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Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge
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    Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge
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    • Felix Strieth-Kalthoff
      Felix Strieth-Kalthoff
      University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
      University of Toronto, Department of Computer Science, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
    • Sara Szymkuć
      Sara Szymkuć
      Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
      Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
    • Karol Molga
      Karol Molga
      Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
      Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
      More by Karol Molga
    • Alán Aspuru-Guzik
      Alán Aspuru-Guzik
      University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
      University of Toronto, Department of Computer Science, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
      Vector Institute for Artificial Intelligence, 661 University Ave., Toronto, Ontario M5G 1M1, Canada
      University of Toronto, Department of Chemical Engineering and Applied Chemistry, 200 College St., Toronto, Ontario M5S 3E5, Canada
      University of Toronto, Department of Materials Science and Engineering, 184 College St., Toronto, Ontario M5S 3E4, Canada
    • Frank Glorius*
      Frank Glorius
      Universität Münster, Organisch-Chemisches Institut, Corrensstr. 36, 48149 Münster, Germany
      *Email: [email protected]
    • Bartosz A. Grzybowski*
      Bartosz A. Grzybowski
      Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
      IBS Center for Algorithmic and Robotized Synthesis, CARS, UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
      Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
      *Email: [email protected]
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    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2024, 146, 16, 11005–11017
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    https://doi.org/10.1021/jacs.4c00338
    Published April 10, 2024
    Copyright © 2024 American Chemical Society

    Abstract

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    Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.

    Copyright © 2024 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jacs.4c00338.

    • Further examples of inconsistencies in chemical reaction databases (PDF)

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    Cited By

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    This article is cited by 4 publications.

    1. Chuanghui Wang, Yunqing Yang, Jinshuai Song, Xiaofei Nan. Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry. Journal of Chemical Information and Modeling 2024, 64 (19) , 7189-7213. https://doi.org/10.1021/acs.jcim.4c00791
    2. Rodolfo I. Teixeira, Michael Andresini, Renzo Luisi, Brahim Benyahia. Computer-Aided Retrosynthesis for Greener and Optimal Total Synthesis of a Helicase-Primase Inhibitor Active Pharmaceutical Ingredient. JACS Au 2024, Article ASAP.
    3. Olaf Wiest, Christoph Bauer, Paul Helquist, Per-Ola Norrby, Samuel Genheden. Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions. Journal of Chemical Information and Modeling 2024, 64 (15) , 5796-5805. https://doi.org/10.1021/acs.jcim.4c00370
    4. Luhan Dai, Yulong Fu, Mengran Wei, Fangyuan Wang, Bailin Tian, Guoqiang Wang, Shuhua Li, Mengning Ding. Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions. Journal of the American Chemical Society 2024, 146 (28) , 19019-19029. https://doi.org/10.1021/jacs.4c03085

    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2024, 146, 16, 11005–11017
    Click to copy citationCitation copied!
    https://doi.org/10.1021/jacs.4c00338
    Published April 10, 2024
    Copyright © 2024 American Chemical Society

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