Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Beker, Wiktor; Gajewska, Ewa; Badowski, Tomasz; Grzybowski, Bartosz A.

doi:10.1002/ange.201806920

Cited by 29 publications

(20 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In that regard, adversarial controls should be designed to disprove alternative model performance hypotheses and distinguish between the exploitation of confounding variables/experimental artefacts and chemically meaningful patterns. Sensible control recommendations, paralleling good wet-laboratory practice, can be found elsewhere 88 and have actually shown utility 89 . In one instance, different dummy variable systems, decorrelated from chemical insight, were used to validate random forests as true high-performance classifiers of regioselectivity, site selectivity and diastereoselectivity in Diels-Alder reactions, only when a conjugation of electronic and steric indices are employed as features (74-83% accuracy for dummy variables versus 93% accuracy for Hammettsteric descriptors) 89 .…”

Section: Softmax Layermentioning

confidence: 99%

Synthetic organic chemistry driven by artificial intelligence

2019

View full text Add to dashboard Cite

Synthetic organic chemistry underpins several areas of chemistry, including drug discovery, chemical biology, materials science and engineering. However, the execution of complex chemical syntheses in itself requires expert knowledge, usually acquired over many years of study and hands-on laboratory practice. The development of technologies with potential to streamline and automate chemical synthesis is a half-century-old endeavour yet to be fulfilled. Renewed interest in artificial intelligence (AI), driven by improved computing power, data availability and algorithms, is overturning the limited success previously obtained. In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature. By examining the underlying concepts, we aim to demystify AI for bench chemists in order that they may embrace it as a tool rather than fear it as a competitor, spur future research by pinpointing the gaps in knowledge and delineate how chemical AI will run in the era of digital chemistry.

show abstract

Section: Softmax Layermentioning

confidence: 99%

Synthetic organic chemistry driven by artificial intelligence

2019

View full text Add to dashboard Cite

show abstract

“…We opted for physical organic chemistry features which would be chemically understandable and transferable to other reactions. 28 We selected features associated with nucleophilicity, electrophilicity, sterics, dispersion and bonding as well as features describing the solvent.…”

Section: Reaction Feature Generationmentioning

confidence: 99%

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

Jorner

Brinck²,

Norrby³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Diverse domains of modern science have already embraced the benefits of machine learning (ML) with an impressive degree of success (7)(8)(9)(10)(11). There have also been some important applications of ML in chemistry at large and catalytic reactions in particular (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27), such as for predicting selectivities (28)(29)(30)(31)(32)(33)(34)(35)(36). We intend to design a practically useful ML protocol to be deployed for asymmetric hydrogenation of substrates bearing C = C and C = N bonds using axially chiral binaphthyl-derived catalyst families.…”

mentioning

confidence: 99%

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Singh

Pareek

Changotra

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate–catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

show abstract

Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Cited by 29 publications

References 47 publications

Synthetic organic chemistry driven by artificial intelligence

Synthetic organic chemistry driven by artificial intelligence

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

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Contact Info

Product

Resources

About