Network Pharmacology of Natural Products from Computational Prediction to Experimental Validation

Natural products are important sources of bioactive compounds, but their complex chemical composition and multi-target effects make their mechanisms difficult to investigate using conventional approaches. Network pharmacology provides a systems-level framework for integrating chemical, biological, and disease-related information to investigate these complex interactions. This review describes the network pharmacology workflow, from active-compound screening, ADMET evaluation and target prediction to network construction and pathway enrichment, and the complementary roles of molecular docking and molecular dynamics. The workflow is organised around the strength of evidence, using a hierarchy that grades compound–target evidence, and the limits of validation and the items needed for reproducible reporting are specified. In addition, strategies for experimental validation, including biophysical and biochemical assays, cellular studies, omics approaches, pharmacokinetic evaluation, and in vivo models, are considered. Important limitations, including database bias, false-positive predictions, inappropriate network interpretation, limited consideration of in vivo exposure, and metabolite-related effects, are highlighted. Future developments involving multi-omics, microbiome data, single-cell and spatial analysis, and artificial intelligence may further improve the biological relevance and predictive value of network pharmacology. Overall, network pharmacology can serve as a useful bridge between natural product chemistry, computational analysis, and experimental pharmacology when supported by rigorous validation.