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rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids

Overview of attention for article published in PLoS Computational Biology, April 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

blogs
2 blogs
twitter
19 X users
wikipedia
1 Wikipedia page

Citations

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405 Dimensions

Readers on

mendeley
529 Mendeley
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Title
rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids
Published in
PLoS Computational Biology, April 2014
DOI 10.1371/journal.pcbi.1003571
Pubmed ID
Authors

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

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/

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 529 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 4 <1%
Spain 4 <1%
United States 3 <1%
Germany 2 <1%
Italy 2 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Brazil 1 <1%
Finland 1 <1%
Other 2 <1%
Unknown 508 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 109 21%
Researcher 101 19%
Student > Master 48 9%
Student > Bachelor 42 8%
Student > Doctoral Student 37 7%
Other 79 15%
Unknown 113 21%
Readers by discipline Count As %
Chemistry 110 21%
Biochemistry, Genetics and Molecular Biology 104 20%
Agricultural and Biological Sciences 74 14%
Computer Science 32 6%
Pharmacology, Toxicology and Pharmaceutical Science 29 5%
Other 43 8%
Unknown 137 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 September 2023.
All research outputs
#1,472,902
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#1,235
of 8,964 outputs
Outputs of similar age
#14,525
of 241,476 outputs
Outputs of similar age from PLoS Computational Biology
#21
of 152 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 241,476 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.