An autonomous organic reaction search engine for chemical reactivity
The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible rea...
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Published in: | Nature communications Vol. 8; no. 1; p. 15733 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
London
Nature Publishing Group UK
09-06-2017
Nature Publishing Group Nature Portfolio |
Subjects: | |
Online Access: | Get full text |
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Summary: | The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.
While automated reaction systems typically work for the synthesis of pre-defined molecules, automated systems to discover reactivity are more challenging. Here the authors report an autonomous organic reaction search engine that allows discovery of the most reactive pathways in a multi-reagent, multistep reaction system. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/ncomms15733 |