Main Article Content
Abstract
Molecular docking is a vital computational technique widely used in drug discovery and structural biology. It predicts how small molecules, such as potential drugs, bind to a target protein and helps evaluate the strength and nature of their interactions. This review explores the key concepts, types of molecular interactions, docking principles, methods, and various tools involved in the docking process. It discusses important models like lock-and-key, induced fit, and conformational selection, as well as flexible docking approaches that enhance prediction accuracy. The study also highlights popular software tools such as AutoDock, Vina, and GOLD, along with their mechanisms and applications in hit identification, lead optimization, and bioremediation. Additionally, it addresses the current challenges and limitations, including issues with protein flexibility, ligand dynamics, and scoring function accuracy. Recent advancements, including the integration of artificial intelligence and machine learning, have significantly improved the efficiency and reliability of docking studies. Looking ahead, molecular docking is expected to play a transformative role in personalized medicine and the development of targeted therapies for complex diseases.