Integrating network pharmacology, machine learning, and molecular docking to explore the therapeutic mechanisms of Huangqin in atopic dermatitis: A STROBE-compliant observational study

Atopic dermatitis (AD) is a chronic inflammatory skin disorder with complex pathogenesis, and current therapies face limitations in efficacy and safety. Huangqin (Scutellaria baicalensis) exhibits anti-inflammatory properties, yet its multi-target mechanisms against AD remain unclear. A systems pharmacology approach integrating multi-omics profiling was utilized to decode Huangqin anti-AD mechanisms. First, bioactive compounds and their potential targets were systematically identified, followed by constructing compound-target networks and enriching key pathways. Then, machine learning algorithms (Support Vector Machine/Recursive Feature/Least Absolute Shrinkage and Selection Operator) were applied to prioritize hub targets from network-derived candidates. Finally, molecular docking was conducted to validate ligand-receptor binding affinity. Twenty-nine bioactive compounds were identified, interacting with 55 AD-related targets. AKT1 emerged as the most central hub in the protein-protein interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed Huangqin potential roles in modulating bacterial infection responses and regulating pathways such as IL-17, TNF, HIF-1α, and PI3K-AKT signaling. Machine learning algorithms were applied to prioritize key genes, which highlighted AKT1, solute carrier family 6 member 4, and chemokine ligand 2 as core targets, with molecular docking confirming strong binding between wogonin, baicalein, beta-sitosterol, and these targets. These findings suggest that Huangqin exerts multi-target effects on AD, centered on AKT1-mediated signaling crosstalk, to regulate inflammatory and immune pathways. This mechanistic insight establishes a foundation for clinical translation and AKT1-focused drug development.