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.

  • News & Views
  • Published:

Drug discovery

Progress in using deep learning to treat cancer

Deep learning approaches have potential to substantially reduce the astronomical costs and long timescales involved in drug discovery. KarmaDock proposes a deep learning workflow for ligand docking that shows improved performance against both benchmark cases and in a real-world virtual screening experiment.

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

Access options

Buy this article

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

Fig. 1: Overview of the KarmaDock workflow7.

References

  1. Dowden, H. & Munro, P. Nat. Rev. Drug. Discov. 18, 495–496 (2019).

    Article  Google Scholar 

  2. Measuring the Return from Pharmaceutical Innovation (Deloitte, 2018)

  3. Ban, F. et al. J. Chem. Inf. Model. 57, 1018–1028 (2017).

    Article  Google Scholar 

  4. Schneider, P. & Schneider, G. J. Med. Chem. 59, 4077–4068 (2016).

    Article  Google Scholar 

  5. Gentile, F. et al. ACS Cent. Sci. 6, 939–949 (2020).

    Article  Google Scholar 

  6. Playe, B. & Stoven, V. J. Chem. Inf. 12, 11 (2020).

    Google Scholar 

  7. Zhang, X. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00511-5 (2023).

    Article  Google Scholar 

  8. Cooper, A., Sequist, L. V., Johnson, T. W. & Lin, J. J. Cancer Cell 40, 23–25 (2022).

    Article  Google Scholar 

  9. Satorras, V. c. G., Hoogeboom, E. & Welling, M. In Proceedings of the 38th International Conference on Machine Learning (eds Marina, M. & Tong, Z.) Vol. 139, 9323–9332 (PMLR, 2021).

  10. Ho, J., Jain, A. & Abbeel, P. In Advances in Neural Information Processing Systems (eds Larochelle, H. et al.) 6840–6851 (Curran Associates, Inc., 2020).

  11. Zhang, J., He, K., Dong, T. & Wu, J. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-1454132/v1 (2022).

  12. Shen, C. et al. J. Med. Chem. 65, 10691–10706 (2022).

    Article  MathSciNet  Google Scholar 

  13. Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L. & Dror, R. Preprint at https://arxiv.org/abs/2009.01411 (2020).

  14. Santos-Martins, D. et al. J. Chem. Theory Comput. 17, 1060–1073 (2021).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shina Caroline Lynn Kamerlin.

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

Kamerlin, S.C.L. Progress in using deep learning to treat cancer. Nat Comput Sci 3, 739–740 (2023). https://doi.org/10.1038/s43588-023-00514-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-023-00514-2

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