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Optimizing microflow sequential coupling and cyclization of multiple linear substrates guided by Bayesian optimization
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DOI：10.1093/bulcsj/uoaf022.png)
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Abstract
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Sequential coupling and cyclization of 2 linear substrates containing multiple reaction sites enables single-step synthesis of useful cyclic compounds; however, if the reaction sites are highly reactive, overreactions tend to occur. Although the use of microflow synthesis can prevent overreactions, careful optimization of multiple variables is required. Herein, we optimized the microflow synthesis of a cyclic sulfamide via sequential coupling and cyclization of 2 linear substrates with 2 highly reactive sites. The traditional one-variant-at-a-time-based approach revealed nonlinear correlations between the variables and the yield of cyclic sulfamide, and the optimal conditions afforded a 90% yield. Subsequent re-optimization using the Bayesian optimization (BO)-based approach identified significantly different optimal conditions, giving the product in 94% yield. Additional experiments and simulations were conducted to investigate the key factors influencing the optimal conditions for the BO-based approach.
Keywords:
Bayesian optimization
flow synthesis
sulfamide
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