When there is a crystal structure or a high-quality homology model (verified via PROCHECK or others softwares) is available for the target, it is possible to performed molecular docking. The crystal structure can be utilized in several ways. One of the ways is to investigate how a molecules might be modified to improve its potency, selectivity and ADME properties. Generally, docking consists of two parts, which are sampling and scoring. In the sampling part, the ligand is docked into the protein cavity in many different ways using different conformations of the ligand. Ligand conformation refers to the same chemical composition but having different bond-angles. Ligand orientation is different from conformation in a way that the bond angles are the same between multiple ligands but the orientation (transition, global rotation) is different. Ligand pose is the binding-mode of a ligand in a protein binding site, which considered a combination of orientation and conformations. Generally, a ligand can act as a receptor agonist-a molecule that triggers a response or as a antagonist, a molecule that suppress the activity of the receptor. In practice, oftern a large library of small drug-like compounds (ligands) has to be screened in order to find out a promising lead compound. Therefore, accuracy and computational efficiency is a major concern in the develpment of molecular docking software. For the evaluation of a scoring function, an experimentally observed protein-ligand complex can be used as positive control. To perform molecular docking, we will use AutoDock and MGLTools, which make use of a computationaly inexpensive “hybrid” force field that contains terms based on molecular machanics and empirical terms. The prediction of absolute binding energies may be less accurate compared to more computationaly expensive (e.g., force field-based methods). I did the molecular docking in Jupyter notebook and you can get the data set as well as resulting output in my github