Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Towards the prediction of general biomolecular interactions with AI

Nature Methods volume 21, pages 1382–1383 (2024)Cite this article

Subjects

Methods for predicting bimolecular interactions are seeing tremendous growth, but challenges remain in capturing the full physical complexity of these interactions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

References

  1. Jumper, J. et al. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Baek, M. et al. Science 373, 871–876 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wang, W. et al. Nat. Commun. 14, 7266 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Li, Y. et al. Nat. Commun. 14, 5745 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Baek, M. et al. Nat. Methods 21, 117–121 (2024).

    Article  CAS  PubMed  Google Scholar 

  6. Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. Preprint at https://doi.org/10.48550/arXiv.2210.01776 (2022).

  7. Alcaide, E. et al. Preprint at https://doi.org/10.48550/arXiv.2405.11769 (2024).

  8. Krishna, R. et al. Science 384, eadl2528 (2024).

    Article  CAS  PubMed  Google Scholar 

  9. Abramson, J. et al. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rao, R., Meier, J., Sercu, T., Ovchinnikov, S. & Rives, A. Preprint at bioRxiv https://doi.org/10.1101/2020.12.15.422761 (2020).

  11. Chen, J. et al. Preprint at https://doi.org/10.48550/arXiv.2204.00300 (2022).

  12. Zdrazil, B. et al. Nucleic Acids Res. 52, D1180–D1192 (2024).

    Article  PubMed  Google Scholar 

  13. Kim, S. et al. Nucleic Acids Res. 51, D1373–D1380 (2023).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by Microsoft, IITP/MSIT (RS-2023-00220628), NRF/MSIT (RS-2023-00210147), and the New Faculty Startup Fund from Seoul National University.

Author information

Authors and Affiliations

  1. School of Biological Sciences, Seoul National University, Seoul, Republic of Korea

    Minkyung Baek

Authors
  1. Minkyung Baek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minkyung Baek.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baek, M. Towards the prediction of general biomolecular interactions with AI. Nat Methods 21, 1382–1383 (2024). https://doi.org/10.1038/s41592-024-02350-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-024-02350-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing