Bayesian optimisation for additive screening and yield improvements in chemical reactions – beyond one-hot encoding

Reaction additives play a significant role in controlling the reactivity and outcomes of chemical reactions. For example, a recent high-throughput additive screening identified a phthalimide ligand additive for Ni-catalysed photoredox decarboxylative arylations. This discovery enabled a 4-fold yield improvement by stabilising oxidative addition complexes and breaking up deactivated catalyst aggregates. Despite the promise of such large-scale screenings, they remain inaccessible to most research groups due to their cost and complexity. In this work, we demonstrate how to uncover similar results under much lower experimental budgets using Bayesian optimisation empowered methodology. We consider a unique reaction screening setting with 720 additives, forcing us to go beyond simple one-hot encoding of the reaction components. By investigating a range of molecular and reaction representations, we demonstrate convincing improvements over random search-inspired baselines. Importantly, our approach is generalisable and not limited to Ni-catalysed reactions, but can be applied to achieve yield improvements in diverse cross-couplings or other reactions, potentially unlocking access to new chemical spaces that are of interest to the chemical and pharmaceutical industries. The code is available at: https://github.com/schwallergroup/chaos.