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Target enhanced 2D similarity search by using explicit biological activity annotations and profiles

Overview of attention for article published in Journal of Cheminformatics, November 2015
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Title
Target enhanced 2D similarity search by using explicit biological activity annotations and profiles
Published in
Journal of Cheminformatics, November 2015
DOI 10.1186/s13321-015-0103-5
Pubmed ID
Authors

Xiang Yu, Lewis Y. Geer, Lianyi Han, Stephen H. Bryant

Abstract

The enriched biological activity information of compounds in large and freely-accessible chemical databases like the PubChem Bioassay Database has become a powerful research resource for the scientific research community. Currently, 2D fingerprint based conventional similarity search (CSS) is the most common widely used approach for database screening, but it does not typically incorporate the relative importance of fingerprint bits to biological activity. In this study, a large-scale similarity search investigation has been carried out on 208 well-defined compound activity classes extracted from PubChem Bioassay Database. An analysis was performed to compare the search performance of three types of 2D similarity search approaches: 2D fingerprint based conventional similarity search approach (CSS), iterative similarity search approach with multiple active compounds as references (ISS), and fingerprint based iterative similarity search with classification (ISC), which can be regarded as the combination of iterative similarity search with active references and a reversed iterative similarity search with inactive references. Compared to the search results returned by CSS, ISS improves recall but not precision. Although ISC causes the false rejection of active hits, it improves the precision with statistical significance, and outperforms both ISS and CSS. In a second part of this study, we introduce the profile concept into the three types of searches. We find that the profile based non-iterative search can significantly improve the search performance by increasing the recall rate. We also find that profile based ISS (PBISS) and profile based ISC (PBISC) significantly decreases ISS search time without sacrificing search performance. On the basis of our large-scale investigation directed against a wide spectrum of pharmaceutical targets, we conclude that ISC and ISS searches perform better than 2D fingerprint similarity searching and that profile based versions of these algorithms do nearly as well in less time. We also suggest that the profile version of the iterative similarity searches are both better performing and potentially quicker than the standard algorithm.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 4%
United Kingdom 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 22%
Student > Master 4 15%
Student > Doctoral Student 4 15%
Professor > Associate Professor 3 11%
Professor 2 7%
Other 8 30%
Readers by discipline Count As %
Chemistry 11 41%
Computer Science 7 26%
Agricultural and Biological Sciences 4 15%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Unspecified 2 7%
Other 1 4%

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 25 November 2015.
All research outputs
#10,467,027
of 13,132,528 outputs
Outputs from Journal of Cheminformatics
#497
of 529 outputs
Outputs of similar age
#240,468
of 353,787 outputs
Outputs of similar age from Journal of Cheminformatics
#61
of 69 outputs
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We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.