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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery
|
|---|---|
| Published in |
Pharmaceutical Research, October 2013
|
| DOI | 10.1007/s11095-013-1172-7 |
| Pubmed ID | |
| Authors |
Sean Ekins, Joel S. Freundlich, Judith V. Hobrath, E. Lucile White, Robert C. Reynolds |
| Abstract |
Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. |
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 % |
|---|---|---|
| United States | 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 3% |
| Indonesia | 1 | 1% |
| United States | 1 | 1% |
| Italy | 1 | 1% |
| Unknown | 70 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 17% |
| Researcher | 12 | 16% |
| Student > Master | 10 | 13% |
| Student > Bachelor | 5 | 7% |
| Student > Doctoral Student | 4 | 5% |
| Other | 13 | 17% |
| Unknown | 18 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 11 | 15% |
| Chemistry | 9 | 12% |
| Agricultural and Biological Sciences | 6 | 8% |
| Medicine and Dentistry | 4 | 5% |
| Other | 13 | 17% |
| Unknown | 21 | 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 21 February 2020.
All research outputs
#3,925,789
of 22,727,570 outputs
Outputs from Pharmaceutical Research
#395
of 2,852 outputs
Outputs of similar age
#37,039
of 211,883 outputs
Outputs of similar age from Pharmaceutical Research
#3
of 32 outputs
Altmetric has tracked 22,727,570 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 2,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 86% 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 211,883 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 82% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.