Thousands of reactants and transition states for competing E2 and S
	    
	     N
	    
	  2 reactions

Rudorff, Guido Falk von; Heinen, Stefan; Bragato, Marco; Lilienfeld, O. Anatole von

doi:10.1088/2632-2153/aba822

Cited by 61 publications

(83 citation statements)

References 78 publications

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“…Our approach to yield predictions can be extended to any reaction regression task, for example, for the prediction of reaction activation energies [34,35], and is expected to have a broad impact in the field of organic chemistry.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Chemical Reaction Yields using Deep Learning

Schwaller¹,

Vaucher²,

Laino³

et al. 2020

Preprint

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<div>Artificial intelligence is driving one of the most important revolutions in organic chemistry. </div><div>Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic chemists' daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, reaction yields models have been less investigated, despite the enormous potential of accurately predicting them. Reaction yields models, describing the percentage of the reactants that is converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the dataset applicability in reaction yields predictions.</div>

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Section: Discussionmentioning

confidence: 99%

Prediction of Chemical Reaction Yields using Deep Learning

Schwaller¹,

Vaucher²,

Laino³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Our approach to yield predictions can be extended to any reaction regression task, for example, for predicting reaction activation energies [36,37,38], and is expected to have a broad impact in the field of organic chemistry.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Chemical Reaction Yields using Deep Learning

Schwaller¹,

Vaucher²,

Laino³

et al. 2020

Preprint

View full text Add to dashboard Cite

<div>Artificial intelligence is driving one of the most important revolutions in organic chemistry. </div><div>Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic chemists' daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, the prediction of reaction yields has received less attention in spite of the enormous potential of accurately predicting reaction conversion rates. Reaction yields models, describing the percentage of the reactants converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the dataset applicability in reaction yields predictions. </div>

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“…We envision that this problem will be solved in the near future by deep learning approaches that can predict both TS geometries 67 and DFT-computed barriers 27 based on large, publicly available datasets. 68,69 In the end, machine learning for reaction prediction needs to reproduce experiment, and transfer learning will probably be key to utilizing small high-quality kinetic datasets together with large amounts of computationally generated data. Regardless of their construction, accurate reaction prediction models will be key components of accelerated route design, reaction optimization and process design enabling the delivery of medicines to patients faster and with reduced costs.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

Jorner

Brinck²,

Norrby³

et al. 2020

Preprint

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Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal/mol for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints.

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Thousands of reactants and transition states for competing E2 and S N 2 reactions

Cited by 61 publications

References 78 publications

Prediction of Chemical Reaction Yields using Deep Learning

Prediction of Chemical Reaction Yields using Deep Learning

Prediction of Chemical Reaction Yields using Deep Learning

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

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