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Scientific discovery in the age of artificial intelligence

Nature volume 620, pages 47–60 (2023)Cite this article

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A Publisher Correction to this article was published on 30 August 2023

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Abstract

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

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Fig. 1: Science in the age of artificial intelligence.
Fig. 2: Learning meaningful representations of scientific data.
Fig. 3: AI-guided generation of scientific hypotheses.
Fig. 4: Integration of AI with scientific experiments and simulation.

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Acknowledgements

M.Z. gratefully acknowledges the support of the National Institutes of Health under R01HD108794, U.S. Air Force under FA8702-15-D-0001, awards from Harvard Data Science Initiative, Amazon Faculty Research, Google Research Scholar Program, Bayer Early Excellence in Science, AstraZeneca Research, Roche Alliance with Distinguished Scientists, and Kempner Institute for the Study of Natural and Artificial Intelligence. C.P.G. and Y.D. acknowledge the support from the U.S. Air Force Office of Scientific Research under Multidisciplinary University Research Initiatives Program (MURI) FA9550-18-1-0136, Defense University Research Instrumentation Program (DURIP) FA9550-21-1-0316, and awards from Scientific Autonomous Reasoning Agent (SARA), and AI for Discovery Assistant (AIDA). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. We thank D. Hassabis, A. Davies, S. Mohamed, Z. Li, K. Ma, Z. Qiao, E. Weinstein, A. V. Weller, Y. Zhong and A. M. Brandt for discussions on the paper.

Author information

Author notes

  1. Hanchen Wang

    Present address: Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA

  2. Hanchen Wang

    Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

  3. These authors contributed equally: Hanchen Wang, Tianfan Fu, Yuanqi Du

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Cambridge, UK

    Hanchen Wang & Joan Lasenby

  2. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Hanchen Wang & Anima Anandkumar

  3. Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Tianfan Fu

  4. Department of Computer Science, Cornell University, Ithaca, NY, USA

    Yuanqi Du & Carla P. Gomes

  5. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Wenhao Gao & Connor W. Coley

  6. Department of Computer Science, Stanford University, Stanford, CA, USA

    Kexin Huang & Jure Leskovec

  7. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ziming Liu

  8. Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA

    Payal Chandak

  9. Mila – Quebec AI Institute, Montreal, Quebec, Canada

    Shengchao Liu, Andreea Deac, Jian Tang & Yoshua Bengio

  10. Université de Montréal, Montreal, Quebec, Canada

    Shengchao Liu, Andreea Deac & Yoshua Bengio

  11. Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

    Peter Van Katwyk & Karianne Bergen

  12. Data Science Institute, Brown University, Providence, RI, USA

    Peter Van Katwyk & Karianne Bergen

  13. NVIDIA, Santa Clara, CA, USA

    Anima Anandkumar

  14. Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

    Shirley Ho

  15. Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA

    Shirley Ho

  16. Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA

    Shirley Ho

  17. Department of Physics and Center for Data Science, New York University, New York, NY, USA

    Shirley Ho

  18. Google DeepMind, London, UK

    Pushmeet Kohli & Petar Veličković

  19. Microsoft Research, Beijing, China

    Tie-Yan Liu

  20. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Arjun Manrai & Marinka Zitnik

  21. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Debora Marks

  22. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Debora Marks & Marinka Zitnik

  23. Deep Forest Sciences, Palo Alto, CA, USA

    Bharath Ramsundar

  24. BioMap, Beijing, China

    Le Song

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

    Le Song

  26. University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Jimeng Sun

  27. HEC Montréal, Montreal, Quebec, Canada

    Jian Tang

  28. CIFAR AI Chair, Toronto, Ontario, Canada

    Jian Tang

  29. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Petar Veličković

  30. University of Amsterdam, Amsterdam, Netherlands

    Max Welling

  31. Microsoft Research Amsterdam, Amsterdam, Netherlands

    Max Welling

  32. DP Technology, Beijing, China

    Linfeng Zhang

  33. AI for Science Institute, Beijing, China

    Linfeng Zhang

  34. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

  35. Harvard Data Science Initiative, Cambridge, MA, USA

    Marinka Zitnik

  36. Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA

    Marinka Zitnik

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Contributions

All authors contributed to the design and writing of the paper, helped shape the research, provided critical feedback, and commented on the paper and its revisions. H.W., T.F., Y.D. and M.Z conceived the study and were responsible for overall direction and planning. W.G., K.H. and Z.L. contributed equally to this work (equal second authorship) and are listed alphabetically.

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Correspondence to Marinka Zitnik.

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Wang, H., Fu, T., Du, Y. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). https://doi.org/10.1038/s41586-023-06221-2

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