Thinking outside the library: cluster synthesis of diverse molecules on a single robotic platform

The development of a general autonomous platform for organic synthesis that enables faster, flexible and efficient delivery of target molecules is an attractive strategy for many fields such as drug discovery and materials science. Traditionally, automated parallel synthesis relies on the synthesis of libraries of various sizes, sharing the same transformations and name reactions with defined reaction conditions. Herein, we report on the development of our platform and a paradigm shift in high throughput robotic synthesis: from mono-reaction type libraries to multi-reaction type clusters. This fundamentally distinct approach differs from the current strategies by clustering reactions based on their reaction conditions, defined as ranges of acceptable temperature and reaction time by expert chemists. As a result, many different reactions can be merged into a cluster. An algorithm has been developed to help chemists organize the workload into the minimum number of clusters, taking into account the physical and chemical constraints of the platform. We applied this strategy to efficiently organize the synthesis of 135 molecules, using 27 different name reactions in only 6 clusters and 3 synthetic campaigns.

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Publication Details

Journal
Chemical Science
Published
2025-11-28
DOI
https://doi.org/10.1039/d5sc07668d
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Thinking outside the library: cluster synthesis of diverse molecules on a single robotic platform

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1 citations
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Machine Learning in Materials Science
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article

Thinking outside the library: cluster synthesis of diverse molecules on a single robotic platform

Nicolas Do Huu, Quentin Perron, Jean‐Christophe Meillon, Franck Le Vaillant, Clémentine Pescheteau, Luidgi Gromat, Nicolas Ducrot, Aurélien Demilly
article en
1 citations

Abstract

The development of a general autonomous platform for organic synthesis that enables faster, flexible and efficient delivery of target molecules is an attractive strategy for many fields such as drug discovery and materials science. Traditionally, automated parallel synthesis relies on the synthesis of libraries of various sizes, sharing the same transformations and name reactions with defined reaction conditions. Herein, we report on the development of our platform and a paradigm shift in high throughput robotic synthesis: from mono-reaction type libraries to multi-reaction type clusters. This fundamentally distinct approach differs from the current strategies by clustering reactions based on their reaction conditions, defined as ranges of acceptable temperature and reaction time by expert chemists. As a result, many different reactions can be merged into a cluster. An algorithm has been developed to help chemists organize the workload into the minimum number of clusters, taking into account the physical and chemical constraints of the platform. We applied this strategy to efficiently organize the synthesis of 135 molecules, using 27 different name reactions in only 6 clusters and 3 synthetic campaigns.

Chemical ScienceVol. 17(6)
GlaxoSmithKline (France) (FR)
European Commission
Openalex Percentile: Top 30%
Machine Learning in Materials Science
0.52
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