Computational planning of the synthesis of complex natural products

Barbara Mikulak-Klucznik, Patrycja Gołębiowska, Alison A. Bayly, Oskar Popik, Tomasz Klucznik, Sara Szymkuć, Ewa P. Gajewska, Piotr Dittwald, Olga Staszewska-Krajewska, Wiktor Beker, Tomasz Badowski, Karl A. Scheidt, Karol Molga (), Jacek Mlynarski (), Milan Mrksich () and Bartosz A. Grzybowski ()
Additional contact information
Barbara Mikulak-Klucznik: Polish Academy of Sciences
Patrycja Gołębiowska: Polish Academy of Sciences
Alison A. Bayly: Northwestern University
Oskar Popik: Polish Academy of Sciences
Tomasz Klucznik: Polish Academy of Sciences
Sara Szymkuć: Polish Academy of Sciences
Ewa P. Gajewska: Polish Academy of Sciences
Piotr Dittwald: Polish Academy of Sciences
Olga Staszewska-Krajewska: Polish Academy of Sciences
Wiktor Beker: Polish Academy of Sciences
Tomasz Badowski: Polish Academy of Sciences
Karl A. Scheidt: Northwestern University
Karol Molga: Polish Academy of Sciences
Jacek Mlynarski: Polish Academy of Sciences
Milan Mrksich: Northwestern University
Bartosz A. Grzybowski: Polish Academy of Sciences

Nature, 2020, vol. 588, issue 7836, 83-88

Abstract: Abstract Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years1–7. However, the field has progressed greatly since the development of early programs such as LHASA1,7, for which reaction choices at each step were made by human operators. Multiple software platforms6,8–14 are now capable of completely autonomous planning. But these programs ‘think’ only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary15,16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program’s knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships17,18, allowing it to ‘strategize’ over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization.

Date: 2020
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DOI: 10.1038/s41586-020-2855-y

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