Phenprobamate

Network pharmacology has become a widely used approach for studying complex herbal medicines. This method helps researchers identify potentially active compounds and mechanisms, which can then guide further experiments. However, current network pharmacology methods often face issues like inconsistent compound records and unreliable screening standards, leading to inaccurate results. To address these challenges, we developed an improved workflow using Chai Hu Gui Zhi Tang (CHGZT) as an example. First, we used advanced analytical techniques (ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry) to rapidly identify chemical components in the herbal formula. Next, we created a machine learning model to predict compounds with anti‐allergic rhinitis activity, allowing systematic selection of key components for network analysis. Our results showed that specific compounds like cinnamic acid and citric acid may combat allergic rhinitis by regulating immune‐related genes (interleukin [IL]‐4 and IL‐5) while influencing biological processes such as “stress response” and “metabolism of foreign substances.” These findings confirm the effectiveness of our optimized method and highlight CHGZT's potential as a therapeutic option for allergic rhinitis.