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GPURFSCREEN: a GPU based virtual screening tool using random forest classifier

Overview of attention for article published in Journal of Cheminformatics, March 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

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3 tweeters

Citations

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11 Dimensions

Readers on

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24 Mendeley
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Title
GPURFSCREEN: a GPU based virtual screening tool using random forest classifier
Published in
Journal of Cheminformatics, March 2016
DOI 10.1186/s13321-016-0124-8
Pubmed ID
Authors

P. B. Jayaraj, Mathias K. Ajay, M. Nufail, G. Gopakumar, U. C. A. Jaleel

Abstract

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 21%
Other 3 13%
Student > Ph. D. Student 3 13%
Researcher 3 13%
Professor 2 8%
Other 3 13%
Unknown 5 21%
Readers by discipline Count As %
Chemistry 6 25%
Computer Science 5 21%
Agricultural and Biological Sciences 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Mathematics 1 4%
Other 3 13%
Unknown 6 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 March 2016.
All research outputs
#6,987,046
of 13,322,716 outputs
Outputs from Journal of Cheminformatics
#373
of 535 outputs
Outputs of similar age
#102,746
of 268,216 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 2 outputs
Altmetric has tracked 13,322,716 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 535 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 268,216 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.