Our research begins with a hierarchical computational strategy. We employ iGEMDOCK, LibDock and AutoDock for high-throughput virtual screening (HTVS) to identify potential hits from extensive natural product libraries. To ensure the reliability of these hits, we perform Molecular Dynamics (MD) Simulations to evaluate the structural stability and binding persistence of ligand-receptor complexes under simulated physiological conditions. This rigorous in silico validation significantly refines our selection before moving to laboratory experiments. 

To bridge the gap between theoretical modeling and physical samples, we employ HPLC-MS/MS for the rigorous identification and quantification of natural active ingredients. This analytical stage allows us to verify the chemical profile of our extracts and isolated compounds. By correlating mass spectrometry data with our computational predictions, we ensure the structural integrity and purity of the candidates before proceeding to biological evaluation. 

The final validation of our screening platform involves in vitro cell-based assays. We utilize specific cell models to evaluate the biological activity, efficacy, and safety of the identified lead compounds. This "Wet Lab" confirmation is crucial for demonstrating the real-world therapeutic potential of our candidates, providing solid experimental evidence to support our computational findings and driving the development of next-generation metabolic therapeutics.