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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting the protein targets for athletic performance-enhancing substances
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2013
|
| DOI | 10.1186/1758-2946-5-31 |
| Pubmed ID | |
| Authors |
Lazaros Mavridis, John BO Mitchell |
| Abstract |
The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. It would be ideal to be able to identify all substances that have one or more performance-enhancing pharmacological actions in an automated, fast and cost effective way. Here, we use experimental data derived from the ChEMBL database (~7,000,000 activity records for 1,300,000 compounds) to build a database model that takes into account both structure and experimental information, and use this database to predict both on-target and off-target interactions between these molecules and targets relevant to doping in sport. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 38 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 8 | 20% |
| Student > Master | 6 | 15% |
| Student > Ph. D. Student | 6 | 15% |
| Professor > Associate Professor | 4 | 10% |
| Researcher | 3 | 8% |
| Other | 4 | 10% |
| Unknown | 9 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 9 | 23% |
| Computer Science | 4 | 10% |
| Engineering | 3 | 8% |
| Medicine and Dentistry | 3 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 8% |
| Other | 8 | 20% |
| Unknown | 10 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 16 February 2014.
All research outputs
#12,586,202
of 22,712,476 outputs
Outputs from Journal of Cheminformatics
#601
of 828 outputs
Outputs of similar age
#96,870
of 196,319 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 12 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 196,319 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.