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Accessible High-Throughput Virtual Screening Molecular Docking Software for Students and Educators

Overview of attention for article published in PLoS Computational Biology, May 2012
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Title
Accessible High-Throughput Virtual Screening Molecular Docking Software for Students and Educators
Published in
PLoS Computational Biology, May 2012
DOI 10.1371/journal.pcbi.1002499
Pubmed ID
Authors

Reed B. Jacob, Tim Andersen, Owen M. McDougal

Abstract

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms.

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The data shown below were collected from the profiles of 2 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 273 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 <1%
United States 2 <1%
Belgium 2 <1%
United Kingdom 2 <1%
Brazil 2 <1%
Colombia 1 <1%
France 1 <1%
Germany 1 <1%
Portugal 1 <1%
Other 3 1%
Unknown 256 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 19%
Researcher 48 18%
Student > Master 32 12%
Student > Bachelor 29 11%
Professor > Associate Professor 17 6%
Other 37 14%
Unknown 58 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 26%
Biochemistry, Genetics and Molecular Biology 49 18%
Chemistry 36 13%
Pharmacology, Toxicology and Pharmaceutical Science 22 8%
Medicine and Dentistry 11 4%
Other 24 9%
Unknown 61 22%