Multi-objective Bayesian optimisation using q-noisy expected hypervolume improvement (qNEHVI) for the Schotten-Baumann reaction

Amide bond formation is one of the most prevalent reactions in the pharmaceutical industry, among which the Schotten-Baumann reaction with a long history is useful as a potential green amide formation approach. However, the use of water in the reaction system often causes undesired hydrolysis and can generate a multiphase system. This makes the reaction space complex, making it challenging to find the optimal conditions. In this study, the Schotten-Baumann reaction was studied in a continuous flow and was optimised with two objectives using a Bayesian optimisation algorithm based on the q-noisy expected hypervolume improvement (qNEHVI) acquisition function. The algorithm guided the experiment design over a range of electrophiles, equivalents, solvents, and flow rates, and was able to identify the Pareto front of optimal solutions efficiently. Based on the optimisation results, reactions under a flow and batch conditions were compared; undesired hydrolysis was suppressed successfully using the flow conditions. Finally, the relationship between the solvent and flow rate was discussed to gain more insights into this reaction.