Planning chemical syntheses with deep neural networks and symbolic AI

Marwin H. S. Segler (), Mike Preuss and Mark P. Waller ()
Additional contact information
Marwin H. S. Segler: Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität
Mike Preuss: European Research Center for Information Systems
Mark P. Waller: Shanghai University

Nature, 2018, vol. 555, issue 7698, 604-610

Abstract: Abstract To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (26)

Downloads: (external link)
https://www.nature.com/articles/nature25978 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:555:y:2018:i:7698:d:10.1038_nature25978

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/nature25978

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().