↓ Skip to main content

AutoClickChem: Click Chemistry in Silico

Overview of attention for article published in PLoS Computational Biology, March 2012
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
112 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
AutoClickChem: Click Chemistry in Silico
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002397
Pubmed ID
Authors

Jacob D. Durrant, J. Andrew McCammon

Abstract

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 2 2%
United States 2 2%
India 1 <1%
France 1 <1%
Norway 1 <1%
Poland 1 <1%
Unknown 104 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 31%
Student > Ph. D. Student 19 17%
Student > Bachelor 15 13%
Student > Doctoral Student 9 8%
Student > Master 8 7%
Other 20 18%
Unknown 6 5%
Readers by discipline Count As %
Chemistry 34 30%
Agricultural and Biological Sciences 32 29%
Biochemistry, Genetics and Molecular Biology 12 11%
Computer Science 9 8%
Physics and Astronomy 4 4%
Other 12 11%
Unknown 9 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 March 2012.
All research outputs
#21,011,157
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#8,282
of 9,043 outputs
Outputs of similar age
#133,265
of 169,867 outputs
Outputs of similar age from PLoS Computational Biology
#95
of 111 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 4th percentile – i.e., 4% 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 169,867 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.