Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

Nicholas H. Angello et al., Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science 378, 399-405 (2022). DOI:10.1126/science.adc8743

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.

All data and code generated as part of this study are freely accessible either in the supplementary materials or in open repositories. Code for the simulation example, model selection, and substrates clusterization as well machine- and human-readable versions of the reaction data are freely accessible at https://doi.org/10.5281/zenodo.7099435. The automated synthesis code, machine parts list and build guide, datamined commercially available building block set, checklist for reporting and evaluating machine learning models, and tabulated numerical data underlying figures in the manuscript are deposited at https://doi.org/10.5281/zenodo.7106075. The code used in the building block selection process is available at https://doi.org/10.5281/zenodo.7094835. All code related to the closed-loop optimization is freely available at https://doi.org/10.5281/zenodo.6517012.