Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation

Objective To explore the protective effect and mechanism of resveratrol in the treatment of chronic inflammatory diseases through network pharmacology, machine learning and molecular docking techniques. Methods TCMSP, Pharm Mapper, SEA and SwissTargetPrediction and GEO databases were used to identify potential targets associated with resveratrol and chronic inflammatory diseases. These include Alzheimer's disease (AD), atherosclerosis (AS), chronic obstructive pulmonary disease (COPD), hepatitis B (HB), multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). The protein interaction network was constructed using STRING platform, and the KEGG pathway enrichment analysis was performed using R software. Machine learning was used to screen core genes and make molecular docking with resveratrol. GEO database was used to verify the expression of core genes and ROC curve analysis was performed. Results Resveratrol had strong binding force with the core targets (BIRC3, CA3, PGR, CXCL8, TNF, TNFSF10 and NFKBIA). These targets were significantly up-regulated in the gene expression data of the corresponding GEO database and showed good diagnostic value (the area under ROC curve ranged from 0.680 to 0.959). Conclusion These results provide a new molecular target and theoretical basis for the application of resveratrol in the treatment of inflammatory diseases.