In silico docking was performed using Autodock Vina (Trott and Olson, 2010) run through PyRx interface to manage the workflow and PyMol to visualize the results. Ligands were prepared by generating the energy-minimized 3D structures using ChemBioDraw3D followed by processing with Autodock Tools 1.5.4 to assign Gasteiger charges, merge nonpolar hydrogens, and set torsional bonds. Initial docking runs were performed within a 25–30 Å cubic search space surrounding the binding pocket, with solutions found using an exhaustiveness of 8, and output modes ranked according to binding affinity (BA). For a detailed comparison, multiple runs with a reduced search space were run with an increased exhaustiveness of 16 and 32. The Autodock Vina produced ligand poses with the best fit and strongest BA (global minima) using a stochastic algorithm to explore surfaces/pockets of the rigid macromolecule, through an iterative series of local optimizations evaluating both intermolecular (hydrophobic interactions, repulsions, hydrogen bonding, etc.) and intramolecular (torsion, rotational torque) factors. SAR insights are greatly aided by molecular docking analysis but must be taken as putative due to the rigid modeling of the protein target and the potential for conformational bias (Bissantz et al., 2010).