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Bayesian optimisation for additive screening and yield improvements - beyond one-hot encoding
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OA
AI
DOI：10.1039/d3dd00096f.png)
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Abstract
En 中文
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.
Keywords:
NEURAL-NETWORKS
PREDICTION
CLASSIFICATION
TRANSFORMER
LIBRARIES
COMPUTER
OUTCOMES
MODELS
Journal
IF:
5.6
Papers:
981
Citations:
1.7K
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