Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis

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Abstract: 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.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis ; day:13 ; month:12 ; year:2022 ; extent:1
Angewandte Chemie ; (13.12.2022) (gesamt 1)

Creator: Clayton, Adam D.
Pyzer‐Knapp, Edward O.
Purdie, Mark
Jones, Martin F.
Barthelme, Alexandre
Pavey, John
Kapur, Nikil
Chamberlain, Thomas W.
Blacker, A. John
Bourne, Richard A.

DOI: 10.1002/ange.202214511

URN: urn:nbn:de:101:1-2022121414081040844833

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:25 AM CEST

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Associated

Clayton, Adam D.
Pyzer‐Knapp, Edward O.
Purdie, Mark
Jones, Martin F.
Barthelme, Alexandre
Pavey, John
Kapur, Nikil
Chamberlain, Thomas W.
Blacker, A. John
Bourne, Richard A.

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