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Computational molecular docking and simulation-based prediction of natural compounds from nyctanthes arbor-tristis as potential antifungal agents

Sohail Akhtar · Varish Ahmad · Mohammad Aatif · Qazi Mohammad Sajid Jamal
10.25259/jksus_1667_2025 393 Views 1 Citations
1
Citations
393
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Abstract


Mycoses, or fungal illnesses, are common health problems that frequently impact crops, animals, and food. Molecular docking and dynamics simulation techniques were used to predict the multitargeted antifungal potential of phytochemicals from
Nyctanthes arbor-tristis
against multiple drug targets of fungi.


We analyzed the binding interaction and dynamics of phytochemicals of the
Nyctanthes arbor-tristis
plant with multiple drug targets of fungi through a computational study.


The strong binding affinities with the targets studied of fungi were found to be more significant compared to the reference drug (lupeol -11.5 kcal/mol; DB01263, a synthetic azole drug -8.7 kcal/mol). The investigated ligands, Lupeol, Nyctanthic acid, Beta-amyrin, and Apigenin, interacted more significantly with fungal drug targets through hydrogen bonds and hydrophobic interactions. The structural assessment of the 5FSA-ligand complexes showed stability with root mean square deviation (RMSD) values between 0.2 and 0.4nm, also investigated through 500 ns molecular dynamics simulations, which considered different geometric properties and computed the binding free energy. We also observed significant ligand-receptor interactions and high drug-likeness properties for the observed molecules using the pkCSM absorption, distribution, metabolism, and excretion
(
ADMET) method.


This study suggested the screened phytochemicals of this plant, namely Lupeol, Nyctanthic acid, Beta-amyrin, and Apigenin, or their combination, could inhibit fungal pathogens and could be beneficial to control the fungal-mediated food spoilage and aspergillosis in both plants and animals. Nevertheless, more
in vitro
and
in vivo
research is required to validate these findings.

Cite this Article (APA)
Sohail, A., Varish, A., Mohammad, A., Qazi, M. S. J. (2026). Computational molecular docking and simulation-based prediction of natural compounds from nyctanthes arbor-tristis as potential antifungal agents. Journal of King Saud University – Science. https://doi.org/10.25259/jksus_1667_2025
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Published in
ISSN 1018-3647
Quartile Q1
AMS Score 100
Field Natural Sciences
Publisher King Saud University
Country 🇸🇦 Saudi Arabia
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Authors
Publication Details
Year 2026
Language English
Added 14 Jul 2026