Antiviral Activities of Halogenated Emodin Derivatives against Human Coronavirus NL63

The current COVID-19 outbreak has highlighted the need for the development of new vaccines and drugs to combat Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). Recently, various drugs have been proposed as potentially effective against COVID-19, such as remdesivir, infliximab and imatinib. Natural plants have been used as an alternative source of drugs for thousands of years, and some of them are effective for the treatment of various viral diseases. Emodin (1,3,8-trihydroxy-6-methylanthracene-9,10-dione) is a biologically active anthraquinone with antiviral activity that is found in various plants. We studied the selectivity of electrophilic aromatic substitution reactions on an emodin core (halogenation, nitration and sulfonation), which resulted in a library of emodin derivatives. The main aim of this work was to carry out an initial evaluation of the potential to improve the activity of emodin against human coronavirus NL63 (HCoV-NL63) and also to generate a set of initial SAR guidelines. We have prepared emodin derivatives which displayed significant anti-HCoV-NL63 activity. We observed that halogenation of emodin can improve its antiviral activity. The most active compound in this study was the iodinated emodin analogue E_3I, whose anti-HCoV-NL63 activity was comparable to that of remdesivir. Evaluation of the emodin analogues also revealed some unwanted toxicity to Vero cells. Since new synthetic routes are now available that allow modification of the emodin structure, it is reasonable to expect that analogues with significantly improved anti-HCoV-NL63 activity and lowered toxicity may thus be generated.

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Chiral Auxiliaries for Stereoselective Electrophilic Aromatic Substitutions

Synlett ◽

10.1055/s-0040-1707296 ◽

2020 ◽

Author(s):

Severin T. Schneebeli ◽

Mona Sharafi ◽

Joseph P. Campbell ◽

Kyle E. Murphy ◽

Reilly Osadchey Brown

Keyword(s):

Stereoselective Synthesis ◽

Transition Metal Catalysts ◽

Biologically Active ◽

Advanced Materials ◽

Electrophilic Aromatic Substitution ◽

Aromatic Substitution ◽

Chiral Auxiliaries ◽

Aromatic Rings ◽

Substitution Reactions ◽

Important Means

AbstractElectrophilic aromatic substitution reactions are of profound importance for the synthesis of biologically active compounds and other advanced materials. They represent an important means to activate specific aromatic C–H bonds without requiring transition-metal catalysts. Surprisingly, few stereoselective variants are known for electrophilic aromatic substitutions, which limits the utility of these classical reactions for stereoselective synthesis. While many electrophilic aromatic substitutions lead to achiral products (due to the planar nature of aromatic rings), there are important examples where chiral products are produced, including desymmetrization reactions of aromatic cyclophanes and of prochiral substrates with multiple aromatic rings. This Synpacts article now illustrates how chiral arms, when placed precisely above and underneath delocalized carbocations, can act as chiral auxiliaries to convert classical electrophilic aromatic substitution reactions into powerful diastereo- and enantioselective transformations.

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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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