Avila, C, Green, C, Kossenjans, M et al. (6 more authors) (2022) Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation. Chemical Science, 13 (41). pp. 12087-12099. ISSN 2041-6520
Abstract
For the discovery of new candidate molecules in the pharmaceutical industry, library synthesis is a critical step, in which library size, diversity, and time to synthesise are fundamental. In this work we propose stopped-flow synthesis as an intermediate alternative to traditional batch and flow chemistry approaches, suited for small molecule pharmaceutical discovery. This method exploits the advantages of both techniques enabling automated experimentation with access to high pressures and temperatures; flexibility of reaction times, with minimal use of reagents (µmol scale per reaction). In this study, we integrate a stopped-flow reactor into a high-throughput continuous platform designed for the synthesis of combinatory libraries with at-line reaction analysis. This approach allowed 900 reactions to be conducted in an accelerated timeframe (192 hours). The stopped flow approach used ~10% of the reactants and solvents compared to a fully continuous approach. This methodology demonstrates a significantly improved synthesis success rate of smaller libraries by simplifying the implementation of cross-reaction optimisation strategies. The experimental datasets were used to train a feed-forward neural network (FFNN) model providing a framework to guide further experiments, which showed good model predictability and success when tested against an external set with fewer experiments. As a result, this work demonstrates that combining experimental automation with machine learning strategies can deliver optimised analyses and enhanced predictions, enabling more efficient drug discovery investigations across the design, make, test and analysis (DMTA) cycle.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R032807/1 Royal Academy of Engineering RCSRF1920\9\38 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Sep 2022 13:14 |
Last Modified: | 18 Nov 2022 15:40 |
Status: | Published |
Publisher: | Royal Society of Chemistry |
Identification Number: | 10.1039/d2sc03016k |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190994 |