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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space
|
|---|---|
| Published in |
Journal of Cheminformatics, February 2012
|
| DOI | 10.1186/1758-2946-4-4 |
| Pubmed ID | |
| Authors |
Narender Singh, Hongmao Sun, Sidhartha Chaudhury, Mohamed Diwan M AbdulHameed, Anders Wallqvist, Gregory Tawa |
| Abstract |
The current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Czechia | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 64 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 25% |
| Student > Ph. D. Student | 11 | 16% |
| Student > Bachelor | 8 | 12% |
| Student > Master | 8 | 12% |
| Professor > Associate Professor | 3 | 4% |
| Other | 6 | 9% |
| Unknown | 14 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 21 | 31% |
| Agricultural and Biological Sciences | 8 | 12% |
| Medicine and Dentistry | 4 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 4% |
| Engineering | 3 | 4% |
| Other | 9 | 13% |
| Unknown | 19 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 01 March 2012.
All research outputs
#3,902,793
of 22,662,201 outputs
Outputs from Journal of Cheminformatics
#377
of 825 outputs
Outputs of similar age
#34,613
of 247,685 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 7 outputs
Altmetric has tracked 22,662,201 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 825 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has gotten more attention than average, scoring higher than 53% 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 247,685 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 7 others from the same source and published within six weeks on either side of this one.