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Organic reaction mechanism classification using machine learning

Abstract

A mechanistic understanding of catalytic organic reactions is crucial for the design of new catalysts, modes of reactivity and the development of greener and more sustainable chemical processes1,2,3,4,5,6,7,8,9,10,11,12,13. Kinetic analysis lies at the core of mechanistic elucidation by facilitating direct testing of mechanistic hypotheses from experimental data. Traditionally, kinetic analysis has relied on the use of initial rates14, logarithmic plots and, more recently, visual kinetic methods15,16,17,18, in combination with mathematical rate law derivations. However, the derivation of rate laws and their interpretation require numerous mathematical approximations and, as a result, they are prone to human error and are limited to reaction networks with only a few steps operating under steady state. Here we show that a deep neural network model can be trained to analyse ordinary kinetic data and automatically elucidate the corresponding mechanism class, without any additional user input. The model identifies a wide variety of classes of mechanism with outstanding accuracy, including mechanisms out of steady state such as those involving catalyst activation and deactivation steps, and performs excellently even when the kinetic data contain substantial error or only a few time points. Our results demonstrate that artificial-intelligence-guided mechanism classification is a powerful new tool that can streamline and automate mechanistic elucidation. We are making this model freely available to the community and we anticipate that this work will lead to further advances in the development of fully automated organic reaction discovery and development.

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Fig. 1: Relevance and state of the art on kinetic analysis.
Fig. 2: Mechanism scope and composition of data.
Fig. 3: Performance of the machine learning model on the test set with six time points per kinetic profile.
Fig. 4: Effect of error and amount of data points in the performance of the machine learning model.
Fig. 5: Case studies with experimental kinetic data.

Data availability

The datasets generated for training, validation and testing are available from figshare: https://doi.org/10.48420/16965292.

Code availability

Trained models, weights and python scripts are available from https://doi.org/10.48420/16965271.

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Acknowledgements

We thank the European Research Council for an Advanced Grant (no. 833337 to I.L.) and Research IT for assistance given and use of the Computational Shared Facility at The University of Manchester. We thank H. Plenio, P. J. Chirick, A. R. Chianese, M. Albrecht and C. S. Schindler for providing the numerical experimental kinetic data used in this study.

Author information

Authors and Affiliations

Authors

Contributions

I.L. and J.B. conceived the project. J.B. selected the mechanisms with input from I.L. I.L. wrote the code, created the datasets and trained the models with input from J.B. I.L. and J.B. analysed data. I.L. and J.B. wrote the manuscript.

Corresponding authors

Correspondence to Jordi Burés or Igor Larrosa.

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The authors declare no competing interests.

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Nature thanks Tiago Rodrigues and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Additional case study with experimental kinetic data.

Includes the reaction under study, the experimental kinetic data used as input for the AI-model and its output. Symbols correspond to substrate concentration. Red triangles: lowest catalyst loading; yellow squares: medium catalyst loading; blue circles: largest catalyst loading. Data from ref. 47.

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Burés, J., Larrosa, I. Organic reaction mechanism classification using machine learning. Nature 613, 689–695 (2023). https://doi.org/10.1038/s41586-022-05639-4

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