 Latest Revision: 20231111
    • Publication Date:
      20231215
    • Accession Number:
      PMC10431039
    • Accession Number:
      10.1002/psp4.12975
    • Accession Number:
      37475158