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Merging enzymatic and synthetic chemistry with computational synthesis planning

Author

Listed:
  • Itai Levin

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Mengjie Liu

    (Massachusetts Institute of Technology)

  • Christopher A. Voigt

    (Massachusetts Institute of Technology)

  • Connor W. Coley

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which are valuable due to their selectivity and sustainability but are few in number. We report a retrosynthetic search algorithm using two neural network models for retrosynthesis–one covering 7984 enzymatic transformations and one 163,723 synthetic transformations–that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others. Application to (-)-Δ9 tetrahydrocannabinol (THC) (dronabinol) and R,R-formoterol (arformoterol) illustrates how our strategy facilitates the replacement of metal catalysis, high step counts, or costly enantiomeric resolution with more elegant hybrid proposals.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35422-y
    DOI: 10.1038/s41467-022-35422-y
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    References listed on IDEAS

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    1. Shang Zhu & Bharath Ramsundar & Emil Annevelink & Hongyi Lin & Adarsh Dave & Pin-Wen Guan & Kevin Gering & Venkatasubramanian Viswanathan, 2024. "Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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