IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46523-1.html
   My bibliography  Save this article

Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes

Author

Listed:
  • Shingo Harada

    (Chiba University)

  • Hiroki Takenaka

    (Chiba University)

  • Tsubasa Ito

    (Chiba University)

  • Haruki Kanda

    (Chiba University)

  • Tetsuhiro Nemoto

    (Chiba University)

Abstract

The rapid and precise creation of complex molecules while controlling multiple selectivities is the principal objective in synthetic chemistry. Combining data science and organic synthesis to achieve this goal is an emerging trend, but few examples of successful reaction designs are reported. We develop an artificial neural network regression model using bond orbital data to predict chemical reactivities. Actual experimental verification confirms cycloheptatriene-selective [6 + 2]-cycloaddition utilizing nitroso compounds and norcaradiene-selective [4 + 2]-cycloaddition reactions employing benzynes. Additionally, a one-pot asymmetric synthesis is achieved by telescoping the enantioselective dearomatization of non-activated benzenes and cycloadditions. Computational studies provide a rational explanation for the seemingly anomalous occurrence of thermally prohibited suprafacial [6 + 2]-cycloaddition without photoirradiation.

Suggested Citation

  • Shingo Harada & Hiroki Takenaka & Tsubasa Ito & Haruki Kanda & Tetsuhiro Nemoto, 2024. "Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46523-1
    DOI: 10.1038/s41467-024-46523-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46523-1
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-46523-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    2. Hak Joong Kim & Mark W. Ruszczycky & Sei-hyun Choi & Yung-nan Liu & Hung-wen Liu, 2011. "Enzyme-catalysed [4+2] cycloaddition is a key step in the biosynthesis of spinosyn A," Nature, Nature, vol. 473(7345), pages 109-112, May.
    3. Jolene P. Reid & Matthew S. Sigman, 2019. "Holistic prediction of enantioselectivity in asymmetric catalysis," Nature, Nature, vol. 571(7765), pages 343-348, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naudé, Wim, 2020. "Artificial Intelligence against COVID-19: An Early Review," IZA Discussion Papers 13110, Institute of Labor Economics (IZA).
    2. Shu-Wen Li & Li-Cheng Xu & Cheng Zhang & Shuo-Qing Zhang & Xin Hong, 2023. "Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Zuodong Sun & Cooper S. Jamieson & Masao Ohashi & K. N. Houk & Yi Tang, 2022. "Discovery and characterization of a terpene biosynthetic pathway featuring a norbornene-forming Diels-Alderase," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.
    5. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    6. Weihe Zhong & Ziduo Yang & Calvin Yu-Chian Chen, 2023. "Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Wenjing Nie & Qiongqiong Wan & Jian Sun & Moran Chen & Ming Gao & Suming Chen, 2023. "Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    9. Xiaoqian Wang & Yang Huang & Xiaoyu Xie & Yan Liu & Ziyu Huo & Maverick Lin & Hongliang Xin & Rong Tong, 2023. "Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    11. Jie Ding & Runping Ye & Yanghe Fu & Yiming He & Ye Wu & Yulong Zhang & Qin Zhong & Harold H. Kung & Maohong Fan, 2023. "Direct synthesis of urea from carbon dioxide and ammonia," Nature Communications, Nature, vol. 14(1), pages 1-4, December.
    12. Itai Levin & Mengjie Liu & Christopher A. Voigt & Connor W. Coley, 2022. "Merging enzymatic and synthetic chemistry with computational synthesis planning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    13. Marcel Rolf Pfeifer, 2021. "Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
    14. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    15. Hang Xiao & Rong Li & Xiaoyang Shi & Yan Chen & Liangliang Zhu & Xi Chen & Lei Wang, 2023. "An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    16. Diandra S. Hassan & Christian Wolf, 2021. "Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    17. Nathan J. Szymanski & Pragnay Nevatia & Christopher J. Bartel & Yan Zeng & Gerbrand Ceder, 2023. "Autonomous and dynamic precursor selection for solid-state materials synthesis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    18. Umit V. Ucak & Islambek Ashyrmamatov & Junsu Ko & Juyong Lee, 2022. "Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Yuanyuan Jiang & Zongwei Yang & Jiali Guo & Hongzhen Li & Yijing Liu & Yanzhi Guo & Menglong Li & Xuemei Pu, 2021. "Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46523-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.