ArticleFrontiers in pharmacology2025
Network pharmacology-based identification of potential drug targets and bioactive compounds in
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Risk-Aware Computational Prioritization and Validation Route Design for Medicine-Food Homology Plant Compounds in a Parkinson's Disease Context.Biotech (Basel (Switzerland)) · 2026Article
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Abstract
Introduction: Parkinson's disease (PD) is a common neurodegenerative disease characterized by the loss of dopaminergic neurons without any curable treatment. Various traditional Chinese medicines have been employed to manage the progression of PD. Methods: Common disease and drug targets were retrieved and analyzed using protein-protein interactions and Cytoscape networks. Gene ontology enrichment analyses and KEGG pathway analyses were performed on the targets, followed by docking analyses to determine the binding potential of the compounds against these targets. Pharmacokinetic predictions and molecular dynamic simulations were performed to calculate the drug-likeness of compounds and extract the structural and residual fluctuations of top binding complexes, respectively. Results: The network pharmacology approach has identified AKT1, IL-1β, TNF, IL-6, and MAOB as key targets of PD. KEGG pathway analysis has shown that the 'pathway of neurodegeneration-multiple diseases' and 'dopaminergic synapses' are significant pathways of selected targets. Molecular docking studies have shown that the compounds cycloartenol, 24-methylenecycloartenol, lupeol acetate, 24-ethylcholesta-5,22-dienol, and 4α-methyl-24-ethylcholesta-7,24-dienol exhibited better binding potential against the scrutinized targets. AKT1 with 24-ethylcholesta-5,22-dienol (-11.5 kcal/mol), TNF with 4α-methyl-24-ethylcholesta-7,24-dienol (-10 kcal/mol), and MAOB with 24-ethylcholesta-5,22-dienol (-9.7 kcal/mol) exhibited a promising binding potential. The ADMET analysis of the selected five compounds reflects the potential of the drug candidate for effective PD therapies, as these compounds have a high drug-likeness score (0.76-0.78) and low drug-induced Neurotoxicity (<0.1). Conclusion: RMSD analysis of the top docked complexes showed that AKT1 - 24-ethylcholesta-5,22-dienol and MAOB-24-ethylcsecrholesta-5,22-dienol remained stable, whereas 4alpha-methyl-24-ethylcholesta-7,24-dienol exhibited fluctuations with TNF over 100 nanoseconds. These findings indicate that
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