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.

  • Meeting Report
  • Published:

ARTIFICIAL INTELLIGENCE

Improving reaction prediction

An Author Correction to this article was published on 21 August 2020

This article has been updated

The use of automation for chemical research and reaction discovery has seen significant advances in recent years, but there are still problems that need to be solved. Ella M. Gale and Derek J. Durand discuss limitations in the field and how researchers are working to address these issues.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

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

Fig. 1: The process for taking input data through to continuous learning embedding.

Change history

References

  1. Segler, M., Preuss, M. & Waller, M. Nature 555, 604–610 (2018).

    Article  CAS  Google Scholar 

  2. Wang, L. Chemical and Engineering News https://cen.acs.org/articles/95/i25/ChemPlanner-integrate-SciFindern.html (2017).

  3. Rosales, A. R. et al. Nat. Catal. 2, 41–45 (2019).

    Article  CAS  Google Scholar 

  4. Lai, A., Clifton, J., Diaconescu, P. L. & Fey, N. Chem. Commun. 55, 7021–7204 (2019).

    Article  CAS  Google Scholar 

  5. Spicer, R. L. et al. J. Am. Chem. Soc. 142, 2134–2139 (2020).

    Article  CAS  Google Scholar 

  6. Grethe, G., Blanke, G., Kraut, H. & Goodman, J. M. J. Cheminform. 10, 22 (2018).

    Article  Google Scholar 

  7. Press, L. S., Press, J. B. & Taylor, K. T. Graphical Representation Standards for Chemical Reactions (IUPAC Recommendations, 2019).

  8. GitHub https://github.com/PistoiaAlliance/UDM (2020).

  9. GitHub https://github.com/rsc-ontologies/rxno (2019).

  10. Bitbucket https://bitbucket.org/rscapplications/chemlistem/src/master/ (2020).

  11. Kanza, S. & Frey, J. G. Expert Opin. Drug Discov. 14, 433–444 (2019).

    Article  CAS  Google Scholar 

  12. Steiner, S. et al. Science 363, eaav2211 (2019).

    Article  CAS  Google Scholar 

  13. Warr, W., Kanza, S., Frey, J. G. & Whitby, R. J. (eds) AI3SD, Dial-a-Molecule & Directed Assembly: AI for reaction outcome and synthetic route prediction conference report 2020 (University of Southampton, 2020)..

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ella M. Gale.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gale, E.M., Durand, D.J. Improving reaction prediction. Nat. Chem. 12, 509–510 (2020). https://doi.org/10.1038/s41557-020-0478-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-020-0478-4

This article is cited by

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