Ullmann反应条件的高通量筛选及其规律性研究

doi:10.6023/cjoc202507012

有机化学 ›› 2026, Vol. 46 ›› Issue (3): 993-999.DOI: 10.6023/cjoc202507012 上一篇下一篇

研究论文

Ullmann反应条件的高通量筛选及其规律性研究

郭在翔^†, 邹昀^†, 程沛浩, 李富军, 陶胜洋^*()

大连理工大学化学学院辽宁大连 116024

收稿日期:2025-07-09 修回日期:2025-10-16 发布日期:2025-12-09
通讯作者: 陶胜洋
作者简介:
†共同第一作者
基金资助:
国家自然科学基金(22372025); 中央高校基本科研业务费(DUT22LAB607); 中央高校基本科研业务费(DUT22QN226)

High-Throughput Screening of Ullmann Reaction Conditions and Study of Their Regularity

Zaixiang Guo, Yun Zou, Peihao Cheng, Fujun Li, Shengyang Tao^*()

School of Chemistry, Dalian University of Technology, Dalian, Liaoning 116024

Received:2025-07-09 Revised:2025-10-16 Published:2025-12-09
Contact: Shengyang Tao
About author:
†The authors contributed equally to this work.
Supported by:
National Natural Science Foundation of China(22372025); Fundamental Research Funds for the Central Universities(DUT22LAB607); Fundamental Research Funds for the Central Universities(DUT22QN226)

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摘要/Abstract

尽管经典的物理有机化学理论已经有了较大发展, 但在应对每一个具体反应时, 也较难快速地确定反应最优条件和可能的反应机制. 近年来, 高通量实验和数据分析方法成为解决上述问题的有力工具. 此研究利用自动化高通量实验设备, 结合大数据与机器学习技术, 以解决Ullmann偶联合成吡非尼酮(5-甲基-1-苯基吡啶-2-酮)的条件优化和机理分析问题. 自动化高通量实验平台筛选了共984组不同的反应条件, 不仅确定了最佳的铜/配体组合, 还通过机器学习, 结合统计分析的方法, 确定草酰胺配体和短Cu-L相互作用距离, 是提升Ullmann反应合成吡非尼酮中产率的重要影响因素.

关键词: 高通量实验, C—N偶联, 机器学习, 大数据

Although classical physical organic chemistry theories have undergone significant development, it remains challenging to rapidly determine the optimal reaction conditions and elucidate possible mechanisms for specific reactions. In recent years, high-throughput experimentation and data-driven analysis have emerged as powerful tools to address these challenges. In this study, an automated high-throughput experimental platform combined with big data and machine learning techniques was employed to optimize the reaction conditions and investigate the mechanism of the Ullmann coupling for the synthesis of pirfenidone (5-methyl-1-phenylpyridin-2(1H)-one). A total of 984 different reaction conditions were screened using the automated platform. This approach not only identified the optimal copper/ligand combination but also revealed, through a combination of machine learning and statistical analysis, that oxalamide ligands and short Cu-L interaction distances are key factors contributing to higher yields in the Ullmann coupling for pirfenidone synthesis.

Key words: high-throughput experiments, C—N coupling, machine learning, big data

引用此文

郭在翔, 邹昀, 程沛浩, 李富军, 陶胜洋. Ullmann反应条件的高通量筛选及其规律性研究[J]. 有机化学, 2026, 46(3): 993-999.

Zaixiang Guo, Yun Zou, Peihao Cheng, Fujun Li, Shengyang Tao. High-Throughput Screening of Ullmann Reaction Conditions and Study of Their Regularity[J]. Chinese Journal of Organic Chemistry, 2026, 46(3): 993-999.

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图式1. 铜催化的Ullmann偶联反应作为模板反应

Scheme 1. Copper-catalyzed Ullmann coupling reaction as a template reaction

图1. (a)初步筛选的反应产率热图; (b)二次筛选的反应产率热图

Figure 1. (a) Heat map of reaction yields of the primary screening; (b) Heat map of reaction yields of the secondary screening Reaction conditions: 2-hydroxy-5-methylpyridine (1.0 equiv.), bromobenzene (1.5 equiv.), catalyst (10 mol%), ligand (10 mol%), base (2.0 equiv.) and solvent (0.6 mL), at 140 ℃ for 10 h. The yield of the product was determined by HPLC using 1,3,5-trimethoxybenzene as an internal standard

图2. (a)初步筛选不同配体的平均产率; (b)初步筛选不同催化剂的平均产率; (c)初步筛选不同碱的平均产率; (d)初步筛选不同溶剂的平均产率

Figure 2. (a) Average yields of different ligands in preliminary screening; (b) average yields of different catalysts in preliminary screening; (c) average yields of different bases in preliminary screening; (d) average yields of different solvents in preliminary screening

图3. (a)决策树分类模型及其将Ullmann C-N偶联的催化剂-配体组合分类为开启或关闭反应性的准确性; (b)使用Cu-L计算的相互作用距离(d)分析数据

Figure 3. (a) Decision tree classification model and its accuracy in classifying catalyst-ligand combinations as “on” or “off” reactive states in the Ullmann C-N coupling reaction; (b) correlation between reaction yield and the Cu-L interaction distance (d) Red dots represent oxamide ligands, black dots represent non-oxamide ligands. The vertical dashed lines indicate the 60% yield threshold distinguishing “on/off” reactivity and the calculated d threshold of 1.919 Å, highlighting the significance of d in determining reaction yield

图4. (a)产率的分布直方图; (b)产率的正态Q-Q图, 红线对角线为正态参照线; (c)配体类型对产率的影响, 对于草酰胺和非草酰胺两类配体的产率分布; (d) Cu-L相互作用距离(d值)对产率的影响, 对于d<1.919 Å和d≥1.919 Å两类组合的产率分布

Figure 4. (a) Distribution histogram of yield; (b) normal Q-Q plot of yields, the red diagonal line is the normal reference line; (c) the effect of ligand type on yield, the yield distribution of oxamide and non-oxamide ligands; (d) the effect of Cu-L interaction distance (d value) on yield, the yield distribution of the two combinations of d<1.919 Å and d≥1.919 Å

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Ullmann反应条件的高通量筛选及其规律性研究

High-Throughput Screening of Ullmann Reaction Conditions and Study of Their Regularity

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