Predictive Multivariate Models for Bioorthogonal Inverse-Electron Demand Diels–Alder Reactions

Ravasco, João M. J. M.; Coelho, Jaime A. S.

doi:10.1021/jacs.9b11948

Cited by 39 publications

(33 citation statements)

References 36 publications

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“… 698 , 699 ML also enables studies of complicated reaction networks that can allow predictions of regioselective products based on CompChem data, 700 asymmetric catalysis important for natural product synthesis, 701 , 702 and biochemical reactions. 703 Efforts to better understand “above-the-arrow” optimizations of reaction conditions relate back to the challenge of retrosynthetic challenges. 704 , 705 Ideally, these efforts will continue while making use of rapid advances in CompChem+ML that enable predictive atomistic simulations to be run faster and more accurately.…”

Section: Selected Applications and Paths Toward Insightsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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Section: Selected Applications and Paths Toward Insightsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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“…Based on reaction data in different solvents, machine learning models could in principle learn to compensate for both the deficiencies in the DFT energies and the solvation model. Accurate QSRR machine learning models ( Figure 2b) for reaction rates or barriers have been constructed for, e.g., cycloaddition, 15,16 S N 2 substitution, 17 and E2 elimination. 18 While these models are highly encouraging, they treat reactions that occur in a single mechanistic step and they are based on an amount of kinetic data (>500 samples) that is only available for very few reaction classes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

et al. 2021

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“…Based on reaction data in different solvents, machine learning models could in principle learn to compensate for both the deficiencies in the DFT energies and the solvation model. Accurate QSRR machine learning models ( Figure 2b) have been constructed for, e.g., cycloaddition, 15,16 SN2 substitution, 17 and E2 elimination. 18 While these models are highly encouraging, they treat simple reactions that occur in a single mechanistic step and they are based on an amount of kinetic data (>500 samples) that is only available for very few reaction classes.…”

Section: Introductionmentioning

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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Predictive Multivariate Models for Bioorthogonal Inverse-Electron Demand Diels–Alder Reactions

Cited by 39 publications

References 36 publications

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

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

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