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Elucidating the druggability of the human proteome with eFindSite

Overview of attention for article published in Journal of Computer-Aided Molecular Design, March 2019
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Mentioned by

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3 tweeters

Citations

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1 Dimensions

Readers on

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12 Mendeley
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Title
Elucidating the druggability of the human proteome with eFindSite
Published in
Journal of Computer-Aided Molecular Design, March 2019
DOI 10.1007/s10822-019-00197-w
Pubmed ID
Authors

Omar Kana, Michal Brylinski

Abstract

Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite .

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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Ph. D. Student 3 25%
Student > Bachelor 2 17%
Student > Master 1 8%
Student > Doctoral Student 1 8%
Other 0 0%
Unknown 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Pharmacology, Toxicology and Pharmaceutical Science 2 17%
Chemistry 2 17%
Computer Science 2 17%
Engineering 1 8%
Other 0 0%
Unknown 2 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 March 2019.
All research outputs
#10,325,571
of 13,528,132 outputs
Outputs from Journal of Computer-Aided Molecular Design
#483
of 615 outputs
Outputs of similar age
#176,116
of 254,828 outputs
Outputs of similar age from Journal of Computer-Aided Molecular Design
#6
of 9 outputs
Altmetric has tracked 13,528,132 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 615 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 20th percentile – i.e., 20% 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 254,828 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.