Uncovering Competing Cross-Coupling Catalytic Cycles Through Rich Data Analysis of Reaction Outcomes Gained by High-throughput Experiment Screening

通过对高通量实验筛选获得的反应结果进行丰富的数据分析，揭示竞争的交叉偶联催化循环

• 批准号: 2742606
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2742606
• 关键词: Uncovering; Competing; Cross; Coupling; Catalytic

Pd-catalyzed Suzuki-Miyaura Cross-Coupling (SMCC) reactions are widely employed in the pharmaceutical sector, from research discovery and scale-up processes through to manufacture. Despite extensive work on Pd-catalyzed SMCC reactions, involving substrate scoping, catalyst development, and mechanistic work, there remain significant challenges when new cross-coupling substrates (reagents) are tested. The reasons for lower than expected yields of products are typically associated with competing side reactions, especially protodeborylation, protodehalogenation and hydrolysis processes. To understand these competing processes better we will employ a Chemspeed ISYNTH robotic system, combining high throughput experimentation (HTE) and data analytics, with the primary aim of generating a large number of reaction outcomes (leading to the creation of a valuable dataset). We are then able to integrate the data from the reaction outcomes using a bespoke design of experiment approach. There we will assess both quantitative (amounts) and categorical (groupings) variables for the different SMCC reactions (employing various substrate reactivity profiles). We expect trends to emerge in the reactivity profiling. As a consequence, this research project will directly address the urgent need to identify rules governing the reactivity of libraries of pi-rich and pi-deficient organic substrates. As a secondary off-shoot from our work, the approaches developed in HTE and data analytics will be broadly transferable to other catalytic processes.

Pd催化的Suzuki-Miyaura交叉偶联(SMCC)反应广泛应用于制药领域，从研究发现、放大过程到生产。尽管在Pd催化的SMCC反应方面做了大量的工作，包括底物范围、催化剂开发和机械工作，但当测试新的交叉偶联底物(试剂)时，仍然存在重大挑战。产品收率低于预期的原因通常与竞争副反应有关，特别是原脱氧氯化、原脱卤化和水解过程。为了更好地了解这些相互竞争的过程，我们将使用ChemSpeedISYNTH机器人系统，将高通量实验(HTE)和数据分析结合在一起，主要目的是生成大量反应结果(导致创建有价值的数据集)。然后，我们可以使用定制的实验方法设计来整合来自反应结果的数据。在那里，我们将评估不同SMCC反应的定量(数量)和分类(分组)变量(使用不同的底物反应性曲线)。我们预计反应性概况中将出现趋势。因此，这项研究项目将直接解决确定富含和缺乏pi的有机底物文库的反应性的规则的迫切需要。作为我们工作的第二个副产品，在氢化物转化和数据分析中开发的方法将广泛地转移到其他催化过程中。