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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PharmDock: a pharmacophore-based docking program
|
|---|---|
| Published in |
Journal of Cheminformatics, April 2014
|
| DOI | 10.1186/1758-2946-6-14 |
| Pubmed ID | |
| Authors |
Bingjie Hu, Markus A Lill |
| Abstract |
Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. |
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 % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 2% |
| Cuba | 1 | <1% |
| Czechia | 1 | <1% |
| Argentina | 1 | <1% |
| China | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 112 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 21% |
| Researcher | 23 | 19% |
| Student > Master | 12 | 10% |
| Student > Bachelor | 11 | 9% |
| Professor > Associate Professor | 8 | 7% |
| Other | 22 | 18% |
| Unknown | 20 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 30 | 25% |
| Biochemistry, Genetics and Molecular Biology | 17 | 14% |
| Agricultural and Biological Sciences | 17 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 14 | 12% |
| Computer Science | 8 | 7% |
| Other | 13 | 11% |
| Unknown | 22 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 27 April 2014.
All research outputs
#14,195,272
of 22,754,104 outputs
Outputs from Journal of Cheminformatics
#699
of 828 outputs
Outputs of similar age
#107,726
of 203,744 outputs
Outputs of similar age from Journal of Cheminformatics
#16
of 27 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 11.0. This one is in the 12th percentile – i.e., 12% 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 203,744 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.