Multi-objective Bayesian optimisation using q-Noisy Expected Hypervolume Improvement (qNEHVI) for Schotten-Baumann reaction

Amide bond formation is one of the most prevalent reactions in pharmaceutical industry, among which the Schotten-Baumann reaction has attracted attention 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 and challenging to find the optimal conditions. In this study, a Schotten-Baumann reaction was studied in 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, reaction under flow and batch conditions were compared; undesired hydrolysis was suppressed successfully using the flow conditions. Finally, the relationship between solvent and flow rate was discussed to gain more insights into this reaction.