| Title |
Enabling drug discovery project decisions with integrated computational chemistry and informatics
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, October 2016
|
| DOI | 10.1007/s10822-016-9988-y |
| Pubmed ID | |
| Authors |
Vickie Tsui, Daniel F. Ortwine, Jeffrey M. Blaney |
| Abstract |
Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training. |
X Demographics
The data shown below were collected from the profiles of 13 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 | 6 | 46% |
| United Kingdom | 3 | 23% |
| Ireland | 1 | 8% |
| Chile | 1 | 8% |
| Unknown | 2 | 15% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 54% |
| Scientists | 6 | 46% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 33 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 38% |
| Student > Ph. D. Student | 5 | 15% |
| Student > Bachelor | 4 | 12% |
| Professor | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 12 | 35% |
| Agricultural and Biological Sciences | 4 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 9% |
| Computer Science | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Other | 2 | 6% |
| Unknown | 7 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 November 2023.
All research outputs
#3,388,407
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#110
of 949 outputs
Outputs of similar age
#54,544
of 318,846 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#6
of 14 outputs
Altmetric has tracked 25,457,858 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 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 88% 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 318,846 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 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.