Molecular docking is a key method in computational chemistry used to predict interactions between ligands and receptors at the molecular level. The basic principle of this technique is to predict the optimal position and orientation of a small molecule (ligand) when it binds to a protein target, as well as to calculate the binding energy. The docking process involves critical steps such as protein and ligand structure preparation, scoring function selection, model validation, and interaction parameter analysis. In modern drug research and design, molecular docking plays a crucial role in in silico screening of drug candidates, enzyme engineering, toxicology studies, and biochemical mechanism analysis. Despite its advantages in efficiency and speed, this technique faces challenges. Challenges such as the limited accuracy of scoring functions, the flexibility of large molecules, and the influence of physiological environments. Recent developments, such as integration with machine learning, quantum mechanics, and molecular dynamics, have improved the predictive power and reliability of docking results. Therefore, molecular docking is becoming an increasingly important computational method in biotechnology, pharmaceuticals, and nanotechnology.