Understanding the Reliability and Transferability of Machine Learning Methods Used in High Throughput Reaction Discovery and Optimisation

高效的有机合成使制药产品能够以可扩展、稳健、安全和具有成本效益的方式生产；花费大量资源来优化所得产品的收率和纯度。实验设计（DoE）提供了一种结构化的、逻辑的方法来确定最佳反应条件并测试其稳健性。DoE的资源需求随着因素的增加而迅速增加，并且对不连续因素（如溶剂、配体）的处理困难。优化反应的更智能、更快和互补的方法将允许以更低的成本更快地递送目标分子。该项目将反应筛选与底物和配体参数的主成分分析（PCA）以及监督机器学习技术（多元线性回归/随机森林分类）相结合，从依赖精细化学品合成的组织中常规的反应筛选中获得数据驱动的反应理解。这将细化化学空间，在随后的DoE过程中进行探索。重点不是取代合成化学家，而是使用现代数据分析技术来加快他们的工作，减少达到最佳条件所需的时间。我们将重点讨论C-H硼化反应，因为其产物的实用性。我们将首先研究已建立的铱催化Hartwig硼化反应；随后，将使用更便宜和更容易获得的钌进行更雄心勃勃的工作，其中只有有限数量的吡啶和亚胺的C-H硼化反应被报道。C-H硼化有许多缺点，限制了它们在工业上的使用：(i)我们对某些方法在不同底物（如杂环）上的行为有一些了解，但已知的例子并不能涵盖在合成活动中可能出现的所有底物，如GSK所进行的那些；（ii）金属负载可能非常高，特别是对于使用钌的新方法，这在所需的催化剂和所得产品的纯化方面都具有成本影响；(3)反应条件苛刻，需要长时间的高温，这对环境不友好，成本高，并可能导致副反应。该项目将开发创新的新方法来优化反应，并将其与现有方法（例如DoE）进行比较。我们的总体目标是减少达到最佳条件所需的实验次数。

Efficient organic synthesis enables pharmaceutical products to be produced in a scalable, robust, safe, and cost-effective way; significant resource is expended to optimise the yield and purity of the products obtained. Design of Experiments (DoE) provides a structured, logical way to determine optimal reaction conditions and test their robustness. The resource requirements of DoE increase rapidly as more factors are added, and the treatment of discontinuous factors (e.g. solvent, ligand) is difficult. Smarter, faster, and complementary ways to optimise reactions will allow the delivery of target molecules more quickly and at lower cost. This project combines reaction screening with principal components analysis (PCA) of substrate and ligand parameters, and supervised machine learning techniques (multiple linear regression/random forest classification) to derive data-driven reaction understanding from reaction screening that is routine within organisations that rely on the synthesis of fine chemicals. This will refine the chemical space to be explored during a subsequent DoE process. The focus is not on replacing the synthetic chemist, but on using modern data analysis techniques to expedite their work and reduce the amount of time required to achieve the optimum conditions.We will focus on C-H borylation reactions because of the utility of the resulting products. We will study established iridium-catalysed Hartwig borylation reactions initially; subsequent, more ambitious work will be conducted using cheaper and more readily-available ruthenium, for which only a limited number of C-H borylation reactions of pyridines and imines have been reported. C-H borylation has a number of drawbacks that limit their use in industry: (i) we have some understanding of how some methods behave with different substrates, such as heterocycles, but known examples do not cover all substrates that might arise during synthetic campaigns such as those undertaken at GSK; (ii) metal loadings can in be very high - particularly for emerging methods that use ruthenium - which has cost implications both in terms of catalyst required and the purification of the resulting products; and (iii) reaction conditions can be harsh, requiring high temperatures for extended periods, which is environmentally unfriendly and costly, and may lead to side reactions. This project will develop innovative new ways to optimise reactions and compare them to established methods (e.g. DoE). Our overall aim is to reduce the number of experiments required to arrive at the optimum set of conditions.