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  • Perspective
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Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences

Nature Methods (2026)Cite this article

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Abstract

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

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Fig. 1: Barriers to OSAI in the life sciences.
Fig. 2: OSAI: recommendations and ecosystem components.

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Code availability

All data and code are made available under a Creative Commons license CC BY 4.0: OSAI Ecosystem Interactive Website: https://osai.dome-ml.org/ai-ecosystem/; OSAI Ecosystem Data and Interactive Website Code - GitHub: https://github.com/BioComputingUP/OSAI_ecosystem/; GitHub—releases: https://github.com/BioComputingUP/OSAI_ecosystem/releases/; Zenodo—archived releases: https://doi.org/10.5281/zenodo.15391273 (ref. 99); Software Heritage—archived releases: https://archive.softwareheritage.org/browse/directory/d1205b81b070e43af1c5d3e1493287518c5262d7/?origin_url= https://doi.org/10.5281/zenodo.15391273&path=BioComputingUP-OSAI_ecosystem-f7a6068&release=3&snapshot=d46b21f9a6057d0ea05675b2c2eb27745f93c9df.

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Acknowledgements

This work has been supported by ELIXIR, the European infrastructure for life science data. Additional funding was from European Union through NextGenerationEU PNRR project ELIXIRxNextGenIT (grant agreement no. IR0000010, to S.C.E.T.); Horizon Europe projects EVERSE (grant agreement no. 101129744, to S.C.E.T., C.G., T.V. and F.P.) and ELIXIR STEERS (grant agreement no. 101131096, to S.C.E.T., C.G., T.V. and F.P.); and COST Action ML4NGP (grant agreement no. CA21160, to S.C.E.T.), supported by COST (European Cooperation in Science and Technology. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Author information

Authors and Affiliations

  1. Department of Biomedical Sciences, University of Padova, Padova, Italy

    Gavin Farrell, Omar Abdelghani Attafi, Alexander Miguel Monzon & Silvio C. E. Tosatto

  2. Athena Research & Innovation Center, Aigialias & Chalepa, Marousi, Greece

    Eleni Adamidi & Thanasis Vergoulis

  3. VIB Data Core, VIB.AI Center for AI & Computational Biology, Ghent, Belgium

    Rafael Andrade Buono

  4. ELIXIR Europe Hub, EMBL-EBI South Building, Hinxton, UK

    Mihail Anton, Fabio G. Liberante & Peter Maccallum

  5. Barcelona Supercomputing Center (BSC), Barcelona, Spain

    Salvador Capella Gutierrez & Davide Cirillo

  6. Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

    Emidio Capriotti

  7. Computational Genomics Platform, IRCCS University Hospital of Bologna, Bologna, Italy

    Emidio Capriotti

  8. ZB MED Information Centre for Life Sciences, Cologne, Germany

    Leyla Jael Castro

  9. SequenceAnalysis.co.uk, Norwich, UK

    Lisa Crossman

  10. University of East Anglia, Research Park, Norwich, UK

    Lisa Crossman

  11. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Christophe Dessimoz

  12. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Christophe Dessimoz & Lucy Poveda

  13. Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center ‘Alexander Fleming’, Vari, Greece

    Alexandros Dimopoulos

  14. Department of Informatics & Telematics, School of Digital Technology, Harokopio University, Athens, Greece

    Alexandros Dimopoulos

  15. School of Medicine, University College Dublin, Dublin, Ireland

    Raúl Fernández-Díaz & Denis C. Shields

  16. Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland

    Raúl Fernández-Díaz & Denis C. Shields

  17. IBM Research Dublin, Dublin, Ireland

    Raúl Fernández-Díaz

  18. Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Styliani-Christina Fragkouli & Fotis Psomopoulos

  19. Department of Biology, National & Kapodistrian University of Athens, Athens, Greece

    Styliani-Christina Fragkouli

  20. Department of Computer Science, University of Manchester, Manchester, UK

    Carole Goble & Shoaib Sufi

  21. Luxembourg National Data Service, Esch-sur-Alzette, Luxembourg

    Wei Gu

  22. Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia

    John M. Hancock

  23. Department of Psychology, University of Freiburg, Freiburg, Germany

    Alireza Khanteymoori

  24. Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium

    Tom Lenaerts

  25. Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium

    Tom Lenaerts

  26. Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium

    Tom Lenaerts

  27. FARI, AI for the Common Good Institute, ULB-VUB, Brussels, Belgium

    Tom Lenaerts

  28. Center for Human-Compatible AI, University of California, Berkeley, Berkeley, CA, USA

    Tom Lenaerts

  29. Leiden University Medical Center, Leiden, the Netherlands

    Magnus Palmblad

  30. LPHI, University of Montpellier, CNRS, INSERM, Montpellier, France

    Ovidiu Radulescu

  31. Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy

    Silvio C. E. Tosatto

Authors
  1. Gavin Farrell
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  2. Eleni Adamidi
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  3. Rafael Andrade Buono
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  4. Mihail Anton
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  5. Omar Abdelghani Attafi
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  7. Emidio Capriotti
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  8. Leyla Jael Castro
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  9. Davide Cirillo
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  10. Lisa Crossman
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  11. Christophe Dessimoz
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  12. Alexandros Dimopoulos
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  13. Raúl Fernández-Díaz
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  15. Carole Goble
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  16. Wei Gu
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  18. Alireza Khanteymoori
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  22. Alexander Miguel Monzon
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  23. Magnus Palmblad
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  24. Lucy Poveda
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  25. Ovidiu Radulescu
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  28. Thanasis Vergoulis
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  29. Fotis Psomopoulos
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  30. Silvio C. E. Tosatto
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Contributions

All authors contributed to the OSAI ecosystem and subsequent discussion as well as the initial draft of this manuscript. G.F. wrote the final draft with the help of the co-authors. All authors edited and refined the final manuscript. G.F., F.P. and S.C.E.T. initiated and coordinated the project.

Corresponding authors

Correspondence to Fotis Psomopoulos or Silvio C. E. Tosatto.

Ethics declarations

Competing interests

R.F.-D. is an Industrial PhD candidate at University College Dublin with affiliation to IBM Research Dublin, who supports joint PhD research between both sites. L.C. is the director of SequenceAnalysis.co.uk. These affiliations are commercial in nature; however, the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Jian Ma, Benjamin Haibe-Kainsand the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Farrell, G., Adamidi, E., Andrade Buono, R. et al. Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences. Nat Methods (2026). https://doi.org/10.1038/s41592-026-03037-6

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