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Mendeley readers
Attention Score in Context
| Title |
A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem
|
|---|---|
| Published in |
Journal of Cheminformatics, April 2009
|
| DOI | 10.1186/1758-2946-1-4 |
| Pubmed ID | |
| Authors |
William WL Wong, Forbes J Burkowski |
| Abstract |
The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Scientists | 1 | 33% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 58 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 25% |
| Student > Ph. D. Student | 14 | 23% |
| Student > Master | 10 | 17% |
| Student > Bachelor | 6 | 10% |
| Professor > Associate Professor | 4 | 7% |
| Other | 7 | 12% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 16 | 27% |
| Computer Science | 11 | 18% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Agricultural and Biological Sciences | 5 | 8% |
| Engineering | 5 | 8% |
| Other | 10 | 17% |
| Unknown | 8 | 13% |
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 08 November 2016.
All research outputs
#3,016,239
of 22,689,790 outputs
Outputs from Journal of Cheminformatics
#305
of 828 outputs
Outputs of similar age
#10,728
of 92,762 outputs
Outputs of similar age from Journal of Cheminformatics
#5
of 5 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 11.0. This one has gotten more attention than average, scoring higher than 63% 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 92,762 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 88% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.