To confirm network pharmacology predictions, experimental methods like in vitro cell-based assays, receptor-binding assays, gene expression analysis, in vivo animal models, proteomics, and metabolomics can be employed, but testing herbal compounds is challenging due to their complex mixtures of active ingredients, bioavailability issues, and variability in potency; improving computational prediction reliability involves using high-quality data, advanced machine learning models, dynamic modeling, and multilayer validation, which can strengthen the evidence and enhance the real-world applicability of predictions, especially for compounds with synergistic effects like herbal medicines.

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