TY - JOUR
T1 - Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling
AU - Angello, Nicholas H.
AU - Rathore, Vandana
AU - Beker, Wiktor
AU - Wołos, Agnieszka
AU - Jira, Edward R.
AU - Roszak, Rafał
AU - Wu, Tony C.
AU - Schroeder, Charles M.
AU - Aspuru-Guzik, Alán
AU - Grzybowski, Bartosz A.
AU - Burke, Martin D.
N1 - Funding Information:
This work was supported by the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program (cooperative agreement no. HR00111920027 dated 1 August 2019) to M.D.B., B.A.G., and A.A.-G. The content of the information presented in this work does not necessarily reflect the position or the policy of the US government. This work was also supported by funding from the Molecule Maker Lab Institute: An AI Research Institutes program supported by NSF under award no. 2019897 to C.M.S. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation. N.H.A. was supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program. B.A.G. was supported by the Institute for Basic Science, Korea (project code IBS-R020-D1).
Publisher Copyright:
Copyright © 2022 The Authors, some rights reserved;
PY - 2022/10/28
Y1 - 2022/10/28
N2 - General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.
AB - General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.
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U2 - 10.1126/science.adc8743
DO - 10.1126/science.adc8743
M3 - Article
C2 - 36302014
SN - 0036-8075
VL - 378
SP - 399
EP - 405
JO - Science
JF - Science
IS - 6618
ER -