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Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

blogs
1 blog
twitter
12 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
20 Mendeley
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2 CiteULike
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Title
Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses
Published in
Journal of Cheminformatics, April 2017
DOI 10.1186/s13321-017-0214-2
Pubmed ID
Authors

Stefan Senger

Abstract

Patents are an important source of information for effective decision making in drug discovery. Encouragingly, freely accessible patent-chemistry databases are now in the public domain. However, at present there is still a wide gap between relatively low coverage-high quality manually-curated data sources and high coverage data sources that use text mining and automated extraction of chemical structures. To secure much needed funding for further research and an improved infrastructure, hard evidence is required to demonstrate the significance of patent-derived information in drug discovery. Surprisingly little such evidence has been reported so far. To address this, the present study attempts to quantify the relevance of patents for formulating and substantiating hypotheses for compound-target interactions. A manually-curated set of 130 compound-target interaction pairs annotated with what are considered to be the earliest patent and publication has been produced. The analysis of this set revealed that in stark contrast to what has been reported for novel chemical structures, only about 10% of the compound-target interaction pairs could be found in publications in the scientific literature within one year of being reported in patents. The average delay across all interaction pairs is close to 4 years. In an attempt to benchmark current capabilities, it was also examined how much of the benefit of using patent-derived information can be retained when a bioannotated version of SureChEMBL is used as secondary source for the patent literature. Encouragingly, this approach found the patents in the annotated set for 72% of the compound-target interaction pairs. Similarly, the effect of using the bioactivity database ChEMBL as secondary source for the scientific literature was studied. Here, the publications from the annotated set were only found for 46% of the compound-target interaction pairs. Patent-derived information is a significant enabler for formulating compound-target interaction hypotheses even in cases where the respective interaction is later reported in the scientific literature. The findings of this study clearly highlight the significance of future investments in the development and provision of databases and tools that will allow scientists to search patent information in a comprehensive, reliable, and efficient manner.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 15%
Student > Master 3 15%
Student > Doctoral Student 2 10%
Student > Bachelor 2 10%
Student > Postgraduate 2 10%
Other 4 20%
Unknown 4 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 15%
Chemistry 3 15%
Agricultural and Biological Sciences 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Computer Science 1 5%
Other 3 15%
Unknown 7 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 05 May 2017.
All research outputs
#999,387
of 13,174,859 outputs
Outputs from Journal of Cheminformatics
#103
of 530 outputs
Outputs of similar age
#33,322
of 263,159 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,174,859 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 530 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done well, scoring higher than 80% 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 263,159 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 87% 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