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Mendeley readers
Attention Score in Context
| Title |
Improving structural similarity based virtual screening using background knowledge
|
|---|---|
| Published in |
Journal of Cheminformatics, December 2013
|
| DOI | 10.1186/1758-2946-5-50 |
| Pubmed ID | |
| Authors |
Tobias Girschick, Lucia Puchbauer, Stefan Kramer |
| Abstract |
Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods. |
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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 3% |
| Unknown | 32 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 24% |
| Student > Bachelor | 6 | 18% |
| Researcher | 4 | 12% |
| Professor > Associate Professor | 4 | 12% |
| Student > Doctoral Student | 3 | 9% |
| Other | 6 | 18% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 7 | 21% |
| Computer Science | 7 | 21% |
| Agricultural and Biological Sciences | 5 | 15% |
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 9% |
| Other | 6 | 18% |
| Unknown | 1 | 3% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 December 2013.
All research outputs
#19,631,015
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#860
of 891 outputs
Outputs of similar age
#241,174
of 316,952 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 7 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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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.