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Total synthesis of 25 picrotoxanes by virtual library selection

Nature volume 638, pages 980–986 (2025)Cite this article

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Abstract

The synthesis of a complex molecule begins from an initial design stage1,2,3,4 in which possible routes are triaged by strategy and feasibility, on the basis of analogy to similar reactions2,3. However, as molecular complexity increases, predictability decreases5; inevitably, even experienced chemists resort to trial and error to identify viable intermediates en route to the target molecule. We encountered such a problem in the synthesis of picrotoxane sesquiterpenes in which pattern-recognition methods anticipated success, but small variations in structure led to failure. Here, to solve this problem but avoid tedious guess-and-check experimentation, we built a virtual library of elusive late-stage intermediate analogues that were triaged by reactivity and altered the synthesis pathway. The efficiency of this method led to concise routes to 25 naturally occurring picrotoxanes. Costly density-functional-theory transition-state calculations were replaced with faster reactant parameterizations to increase scalability and, in this case, inform the mechanism. This approach can serve as an add-on search to human or computer-assisted synthesis planning applicable to high-complexity targets and/or steps with little representation in the literature or reaction databases.

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Fig. 1: Observations and study design.
Fig. 2: Synthesis entry and virtual library.
Fig. 3: Synthesis of three picrotoxanes using calculated intermediates.
Fig. 4: Parameterization and prediction.
Fig. 5: Navigation of chemical space aided by virtual library selection.

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Data availability

All data are made available in the main text or Supplementary Information, including experimental procedures, copies of NMR spectra and X-ray structure reports. Structural parameters are available from the Cambridge Crystallographic Data Centre (CCDC) under the following reference numbers: des-Bz 19, 2250952; (+)-tutin (1), 2304983; 21, 2304987; SI-6′, 2303677.

Code availability

The code used in Figs. 4 and  5 is in the Supplementary Information (page 203). The source code for AutoDFT is available on Zenodo at https://doi.org/10.5281/zenodo.14366481 (ref. 49).

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Acknowledgements

We acknowledge J. S. Chen, B. Sanchez, Q. N. Wang, J. Lee and B. Orzolek for analysis; L. Pasternack, G. J. Kroon and D.-H. Huang for assistance with NMR spectroscopy; M. Gembicky, J. Bailey and the entire UCSD Crystallography Facility for X-ray crystallographic analysis; and J.-C. Ducom and L. Dong for assistance with the computing cluster. We thank S. Ting for conversations about statistical modelling; and K. Engle, T. Cernak and D. Wang for computational suggestions. Funding was provided by the National Institutes of Health (GM122606) and the Skaggs Graduate School of Chemical and Biological Sciences (Dale Boger Endowed Graduate Fellowship to C.L.).

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Authors and Affiliations

  1. Department of Chemistry, Scripps Research, La Jolla, CA, USA

    Chunyu Li  (李春雨) & Ryan A. Shenvi

  2. Graduate School of Chemical and Biological Sciences, Scripps Research, La Jolla, CA, USA

    Chunyu Li  (李春雨) & Ryan A. Shenvi

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  1. Chunyu Li  (李春雨)
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  2. Ryan A. Shenvi
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Contributions

R.A.S. and C.L. conceived the project. C.L. executed the combined computational and experimental work with guidance and verification by R.A.S., who also oversaw the research. R.A.S. and C.L. composed the paper, C.L. compiled the supplementary materials and R.A.S. provided editorial feedback and oversight.

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Correspondence to Ryan A. Shenvi.

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Li, C., Shenvi, R.A. Total synthesis of 25 picrotoxanes by virtual library selection. Nature 638, 980–986 (2025). https://doi.org/10.1038/s41586-024-08538-y

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