Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis
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Date
2024
Publication Type
Journal Article
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yes
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Abstract
Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.
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published
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Journal / series
Volume
20
Pages / Article No.
2280 - 2304
Publisher
Beilstein-Institut zur Förderung der Chemischen Wissenschaften
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Subject
catalyst design; machine learning; modelling; organocatalysis; selectivity prediction
Organisational unit
09781 - Jorner, Kjell / Jorner, Kjell
Notes
Funding
180544 - NCCR Catalysis (phase I) (SNF)