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

pd催化的Suzuki-Miyaura交叉偶联（SMCC）反应广泛应用于制药行业，从研究发现和放大过程到生产。尽管在pd催化的SMCC反应方面做了大量的工作，包括底物范围的确定、催化剂的开发和机理研究，但在测试新的交叉偶联底物（试剂）时，仍然存在重大挑战。产物产率低于预期的原因通常与相互竞争的副反应有关，特别是原黛波酰化、原脱卤化和水解过程。为了更好地理解这些相互竞争的过程，我们将采用Chemspeed ISYNTH机器人系统，结合高通量实验（HTE）和数据分析，其主要目标是生成大量的反应结果（导致创建有价值的数据集）。然后，我们能够使用定制设计的实验方法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（采用不同的底物反应性曲线）。我们期望在反应性分析中出现趋势。因此，该研究项目将直接解决确定富pi和缺pi有机底物库反应性规则的迫切需要。作为我们工作的次要分支，HTE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。

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.