All docking calculations were performed on the Azure cloud platform using our automated cloud application, which submits all docking tasks for a given target to a selected Azure region. This setup employs a specialized Docker image containing both our locally modified Smina executable and the Vina executable. Smina , a modern C++ fork of the AutoDock family, incorporates an optimized scoring function known as the dkoes scoring function. This function differs from the traditional vina and vinardo scoring functions by including an explicit desolvation energy term. It has demonstrated good accuracy not only in the original publication , but also in a recent benchmark study , where it produced binding free energy calculations comparable to those of leading commercial docking programs. In addition to the dkoes scoring function, the Smina program supports both the traditional vina and vinardo scoring functions and has improved optimization compared to the original vina program. Given these advantages, I selected Smina for this docking study. I further enhanced performance by accelerating certain computational steps with AVX512 vector instructions and compiling the program with Intel C++ Classic Compiler, which provides efficient execution on modern Xeon-based CPUs available on Azure. Because our cloud workflow relies exclusively on spot instances, we achieved rapid computation at moderate cost. All docking calculations were performed using the Smina program with both the vinardo and dkoes scoring functions. In a small percentage of cases, Smina encountered atom typing errors; in these instances, we automatically reverted to the Vina application in its vinardo scoring mode. For cases involving the dkoes scoring function, those small percent of problematic calculations were skipped. Protein preparation was conducted locally using the pdb4amber.py program, which also employs the reduce program to add hydrogens appropriate for a physiological pH. For all docking calculations reported here, we used a 20 Å × 20 Å × 20 Å cubic docking box and the docking box was centered at the geometric center of the native ligand from the PDB. Our docking application calls the mk_prepare_ligand.py script from Meeko to prepare ligands and convert them into the required PDBQT format for docking, and employs mk_export.py to export the results into standard SDF format. If mk_export.py fails, the application defaults to using OpenBabel to produce the SDF files. We also invoke wk_prepare_receptor.py from the Waterkit program , followed by mk_prepare_receptor.py python program, for additional (and sometimes redundant) protein preparation steps required for vina-type docking. Since cofactors are not handled automatically in Smina or Vina programs I have written a small program to build the proper PDBQT file including both the protein and the cofactor. The exhaustiveness parameter was set to 32 for all docking runs, and the program retained the top three docking poses for each conformation.