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

Abstract

Reaction additives are critical in dictating the outcomes of chemical processes making their effective screening vital for research. Conventional high-throughput experimentation tools can screen multiple reaction components rapidly. However, they are prohibitively expensive, which puts them out of reach for many research groups. This work introduces a cost-effective alternative using Bayesian optimisation. We consider a unique reaction screening scenario evaluating a set of 720 additives across four different reactions, aiming to maximise UV210 product area absorption. The complexity of this setup challenges conventional methods for depicting reactions, such as one-hot encoding, rendering them inadequate. This constraint forces us to move towards more suitable reaction representations. We leverage a variety of molecular and reaction descriptors, initialisation strategies and Bayesian optimisation surrogate models and demonstrate convincing improvements over random search-inspired baselines. Importantly, our approach is generalisable and not limited to chemical additives, 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.