Evidence map›Paper›PMID 35759413›Full record

ArticleJournal of chemical information and modeling2022

SEEKR2: Versatile Multiscale Milestoning Utilizing the OpenMM Molecular Dynamics Engine.

Lane W Votapka, Andrew M Stokely, Anupam A Ojha, Rommie E Amaro

Open access · hybridAbstract read
In one paragraph

Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 41 citations in OpenAlex.

  1. Article
  2. Article
  3. Prediction of αBiophysical journal · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Toward Grid-Based Models for Molecular Association.Journal of chemical theory and computation · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. OneOPES, a Combined Enhanced Sampling Method to Rule Them All.Journal of chemical theory and computation · 2023
    Article
  18. Flexible Topology: A Dynamic Model of a Continuous Chemical Space.Journal of chemical theory and computation · 2023
    Article
  19. Predicting Biomolecular Binding Kinetics: A Review.Journal of chemical theory and computation · 2023
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Lane W VotapkaUniversity of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States.
Andrew M StokelyUniversity of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States.ORCID 0000-0002-3750-5323
Anupam A OjhaUniversity of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States.ORCID 0000-0001-6588-3092
Rommie E AmaroUniversity of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States.ORCID 0000-0002-9275-9553
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present SEEKR2 (simulation-enabled estimation of kinetic rates version 2)─the latest iteration in the family of SEEKR programs for using multiscale simulation methods to computationally estimate the kinetics and thermodynamics of molecular processes, in particular, ligand-receptor binding. SEEKR2 generates equivalent, or improved, results compared to the earlier versions of SEEKR but with significant increases in speed and capabilities. SEEKR2 has also been built with greater ease of usability and with extensible features to enable future expansions of the method. Now, in addition to supporting simulations using NAMD, calculations may be run with the fast and extensible OpenMM simulation engine. The Brownian dynamics portion of the calculation has also been upgraded to Browndye 2. Furthermore, this version of SEEKR supports hydrogen mass repartitioning, which significantly reduces computational cost, while showing little, if any, loss of accuracy in the predicted kinetics.

Indexed as

Molecular Dynamics SimulationKineticsLigandsProtein BindingThermodynamicsLigands

Identifiers

PMID35759413
PMCPMC9277580
OpenAlexW4283584533

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.