Bayesian optimization is a promising approach towards a more environmentally friendly chemical synthesis, in line with the Sustainable Development Goals. It can aid chemists to explore vast chemical spaces and find green reaction conditions with few experiments, decreasing resource consumption and waste generation while reducing discovery timelines and costs.
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References
Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11, 233 (2020).
Guo, J., Ranković, B. & Schwaller, P. Bayesian optimization for chemical reactions. Chimia 77, 31–38 (2023).
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).
Sheldon, R. A. Metrics of green chemistry and sustainability: past, present, and future. ACS Sustain. Chem. Eng. 6, 32–48 (2018).
Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).
Garrido Torres, J. A. et al. A multi-objective active learning platform and web app for reaction optimization. J. Am. Chem. Soc. 144, 19999–20007 (2022).
Häse, F., Aldeghi, M., Hickman, R. J., Roch, L. M. & Aspuru-Guzik, A. GRYFFIN: an algorithm for Bayesian optimization of categorical variables informed by expert knowledge. Appl. Phys. Rev. 8, 031406 (2021).
Braconi, E. & Godineau, E. Bayesian optimization as a sustainable strategy for early-stage process development? A case study of Cu-catalyzed C–N coupling of sterically hindered pyrazines. ACS Sustain. Chem. Eng. 11, 10545–10554 (2023).
Clayton, A. D. et al. Bayesian self-optimization for telescoped continuous flow synthesis. Angew. Chem. Int. Ed. 62, e202214511 (2023).
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Acknowledgements
The author thanks E. Godineau, O. Lahtigui and S. Bell for valuable discussions and acknowledges Syngenta Crop Protection AG for financial support.
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Related links
Chimera: https://github.com/aspuru-guzik-group/chimera
EDBO+: https://www.edbowebapp.com/
Gryffin: https://github.com/aspuru-guzik-group/gryffin
Phoenics: https://github.com/aspuru-guzik-group/phoenics
Sustainable Development Goals: https://sdgs.un.org/2030agenda
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Braconi, E. Bayesian optimization as a valuable tool for sustainable chemical reaction development. Nat Rev Methods Primers 3, 74 (2023). https://doi.org/10.1038/s43586-023-00266-3
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DOI: https://doi.org/10.1038/s43586-023-00266-3
