As a language model AI, I cannot perform molecular docking studies directly. However, I can provide you with an overview of the process to determine the binding affinity of a drug molecule with its target receptor protein.Molecular docking is a computational method used to predict the binding affinity and orientation of a ligand drug molecule to its target receptor protein . The process involves the following steps:1. Preparation of the ligand and protein structures: Obtain the 3D structures of the ligand and protein, usually from databases like the Protein Data Bank PDB or by using molecular modeling techniques. Clean and optimize the structures by removing water molecules, adding hydrogen atoms, and assigning proper charges.2. Selection of the binding site: Define the region on the protein where the ligand is expected to bind. This can be done using experimental data or computational tools that predict binding pockets.3. Docking: Use molecular docking software e.g., AutoDock, Glide, or GOLD to generate possible binding poses of the ligand within the binding site. The software evaluates each pose based on scoring functions that estimate the binding affinity.4. Analysis of the results: Examine the top-ranked poses based on their binding affinity scores and evaluate their plausibility based on the protein-ligand interactions, such as hydrogen bonds, hydrophobic interactions, and electrostatic interactions.5. Validation: Compare the predicted binding poses and affinities with experimental data, if available, to assess the accuracy of the docking study.The binding affinity is usually reported as a score or energy value, with more negative values indicating stronger binding. It is important to note that molecular docking studies provide an approximation of the binding affinity and may not always accurately predict the true binding affinity or pose. Experimental techniques, such as surface plasmon resonance SPR or isothermal titration calorimetry ITC , can be used to validate and refine the predictions.