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Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation

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 che...

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Autores principales: Avila, Claudio, Cassani, Carlo, Kogej, Thierry, Mazuela, Javier, Sarda, Sunil, Clayton, Adam D., Kossenjans, Michael, Green, Clive P., Bourne, Richard A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601405/
https://www.ncbi.nlm.nih.gov/pubmed/36349112
http://dx.doi.org/10.1039/d2sc03016k
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author Avila, Claudio
Cassani, Carlo
Kogej, Thierry
Mazuela, Javier
Sarda, Sunil
Clayton, Adam D.
Kossenjans, Michael
Green, Clive P.
Bourne, Richard A.
author_facet Avila, Claudio
Cassani, Carlo
Kogej, Thierry
Mazuela, Javier
Sarda, Sunil
Clayton, Adam D.
Kossenjans, Michael
Green, Clive P.
Bourne, Richard A.
author_sort Avila, Claudio
collection PubMed
description 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.
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spelling pubmed-96014052022-11-07 Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation Avila, Claudio Cassani, Carlo Kogej, Thierry Mazuela, Javier Sarda, Sunil Clayton, Adam D. Kossenjans, Michael Green, Clive P. Bourne, Richard A. Chem Sci Chemistry 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. The Royal Society of Chemistry 2022-09-13 /pmc/articles/PMC9601405/ /pubmed/36349112 http://dx.doi.org/10.1039/d2sc03016k Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Avila, Claudio
Cassani, Carlo
Kogej, Thierry
Mazuela, Javier
Sarda, Sunil
Clayton, Adam D.
Kossenjans, Michael
Green, Clive P.
Bourne, Richard A.
Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title_full Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title_fullStr Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title_full_unstemmed Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title_short Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
title_sort automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601405/
https://www.ncbi.nlm.nih.gov/pubmed/36349112
http://dx.doi.org/10.1039/d2sc03016k
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