Identifying active compounds and revealing integrated mechanism of phytomedicines via AI-driven chemical-biological information fusion: a case study of Weifuchun - PubMed
4 hours ago
- #AI-driven bioinformatics
- #network pharmacology
- #phytomedicine mechanism
- An AI-driven chemical-biological information fusion framework was developed to identify active compounds and integrated mechanisms of action (MOAs) of phytomedicines, using Weifuchun for chronic atrophic gastritis as a case study.
- Mass spectrometry, disease-related data, and transcriptomics were integrated to construct networks, with a GCN-GAT-A* model developed to identify key action paths and novel targets.
- Weifuchun significantly reduced inflammatory markers in vivo and in vitro, with naringenin, ginsenoside Rc, and diosmetin identified as core active compounds regulating targets like DUSP1, IRS1, and FLT1.
- Synergistic effects of compounds modulated pathways such as PI3K/AKT and MAPK, leading to anti-inflammatory, antioxidant, anti-metaplasia, and mucosal repair activities in chronic atrophic gastritis.
- The approach provides an intelligent virtual screening method for efficient identification of novel compounds and targets, offering a robust tool for phytomedicine research and drug discovery.