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The digitization of organic synthesis

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  • Ian W. Davies

    (Princeton University)

Abstract

Organic chemistry has largely been conducted in an ad hoc manner by academic laboratories that are funded by grants directed towards the investigation of specific goals or hypotheses. Although modern synthetic methods can provide access to molecules of considerable complexity, predicting the outcome of a single chemical reaction remains a major challenge. Improvements in the prediction of ‘above-the-arrow’ reaction conditions are needed to enable intelligent decision making to select an optimal synthetic sequence that is guided by metrics including efficiency, quality and yield. Methods for the communication and the sharing of data will need to evolve from traditional tools to machine-readable formats and open collaborative frameworks. This will accelerate innovation and require the creation of a chemistry commons with standardized data handling, curation and metrics.

Suggested Citation

  • Ian W. Davies, 2019. "The digitization of organic synthesis," Nature, Nature, vol. 570(7760), pages 175-181, June.
    • Handle: RePEc:nat:nature:v:570:y:2019:i:7760:d:10.1038_s41586-019-1288-y
    DOI: 10.1038/s41586-019-1288-y
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    Cited by:

    1. Yu Shee & Haote Li & Pengpeng Zhang & Andrea M. Nikolic & Wenxin Lu & H. Ray Kelly & Vidhyadhar Manee & Sanil Sreekumar & Frederic G. Buono & Jinhua J. Song & Timothy R. Newhouse & Victor S. Batista, 2024. "Site-specific template generative approach for retrosynthetic planning," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Artem I. Leonov & Alexander J. S. Hammer & Slawomir Lach & S. Hessam M. Mehr & Dario Caramelli & Davide Angelone & Aamir Khan & Steven O’Sullivan & Matthew Craven & Liam Wilbraham & Leroy Cronin, 2024. "An integrated self-optimizing programmable chemical synthesis and reaction engine," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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