Evidence map›Paper›PMID 37676600›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Accelerating Molecular Dynamics Simulations for Drug Discovery.

Kushal Koirala, Keya Joshi, Victor Adediwura, Jinan Wang, Hung Do, Yinglong Miao

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

6 authors.

Kushal KoiralaComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
Keya JoshiComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
Victor AdediwuraComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
Jinan WangComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
Hung DoComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
Yinglong MiaoComputational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA. miao@ku.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of ligand binding thermodynamics and kinetics is crucial in drug design. However, it remains challenging for conventional molecular dynamics (MD) simulations due to sampling issues. Gaussian accelerated MD (GaMD) is an enhanced sampling method that adds a harmonic boost to overcome energy barriers, which has demonstrated significant benefits in exploring protein-ligand interactions. Especially, the ligand GaMD (LiGaMD) applies a selective boost potential to the ligand nonbonded potential energy, significantly improving sampling for ligand binding and dissociation. Furthermore, a selective boost potential is applied to the potential of both ligand and protein residues around binding pocket in LiGaMD2 to further increase the sampling of protein-ligand interaction. LiGaMD and LiGaMD2 simulations could capture repetitive ligand binding and unbinding events within microsecond simulations, allowing to simultaneously characterize ligand binding thermodynamics and kinetics, which is expected to greatly facilitate drug design. In this chapter, we provide a brief review of the status of LiGaMD in drug discovery and outline its usage.

Indexed as

Drug DiscoveryMolecular Dynamics SimulationDrug DesignKineticsLigandsLigandsEnhanced samplingKineticsLigand binding and unbindingLigand Gaussian accelerated molecular dynamics (LiGaMD)

Identifiers

PMID37676600

What OpenQuestion holds

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Registered trials

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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.