AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings

By Jérôme Eberhardt, Diogo Santos-Martins, Andreas F. Tillack, Stefano Forli. 2021

6,071 citations recorded by the source corpus.

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of these functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, methodology): “One of the other servers is an implementation of a leading non-deep learning protein small molecule docking method AutoDock Vina by the CAMEO organizers that predicts the protein structure by homology modeling(24–29), runs AutoDock to dock the small molecule, and ranks the poses using the Vina scori”
End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, methodology): “0 [8] were used in blind self-docking settings with default parameters, except we increased exhaustiveness from 8 to 64 and num_modes to 64.”
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery (2022, methodology): “Different software for performing molecular docking are widely available and commonly used platforms include AutoDock Vina (Eberhardt et al , 2021 ) and DOCK (Allen et al , 2015 ).”
CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, methodology): “It employed our protein-surface-curvature-based cavity detection approach (CurPocket) (13,14) to guide the molecular docking with AutoDock Vina (version 1.1.2) (15,16).”
CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome (2022, methodology): “Docking study with Autodock Vina 1.2 [36] showed that zaﬁrlukast can potentially bind to these cavities with high binding afﬁnity (Table S2).”
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “Target Binding Afﬁnity Figure 4 shows the median Vina energy (computed by AutoDock Vina (Eberhardt et al., 2021)) of all generated molecules for each binding pocket.”
SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina (2024, methodology): “Attracting Cavities 2.0 and AutoDock Vina have undergone comprehensive benchmarking to evaluate their performance ( 7 , 10 , 32 ).”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “Tools such as AutoDock4 [67], AutoDock Vina [63, 64], and 323 Smina [66] are among the most used docking programs.”
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, background): “Traditional Molecular Docking Approaches AutoDock and its variants [1, 17, 18, 19] are widely used in molecular docking.”