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Database fingerprint (DFP): an approach to represent molecular databases

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
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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1 news outlet
blogs
2 blogs
twitter
10 X users
facebook
1 Facebook page

Citations

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51 Dimensions

Readers on

mendeley
173 Mendeley
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Title
Database fingerprint (DFP): an approach to represent molecular databases
Published in
Journal of Cheminformatics, February 2017
DOI 10.1186/s13321-017-0195-1
Pubmed ID
Authors

Eli Fernández-de Gortari, César R. García-Jacas, Karina Martinez-Mayorga, José L. Medina-Franco

Abstract

Molecular fingerprints are widely used in several areas of chemoinformatics including diversity analysis and similarity searching. The fingerprint-based analysis of chemical libraries, in particular of large collections, usually requires the molecular representation of each compound in the library that may lead to issues of storage space and redundant calculations. In fact, information redundancy is inherent to the data, resulting on binary digit positions in the fingerprint without significant information. Herein is proposed a general approach to represent an entire compound library with a single binary fingerprint. The development of the database fingerprint (DFP) is illustrated first using a short fingerprint (MACCS keys) for 10 data sets of general interest in chemistry. The application of the DFP is further shown with PubChem fingerprints for the data sets used in the primary example but with a larger number of compounds, up to 25,000 molecules. The performance of DFP were studied through differential Shannon entropy, k-mean clustering, and DFP/Tanimoto similarity. The DFP is designed to capture key information of the compound collection and can be used to compare and assess the diversity of molecular libraries. This Preliminary Communication shows the potential of the novel fingerprint to conduct inter-library relationships. A major future goal is to apply the DFP for virtual screening and developing DFP for other data sets based on several different type of fingerprints.Graphical AbstractDatabase fingerprint captures the key information of molecular databases to perform chemical space characterization and virtual screening.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Unknown 172 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 47 27%
Student > Master 25 14%
Student > Ph. D. Student 22 13%
Researcher 13 8%
Other 9 5%
Other 24 14%
Unknown 33 19%
Readers by discipline Count As %
Chemistry 64 37%
Biochemistry, Genetics and Molecular Biology 22 13%
Computer Science 16 9%
Pharmacology, Toxicology and Pharmaceutical Science 9 5%
Agricultural and Biological Sciences 7 4%
Other 24 14%
Unknown 31 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 31 January 2022.
All research outputs
#1,369,059
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#89
of 891 outputs
Outputs of similar age
#30,315
of 427,429 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 23 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done particularly well, scoring higher than 90% 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 427,429 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.