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An Artificial Intelligence that Discovers Unpredictable Chemical Reactions

preprint
submitted on 07.09.2020 and posted on 08.09.2020 by Dario Caramelli, Jaroslaw Granda, Dario Cambié, Hessam Mehr, Alon Henson, Leroy Cronin

We present an artificial intelligence, built to autonomously explore chemical reactions in the laboratory using deep learning. The reactions are performed automatically, analysed online, and the data is processed using a convolutional neural network (CNN) trained on a small reaction dataset to assess the reactivity of reaction mixtures. The network can be used to predict the reactivity of an unknown dataset, meaning that the system is able to abstract the reactivity assignment regardless the identity of the starting materials. The system was set up with 15 inputs that were combined in 1018 reactions, the analysis of which lead to the discovery of a ‘multi-step, single-substrate’ cascade reaction and a new mode of reactivity for methylene isocyanides. p-Toluenesulfonylmethyl isocyanide (TosMIC) in presence of an activator reacts consuming six equivalents of itself to yield a trimeric product in high (unoptimized) yield (47%) with formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centres. A cheminformatics analysis reveals that this transformation is both highly unpredictable and able to generate an increase in complexity like a one-pot multicomponent reaction.

Funding

EPSRC (Grant Nos EP/S030603/1, EP/S019472/1, EP/S017046/1, EP/L015668/1, EP/L023652/1), the ERC (project 670467 SMART-POM)

History

Email Address of Submitting Author

lee.cronin@glasgow.ac.uk

Institution

University of Glasgow

Country

United Kingdom

ORCID For Submitting Author

0000-0001-8035-5757

Declaration of Conflict of Interest

No conflict of interest

Version Notes

This it the first version.

Exports