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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Bright Future for Evolutionary Methods in Drug Design
|
|---|---|
| Published in |
ChemMedChem, June 2015
|
| DOI | 10.1002/cmdc.201500161 |
| Pubmed ID | |
| Authors |
Tu C. Le, David A. Winkler |
| Abstract |
Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 40 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 24% |
| Student > Ph. D. Student | 7 | 17% |
| Student > Master | 6 | 14% |
| Professor | 5 | 12% |
| Other | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 8 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 36% |
| Computer Science | 4 | 10% |
| Agricultural and Biological Sciences | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 4 | 10% |
| Unknown | 14 | 33% |
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 03 July 2016.
All research outputs
#15,519,198
of 24,590,593 outputs
Outputs from ChemMedChem
#1,995
of 3,504 outputs
Outputs of similar age
#143,289
of 271,235 outputs
Outputs of similar age from ChemMedChem
#29
of 54 outputs
Altmetric has tracked 24,590,593 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,504 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 39th percentile – i.e., 39% 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 271,235 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 54 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.