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、坚固、安全和成本效益的方式生产；大量资源被花费来优化所获得产品的产量和纯度。实验设计(DoE)提供了一种结构化、合乎逻辑的方法来确定最佳反应条件并测试其稳健性。随着因素的增加，DOE对资源的需求迅速增加，不连续因素(如溶剂、配体)的处理变得困难。更智能、更快、更互补的优化反应的方法将使目标分子的输送更快、成本更低。该项目将反应筛选与底物和配体参数的主成分分析(PCA)以及有监督的机器学习技术(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的常规反应筛选中得出数据驱动的反应理解。这将完善后续能源部过程中要探索的化学空间。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。由于生成的产品的实用性，我们将重点放在C-H硼化反应上。我们将首先研究已建立的Ir催化的Hartwig硼化反应；随后，将使用更便宜和更容易获得的Ru进行更雄心勃勃的工作，对于这些反应，只有有限数量的吡啶和亚胺的C-H硼化反应已被报道。C-H硼化反应有一些缺点限制了其在工业中的使用：(I)我们对一些方法在不同底物(如杂环)上的行为有一定的了解，但已知的例子并不包括在合成活动中可能出现的所有底物，例如GSK进行的那些底物；(Ii)金属负载量可能非常高--特别是对于使用Ru的新兴方法--这对所需催化剂和产物的纯化都有成本影响；(Iii)反应条件可能很苛刻，需要较长时间的高温，这不环保且成本高昂，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，并将它们与现有的方法(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.