Molecular docking is a widely used in silico structure-based method in drug discovery, enabling the identification of novel compounds, predicting ligand-target interactions, and delineating structure-activity relationships. Initially developed to understand molecular recognition between small and large molecules, its applications have expanded significantly over the years. This review discusses the evolution of molecular docking from its early uses in drug discovery to its current applications, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling. The review highlights the integration of docking with other computational methods such as ligand-based approaches, molecular dynamics, binding free energy estimations, and artificial intelligence (AI) to improve prediction accuracy and efficiency. Recent advancements in docking algorithms and the availability of large-scale data have further enhanced its utility in drug discovery. The review also explores the use of reverse docking for target fishing and profiling, the prediction of adverse drug reactions, and the design of multi-target ligands. Finally, the potential of docking in drug repurposing is discussed, emphasizing its role in identifying novel therapeutic uses for existing drugs, natural compounds, and synthesized ligands.Molecular docking is a widely used in silico structure-based method in drug discovery, enabling the identification of novel compounds, predicting ligand-target interactions, and delineating structure-activity relationships. Initially developed to understand molecular recognition between small and large molecules, its applications have expanded significantly over the years. This review discusses the evolution of molecular docking from its early uses in drug discovery to its current applications, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling. The review highlights the integration of docking with other computational methods such as ligand-based approaches, molecular dynamics, binding free energy estimations, and artificial intelligence (AI) to improve prediction accuracy and efficiency. Recent advancements in docking algorithms and the availability of large-scale data have further enhanced its utility in drug discovery. The review also explores the use of reverse docking for target fishing and profiling, the prediction of adverse drug reactions, and the design of multi-target ligands. Finally, the potential of docking in drug repurposing is discussed, emphasizing its role in identifying novel therapeutic uses for existing drugs, natural compounds, and synthesized ligands.