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Historeceptomic Fingerprints for Drug-Like Compounds

Overview of attention for article published in Frontiers in Physiology, December 2015
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
Historeceptomic Fingerprints for Drug-Like Compounds
Published in
Frontiers in Physiology, December 2015
DOI 10.3389/fphys.2015.00371
Pubmed ID
Authors

Evgeny Shmelkov, Arsen Grigoryan, James Swetnam, Junyang Xin, Doreen Tivon, Sergey V. Shmelkov, Timothy Cardozo

Abstract

Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.

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X Demographics

The data shown below were collected from the profiles of 7 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 4 31%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Unknown 3 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 31%
Chemistry 3 23%
Agricultural and Biological Sciences 1 8%
Medicine and Dentistry 1 8%
Engineering 1 8%
Other 0 0%
Unknown 3 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 May 2016.
All research outputs
#7,157,973
of 22,835,198 outputs
Outputs from Frontiers in Physiology
#3,439
of 13,604 outputs
Outputs of similar age
#114,509
of 388,246 outputs
Outputs of similar age from Frontiers in Physiology
#46
of 130 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 13,604 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 74% 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 388,246 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 70% of its contemporaries.
We're also able to compare this research output to 130 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.