Machine Learning and Experimental Verification Identify Anti-Influenza Natural Products

The influenza A virus (IAV) has been responsible for multiple seasonal epidemics and poses a pandemic threat, and the growing number of variant strains constitutes a persistent threat to humanity. This study aimed to identify anti-influenza compounds from a traditional Chinese medicine (TCM) monomer library using a machine learning approach, with calmodulin as a hypothesis-driven target. The antiviral efficacy of the compounds with the highest predicted binding scores from virtual screening was evaluated using integrated computational and experimental approaches. A pre-trained protein language model (ConPLex) was employed for virtual screening. Molecular docking was used to predict binding characteristics, and network pharmacology was applied to generate hypotheses on multi-target mechanisms. The cytotoxicity and anti-H1N1 activity of the selected compounds were assessed in vitro, followed by in vivo evaluation of survival, lung pathology, viral load, and inflammatory mediators in a lethal mouse infection model. Sodium deoxycholate (NaDC) and deoxycholic acid (DCA) were identified as promising lead compounds. Both exhibited dose-dependent inhibition of viral replication in vitro with low cytotoxicity. Treatment with NaDC and DCA significantly improved survival rates and reduced lung pathology in H1N1-infected mice. Treatment was associated with suppression of nuclear factor kappa-B (NF-κB) activation, reduced pro-inflammatory cytokines, and elevated interleukin-10 (IL-10) levels. Molecular docking predictions indicated that NaDC and DCA exhibit moderate binding affinity for calmodulin, with binding energies of -8.38 kcal/mol and -7.61 kcal/mol, respectively. Furthermore, network pharmacology analysis suggested that these compounds may modulate pathways related to viral infection, inflammation, and immune regulation. NaDC and DCA demonstrate anti-influenza activity both in vitro and in vivo, reducing viral replication and alleviating inflammatory lung injury. These findings position NaDC and DCA as promising lead compounds for anti-influenza drug development and provide a foundation for further mechanistic validation.