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Computational prediction of complex cationic rearrangement outcomes

Abstract

Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction-network and neural-network algorithms which can plan multi-step synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. Such methods typically operate on full, literature-derived “substrate(s)-to-product” reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical (QM) and kinetic calculations, can use a reaction-network approach to analyze the mechanisms of some of the most complex organic transformations – namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail dramatic changes in the molecule’s carbon skeleton8-12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences, and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would likely prove challenging even to highly trained chemists: (i) predicting the outcomes of Tail-to-Head Terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (ii) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule, and (iii) analyzing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types1-7 but will help rationalize and discover new, mechanistically complex transformations.

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Author notes

  1. These authors contributed equally: Tomasz Klucznik, Leonidas-Dimitrios Syntrivanis, Wiktor Beker

Authors and Affiliations

  1. Allchemy, Highland, IN, USA

    Tomasz Klucznik, Barbara Mikulak-Klucznik, Martyna Moskal, Sara Szymkuć, Wiktor Beker & Bartosz A. Grzybowski

  2. Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw, Poland

    Tomasz Klucznik, Sebastian Baś, Barbara Mikulak-Klucznik, Jacek Mlynarski, Louis Gadina, Wiktor Beker & Bartosz A. Grzybowski

  3. Roger Adams Laboratory, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Leonidas-Dimitrios Syntrivanis & Martin D. Burke

  4. Department of Chemistry, University of Basel, Basel, Switzerland

    Leonidas-Dimitrios Syntrivanis & Konrad Tiefenbacher

  5. Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, Krakow, Poland

    Sebastian Baś

  6. Molecule Maker Lab Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Martin D. Burke

  7. Molecule Maker Lab at the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Martin D. Burke

  8. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Martin D. Burke

  9. Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Martin D. Burke

  10. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Konrad Tiefenbacher

  11. IBS Center for Algorithmic and Robotized Synthesis, CARS, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, South Korea

    Bartosz A. Grzybowski

  12. Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, South Korea

    Bartosz A. Grzybowski

Authors

  1. Tomasz Klucznik
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  2. Leonidas-Dimitrios Syntrivanis
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  3. Sebastian Baś
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  4. Barbara Mikulak-Klucznik
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  5. Martyna Moskal
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  6. Sara Szymkuć
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  7. Jacek Mlynarski
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  8. Louis Gadina
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  9. Wiktor Beker
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  10. Martin D. Burke
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  11. Konrad Tiefenbacher
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  12. Bartosz A. Grzybowski
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Corresponding authors

Correspondence to
Leonidas-Dimitrios Syntrivanis, Wiktor Beker, Martin D. Burke, Konrad Tiefenbacher or Bartosz A. Grzybowski.

Supplementary information

Supplementary Information

Supplementary Sections 1–10, including Supplementary Tables 1–3 and Supplementary Figs 1–256.

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Klucznik, T., Syntrivanis, LD., Baś, S. et al. Computational prediction of complex cationic rearrangement outcomes.
Nature (2023). https://doi.org/10.1038/s41586-023-06854-3

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  • Received: 05 July 2022

  • Accepted: 08 November 2023

  • Published: 15 November 2023

  • DOI: https://doi.org/10.1038/s41586-023-06854-3

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