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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

Overview of attention for article published in Perspectives in Drug Discovery and Design, October 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 (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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1 blog
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Citations

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245 Mendeley
Title
Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset
Published in
Perspectives in Drug Discovery and Design, October 2016
DOI 10.1007/s10822-016-9977-1
Pubmed ID
Authors

Michael R. Shirts, Christoph Klein, Jason M. Swails, Jian Yin, Michael K. Gilson, David L. Mobley, David A. Case, Ellen D. Zhong

Abstract

We describe our efforts to prepare common starting structures and models for the SAMPL5 blind prediction challenge. We generated the starting input files and single configuration potential energies for the host-guest in the SAMPL5 blind prediction challenge for the GROMACS, AMBER, LAMMPS, DESMOND and CHARMM molecular simulation programs. All conversions were fully automated from the originally prepared AMBER input files using a combination of the ParmEd and InterMol conversion programs. We find that the energy calculations for all molecular dynamics engines for this molecular set agree to better than 0.1 % relative absolute energy for all energy components, and in most cases an order of magnitude better, when reasonable choices are made for different cutoff parameters. However, there are some surprising sources of statistically significant differences. Most importantly, different choices of Coulomb's constant between programs are one of the largest sources of discrepancies in energies. We discuss the measures required to get good agreement in the energies for equivalent starting configurations between the simulation programs, and the energy differences that occur when simulations are run with program-specific default simulation parameter values. Finally, we discuss what was required to automate this conversion and comparison.

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The data shown below were collected from the profiles of 6 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 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 <1%
Czechia 1 <1%
Brazil 1 <1%
Unknown 242 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 27%
Researcher 45 18%
Student > Master 26 11%
Student > Doctoral Student 12 5%
Other 11 4%
Other 40 16%
Unknown 45 18%
Readers by discipline Count As %
Chemistry 79 32%
Biochemistry, Genetics and Molecular Biology 34 14%
Engineering 14 6%
Chemical Engineering 12 5%
Physics and Astronomy 10 4%
Other 41 17%
Unknown 55 22%
Attention Score in Context

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 13 June 2023.
All research outputs
#3,226,472
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#101
of 949 outputs
Outputs of similar age
#52,219
of 321,244 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#5
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 87th 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 89% 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 321,244 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 83% 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 64% of its contemporaries.