The DrugBank database was utilized to obtain the 2D structure of afatinib, which was subsequently converted from SDF format to PDB format via Open Babel version 2.3.2 [55 (link)]. An analysis of the crystal structure of the CDH3 protein was conducted (PDB: 4oy9), during which the receptor protein underwent desolvation and ligand removal through the application of PyMOL version 2.3.4. Additional modifications, including the addition of hydrogen atoms, were executed via AutoDock Tools, facilitating the conversion of both the receptor protein and the ligand small molecules into pdbqt format [56 (link)]. Molecular docking was performed with AutoDock Vina version 1.1.2, with the conformation exhibiting the lowest binding energy selected as the docking outcome. Typically, a binding energy threshold of < -5.0 kcal/mol is indicative of favourable binding potential. The results of the molecular docking were visualized via PyMOL software.
Tools
Manufactured by AutoDock
2 809 citations
Sourced in United States, Hungary
About the product
AutoDock Tools is a software suite designed to perform molecular docking simulations. It provides a graphical user interface (GUI) for preparing input files, running docking calculations, and analyzing the results. The core function of AutoDock Tools is to predict the preferred binding orientations and affinities between a small molecule and a target protein.
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Market Availability & Pricing
AutoDockTools (ADT) is a graphical user interface developed by Scripps Research to facilitate the preparation and analysis of docking simulations using the AutoDock suite. It assists users in tasks such as setting up ligand and receptor files, defining rotatable bonds, and analyzing docking results.
ADT is distributed freely under the GNU General Public License (GPL) and can be downloaded from the official AutoDock website. As open-source software, it is available at no cost.
The latest stable version of ADT is 1.5.6, released in 2009. While this version remains available, users are encouraged to explore newer tools and interfaces that have been developed to enhance the docking experience, such as AutoDock-GPU and AutoDock Vina, which offer improved performance and features.
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Check compatibility
2 809 protocols using «tools»
1
Molecular Docking of Afatinib to CDH3 Protein
2
Molecular Docking of Compound 7a against DNA Gyrase
First, a molecular docking evaluation was carried out [32 (link)] in order to determine whether molecule 7a would interact with DNA gyrase (the usual target of fluoroquinolones). We selected and downloaded the crystallized protein from the RSCB Protein Data Bank (PDB) with the ID 2xct, a 3.35 Å structure of S. aureus gyrase co-crystalized with ciprofloxacin.
The active sites of the protein were predicted in the Protein Plus server (https://proteins.plus/#dogsite , accessed on 5 February 2025). Structures adjacent to a single protein, such as the solvent, were removed with UCSF Chimera (1.16), except for the DNA double strand and the Mg2+ ion, which were retained. The same software allowed us to store the energy minimization of the protein, whose computations were performed with the PDB2PQR parameter set. We used the AutoDock Tools software (1.5.7) to give the receptor the protonation state.
The structure of7a was drafted in BIOBIA Draw software (19.1.0), and later opened in Avogadro (1.2.0) to conduct geometrical optimization with the UFF force field by using the steepest descent algorithm with 4 steps per update. AutoDock Tools (1.5.7) allowed us to establish the torsion tree of the ligand (molecule 7a ), and to design the grid center for use in the receptor. AutoDock Vina (1.1.2) was used to carry out the docking of 7a against the gyrase protein. The post-dock results were analyzed and visualized using the Receptor–Ligand interactions on Discovery Studio Visualizer (21.1.0).
The active sites of the protein were predicted in the Protein Plus server (
The structure of
3
Automated Docking Simulation Protocol
Docking simulations were facilitated by AutoDock Tools (version 1.5.65) and Chimera (version 1.112) for grid generation and validation. The grid parameters were established based on the co-crystal ligand orientation whenever available. Alternatively, the CASTp6 server was employed for apo-state proteins to determine favourable ligand binding pockets. The dimensions of the docking box were minimized to ensure optimal alignment of geometry and character with the active site of protein to accommodate the anticipated ligand structures.
4
Rutin-Protein Binding Affinity Analysis
The relationship between the core targets and rutin was verified by selecting the core targets in the protein–protein interaction network for molecular docking. Initially, the PDB database was utilized to acquire the structure of the central target proteins, subsequently eliminating water and small molecules. The protein was hydrogenated, and its charge was calculated using AutoDock Tools (version 1.5.6). The Rutin structure was then taken from the PubChem database and analyzed using AutoDock Tools to observe charge balance and rotatable bonds. Finally, AutoDock Vina was used to determine the receptor–ligand docking. The structure with the highest binding affinity, i.e., corresponding to the least free binding energy, was selected before visualization with PyMol (version 2.4.1) software.
5
Molecular Docking of TCM Compounds
Molecular
docking between the core components and the core targets was conducted
based on the aforementioned analysis. The three-dimensional crystal
structures of the target proteins were retrieved from the RCSB PDB.
Docking parameters and the details of the targets are presented inTable 2 . The corresponding
PDB format files were downloaded, dewatered, and hydrogenated using
AutoDockTools. These files were then selected as receptors and saved
in PDBQT format. The MOL2 files of the active components were obtained
from the TCMSP database, hydrogenated in AutoDockTools, designated
as ligands, and exported as PDBQT files. Molecular docking was performed
using AutoDock Vina. Components and targets exhibiting strong binding
activity were screened based on their affinity and visualized using
PyMOL.
docking between the core components and the core targets was conducted
based on the aforementioned analysis. The three-dimensional crystal
structures of the target proteins were retrieved from the RCSB PDB.
Docking parameters and the details of the targets are presented in
PDB format files were downloaded, dewatered, and hydrogenated using
AutoDockTools. These files were then selected as receptors and saved
in PDBQT format. The MOL2 files of the active components were obtained
from the TCMSP database, hydrogenated in AutoDockTools, designated
as ligands, and exported as PDBQT files. Molecular docking was performed
using AutoDock Vina. Components and targets exhibiting strong binding
activity were screened based on their affinity and visualized using
PyMOL.
Top 5 most cited protocols using «tools»
1
Structural Comparison and Molecular Docking
Structure images were created in PyMOL69 , and PyMOL align was used to compute r.m.s.d.s (outlier rejection is described in the text where applicable).
For docking against DGAT2, P2Rank65 (link) was used to identify ligand-binding pockets in the AlphaFold structure. AutoDockTools70 (link) was used to convert the AlphaFold prediction to PDBQT format. For the ligands, DGAT2-specific inhibitor (CAS number 1469284-79-4) and DGAT1-specific inhibitor (CAS number 942999-61-3) were also prepared in PDBQT format using AutoDockTools. AutoDock Vina71 (link) was run with an exhaustiveness parameter of 32, a seed of 0 and a docking search space of 25 × 25 × 25 Å3 centred at the point identified by P2Rank.
For identifying the most similar structure to wolframin, TM-align42 (link) was used to compare against all PDB chains (downloaded 15 February 2021) with our prediction as the reference. This returned 3F1Z with a TM-score of 0.472.
For docking against DGAT2, P2Rank65 (link) was used to identify ligand-binding pockets in the AlphaFold structure. AutoDockTools70 (link) was used to convert the AlphaFold prediction to PDBQT format. For the ligands, DGAT2-specific inhibitor (CAS number 1469284-79-4) and DGAT1-specific inhibitor (CAS number 942999-61-3) were also prepared in PDBQT format using AutoDockTools. AutoDock Vina71 (link) was run with an exhaustiveness parameter of 32, a seed of 0 and a docking search space of 25 × 25 × 25 Å3 centred at the point identified by P2Rank.
For identifying the most similar structure to wolframin, TM-align42 (link) was used to compare against all PDB chains (downloaded 15 February 2021) with our prediction as the reference. This returned 3F1Z with a TM-score of 0.472.
Corresponding organizations : DeepMind (United Kingdom), European Bioinformatics Institute
2
Optimization of Ligand-Receptor Complexes
+ Expand
Corresponding organizations : University of California, San Diego, Howard Hughes Medical Institute
3
Molecular Docking Protocol for Drug Discovery
Intermediary steps, such as pdbqt files for protein and ligands preparation and grid box creation were completed using Graphical User Interface program AutoDock Tools (ADT). ADT assigned polar hydrogens, united atom Kollman charges, solvation parameters and fragmental volumes to the protein. AutoDock saved the prepared file in PDBQT format. AutoGrid was used for the preparation of the grid map using a grid box. The grid size was set to 60 × 60 × 60 xyz points with grid spacing of 0.375 Å and grid center was designated at dimensions (x, y, and z): -1.095, -1.554 and 3.894. A scoring grid is calculated from the ligand structure to minimize the computation time. AutoDock/Vina was employed for docking using protein and ligand information along with grid box properties in the configuration file. AutoDock/Vina employs iterated local search global optimizer [34 ,35 ]. During the docking procedure, both the protein and ligands are considered as rigid. The results less than 1.0 Å in positional root-mean-square deviation (RMSD) was clustered together and represented by the result with the most favorable free energy of binding. The pose with lowest energy of binding or binding affinity was extracted and aligned with receptor structure for further analysis.
Corresponding organizations : Quaid-i-Azam University
4
In Silico Inositol Phosphate Docking
Docking studies were carried out with AutoDock4 (ref. 64 (link)), utilizing the AutoDockTools 1.5.6 GUI. Non-polar hydrogens and Gasteiger atomic charges were added to the HDAC3:SMRT DAD atomic coordinates (PDB ID: 4A69) in AutoDockTools. The inositol phosphate-binding site was as defined in ref. 2 (link). Probes were calculated at every 0.375 Å grid position of a grid box (box size x, y, z=21.406, 50.64, 23.036 Å, respectively), centred upon the inositol ring. The docking of the inositol phosphates was run using the Lamarckian genetic algorithm in AutoDock4. Other parameters were set to the default values.
Corresponding organizations : Institute of Structural and Molecular Biology, University of Leicester, University of Bath, University of Oxford
5
Identifying SARS-CoV-2 Main Protease Inhibitors
To identify the compounds with the favorable interaction with the main protease of SARS-CoV-2, 1615 FDA-approved and 4266 world approved drugs were screened with molecular docking simulations over the binding pocket of the main protease of SARS-CoV-2. The newly released crystal structure of SARS-CoV-2 main protease was retrieved from protein data bank (www.rcsb.org ) with PDB ID: 6LU7 [13 ]. AutoDockTools (ADT, Ver.1.5.6) [14 (link)] was used for preparing the input files and analyzing the result. For the preparation of protein input files, all water molecules, ligands, and ions were removed from the PDB file. Then polar hydrogens were added and the Kollman-united charge was used to calculate the partial atomic charge and the prepared file was saved in pdbqt format to use in the following steps.
3D structures of FDA and world approved drugs were downloaded from the ZINC database [15 ] in structure-data file (SDF) format which contains a total of 5881 compounds. Then OpenBabel (version 2.3.1) [16 ] was used to convert SDF to PDB format. Rotatable bonds and Gasteiger-Marsili charges were assigned to all ligands and saved in pdbqt for further docking process using AutoDock 4.2. A 50 × 50 × 50 Å (x, y, and z) grid box was centered on the protease binding pocket with 0.375 nm spacing for each dimension. AutoGrid 4.2 was used to prepare grid maps. Docking parameters were set as follows: the number of Lamarckian job = 40, initial population = 150, the maximum number of energy evaluation = 2.5 × 105, other parameters were set in their default value, and finally, docking was performed by AutoDock 4.2.
All docking results were sorted from the lowest to highest of the docking score. Docking procedures were done automatically by scripts written in-house. Also, docking validation was carried out using previously published methods [17 (link)] with re-docking of the co-crystal structure as an inhibitor in the main protease of SARS-CoV-2 with the above-mentioned parameters and values. Visualization of docking results has been done by Discovery Studio visualizer version 17.2 [18 ] and PyMol version 1.1evel [19 ]. The best complexes with the lowest docking score were used for further investigation as input files for molecular dynamics simulation.
3D structures of FDA and world approved drugs were downloaded from the ZINC database [15 ] in structure-data file (SDF) format which contains a total of 5881 compounds. Then OpenBabel (version 2.3.1) [16 ] was used to convert SDF to PDB format. Rotatable bonds and Gasteiger-Marsili charges were assigned to all ligands and saved in pdbqt for further docking process using AutoDock 4.2. A 50 × 50 × 50 Å (x, y, and z) grid box was centered on the protease binding pocket with 0.375 nm spacing for each dimension. AutoGrid 4.2 was used to prepare grid maps. Docking parameters were set as follows: the number of Lamarckian job = 40, initial population = 150, the maximum number of energy evaluation = 2.5 × 105, other parameters were set in their default value, and finally, docking was performed by AutoDock 4.2.
All docking results were sorted from the lowest to highest of the docking score. Docking procedures were done automatically by scripts written in-house. Also, docking validation was carried out using previously published methods [17 (link)] with re-docking of the co-crystal structure as an inhibitor in the main protease of SARS-CoV-2 with the above-mentioned parameters and values. Visualization of docking results has been done by Discovery Studio visualizer version 17.2 [18 ] and PyMol version 1.1evel [19 ]. The best complexes with the lowest docking score were used for further investigation as input files for molecular dynamics simulation.
Corresponding organizations : Tehran University of Medical Sciences
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