Fast and Accurate Prediction of the Regioselectivity of Electrophilic Aromatic Substitution Reactions

While computational prediction of chemical reactivity is possible it usually requires expert knowledge and there are relatively few computational tools that can be used by a bench chemist to help guide synthesis. The RegioSQM method for predicting the regioselectivity of electrophilic aromatic substitution reactions of heteroaromatic systems is presented in this paper. RegioSQM protonates all aromatic C-H carbon atoms and identifies those with the lowest free energies in chloroform using the PM3 semiempirical method as the most nucleophilic center. These positions are found to correlate qualitatively with the regiochemical outcome in a retrospective analysis of 96% of more than 525 literature examples of electrophilic aromatic halogenation reactions. The method is automated and requires only a SMILES string of the molecule of interest, which can easily be generated using chemical drawing programs such as ChemDraw. The computational cost is 1-10 minutes per molecule depending on size, using relatively modest computational resources and the method is freely available via a web server at regiosqm.org. RegioSQM should therefore be of practical use in the planning of organic synthesis.

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Electrophilic Addition of Brominated Poly(Isobutylene-co-4-Methylstyrene) to Olefins Catalyzed by Zinc Salts: A Model Study

Rubber Chemistry and Technology ◽

10.5254/1.3538303 ◽

1993 ◽

Vol 66 (1) ◽

pp. 98-108 ◽

Cited By ~ 6

Author(s):

J. M. J. Fréchet ◽

R. Bielski ◽

H-C. Wang ◽

J. V. Fusco ◽

K. W. Powers

Keyword(s):

Induction Period ◽

Chemical Reactivity ◽

Zinc Stearate ◽

Gas Chromatography Mass Spectrometry ◽

Electrophilic Aromatic Substitution ◽

Aromatic Substitution ◽

Substitution Reactions ◽

Model Study ◽

Zinc Salts ◽

Electrophilic Additions

Abstract The chemical reactivity of a new elastomer based on brominated poly(isobutylene-co-4-methylstyrene) in electrophilic additions to olefins has been investigated using model compounds as well as appropriate polymers. The reactions catalyzed by zinc salts are influenced by the solubility as well as the composition of the catalyst. While the reactivity of zinc bromide is limited by its low solubility in nonpolar medium, zinc oxide and zinc stearate can afford excellent results once an induction period has elapsed. The induction period likely corresponds to the formation of more reactive zinc based moieties through interchange reactions with the benzylic bromide groups. The mechanism of the addition process involves initial formation of carbocationic complexes with the zinc salts, followed by addition to the double bonds of the olefins. The products resulting from these additions have been characterized by NMR as well as gas chromatography-mass spectrometry. A comparison of potential crosslinking processes involving olefin addition or electrophilic aromatic substitution reactions shows that the former process is preferred. The findings of this study are directly applicable to the co-curing of elastomers based on brominated poly-(isobutylene-co-4-methylstyrene) with polyolefins.

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Computational Study of Chemical Reactivity Using Information-Theoretic Quantities from Density Functional Reactivity Theory for Electrophilic Aromatic Substitution Reactions

The Journal of Physical Chemistry A ◽

10.1021/acs.jpca.5b04309 ◽

2015 ◽

Vol 119 (29) ◽

pp. 8216-8224 ◽

Cited By ~ 26

Author(s):

Wenjie Wu ◽

Zemin Wu ◽

Chunying Rong ◽

Tian Lu ◽

Ying Huang ◽

...

Keyword(s):

Density Functional ◽

Computational Study ◽

Chemical Reactivity ◽

Electrophilic Aromatic Substitution ◽

Aromatic Substitution ◽

Substitution Reactions ◽

Information Theoretic

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Unveiling the Regioselectivity in Electrophilic Aromatic Substitution Reactions of Deactivated Benzenes through Molecular Electron Density Theory

New Journal of Chemistry ◽

10.1039/d1nj02435c ◽

2021 ◽

Author(s):

Luis R. Domingo ◽

Mar Ríos-Gutiérrez ◽

María José Aurell

Keyword(s):

Electron Density ◽

Electrophilic Aromatic Substitution ◽

Benzene Derivatives ◽

Aromatic Substitution ◽

Substitution Reactions ◽

Molecular Electron ◽

Molecular Electron Density Theory

The origin of the meta regioselectivity in electrophilic aromatic substitution (EAS) reactions of deactivated benzene derivatives is herein analysed through Molecular Electron Density Theory (MEDT). To this end, the EAS...

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Intramolecular Electrophilic Aromatic Substitution Reactions of 2-Amidoacroleins: A New Method for the Preparation of Tetrahydroisoquinolines, Tetrahydro-3-benzazepines, and Hexahydro-3-benzazocines

Organic Letters ◽

10.1021/ol016592n ◽

2001 ◽

Vol 3 (21) ◽

pp. 3349-3351 ◽

Cited By ~ 33

Author(s):

James R. Fuchs ◽

Raymond L. Funk

Keyword(s):

New Method ◽

Electrophilic Aromatic Substitution ◽

Aromatic Substitution ◽

Substitution Reactions

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RegioML: Predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning

10.33774/chemrxiv-2021-l2fvl ◽

2021 ◽

Author(s):

Nicolai Ree ◽

Andreas H. Göller ◽

Jan H. Jensen

Keyword(s):

Machine Learning ◽

Tight Binding ◽

Reaction Centers ◽

Gradient Boosting ◽

Electrophilic Aromatic Substitution ◽

Aromatic Substitution ◽

Substitution Reactions ◽

Test Set ◽

Light Gradient ◽

Out Of Sample

We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. The model relies on CM5 atomic charges computed using semiempirical tight binding (GFN1-xTB) combined with the ensemble decision tree variant light gradient boosting machine (LightGBM). The model is trained and tested on 21,201 bromination reactions with 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% for the test set and 90% for the out-of-sample set, while the precision (the percentage of positive predictions that are correct) is 88% and 80%, respectively. The test-set performance is very similar to the graph-based WLN method developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) though the comparison is complicated by the possibility that some of the test and out-of-sample molecules are used to train WLN. RegioML out-performs our physics-based RegioSQM20 method (J. Cheminform. 2021, 13:10) where the precision is only 75%. Even for the out-of-sample dataset, RegioML slightly outperforms RegioSQM20. The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. However, for reactions where there is little experimental data, physics-based approaches like RegioSQM20 can be used to generate synthetic data for model training. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML-model trained on RegioSQM20-generated data.

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