The AutoDock suite is a set of free, open-source software tools for computational docking and virtual screening of small molecules to macromolecular receptors. The suite includes AutoDock Vina, AutoDock, Raccoon2, AutoDockTools, and AutoLigand. These tools are used for predicting the binding conformations and free energies of small molecule ligands to macromolecular targets, and are widely used in structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. The protocol covers docking and virtual screening methods provided by the AutoDock suite, including basic docking of a drug molecule with an anticancer target, virtual screening of this target with a small ligand library, docking with selective receptor flexibility, active site prediction, and docking with explicit hydration. The entire protocol will require approximately 5 hours. Computational docking is widely used for studying protein-ligand interactions and for drug discovery and development. The process typically starts with a target of known structure, such as a crystallographic structure of an enzyme of medicinal interest. Docking is then used to predict the bound conformation and binding free energy of small molecules to the target. Single docking experiments are useful for exploring the function of the target, and virtual screening, where a large library of compounds are docked and ranked, may be used to identify new inhibitors for drug development. AutoDock is a suite of free open-source software for computational docking and virtual screening of small molecules to macromolecular receptors. The suite currently includes several complementary tools: AutoDock Vina, AutoDock, Raccoon2, AutoDockTools, and AutoLigand. The AutoDock suite, including source, is freely available, and has been widely used in research and drug discovery. AutoDock Vina is a turnkey computational docking program based on a simple scoring function and rapid gradient-optimization conformational search. AutoDock is a computational docking program based on an empirical free energy force field and rapid Lamarckian genetic algorithm search method. Raccoon2 is an interactive graphical tool for virtual screening and analysis. AutoDockTools is an interactive graphical tool for coordinate preparation, docking, and analysis. AutoLigand is a program for predicting optimal sites of ligand binding on receptors. Both AutoDock and AutoDock Vina are designed to be generic computational docking tools, accepting coordinate files for receptor and ligand, and predicting optimal docked conformations. Typically, users start with receptor coordinates from crystallography or NMR spectroscopy, and ligand coordinates generated from SMILES strings or other methods. Because the search methods are stochastic, a set of optimal docked conformations is predicted, then typically clustered spatially to analyze consistency of the results. Highly clustered results are an indication that the conformational search procedure is exhaustive enough to ensure coverage of the accessible conformational space. Due to the stochastic nature of the search, theThe AutoDock suite is a set of free, open-source software tools for computational docking and virtual screening of small molecules to macromolecular receptors. The suite includes AutoDock Vina, AutoDock, Raccoon2, AutoDockTools, and AutoLigand. These tools are used for predicting the binding conformations and free energies of small molecule ligands to macromolecular targets, and are widely used in structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. The protocol covers docking and virtual screening methods provided by the AutoDock suite, including basic docking of a drug molecule with an anticancer target, virtual screening of this target with a small ligand library, docking with selective receptor flexibility, active site prediction, and docking with explicit hydration. The entire protocol will require approximately 5 hours. Computational docking is widely used for studying protein-ligand interactions and for drug discovery and development. The process typically starts with a target of known structure, such as a crystallographic structure of an enzyme of medicinal interest. Docking is then used to predict the bound conformation and binding free energy of small molecules to the target. Single docking experiments are useful for exploring the function of the target, and virtual screening, where a large library of compounds are docked and ranked, may be used to identify new inhibitors for drug development. AutoDock is a suite of free open-source software for computational docking and virtual screening of small molecules to macromolecular receptors. The suite currently includes several complementary tools: AutoDock Vina, AutoDock, Raccoon2, AutoDockTools, and AutoLigand. The AutoDock suite, including source, is freely available, and has been widely used in research and drug discovery. AutoDock Vina is a turnkey computational docking program based on a simple scoring function and rapid gradient-optimization conformational search. AutoDock is a computational docking program based on an empirical free energy force field and rapid Lamarckian genetic algorithm search method. Raccoon2 is an interactive graphical tool for virtual screening and analysis. AutoDockTools is an interactive graphical tool for coordinate preparation, docking, and analysis. AutoLigand is a program for predicting optimal sites of ligand binding on receptors. Both AutoDock and AutoDock Vina are designed to be generic computational docking tools, accepting coordinate files for receptor and ligand, and predicting optimal docked conformations. Typically, users start with receptor coordinates from crystallography or NMR spectroscopy, and ligand coordinates generated from SMILES strings or other methods. Because the search methods are stochastic, a set of optimal docked conformations is predicted, then typically clustered spatially to analyze consistency of the results. Highly clustered results are an indication that the conformational search procedure is exhaustive enough to ensure coverage of the accessible conformational space. Due to the stochastic nature of the search, the