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  • Viewpoint
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Artificial intelligence in metabolic research

Nature Metabolism (2025)Cite this article

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The recent developments in artificial intelligence (AI) have created both intrigue and apprehension in the world of research. In this Viewpoint, we asked 12 experts in the field of metabolism to share their — differing — opinions on the use of AI in pre-clinical and clinical metabolic research.

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Author information

Authors and Affiliations

  1. Department of Biomedical Sciences, University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark

    Jens Juul Holst

  2. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Jens Juul Holst, Camilla Schéele & Ruth J. F. Loos

  3. Touchstone Diabetes Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Philipp E. Scherer

  4. Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Weiping Jia

  5. Institute for Proactive Healthcare, Shanghai Jiao Tong University, Shanghai, China

    Weiping Jia

  6. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Eran Segal

  7. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Eran Segal

  8. Department of Bioengineering, Northeastern University, Boston, MA, USA

    Nikolai Slavov

  9. Parallel Squared Technology Institute, Watertown, MA, USA

    Nikolai Slavov

  10. Charles Bronfman Institute for Personalized Medicine, Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ruth J. F. Loos

  11. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Golnaz Vahedi

  12. Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Golnaz Vahedi

  13. Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Golnaz Vahedi

  14. Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Golnaz Vahedi

  15. TaiKang Center for Life and Medical Sciences, TaiKang Medical School, Wuhan University, Wuhan, China

    Lei Sun

  16. Cardiovascular & Metabolic Disorders, DUKE-NUS Medical School, Singapore, Singapore

    Lei Sun

  17. State Key Laboratory of Metabolism and Regulation in Complex Organisms, Wuhan University, Wuhan, China

    Lei Sun

  18. St. Jude Children’s Research Hospital, Memphis, TN, USA

    M. Madan Babu

  19. Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia, Australia

    Melissa D. McCradden

  20. Women’s and Children’s Health Network, North Adelaide, South Australia, Australia

    Melissa D. McCradden

  21. SickKids Research Institute, Toronto, Ontario, Canada

    Melissa D. McCradden

  22. Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, OR, USA

    Peter G. Jacobs

  23. School of Chemical, Biological, and Environmental Engineering, College of Engineering, Oregon State University, Corvallis, OR, USA

    Peter G. Jacobs

  24. Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA

    Peter G. Jacobs

Authors
  1. Jens Juul Holst
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  2. Camilla Schéele
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  3. Philipp E. Scherer
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  4. Weiping Jia
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  5. Eran Segal
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  6. Nikolai Slavov
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  7. Ruth J. F. Loos
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  8. Golnaz Vahedi
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  9. Lei Sun
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  10. M. Madan Babu
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  11. Melissa D. McCradden
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  12. Peter G. Jacobs
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Corresponding authors

Correspondence to Jens Juul Holst, Camilla Schéele, Philipp E. Scherer, Weiping Jia, Eran Segal, Nikolai Slavov, Ruth J. F. Loos, Golnaz Vahedi, Lei Sun, M. Madan Babu, Melissa D. McCradden or Peter G. Jacobs.

Ethics declarations

Competing interests

J.J.H. has served on scientific advisory panels and/or as a speaker or consultant for several companies including Novo Nordisk, Eli Lilly, MSD Denmark and Mayo Clinic; is a co-founder and owns stock in Antag Therapeutics; and is a member of the board of Antag Therapeutics, which is unpaid. C.S. has received research funding from Novo Nordisk and Eli Lilly, and consulting fees from Zealand Pharma and Tribune Therapeutics. N.S. is a founding director and CEO of Parallel Squared Technology Institute, which is a non-profit research institute. R.J.F.L. has received speaker fees from Novo Nordisk and Eli Lilly; has served as a member of an advisory board for Eli Lilly, and as a consultant for Novo Nordisk, Eli Lilly, Foghorn Therapeutics Inc. and Maze Therapeutics Inc. M.D.M. reports receiving consulting fees for services to Google Health, Cephalgo and iheed. P.G.J. receives research support from Dexcom, Eli Lilly and SFC Fluidics; and is a co-founder and shareholder in Pacific Diabetes Technologies Inc., a company that may have a commercial interest in this type of research. The remaining authors declare no competing interests related to this manuscript.

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Cite this article

Holst, J.J., Schéele, C., Scherer, P.E. et al. Artificial intelligence in metabolic research. Nat Metab (2025). https://doi.org/10.1038/s42255-025-01395-7

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  • DOI: https://doi.org/10.1038/s42255-025-01395-7

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