Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis
Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis
复制标题
伸缩连续流合成的贝叶斯自优化
DOI：
10.1002/ange.202214511
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发表时间：
2022
影响因子：
--
通讯作者：
Clayton A
中科院分区：
文献类型：
--
作者：
Clayton A
多步化学合成的优化对于新药的快速开发至关重要。然而，由于步骤之间的化学相互依赖性，串联单独优化的反应可能导致低效的多步合成。在此，我们开发了一个自动化的连续流平台，用于同时优化伸缩反应。我们的方法应用于Heck环化-脱保护反应序列，用于合成1-甲基四氢异喹啉C5官能化的前体。设计了一种使用单个在线HPLC仪器进行多点采样的简单方法，可以准确定量每个反应，并深入了解反应途径。值得注意的是，贝叶斯优化技术的整合在仅14小时内鉴定出81%的总产率，并揭示了形成所需产物的有利竞争途径。
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.