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The evolution of drug design at Merck Research Laboratories

Overview of attention for article published in Perspectives in Drug Discovery and Design, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
1 blog
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9 X users
googleplus
1 Google+ user

Citations

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15 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
1 CiteULike
Title
The evolution of drug design at Merck Research Laboratories
Published in
Perspectives in Drug Discovery and Design, November 2016
DOI 10.1007/s10822-016-9993-1
Pubmed ID
Authors

Frank K. Brown, Edward C. Sherer, Scott A. Johnson, M. Katharine Holloway, Bradley S. Sherborne

Abstract

On October 5, 1981, Fortune magazine published a cover article entitled the "Next Industrial Revolution: Designing Drugs by Computer at Merck". With a 40+ year investment, we have been in the drug design business longer than most. During its history, the Merck drug design group has had several names, but it has always been in the "design" business, with the ultimate goal to provide an actionable hypothesis that could be tested experimentally. Often the result was a small molecule but it could just as easily be a peptide, biologic, predictive model, reaction, process, etc. To this end, the concept of design is now front and center in all aspects of discovery, safety assessment and early clinical development. At present, the Merck design group includes computational chemistry, protein structure determination, and cheminformatics. By bringing these groups together under one umbrella, we were able to align activities and capabilities across multiple research sites and departments. This alignment from 2010 to 2016 resulted in an 80% expansion in the size of the department, reflecting the increase in impact due to a significant emphasis across the organization to "design first" along the entire drug discovery path from lead identification (LID) to first in human (FIH) dosing. One of the major advantages of this alignment has been the ability to access all of the data and create an adaptive approach to the overall LID to FIH pathway for any modality, significantly increasing the quality of candidates and their probability of success. In this perspective, we will discuss how we crafted a new strategy, defined the appropriate phenotype for group members, developed the right skillsets, and identified metrics for success in order to drive continuous improvement. We will not focus on the tactical implementation, only giving specific examples as appropriate.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United States 1 1%
Unknown 91 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 28%
Student > Bachelor 11 12%
Student > Master 10 11%
Student > Ph. D. Student 7 7%
Professor > Associate Professor 6 6%
Other 14 15%
Unknown 20 21%
Readers by discipline Count As %
Chemistry 31 33%
Pharmacology, Toxicology and Pharmaceutical Science 12 13%
Biochemistry, Genetics and Molecular Biology 7 7%
Agricultural and Biological Sciences 7 7%
Computer Science 6 6%
Other 8 9%
Unknown 23 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 13 November 2020.
All research outputs
#2,752,160
of 25,727,480 outputs
Outputs from Perspectives in Drug Discovery and Design
#74
of 955 outputs
Outputs of similar age
#50,959
of 417,623 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#3
of 9 outputs
Altmetric has tracked 25,727,480 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 955 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 particularly well, scoring higher than 92% 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 417,623 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 87% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.