Computational Insights into the Molecular Mechanisms of Coptis chinensis Franch. in Treating Chronic Atrophic Gastritis: An Integrated Network Pharmacology, Machine Learning, and Molecular Dynamics Study

Chronic atrophic gastritis (CAG) is a precancerous gastric condition with limited therapeutic interventions, and the mechanisms underlying the benefits of <i>Coptis chinensis</i> Franch. (CCF) remain insufficiently defined. This study employed an integrated computational strategy to clarify the molecular basis of CCF activity against CAG. Network pharmacology was used to identify potential targets of the major CCF constituents berberine, coptisine, and palmatine, followed by molecular docking, machine learning-based IC₅₀ prediction, and molecular dynamics simulations. Fifty-eight overlapping targets between CCF compounds and CAG-related genes were identified, highlighting SRC, STAT3, MAPK1, and NFKB1 as central nodes enriched in inflammatory and immune pathways, including TNF and MAPK signaling. Docking analyses revealed strong interactions between all three compounds and SRC kinase, and machine learning models predicted IC₅₀ values in the low micromolar range (1.38-1.82 μM). Molecular dynamics simulations further suggest that berberine may stabilize the crucial regulatory regions of SRC, specifically the activation loop. It is hypothesized that this stabilization maintains the inactive conformation of the kinase domain and potentially shields Tyr416 from phosphorylation, thus potentially influencing kinase activation. These findings suggest that CCF may modulate key inflammatory and immune pathways implicated in CAG progression, with SRC emerging as a central node for further investigation.