From the computational point of view, several methods can be useful validating the docking results. Re-docking and Crossdocking: Re-docking involves docking the co-crystallized ligand back into the binding site of the protein to see if the docking method can reproduce the experimentally observed binding mode. Cross-docking tests the docking protocol on a set of proteins with known ligands to assess its robustness. Scoring Function Evaluation: Comparing different scoring functions and evaluating their performance on benchmark datasets or against experimental data can help validate the docking results. The use of consensus scoring, where multiple scoring functions are applied, can also improve validation. Ensemble Docking: Docking against multiple conformations of the protein, generated through techniques like molecular dynamics simulations, can account for receptor flexibility and provide a more comprehensive validation of the docking results. Decoy Sets: Docking a set of inactive compounds (decoys) seeded with known active compounds can help assess the ability of the docking protocol to distinguish between binders and non-binders, providing a measure of its predictive accuracy. Molecular Dynamics Simulations: Post-docking molecular dynamics simulations can be used to assess the stability of the ligand-receptor complex over time, providing further validation of the docking results. Area Under Curve (AUC) and Enrichment Factor (E.F): The AUC value from receiver operating characteristic (ROC) curves and the enrichment factor at certain percentages can be used to validate the docking tool's ability to rank active compounds higher than decoys. Ligand Efficiency Metrics: Metrics such as ligand efficiency (LE) and binding efficiency index (BEI) can be used to evaluate the quality of the docking poses in terms of binding affinity relative to the size of the ligand .