Evidence mapPaperPMID 41003729Full record

ReviewBiological cybernetics2025

Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.

Bita Farhadi, Mahnoush Beygisangchin, Nakisa Ghamari, Jaroon Jakmunee, Tang Tang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Biological cybernetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Frontiers in cellular and infection microbiology · 2026
    Article
  7. 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

5 authors.

Bita Farhadi *EIT Data Science and Communication College, Zhejiang Yuexiu University, Shaoxing, China. 20242705@zyufl.edu.cn.
Mahnoush BeygisangchinResearch Laboratory for Analytical Instrument and Electrochemistry Innovation, Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand. m.beygi2300@gmail.com.
Nakisa Ghamari *Department of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, 6714967346, I.R. of Iran.
Jaroon JakmuneeResearch Laboratory for Analytical Instrument and Electrochemistry Innovation, Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.
Tang TangEIT Data Science and Communication College, Zhejiang Yuexiu University, Shaoxing, China.

Funding

Chiang Mai University and Center of Excellence for Innovation in Chemistry. Partially supported by CMU Proactive Researcher Scheme (2024), Chiang Mai University Fundamental Fund 2025
6 · The paper itself

Abstract

Molecular dynamics (MD) simulations have emerged as a powerful and extensively employed tool in biomedical research, offering insights into intricate biomolecular processes such as structural flexibility and molecular interactions, and playing a pivotal role in the development of therapeutic approaches. Although MD techniques are applied to a variety of biomolecules including DNA, RNA, proteins, and their assemblies, this review focuses specifically on the role of MD in elucidating protein behavior and their interactions with inhibitors across different disease contexts. The selection of an appropriate force field is essential, as it greatly influences the reliability of simulation outcomes. Widely adopted MD software packages such as GROMACS, DESMOND, and AMBER leverage rigorously tested force fields and have shown consistent performance across diverse biological applications. Despite current successes, challenges remain in narrowing the gap between computational models and actual cellular conditions. The integration of machine learning and deep learning technologies is expected to accelerate progress in this evolving field.

Indexed as

Chemistry, PharmaceuticalDrug DevelopmentMolecular Dynamics SimulationProteinsHumansProteinsBiomoleculesDrug designInhibitor developmentMolecular dynamic simulationsProteins

Identifiers

What Socratic holds

Textmetadata
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 Socratic graph.