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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 (91st percentile)

Mentioned by

1 news outlet
1 blog
10 tweeters
1 Facebook page


11 Dimensions

Readers on

89 Mendeley
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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

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


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.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 88 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 37 42%
Student > Master 10 11%
Student > Ph. D. Student 9 10%
Researcher 8 9%
Other 8 9%
Other 11 12%
Unknown 6 7%
Readers by discipline Count As %
Chemistry 52 58%
Pharmacology, Toxicology and Pharmaceutical Science 7 8%
Biochemistry, Genetics and Molecular Biology 7 8%
Agricultural and Biological Sciences 6 7%
Computer Science 4 4%
Other 6 7%
Unknown 7 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 November 2019.
All research outputs
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Outputs of similar age
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Outputs of similar age from Journal of Cheminformatics
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Altmetric has tracked 14,056,542 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 569 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has done well, scoring higher than 87% 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 349,671 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 91% 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