Q1 2026

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 395 المشاهدات 1 الاقتباسات
1
الاقتباسات
395
المشاهدات
الملخص


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.

الاستشهاد بهذا المقال (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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الوصول
عرض النص الكامل عبر DOI
نُشر في
الرقم الدولي ISSN 1018-3647
الربعية Q1
درجة المؤشر القياس العربي 100
التخصص Natural Sciences
الناشر King Saud University
الدولة 🇸🇦 Saudi Arabia
عرض ملف المجلة →
المؤلفون
تفاصيل النشر
السنة 2026
اللغة English
أُضيف في 14 Jul 2026