Skip to main content

Advertisement

SpringerLink
Log in

MACHINE LEARNING

Fast track to structural biology

  • News & Views
  • Published:

From Nature Chemistry

View current issue Submit your manuscript

Machine learning algorithms are fast surpassing human abilities in multiple tasks, from image recognition to medical diagnostics. Now, machine learning algorithms have been shown to be capable of accurately predicting the folded structures of proteins.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: AlphaFold2 prediction of protein structures in three different cases.

References

  1. Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research. Protein Structure Prediction Center (30 November 2020); https://predictioncenter.org/casp14/doc/CASP14_press_release.html

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

    Article  CAS  Google Scholar 

  3. Baek, M. et al. RoseTTAFold: the first release of RoseTTAFold. Zenodo https://zenodo.org/record/5068265 (2021).

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

    Article  CAS  Google Scholar 

  5. Tunyasuvunakool, K. et al. Nature 596, 590–596 (2021).

    Article  CAS  Google Scholar 

  6. AlQuraishi, M. The AlphaFold2 method paper: A fount of good ideas. Wordpress https://moalquraishi.wordpress.com/2021/07/25/the-alphafold2-method-paper-a-fount-of-good-ideas/ (2021).

  7. Noé, F., Tkatchenko, A., Müller, K.-R. & Clementi, C. Annu. Rev. Phys. Chem. 71, 361–390 (2020).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics, The Free University of Berlin, Berlin, Germany

    Cecilia Clementi

  2. Center for Theoretical Biological Physics, Department of Chemistry, Department of Physics, and Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA

    Cecilia Clementi

Authors
  1. Cecilia Clementi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cecilia Clementi.

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

Clementi, C. Fast track to structural biology. Nat. Chem. 13, 1032–1034 (2021). https://doi.org/10.1038/s41557-021-00814-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-021-00814-y

  • Springer Nature Limited

This article is cited by

Search

Navigation