Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Reveal the Multi-target Anti-influenza a Virus Potential of Cynanchum Atratum.

Abstract

Introduction: Influenza A Virus (IAV) remains a major global health threat due to its high mutability and transmissibility, while drug resistance and dysregulated immune responses limit current therapeutic options. To address these challenges, this study developed a molecular dockingbased screening platform to efficiently identify anti-IAV herbal candidates from Traditional Chinese Medicine (TCM) and further elucidated the pharmacological mechanisms of the selected compounds.

Methods: A standardized herb-compound dataset was first established using the TCMSP. Key IAVrelated targets were then collected through literature mining, followed by structure-based virtual screening of bioactive compounds by molecular docking. On this basis, the source herb was reversetraced and further investigated using network pharmacology, including target intersection analysis, protein-protein interaction network construction, and GO and KEGG enrichment analyses. Molecular docking and Molecular Dynamics (MD) simulations were subsequently performed to evaluate the stability of representative compound-target complexes.

Results: Retrospective analysis revealed a strong association between multiple constituents of Cynanchum atratum (CA) and IAV inhibition. Using network pharmacology, we systematically evaluated the therapeutic potential and mechanisms of CA against IAV, identifying 21 key bioactive compounds, 291 potential targets, and 136 shared targets between CA and IAV, with AKT1, IL6, EGFR, and JUN likely serving as core targets. GO and KEGG enrichment analyses showed that the shared targets were mainly associated with the TNF, IL-17, and MAPK signaling pathways, Th17 cell differentiation, and COVID-19-related pathways. Molecular docking showed that the core constituents of CA bound favourably to the key targets, with the strongest binding energies reaching -9.8 kcal/mol against JUN and -9.6 kcal/mol against EGFR. MD simulations further corroborated these docking results, among which the EGFR-ethyl oleate complex exhibited the greatest conformational stability over 100 ns, with a mean RMSD of 0.228 ± 0.028 nm and an MM/PBSA binding free energy of -19.6 kcal/mol.

Discussion: The convergence of target-network, pathway-enrichment, molecular docking, and MD simulation results indicates that CA may act on multiple IAV-related targets and regulate both viral infection-associated and host inflammatory responses. These findings are consistent with the multicomponent and multi-target pharmacological characteristics of TCM and support the applicability of the integrated computational platform for anti-IAV herbal discovery. Nevertheless, the predicted compound-target interactions and biological mechanisms require further validation in cellular and animal models.

Conclusion: This study developed a comprehensive computational strategy for discovering TCMs against IAV, and identified CA as a potential herbal candidate against IAV infection. This work offers broader insights for the novel research and development of anti-IAV drugs.