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research article

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

Rankovic, Bojana  
•
Griffiths, Ryan-Rhys
•
Moss, Henry B.
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April 17, 2024
Digital Discovery

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.|Cost-effective Bayesian optimisation screening of 720 additives on four complex reactions, achieving substantial yield improvements over baselines using chemical reaction representations beyond one-hot encoding.

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d3dd00096f.pdf

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http://purl.org/coar/version/c_970fb48d4fbd8a85

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openaccess

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CC BY

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607952799355a8301641129c1b22bb74

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