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Mendeley readers
Attention Score in Context
| Title |
Natural product-likeness score revisited: an open-source, open-data implementation
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-106 |
| Pubmed ID | |
| Authors |
Kalai Vanii Jayaseelan, Pablo Moreno, Andreas Truszkowski, Peter Ertl, Christoph Steinbeck |
| Abstract |
Natural product-likeness of a molecule, i.e. similarity of this molecule to the structure space covered by natural products, is a useful criterion in screening compound libraries and in designing new lead compounds. A closed source implementation of a natural product-likeness score, that finds its application in virtual screening, library design and compound selection, has been previously reported by one of us. In this note, we report an open-source and open-data re-implementation of this scoring system, illustrate its efficiency in ranking small molecules for natural product likeness and discuss its potential applications. |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| Portugal | 1 | <1% |
| Colombia | 1 | <1% |
| South Africa | 1 | <1% |
| United Kingdom | 1 | <1% |
| China | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 107 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 22% |
| Researcher | 19 | 17% |
| Student > Master | 15 | 13% |
| Student > Postgraduate | 8 | 7% |
| Other | 8 | 7% |
| Other | 16 | 14% |
| Unknown | 24 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 26 | 23% |
| Agricultural and Biological Sciences | 20 | 17% |
| Computer Science | 15 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 7% |
| Biochemistry, Genetics and Molecular Biology | 7 | 6% |
| Other | 12 | 10% |
| Unknown | 27 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 03 June 2022.
All research outputs
#3,234,799
of 22,665,794 outputs
Outputs from BMC Bioinformatics
#1,205
of 7,247 outputs
Outputs of similar age
#22,189
of 163,617 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 104 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 83% 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 163,617 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 86% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.