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A neural network model informs the total synthesis of clovane sesquiterpenoids

Nature Synthesis volume 2, pages 527–534 (2023)Cite this article

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Abstract

Efficient syntheses of complex small molecules, such as bioactive natural products, often involve detailed retrosynthetic planning and experimental evaluation of speculative synthetic routes. The central challenge of such an approach is that experimental evaluation of high-risk strategies is resource intensive because it requires iterative attempts at unsuccessful strategies. Along with the rapid development of cheminformatics and artificial intelligence, computer-aided synthetic planning has emerged to address this challenge. Herein, we report a complementary strategy that combines human-generated synthetic plans with computational prediction of the feasibility of key steps in the proposed synthesis. A neural network model (NNET) was trained on a literature-based dataset (from Reaxys) to predict the outcome of a generally disfavoured transformation, 6-endo-trig radical cyclization. The model performance was rigorously tested by experimental validation. On the basis of the virtual screening of potential substrates with our NNET model, optimal disconnections and structural modifications were chosen, resulting in five- to eight-step syntheses of three clovane sesquiterpenoids. This work establishes how a machine learning model informs human design and guides multistep syntheses of complex small molecules.

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Fig. 1: ML model informs synthetic plan for clovane sesquiterpenoids.
Fig. 2: ML model development.
Fig. 3: ML model informed synthesis of clovan-2,9-dione.
Fig. 4: Experimental validation of NNET model and application to the total synthesis of clovane sesquiterpenoids.

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Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information.

Code availability

All code used to support the findings of this work is supplied as Supplementary Information. The code is also available on GitHub (https://github.com/Newhouse-Group/6-Endo-Radical-Cyclization). Source data are provided with this paper.

References

  1. Corey, E. J. Robert Robinson Lecture. Retrosynthetic thinking—essentials and examples. Chem. Soc. Rev. 17, 111–133 (1988).

    Article  CAS  Google Scholar 

  2. Shen, Y. et al. Automation and computer-assisted planning for chemical synthesis. Nat. Rev. Methods Primers 1, 23 (2021).

    Article  CAS  Google Scholar 

  3. Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part I: progress. Angew. Chem. Int. Ed. 59, 22858–22893 (2020).

    Article  CAS  Google Scholar 

  4. Segler, M. H. S., Preuss, M. & Waller, M. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Szymkuc, S. et al. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. Int. Ed. 55, 5904–5937 (2016).

    Article  CAS  Google Scholar 

  6. Marth, C. J. et al. Network-analysis-guided synthesis of weisaconitine D and liljestrandinine. Nature 528, 493–498 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mo, Y. et al. Evaluating and clustering retrosynthesis pathways with learned strategy. Chem. Sci. 12, 1469–1478 (2021).

    Article  CAS  Google Scholar 

  8. Mikulak-Klucznik, B. et al. Computational planning of the synthesis of complex natural products. Nature 588, 83–88 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Bideau, F. L. et al. Tricyclic sesquiterpenes from marine origin. Chem. Rev. 117, 6110–6159 (2017).

    Article  PubMed  Google Scholar 

  10. Chung, H.-M. et al. Rumphellclovane B, a novel clovane analogue from the gorgonian coral rumphella antipathies. Bull. Chem. Soc. Jpn 84, 119–121 (2011).

    Article  CAS  Google Scholar 

  11. Chung, H.-M. et al. Rumphellclovanes C–E, new clovane-type sesquiterpenoids from the gorgonian coral Rumphella antipathies. Tetrahedron Lett. 69, 2740–2744 (2013).

    Article  CAS  Google Scholar 

  12. Cheng, X. et al. Neuronal growth promoting sesquiterpene–neolignans; syntheses and biological studies. Org. Biomol. Chem. 10, 383–393 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Yang, J. et al. Asymmetric total synthesis of (−)-clovan-2,9-dione using Rh(I)-catalyzed [3+2+1] cycloaddition of 1-yne-vinylcyclopropane and CO. Org. Lett. 19, 6040–6043 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Liu, G., Zhang, Z., Fu, S. & Liu, B. Asymmetric total synthesis of rumphellclovane E. Org. Lett. 23, 290–295 (2021).

    Article  CAS  PubMed  Google Scholar 

  15. de Souza, G. G. et al. Biotransformation of clovane derivatives. Whole cell fungi mediated domino synthesis of rumphellclovane A. Org. Biomol. Chem. 10, 3315–3320 (2012).

    Article  PubMed  Google Scholar 

  16. Wright, P. M., Seiple, I. B. & Myers, A. G. The evolving role of chemical synthesis in antibacterial drug discovery. Angew. Chem. Int. Ed. 53, 8840–8869 (2014).

    Article  CAS  Google Scholar 

  17. Romero, K. J., Galliher, M. S., Pratt, D. A. & Stephenson, C. R. J. Radicals in natural product synthesis. Chem. Soc. Rev. 47, 7851–7866 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sha, C.-K., Jean, T.-S. & Wang, D.-C. Intramolecular radical cyclization of silylacetylenic or olefinic α-iodo ketones: application to the total synthesis of (±)-modhephene. Tetrahedron Lett. 31, 3745–3748 (1990).

    Article  CAS  Google Scholar 

  19. Baldwin, J. E. Rules for ring closure. J. Chem. Soc., Chem. Commun. 18, 734–736 (1976).

    Article  Google Scholar 

  20. Jorner, K. et al. Organic reactivity from mechanism to machine learning. Nat. Rev. Chem. 5, 240–255 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Reid, J. P. & Sigman, M. S. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature 571, 343–348 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ahneman, D. T. et al. Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 186–190 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Zahrt, A. F. et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363, eaau5631 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Schwaller, P., Vaucher, A., Laino, T. & Reymond, J.-L. Prediction of chemical reaction yields using deep learning. Mach. Learn.: Sci. Technol. 2, 015016 (2021).

    Google Scholar 

  26. Kim, D. E., Zweig, J. E. & Newhouse, T. R. Total synthesis of paspaline A and emindole PB enabled by computational augmentation of a transform-guided retrosynthetic strategy. J. Am. Chem. Soc. 141, 1479–1483 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Beckwith, A. L. J. & Schiesser, C. H. Regio- and stereo-selectivity of alkenyl radical ring closure: a theoretical study. Tetrahedron 41, 3925–3941 (1985).

    Article  CAS  Google Scholar 

  28. Wenderski, T. A. et al. Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries. Methods Mol. Biol. 1263, 225–242 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Estrada, J. G. et al. Response to comment on ‘Predicting reaction performance in C–N cross-coupling using machine learning’. Science 362, eaat8763 (2018).

    Article  PubMed  Google Scholar 

  30. Valerio, V., Mostinski, Y., Kotikalapudi, R. & Tsvelikhovsky, D. Stereo- and regioselective synthesis of tricyclic spirolactones by diastereoisomeric differentiation of a collective key precursor. Chem. Eur. J. 22, 2640–2647 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Siewert, J., Textor, A., Grond, S. & von Zezschwitz, P. Spirodionic acid, a novel metabolite from Streptomyces sp., part 2: total synthesis through a twofold Michael addition as a selective spiroannelation strategy. Chem. Eur. J. 13, 7424–7431 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Benohoud, M., Tuokko, S. & Pihko, P. M. Stereoselective hydrosilylation of enals and enones catalysed by palladium nanoparticles. Chem. Eur. J. 17, 8404–8413 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Chung, H.-M. et al. Rumphellclovane A, a novel clovane-related sesquiterpenoid from the gorgonian coral Rumphella antipathies. Tetrahedron Lett. 51, 2734–2736 (2010).

    Article  CAS  Google Scholar 

  34. Matsumoto, T. et al. Lignan dicarboxylates and terpenoids from the flower buds of cananga odorata and their inhibitory effects on melanogenesis. J. Nat. Prod. 77, 990–999 (2014).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful for financial support from Yale University, the Sloan Foundation, Boehringer Ingelheim, Genentech and the National Institutes of Health (grant no. GR100045). Student support included Chemical Biology Training grant (no. T32 GM067543 to R.L.C.) and an Anderson Postdoctoral Fellowship (P.Z.). We gratefully acknowledge Yale University’s High-Performance Computing Center for providing resources for this work. F. Menges of Yale Chemical and Biophysical Instrumentation Center is gratefully acknowledged for obtaining the high-resolution mass spectrometry data.

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Author notes

  1. These authors contributed equally: Pengpeng Zhang, Jungmin Eun.

Authors and Affiliations

  1. Department of Chemistry, Yale University, New Haven, CT, USA

    Pengpeng Zhang, Jungmin Eun, Masha Elkin, Yizhou Zhao, Rachel L. Cantrell & Timothy R. Newhouse

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Contributions

M.E., P.Z. and T.R.N. initiated the project. Y.Z. and J.E. synthesized clovan-2,9-dione. P.Z. and R.L.C. synthesized rumphellclovane A and canangaterpene II. J.E. and R.L.C. carried out DFT and nuclear magnetic resonance spectroscopy calculations. P.Z., M.E. and Y.Z. performed the ML modelling. P.Z. and J.E. carried out experimental validation. All co-authors wrote and edited the manuscript.

Corresponding author

Correspondence to Timothy R. Newhouse.

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The authors declare no competing interests.

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Peer review information

Nature Synthesis thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Peter Seavill, in collaboration with the Nature Synthesis team.

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Supplementary information

Supplementary Information

Experimental Details, Supplementary Sections I–X, Figs. 1–15 and Tables 1–7.

Source data

Source Data Fig. 2

Reported 6-endo-trig radical cyclization yields with calculated ΔG of reactions.

Source Data Fig. 4

ML-predicted yields and experimental yields.

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Zhang, P., Eun, J., Elkin, M. et al. A neural network model informs the total synthesis of clovane sesquiterpenoids. Nat. Synth 2, 527–534 (2023). https://doi.org/10.1038/s44160-023-00271-0

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