Unveiling the Multitarget Anti-Aging Mechanisms of Andaliman (Zanthoxylum acanthopodium) Essential Oil through Integrated in Silico Network Pharmacology and Docking Analysis

Research on natural anti-aging agents often encounters challenges due to the complex and multifactorial nature of skin-aging mechanisms, as well as the limited exploration of essential oils with multitarget actions. Addressing these gaps, this study aimed to elucidate the multitarget molecular mechanisms of andaliman (Zanthoxylum acanthopodium) essential oil against skin aging using an integrated in silico network pharmacology and molecular docking approach. The study identified three dominant bioactive compounds, geranyl acetate, citronellal, and citronellol acetate, through literature-based GC–MS data. Their potential protein targets were predicted via Swiss Target Prediction and compared with aging-related genes, followed by protein–protein interaction (PPI) analysis, hub gene identification, and pathway enrichment using STRING, Cytoscape, and Shiny GO platforms. Molecular docking was then conducted to validate the interactions of key compounds with the core proteins. The analysis revealed six central target genes (CDK1, CCND1, EGFR, SRC, GSK3B, and HDAC1) that regulate cell proliferation, adhesion, and epigenetic modification. Pathway enrichment indicated significant involvement in cell cycle, focal adhesion, and thyroid hormone signaling pathways. Docking simulations demonstrated stable ligand–protein interactions, with binding affinities ranging from −5.67 to −7.37 kcal/mol, particularly between geranyl acetate and CDK1 as well as citronellol acetate and CDK1. These findings provide comprehensive computational evidence that andaliman essential oil exerts anti-aging activity through simultaneous modulation of proliferative, adhesive, and epigenetic pathways. The results reinforce its potential application as a natural multitarget active ingredient for anti-aging cosmetic formulations, offering a molecular foundation for further experimental validation in biological models.