Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 572

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina

Open access
Bugnon, Marine, Röhrig, Ute F, Goullieux, Mathilde, Perez, Marta A S, Daina, Antoine, Michielin, Olivier, Zoete, Vincent·2024·Nucleic Acids Research
DOI 10.1093/nar/gkae300
Open this paper in CoChat — free account

Abstract

Drug discovery aims to identify potential therapeutic compounds capable of modulating the activity of specific biological targets. Molecular docking can efficiently support this process by predicting binding interactions between small molecules and macromolecular targets and potentially accelerating screening campaigns. SwissDock is a computational tool released in 2011 as part of the SwissDrugDesign project, providing a free web-based service for small-molecule docking after automatized preparation of ligands and targets. Here, we present the latest version of SwissDock, in which EADock DSS has been replaced by two state-of-the-art docking programs, i.e. Attracting Cavities and AutoDock Vina. AutoDock Vina provides faster docking predictions, while Attracting Cavities offers more accurate results. Ligands can be imported in various ways, including as files, SMILES notation or molecular sketches. Targets can be imported as PDB files or identified by their PDB ID. In addition, advanced search options are available both for ligands and targets, giving users automatized access to widely-used databases. The web interface has been completely redesigned for interactive submission and analysis of docking results. Moreover, we developed a user-friendly command-line access which, in addition to all options of the web site, also enables covalent ligand docking with Attracting Cavities. The new version of SwissDock is freely available at https://www.swissdock.ch/.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkae300

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 36

  1. Kuntz, 1982, A geometric approach to macromolecule-ligand interactions, J. Mol. Biol., 161, 269, 10.1016/0022-2836(82)90153-X · DOI
  2. Stanzione, 2021, Use of molecular docking computational tools in drug discovery, 273
  3. Ferreira, 2015, Molecular docking and structure-based drug design strategies, Molecules, 20, 13384, 10.3390/molecules200713384 · DOI
  4. Grosdidier, 2011, Fast docking using the CHARMM force field with EADock DSS, J. Comput. Chem., 32, 2149, 10.1002/jcc.21797 · DOI · Paper page
  5. Zoete, 2016, Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape, J. Comput. Chem., 37, 437, 10.1002/jcc.24249 · DOI
  6. Röhrig, 2023, Attracting cavities 2.0: improving the flexibility and robustness for small-molecule docking, J. Chem. Inf. Model., 63, 3925, 10.1021/acs.jcim.3c00054 · DOI
  7. Morris, 2009, AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility, J. Comput. Chem., 30, 2785, 10.1002/jcc.21256 · DOI · Paper page
  8. Eberhardt, 2021, AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings, J. Chem. Inf. Model., 61, 3891, 10.1021/acs.jcim.1c00203 · DOI
  9. Quiroga, 2016, Vinardo: a scoring function based on autodock vina improves scoring, docking, and virtual screening, PLoS One, 11, e0155183, 10.1371/journal.pone.0155183 · DOI · Paper page
  10. Kochnev, 2020, Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser, Bioinformatics, 36, 4513, 10.1093/bioinformatics/btaa579 · DOI
  11. Irwin, 2009, Automated Docking Screens: a feasibility study, J. Med. Chem., 52, 5712, 10.1021/jm9006966 · DOI · Paper page
  12. Gupta, 2007, ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes, Protein Pept. Lett., 14, 632, 10.2174/092986607781483831 · DOI
  13. Murail, 2021, SeamDock: an interactive and collaborative online docking resource to assist small compound molecular docking, Front. Mol. Biosci., 8, 716466, 10.3389/fmolb.2021.716466 · DOI
  14. Schöning-Stierand, 2022, ProteinsPlus: a comprehensive collection of web-based molecular modeling tools, Nucleic Acids Res., 50, W611, 10.1093/nar/gkac305 · DOI
  15. Grosdidier, 2011, SwissDock, a protein-small molecule docking web service based on EADock DSS, Nucleic Acids Res., 39, W270, 10.1093/nar/gkr366 · DOI · Paper page
  16. Daina, 2019, Application of the swissdrugdesign online resources in virtual screening, Int. J. Mol. Sci., 20, 4612, 10.3390/ijms20184612 · DOI
  17. Al-Khafaji, 2021, Using integrated computational approaches to identify safe and rapid treatment for SARS-CoV-2, J. Biomol. Struct. Dyn., 39, 3387
  18. Basu, 2020, Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2, Sci. Rep., 10, 17699, 10.1038/s41598-020-74715-4 · DOI
  19. Bugnon, 2023, SwissParam 2023: a modern web-based tool for efficient small molecule parametrization, J. Chem. Inf. Model., 63, 6469, 10.1021/acs.jcim.3c01053 · DOI · Paper page
  20. Bragina, 2022, The SwissSimilarity 2021 web tool: novel chemical libraries and additional methods for an enhanced ligand-based virtual screening experience, Int. J. Mol. Sci., 23, 811, 10.3390/ijms23020811 · DOI
  21. Cuozzo, 2022, SwissBioisostere 2021: updated structural, bioactivity and physicochemical data delivered by a reshaped web interface, Nucleic Acids Res., 50, D1382, 10.1093/nar/gkab1047 · DOI
  22. Hastings, 2016, ChEBI in 2016: improved services and an expanding collection of metabolites, Nucleic Acids Res., 44, D1214, 10.1093/nar/gkv1031 · DOI · Paper page
  23. Westbrook, 2015, The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank, Bioinformatics, 31, 1274, 10.1093/bioinformatics/btu789 · DOI
  24. Armstrong, 2020, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res., 48, D335
  25. Bienert, 2017, The SWISS-MODEL Repository-new features and functionality, Nucleic Acids Res., 45, D313, 10.1093/nar/gkw1132 · DOI · Paper page
  26. Daina, 2019, SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules, Nucleic Acids Res., 47, W357, 10.1093/nar/gkz382 · DOI · Paper page
  27. Goullieux, 2023, Two-step covalent docking with attracting cavities, J. Chem. Inf. Model., 63, 7847, 10.1021/acs.jcim.3c01055 · DOI
  28. Mackerell, 1998, All-atom empirical potential for molecular modeling and dynamics studies of proteins, J. Phys. Chem. B, 102, 3586, 10.1021/jp973084f · DOI · Paper page
  29. Best, 2012, Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles, J. Chem. Theory Comput., 8, 3257, 10.1021/ct300400x · DOI · Paper page
  30. Huang, 2017, CHARMM36m: an improved force field for folded and intrinsically disordered proteins, Nat. Methods, 14, 71, 10.1038/nmeth.4067 · DOI
  31. Zoete, 2011, SwissParam: a fast force field generation tool for small organic molecules, J. Comput. Chem., 32, 2359, 10.1002/jcc.21816 · DOI · Paper page
  32. Ravindranath, 2015, AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility, PLOS Comput. Biol., 11, e1004586, 10.1371/journal.pcbi.1004586 · DOI
  33. Croitoru, 2021, Additive CHARMM36 force field for nonstandard amino acids, J. Chem. Theory Comput., 17, 3554, 10.1021/acs.jctc.1c00254 · DOI
  34. Feng, 2004, Ligand Depot: a data warehouse for ligands bound to macromolecules, Bioinforma. Oxf. Engl., 20, 2153, 10.1093/bioinformatics/bth214 · DOI
  35. Pettersen, 2004, UCSF Chimera–a visualization system for exploratory research and analysis, J. Comput. Chem., 25, 1605, 10.1002/jcc.20084 · DOI · Paper page
  36. Goddard, 2018, UCSF ChimeraX: meeting modern challenges in visualization and analysis, Protein Sci. Publ. Protein Soc., 27, 14, 10.1002/pro.3235 · DOI · Paper page

Cited by

Metadata

Authors
Bugnon, M., Röhrig, U. F., Goullieux, M., Perez, M. A. S., Daina, A., Michielin, O., & Zoete, V.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-04-30
Volume
52
Issue
W1
Pages
W324-W332
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkae300
OpenAlex
W4396519787
Cited by
572
References
7
FWCI
112.13
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
SIB

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.