Network Pharmacology and Multitarget Analysis of Artemisia annua in the Management of the Influenza Virus: A Computational Study

Background: Influenza viruses pose a significant global health threat due to their rapid mutation and resistance to conventional therapies. Objectives: This study uses network pharmacology and computational approaches to explore the multitarget antiviral potential of Artemisia annua against influenza. Methods: Seventeen bioactive compounds from A. annua were screened for drug-likeness and non-toxicity via ADMET/Protox analyses. Results: Using network pharmacology, 194 common targets between A. annua compounds and influenzarelated genes were identified, with hub genes TNF, AKT1, SRC, EGFR, and STAT3 implicated in immune modulation and viral replication. Molecular docking revealed strong binding affinities (docking scores: -6.5 to -15 kcal/mol) between key compounds (e.g., bauerenol, 6-epi-β-bisabolol) and influenza-associated proteins, outperforming controls like baloxavir marboxil. Molecular dynamics simulations over 100 ns demonstrated stable ligand-receptor complexes, with RMSD fluctuations ≤6 Å and favorable MM-GBSA binding energies (-50 to -150 kcal/mol). Findings of this study suggest that A. annua exerts synergistic, multitarget antiviral effects via its bioactive compounds, with several components outperforming conventional therapies in computational assays. A. annua primarily disrupts viral pathogenesis through immunomodulation and direct antiviral mechanisms. Conclusion: This study provides robust computational evidence for the therapeutic potential of A. annua against influenza, identifying critical compound-target-pathway interactions. Further experimental validation is recommended to confirm efficacy and translation of insights into clinical applications.