Highly parallel optimisation of chemical reactions through automation and machine intelligence

Joshua W. Sin (), Siu Lun Chau, Ryan P. Burwood, Kurt Püntener, Raphael Bigler and Philippe Schwaller ()
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Joshua W. Sin: F. Hoffmann-La Roche AG
Siu Lun Chau: CISPA Helmholtz Center for Information Security
Ryan P. Burwood: F. Hoffmann-La Roche AG
Kurt Püntener: F. Hoffmann-La Roche AG
Raphael Bigler: F. Hoffmann-La Roche AG
Philippe Schwaller: EPFL

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract We report the development and application of a scalable machine learning (ML) framework (Minerva) for highly parallel multi-objective reaction optimisation with automated high-throughput experimentation (HTE). Minerva demonstrates robust performance with experimental data-derived benchmarks, efficiently handling large parallel batches, high-dimensional search spaces, reaction noise, and batch constraints present in real-world laboratories. Validating our approach experimentally, we apply Minerva in a 96-well HTE reaction optimisation campaign for a nickel-catalysed Suzuki reaction, tackling challenges in non-precious metal catalysis. Our approach effectively navigates the complex reaction landscape with unexpected chemical reactivity, outperforming traditional experimentalist-driven methods. Extending to industrial applications, we deploy Minerva in pharmaceutical process development, successfully optimising two active pharmaceutical ingredient (API) syntheses. For both a Ni-catalysed Suzuki coupling and a Pd-catalysed Buchwald-Hartwig reaction, our approach identifies multiple conditions achieving >95 area percent (AP) yield and selectivity, directly translating to improved process conditions at scale.

Date: 2025
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DOI: 10.1038/s41467-025-61803-0

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