Sin Joshua W, Chau Siu Lun, Burwood Ryan P, Püntener Kurt, Bigler Raphael, Schwaller Philippe
Process Chemistry & Catalysis, Synthetic Molecules Technical Development, F. Hoffmann-La Roche AG, Basel, Switzerland.
Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland.
Nat Commun. 2025 Jul 12;16(1):6464. doi: 10.1038/s41467-025-61803-0.
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.
我们报告了一种可扩展的机器学习（ML）框架（密涅瓦）的开发与应用，该框架用于通过自动化高通量实验（HTE）进行高度并行的多目标反应优化。密涅瓦在基于实验数据的基准测试中展现出强大的性能，能够有效处理大型并行批次、高维搜索空间、反应噪声以及现实实验室中存在的批次约束。通过实验验证我们的方法，我们将密涅瓦应用于镍催化的铃木反应的96孔HTE反应优化实验中，应对非贵金属催化方面的挑战。我们的方法有效地应对了具有意外化学反应性的复杂反应态势，优于传统的实验人员主导的方法。扩展到工业应用中，我们将密涅瓦应用于制药工艺开发，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反应和钯催化的布赫瓦尔德-哈特维希反应，我们的方法都确定了多种条件，实现了>95面积百分比（AP）的产率和选择性，直接转化为大规模的改进工艺条件。
