In Silico Study: <scp>DFT</scp>/ <scp>TDDFT</scp>/Admet and Molecular Docking, of Some Phytochemical Components of Plants Widely Used for the Treatment of COVID‐19

ABSTRACT This research work focuses on the theoretical investigation and evaluation of the biological activities of three medicinal plants: black cumin and Artemisia , which have been widely used in the treatment of coronavirus, and Euphorbia Atlantica Coss , belonging to the Euphorbiaceous family. The molecules that are the subject of our work are: thymol, thymoquinone, thujone, and artemisinin, as well as methyl galat (M1) and pholoroacetophenone 4‐O‐B‐D‐glucopyranoside (M2); molecules extracted from the plant Euphorbia Atlantica Coss . Structural and orbital studies are carried out on these species, with the aim of establishing a structure‐biological activity relationship. The obtained results an energy gap of the order of ~5.5 eV is found for most of the molecules. Potential electrostatic calculations show that Thymol, M1, and M2 are susceptible to electrophilic attack. Thymol has a high polarizability and therefore a good activity, while a good electronic transfer is observed for thujone and artemisinin. Our study showed that black cumin has much more intense absorbance peaks than other plants. AIM analysis identifies critical points at CO and OH bonds, with strong hydrogen bonding in artemisinin. Molecular docking shows promising inhibitory potential of artemisinin. Assessment of the main ADME properties showed that all compounds except M1 and M2 comply with Lipinski's rule, which predicts good oral bioavailability.

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Publication Details

Journal
International Journal of Quantum Chemistry
Published
2025-04-11
DOI
https://doi.org/10.1002/qua.70025
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

In Silico Study: DFT/ TDDFT/Admet and Molecular Docking, of Some Phytochemical Components of Plants Widely Used for the Treatment of COVID‐19

Samira Zeroual, Henry Chermette, Rafik Menacer, Saad Bouchekioua et al.
1 citations
International Journal of Quantum Chemistry
Computational Drug Discovery Methods
1.50
article

In Silico Study: DFT/ TDDFT/Admet and Molecular Docking, of Some Phytochemical Components of Plants Widely Used for the Treatment of COVID‐19

Samira Zeroual, Henry Chermette, Rafik Menacer, Saad Bouchekioua, Guillaume Hoffmann
article en
1 citations

Abstract

ABSTRACT This research work focuses on the theoretical investigation and evaluation of the biological activities of three medicinal plants: black cumin and Artemisia , which have been widely used in the treatment of coronavirus, and Euphorbia Atlantica Coss , belonging to the Euphorbiaceous family. The molecules that are the subject of our work are: thymol, thymoquinone, thujone, and artemisinin, as well as methyl galat (M1) and pholoroacetophenone 4‐O‐B‐D‐glucopyranoside (M2); molecules extracted from the plant Euphorbia Atlantica Coss . Structural and orbital studies are carried out on these species, with the aim of establishing a structure‐biological activity relationship. The obtained results an energy gap of the order of ~5.5 eV is found for most of the molecules. Potential electrostatic calculations show that Thymol, M1, and M2 are susceptible to electrophilic attack. Thymol has a high polarizability and therefore a good activity, while a good electronic transfer is observed for thujone and artemisinin. Our study showed that black cumin has much more intense absorbance peaks than other plants. AIM analysis identifies critical points at CO and OH bonds, with strong hydrogen bonding in artemisinin. Molecular docking shows promising inhibitory potential of artemisinin. Assessment of the main ADME properties showed that all compounds except M1 and M2 comply with Lipinski's rule, which predicts good oral bioavailability.

International Journal of Quantum ChemistryVol. 125(8)
Université Claude Bernard Lyon 1 (FR), Centre National de la Recherche Scientifique (FR), University of Batna 1 (DZ), Centre de Recherche sur l'Information Scientifique et Technique (DZ)
Openalex Percentile: Top 18%
Computational Drug Discovery Methods
1.50
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