Journal
SCIENCE
Volume 378, Issue 6618, Pages 399-405Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.adc8743
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Funding
- Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program [HR00111920027]
- Molecule Maker Lab Institute
- NSF [2019897]
- Department of Defense (DoD) through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program
- Institute for Basic Science, Korea [IBS-R020-D1]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [2019897] Funding Source: National Science Foundation
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This study presents a closed-loop workflow that combines data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that significantly improve reaction yield. This research provides a practical approach for addressing multidimensional chemical optimization problems.
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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