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Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis
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DOI：10.1002/anie.202214511.png)
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摘要
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The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization-deprotection reaction sequence, used in the synthesis of a precursor for 1-methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in-depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product.
Keyword:
Bayesian Optimization
Continuous Flow
Machine Learning
Medicinal Chemistry
Sustainable Chemistry
期刊
IF:
16.9
论文数:
5.7W
被引数:
53.0W
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引用论文
Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solidsOAAI
OAAI分享收藏分享收藏Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics.png)
ADVANCED SCIENCEOAAI
ADVANCED SCIENCEIF14.1
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