To reduce high root-mean-square deviation (RMSD) values in a molecular docking project and achieve acceptable levels (typically ≤2 Å), indicating better alignment between predicted and experimental ligand poses, start by ensuring accurate ligand and receptor preparation by verifying the ligand’s 3D structure is correctly protonated, tautomeric states are appropriate, and partial charges are assigned using tools like Schrödinger’s LigPrep or AutoDockTools, while confirming the receptor’s active site is properly defined, protonated, and energy-minimized with tools like PROPKA or GROMACS, removing non-essential water molecules unless critical for binding. Optimize docking parameters by increasing search exhaustiveness (e.g., set to 16 or higher in AutoDock Vina) to explore more conformations, using a tightly focused grid box (20–30 Å) around the binding site, and, for flexible docking, including key active site residues to account for induced-fit effects without excessive flexibility that adds computational noise. Test alternative scoring functions (e.g., Vina’s empirical vs. GOLD’s force-field-based scoring) to better rank poses, and increase the number of docking runs or conformers sampled to capture diverse poses, using algorithms like Lamarckian genetic algorithms in AutoDock for better convergence. Consider ensemble docking with multiple receptor conformations from molecular dynamics or NMR to account for protein flexibility, as a static receptor may misrepresent the binding pocket. Post-docking, rescore top poses with accurate methods like MM-GBSA or MM-PBSA to account for solvation and entropic effects, and run short molecular dynamics simulations (1–5 ns with GROMACS) to refine ligand-receptor interactions before recalculating RMSD. Validate the reference crystal structure for reliability and relevance, performing redocking with a known ligand to test the protocol, as discrepancies in the reference can inflate RMSD. If high RMSD persists, try alternative docking software like Glide, GOLD, or DOCK, or incorporate machine learning-based scoring or pharmacophore constraints based on known interactions like hydrogen bonds to guide docking, addressing ligand-specific issues like high flexibility or multiple binding modes to achieve more accurate, low-RMSD poses.

Abdulhamid Dehghani's suggestions are valid, but they may require considerable time and ultimately lead to a docking procedure that still carries uncertainties since it relies on models. A practical approach would be to validate the interaction using a biological method. If your two molecules—presumably proteins—exhibit biological activity, you should measure that activity. Vigorous biological activity would indicate that docking is likely to be successful.

Alternatively, you could use a molecular probe. If a molecular probe that binds to one protein also binds to another protein in solution, this can demonstrate that a binding interaction exists between the two proteins. This is the foundational principle behind many molecular biology techniques, such as immunoprecipitation, which isolate and study proteins and their interactions.

The binding can be detected and confirmed using various techniques. For example, centrifugation can separate the protein complex from the rest of the solution, while electrophoresis can visualize the bound proteins, and mass spectrometry can identify the specific proteins involved. It is essential to ensure that the binding is stable over time. Some methods utilize fluorescence or luminescence to monitor and quantify protein-protein interactions in real-time, providing insight into the transient nature of these interactions.

RMSD values of 7-14 Å are quite concerning and indicate fundamental issues with your docking setup. From my experience working with Maestro and Glide for large-scale virtual screening, the most common culprit is improper receptor grid generation - you'll want to ensure your grid is properly centered on the actual binding pocket rather than just the protein geometric center, and consider reducing the grid box size to around 20×20×20 Å for more focused docking. I'd also recommend re-running LigPrep on your compounds with appropriate ionization states at pH 7.0 ± 2.0, as incorrect protonation states can dramatically affect binding poses. A good validation approach is to first redock the co-crystallized ligand from your reference structure using identical preparation protocols - this should give you an RMSD below 2 Å if everything is set up correctly. If the reference ligand itself shows high RMSD, then your grid preparation definitely needs adjustment. In my molecular screening work targeting NMDA receptors, I've found that visual inspection of the binding site in Maestro often reveals obvious issues like water molecules interfering with the binding pocket or incorrect chain selections. Try starting with HTVS mode first to get reasonable poses, then refine with SP docking for better accuracy. What protein target are you working with and do you have a co-crystallized ligand structure to validate against?