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The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data

Overview of attention for article published in Journal of Cheminformatics, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

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14 tweeters
googleplus
1 Google+ user

Citations

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28 Dimensions

Readers on

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66 Mendeley
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Title
The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data
Published in
Journal of Cheminformatics, February 2017
DOI 10.1186/s13321-017-0199-x
Pubmed ID
Authors

Mahendra Awale, Jean-Louis Reymond

Abstract

Several web-based tools have been reported recently which predict the possible targets of a small molecule by similarity to compounds of known bioactivity using molecular fingerprints (fps), however predictions in each case rely on similarities computed from only one or two fps. Considering that structural similarity and therefore the predicted targets strongly depend on the method used for comparison, it would be highly desirable to predict targets using a broader set of fps simultaneously. Herein, we present the polypharmacology browser (PPB), a web-based platform which predicts possible targets for small molecules by searching for nearest neighbors using ten different fps describing composition, substructures, molecular shape and pharmacophores. PPB searches through 4613 groups of at least 10 same target annotated bioactive molecules from ChEMBL and returns a list of predicted targets ranked by consensus voting scheme and p value. A validation study across 670 drugs with up to 20 targets showed that combining the predictions from all 10 fps gives the best results, with on average 50% of the known targets of a drug being correctly predicted with a hit rate of 25%. Furthermore, when profiling a new inhibitor of the calcium channel TRPV6 against 24 targets taken from a safety screen panel, we observed inhibition in 5 out of 5 targets predicted by PPB and in 7 out of 18 targets not predicted by PPB. The rate of correct (5/12) and incorrect (0/12) predictions for this compound by PPB was comparable to that of other web-based prediction tools. PPB offers a versatile platform for target prediction based on multi-fingerprint comparisons, and is freely accessible at www.gdb.unibe.ch as a valuable support for drug discovery.Graphical abstract.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
Germany 2 3%
Brazil 1 2%
Unknown 61 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 29%
Student > Ph. D. Student 14 21%
Student > Bachelor 8 12%
Student > Master 8 12%
Other 6 9%
Other 5 8%
Unknown 6 9%
Readers by discipline Count As %
Chemistry 23 35%
Agricultural and Biological Sciences 10 15%
Pharmacology, Toxicology and Pharmaceutical Science 10 15%
Biochemistry, Genetics and Molecular Biology 9 14%
Computer Science 4 6%
Other 3 5%
Unknown 7 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 April 2019.
All research outputs
#2,191,190
of 14,085,376 outputs
Outputs from Journal of Cheminformatics
#236
of 570 outputs
Outputs of similar age
#57,779
of 258,748 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 14,085,376 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 570 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 58% 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 258,748 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them