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Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis

Author

Listed:
  • Zi-Jing Zhang

    (Georg-August-Universität Göttingen)

  • Shu-Wen Li

    (Zhejiang University)

  • João C. A. Oliveira

    (Georg-August-Universität Göttingen)

  • Yanjun Li

    (Georg-August-Universität Göttingen)

  • Xinran Chen

    (Georg-August-Universität Göttingen
    Zhejiang University)

  • Shuo-Qing Zhang

    (Zhejiang University)

  • Li-Cheng Xu

    (Zhejiang University)

  • Torben Rogge

    (Georg-August-Universität Göttingen)

  • Xin Hong

    (Zhejiang University
    Beijing National Laboratory for Molecular Sciences
    Westlake University)

  • Lutz Ackermann

    (Georg-August-Universität Göttingen
    Georg-August-Universität Göttingen)

Abstract

Challenging enantio- and diastereoselective cobalt-catalyzed C–H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C–H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C–N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.

Suggested Citation

  • Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38872-0
    DOI: 10.1038/s41467-023-38872-0
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    References listed on IDEAS

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    1. Jarosław M. Granda & Liva Donina & Vincenza Dragone & De-Liang Long & Leroy Cronin, 2018. "Publisher Correction: Controlling an organic synthesis robot with machine learning to search for new reactivity," Nature, Nature, vol. 562(7728), pages 26-26, October.
    2. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    3. K. N. Houk & Paul Ha-Yeon Cheong, 2008. "Computational prediction of small-molecule catalysts," Nature, Nature, vol. 455(7211), pages 309-313, September.
    4. Jarosław M. Granda & Liva Donina & Vincenza Dragone & De-Liang Long & Leroy Cronin, 2018. "Controlling an organic synthesis robot with machine learning to search for new reactivity," Nature, Nature, vol. 559(7714), pages 377-381, July.
    5. Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
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    Cited by:

    1. Zi-Jing Zhang & Nicolas Jacob & Shilpa Bhatia & Philipp Boos & Xinran Chen & Joshua C. DeMuth & Antonis M. Messinis & Becky Bongsuiru Jei & João C. A. Oliveira & Aleksa Radović & Michael L. Neidig & , 2024. "Iron-catalyzed stereoselective C–H alkylation for simultaneous construction of C–N axial and C-central chirality," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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