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Automatic mapping of atoms across both simple and complex chemical reactions

Author

Listed:
  • Wojciech Jaworski

    (University of Warsaw)

  • Sara Szymkuć

    (Polish Academy of Sciences)

  • Barbara Mikulak-Klucznik

    (Polish Academy of Sciences)

  • Krzysztof Piecuch

    (University of Warsaw)

  • Tomasz Klucznik

    (Polish Academy of Sciences)

  • Michał Kaźmierowski

    (University of Warsaw)

  • Jan Rydzewski

    (University of Warsaw)

  • Anna Gambin

    (University of Warsaw)

  • Bartosz A. Grzybowski

    (Polish Academy of Sciences
    UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun
    UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun)

Abstract

Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer’s help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible “intermediate” atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and – above all – machine extraction of reaction rules underlying modern synthesis-planning programs.

Suggested Citation

  • Wojciech Jaworski & Sara Szymkuć & Barbara Mikulak-Klucznik & Krzysztof Piecuch & Tomasz Klucznik & Michał Kaźmierowski & Jan Rydzewski & Anna Gambin & Bartosz A. Grzybowski, 2019. "Automatic mapping of atoms across both simple and complex chemical reactions," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09440-2
    DOI: 10.1038/s41467-019-09440-2
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    Cited by:

    1. Shuangjia Zheng & Tao Zeng & Chengtao Li & Binghong Chen & Connor W. Coley & Yuedong Yang & Ruibo Wu, 2022. "Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Shuan Chen & Sunggi An & Ramil Babazade & Yousung Jung, 2024. "Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Sunghwan Choi, 2023. "Prediction of transition state structures of gas-phase chemical reactions via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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