Molecular Docking: Principles, Applications, Challenges, and Future Directions

Molecular docking is a fundamental computational technique widely used to predict and analyze molecular interactions, particularly between small ligands and biological macromolecules such as proteins or nucleic acids. This review provides a comprehensive overview of the principles, methodologies, applications, challenges, and recent advancements in molecular docking. The discussion encompasses essential stages of the docking process, including target and ligand preparation, docking type selection, scoring function evaluation, and validation techniques. Applications across biotechnology, computational pharmacology, enzyme engineering, nanotechnology, and toxicology are explored to highlight the method’s versatility and interdisciplinary relevance. Despite its widespread use, challenges such as limited scoring accuracy, receptor flexibility, and environmental variability continue to hinder prediction precision. However, emerging technologies—such as hybrid quantum mechanics/molecular mechanics (QM/MM) methods, molecular dynamics integration, and artificial intelligence–based docking—have significantly improved accuracy and efficiency in virtual screening and drug design. Moreover, data-driven and AI-powered models, including machine learning–enhanced scoring and deep neural network prediction, mark the evolution toward next-generation docking systems. Overall, molecular docking remains an indispensable tool in structure-based drug design and molecular modeling, bridging the gap between computational prediction and experimental validation, while continuously evolving through the integration of advanced computational intelligence and biophysical modeling.