Highly parallel optimisation of chemical reactions through automation and machine intelligence

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Joshua W. Sin, Siu Lun Chau, Ryan P. Burwood, Kurt Püntener, Raphael Bigler, Philippe Schwaller
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引用次数: 0

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.

Abstract Image

通过自动化和机器智能实现化学反应的高度并行优化
我们报告了一个可扩展的机器学习(ML)框架(Minerva)的开发和应用,用于高度并行的多目标反应优化和自动化高通量实验(HTE)。Minerva通过实验数据衍生的基准测试证明了强大的性能,可以有效地处理现实世界实验室中存在的大型并行批次、高维搜索空间、反应噪声和批次约束。通过实验验证了我们的方法,我们将Minerva应用于96井的镍催化铃木反应的HTE反应优化活动,解决了非贵金属催化的挑战。我们的方法通过意想不到的化学反应性有效地导航复杂的反应景观,优于传统的实验驱动方法。扩展到工业应用,我们在制药工艺开发中部署Minerva,成功优化了两种活性药物成分(API)的合成。对于镍催化的铃木偶联和钯催化的Buchwald-Hartwig反应,我们的方法确定了多种条件,达到95%的产率和选择性,直接转化为大规模改进的工艺条件。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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