Comprehensive pharmacokinetic profiling and binding stability assessment of phytochemicals from Aconitum heterophyllum against viral targets using in silico methods

Aconitum Heterophyllum had been used as traditional medicine for treating infectious diseases. This study is a comprehensive in silico investigation to identify the antiviral potential of phytocompounds derived from Aconitum heterophyllum against major viral pathogens, including HIV, chikungunya, SARS-CoV-2, Ebola, and herpes viruses. ADMET screening of 21 phytoconstituents identified Isoatisine as the most promising candidate, exhibiting favorable druglikeness, pharmacokinetic properties, and low toxicity. Molecular docking revealed that Isoatisine shows strong binding affinities (≤ − 6.5 kcal mol⁻1) towards all selected viral targets, indicating its broad-spectrum antiviral potential. Energy minimization calculations done via Density functional theory (DFT) demonstrated significant intramolecular charge transfer between C=C bonds and nitrogen-linked cyclic carbon, indicating interaction capabilities of Isoatisine. Molecular electrostatic potential and frontier molecular orbital analyses further supported the reactive nature and stability of Isoatisine. Docking results were supported via molecular dynamics simulations. The RMSD and RMSF confirmed minimal conformational fluctuations, indicating the formation of a stable protein–ligand complex. Overall, the findings suggest that Isoatisine can effectively interact with the spike glycoprotein of SARS-CoV-2, thereby potentially binding with viral entry. This study provides mechanistic insights and a rational framework for designing spike-targeted antiviral agents based on natural phytochemicals.

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Publication Details

Journal
Discover Chemistry.
Published
2026-03-26
DOI
https://doi.org/10.1007/s44371-026-00596-7
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Comprehensive pharmacokinetic profiling and binding stability assessment of phytochemicals from Aconitum heterophyllum against viral targets using in silico methods

Meenakshi Rana, Shradha Lakhera, Arabinda Ghosh
Discover Chemistry.
Computational Drug Discovery Methods
article

Comprehensive pharmacokinetic profiling and binding stability assessment of phytochemicals from Aconitum heterophyllum against viral targets using in silico methods

Meenakshi Rana, Shradha Lakhera, Arabinda Ghosh
article en

Abstract

Aconitum Heterophyllum had been used as traditional medicine for treating infectious diseases. This study is a comprehensive in silico investigation to identify the antiviral potential of phytocompounds derived from Aconitum heterophyllum against major viral pathogens, including HIV, chikungunya, SARS-CoV-2, Ebola, and herpes viruses. ADMET screening of 21 phytoconstituents identified Isoatisine as the most promising candidate, exhibiting favorable druglikeness, pharmacokinetic properties, and low toxicity. Molecular docking revealed that Isoatisine shows strong binding affinities (≤ − 6.5 kcal mol⁻1) towards all selected viral targets, indicating its broad-spectrum antiviral potential. Energy minimization calculations done via Density functional theory (DFT) demonstrated significant intramolecular charge transfer between C=C bonds and nitrogen-linked cyclic carbon, indicating interaction capabilities of Isoatisine. Molecular electrostatic potential and frontier molecular orbital analyses further supported the reactive nature and stability of Isoatisine. Docking results were supported via molecular dynamics simulations. The RMSD and RMSF confirmed minimal conformational fluctuations, indicating the formation of a stable protein–ligand complex. Overall, the findings suggest that Isoatisine can effectively interact with the spike glycoprotein of SARS-CoV-2, thereby potentially binding with viral entry. This study provides mechanistic insights and a rational framework for designing spike-targeted antiviral agents based on natural phytochemicals.

Discover Chemistry.Vol. 3(1)
Gauhati University (IN), Uttarakhand Open University (IN), Uttarakhand Technical University (IN), Tripura University (IN)
Openalex Percentile: Top 6%
Computational Drug Discovery Methods
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