Evidence map›Paper›PMID 24722481›Full record

ArticlePLoS computational biology2014

rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids.

Sergio Ruiz-Carmona, Daniel Alvarez-Garcia, Nicolas Foloppe, A Beatriz Garmendia-Doval, Szilveszter Juhos, Peter Schmidtke, Xavier Barril, Roderick E Hubbard, S David Morley

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 261 papers.

0numbers the graph read from it
0cells of the map it votes in
261citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

261 citing papers in PubMed.

  1. Review
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  3. Fragment-Based Discovery of KLK6 and KLK7 Inhibitors.Journal of chemical information and modeling · 2026
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  6. Discovery of novel anti-RSC advances · 2026
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  8. Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026
    Review
  9. Benchmarking Docking Protocols on Predicting Alternative Binding Modes.Journal of chemical information and modeling · 2026
    Article
  10. Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026
    Article
  11. Article
  12. Review
  13. Article
  14. Comparative Evaluation of Explicit Solvent Models for RNA-Ligand Docking.Journal of chemical information and modeling · 2026
    Article
  15. Article
  16. Article
  17. Interplay of RNA mFASEB bioAdvances · 2026
    Article
  18. Article
  19. Article
  20. Article

201 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sergio Ruiz-CarmonaDepartament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.
Daniel Alvarez-GarciaDepartament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.
Nicolas FoloppeVernalis (R&D) Ltd, Granta Park, Cambridge, United Kingdom.
A Beatriz Garmendia-DovalAmper Programas, Madrid, Spain.
Szilveszter JuhosOmixon Biocomputing, Budapest, Hungary.
Peter SchmidtkeDiscngine, Paris, France.
Xavier BarrilDepartament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
Roderick E HubbardVernalis (R&D) Ltd, Granta Park, Cambridge, United Kingdom; YSBL, University of York, Heslington, York, United Kingdom.
S David MorleyEnspiral Discovery Limited, Cambridge, United Kingdom; Ariana Pharma, Paris, France.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

Indexed as

Drug DiscoveryLigandsNucleic AcidsProteinsLigandsNucleic AcidsProteins

Identifiers

PMID24722481
PMCPMC3983074

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.