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

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.