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DOI 10.1007/s00894-026-06711-w

Медицинская библиотека · Journal of molecular modeling · 2026

Exploration of potential neuroprotective agents from medicinal plants for the treatment of Alzheimer's disease-approach through in silico ADMET, network pharmacology, docking, and dynamics studies.

Shinde Pooja, Sathiyanarayanan Arulmozhi, Lohidasan Sathiyanarayanan

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Аннотация

CONTEXT: A degenerative brain disorder that causes memory loss is Alzheimer's disease (AD). Phytoconstituents represent a promising therapeutic strategy due to their diverse bioactivities and favourable safety profiles. This study aimed to identify potential neuroprotective phytoconstituents for AD by pharmacokinetic screening, network pharmacology, molecular docking and molecular dynamics simulation. Among 22 phytoconstituents analysed, the network revealed GSK3β, STAT3, MAOB, ESR1, and PTGS2 as key AD-associated targets. Docking results were supported by dynamic stability analysis. The combined computational results support rosmariquinone as a potential neuroprotective lead compound for AD treatment.

METHODS

Pharmacokinetic and toxicity profiling of 22 phytoconstituents was performed using Swiss ADME and ProTox III software. Target prediction and construction of the phytoconstituent-disease target-gene interaction network were conducted using Swiss Target Prediction, Gene Card and Cytoscape. Pathway enrichment was evaluated via KEGG and GO analysis. Molecular docking of all shortlisted phytoconstituents against AD-related targets was carried out using AutoDock Vina, while 500 ns molecular dynamics simulations were performed using the Desmond module of the Schrödinger Suite to assess complex stability, RMSD, RMSF and hydrogen-bond fluctuation.

Для цитирования: Shinde Pooja, Sathiyanarayanan Arulmozhi, Lohidasan Sathiyanarayanan. Exploration of potential neuroprotective agents from medicinal plants for the treatment of Alzheimer's disease-approach through in silico ADMET, network pharmacology, docking, and dynamics studies.. Journal of molecular modeling. 2026. DOI: 10.1007/s00894-026-06711-w.

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