Perspective Chapter: Introduction to Molecular Docking

Molecular docking is a computational approach used to predict the binding orientation and affinity of ligands with target receptors, supporting drug discovery and biomedical research. This chapter outlines the principles, methodology, and recent advancements in docking. The process involves protein and ligand preparation, grid generation, active site prediction, and docking simulations, which collectively help identify stable complexes and optimize lead molecules. Emerging developments, including deep learning frameworks like GNINA, hybrid artificial intelligence approaches, and improved docking algorithms, have enhanced prediction accuracy and efficiency in virtual screening. Applications of molecular docking extend to drug repurposing, anticancer and antidepressant drug design, biosensor development, biomarker identification, and environmental remediation. Case studies include thymol and carvacrol derivatives as anticancer agents, chalcone derivatives with antidepressant potential, and graphene-based composites for dental restoratives. Docking also assists in evaluating interactions with metabolic enzymes, supporting drug safety and specificity. Despite limitations such as scoring accuracy and receptor flexibility, integration with molecular dynamics, machine learning, and quantum computing offers promising future directions. Molecular docking continues to be a cost-effective and versatile tool in drug development, personalized medicine, and translational research.