Molecular docking is a widely used in silico method in drug discovery for identifying novel compounds, predicting ligand-target interactions, and determining structure-activity relationships. Initially developed to understand molecular recognition between small and large molecules, docking has evolved to include applications such as adverse effect prediction, polypharmacology, drug repurposing, and target fishing. Recent advancements in computational methods, including artificial intelligence, have enhanced docking's predictive power and expanded its applications in drug discovery. Docking is now used to identify potential drug targets, predict off-target effects, and design multi-target ligands. It also aids in drug repositioning by identifying new therapeutic uses for existing compounds. Docking is often integrated with other computational approaches like molecular dynamics, binding free energy estimation, and machine learning to improve prediction accuracy and overcome limitations. These combined methods allow for more efficient virtual screening, better identification of drug candidates, and improved understanding of drug-target interactions. The integration of docking with advanced techniques has significantly enhanced its role in drug discovery, enabling the identification of novel therapeutic targets and the prediction of adverse drug reactions. Overall, molecular docking remains a crucial tool in modern drug discovery, offering valuable insights into drug-target interactions and facilitating the development of safer and more effective therapeutic agents.Molecular docking is a widely used in silico method in drug discovery for identifying novel compounds, predicting ligand-target interactions, and determining structure-activity relationships. Initially developed to understand molecular recognition between small and large molecules, docking has evolved to include applications such as adverse effect prediction, polypharmacology, drug repurposing, and target fishing. Recent advancements in computational methods, including artificial intelligence, have enhanced docking's predictive power and expanded its applications in drug discovery. Docking is now used to identify potential drug targets, predict off-target effects, and design multi-target ligands. It also aids in drug repositioning by identifying new therapeutic uses for existing compounds. Docking is often integrated with other computational approaches like molecular dynamics, binding free energy estimation, and machine learning to improve prediction accuracy and overcome limitations. These combined methods allow for more efficient virtual screening, better identification of drug candidates, and improved understanding of drug-target interactions. The integration of docking with advanced techniques has significantly enhanced its role in drug discovery, enabling the identification of novel therapeutic targets and the prediction of adverse drug reactions. Overall, molecular docking remains a crucial tool in modern drug discovery, offering valuable insights into drug-target interactions and facilitating the development of safer and more effective therapeutic agents.