Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis

Clayton, Adam D, Pyzer-Knapp, Edward O ORCID: 0000-0002-8232-8282, Purdie, Mark, Jones, Martin F, Barthelme, Alexandre, Pavey, John, Kapur, Nikil, Chamberlain, Thomas W, Blacker, A John and Bourne, Richard A (2022) Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 62 (3). e202214511-.

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Official URL: http://dx.doi.org/10.1002/anie.202214511

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.

Item Type:	Article
Uncontrolled Keywords:	Bayesian Optimization, Continuous Flow, Machine Learning, Medicinal Chemistry, Sustainable Chemistry
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	02 Mar 2023 09:02
Last Modified:	02 Mar 2023 09:02
DOI:	10.1002/anie.202214511
Open Access URL:	https://doi.org/10.1002/anie.202214511
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168670