近日，美国哈佛大学Jacobsen, Eric N.团队报道了加速发现多催化协同作用。2025年11月3日，《自然》杂志发表了这一成果。

协同催化是指多个催化单元协同作用，是多种重要的合成和机械有机反应的基础。尽管在新的反应环境中具有潜在的效用，但人们发现协同催化剂的方法仍然有限，通常依赖于偶然发现或对单催化剂反应的先验知识。对未预料到的催化剂协同性类型的系统搜索将与巨大的组合复杂性相竞争，因此没有进行。

研究组描述了一种受群体测试启发的池-反卷积算法，该算法以低实验成本识别协同催化剂行为，同时适应催化剂候选物之间潜在的抑制效应。首先在模拟协同性数据上验证了该工作流，然后通过实验确定了之前记录的对映选择性氧乙烷开孔反应中有机催化剂之间的协同性。然后，将该工作流程应用于Pd催化的脱碳交叉偶联反应的发现环境中，能够识别出几种配体对，这些配体对在比以前报道的单配体系统低得多的催化剂负载和温度下促进目标转化。

附：英文原文

Title: Accelerating the discovery of multicatalytic cooperativity

Author: Sak, Marcus H., Liu, Richard Y., Kwan, Eugene E., Jacobsen, Eric N.

Issue&Volume: 2025-11-03

Abstract: Cooperative catalysis, in which multiple catalytic units operate synergistically, underpins a variety of synthetically and mechanistically important organic reactions1–4. Despite its potential utility in new reactivity contexts, approaches to the discovery of cooperative catalysts have been limited, typically relying on serendipity or on prior knowledge of single-catalyst reactivity1,5. Systematic searches for unanticipated types of catalyst cooperativity must contend with vast combinatorial complexity and are therefore not undertaken6–10. Here, we describe a pooling–deconvolution algorithm, inspired by group testing11, that identifies cooperative catalyst behaviors with low experimental cost while accommodating potential inhibitory effects between catalyst candidates. The workflow was validated first on simulated cooperativity data, and then by experimentally identifying previously documented cooperativity between organocatalysts in an enantioselective oxetane-opening reaction. The workflow was then applied in a discovery context to a Pd-catalyzed decarbonylative cross-coupling reaction, enabling the identification of several ligand pairs that promote the target transformation at substantially lower catalyst loading and temperature than previously reported with single ligand systems.

DOI: 10.1038/s41586-025-09813-2

Source: https://www.nature.com/articles/s41586-025-09813-2