Computational protein-ligand docking and virtual drug screening with the AutoDock suite

By Stefano Forli, Ruth Huey, Michael E. Pique, Michel F. Sanner, David S. Goodsell, Arthur J. Olson. 2016

2,356 citations recorded by the source corpus.

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen 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 ∼5 h.

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A Review on Applications of Computational Methods in Drug Screening and Design (2020, background): “Virtual screening then searches chemical libraries to provide possible drug candidates based on drug binding sites on target proteins [5] [6] [7] .”
Key Topics in Molecular Docking for Drug Design (2019, methodology): “AutoDock uses Gasteiger–Marsili atomic charges whereas the closely-related AutoDock Vina does not require the assignment of atomic charges, since the terms that compose its scoring function are charge-independent [3,24].”
Evaluation of AlphaFold2 structures as docking targets (2022, methodology): “Partial charges were assigned using prepare_receptor4.py within Auto-DockTools (Forli et al., 2016; Morris et al., 2009).”