Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (632 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A computer simulation developed to study the motion of molecules over a period of time.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Dynamics Simulation, Molecular; Dynamics Simulations, Molecular; Simulation, Molecular Dynamics; Simulations, Molecular Dynamics; Molecular Dynamics Simulations; Molecular Dynamics; Dynamic, Molecular; Dynamics, Molecular.
Where it fits in the MeSH classification
- Broader topics
- Models, Molecular; Molecular Structure; Computer Simulation
Research landscape
PubMed indexes 64,035 records tagged with the MeSH heading “Molecular Dynamics Simulation” (counts retrieved 2026-10-29). 31,676 of them (49.5%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 32,512 records.
Reviews account for 2,298 records (3.6%) and meta-analyses for 17. Randomized controlled trials number 5 (0.0%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 575 |
| 2010-2019 | 32,512 |
| 2020 onward | 31,676 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 2,298 | 3.6% |
| Meta-analyses | 17 | 0.0% |
| Randomized controlled trials | 5 | 0.0% |
Method: PubMed query "Molecular Dynamics Simulation"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in molecular biology
Molecular biology establishes mechanisms through controlled experiments, often in simplified systems such as purified proteins, cultured cells or model organisms. Such work can show what is possible in a defined system, but whether a pathway matters in a whole organism or in human disease needs further evidence. Check the experimental system, whether findings were reproduced by independent groups, and whether the authors tested the mechanism directly or inferred it from correlation.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Decrypting cryptic pockets with physics-based simulations and artificial intelligence | 2026 | Review article Design not recorded in structured metadata | 9 | cc by PMID 41604887 · DOI · PMC12959236 |
| Dynamic architecture of mycobacterial outer membranes revealed by all-atom simulations | 2026 | Research article Design not recorded in structured metadata | 6 | cc by PMID 42090459 · DOI · PMC13148821 |
| Destabilization of Structured RNAs by OPC and TIP4PD Water Models | 2026 | Research article Design not recorded in structured metadata | 6 | cc by PMID 41665624 · DOI · PMC12937104 |
Decrypting cryptic pockets with physics-based simulations and artificial intelligence [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome.
- Closing excerpt
- This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence-driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (105 words)
Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be "undruggable" due to a lack of pockets in structures of the ground state. However, their transient and hidden nature renders them difficult to detect through conventional experimental screening methods. Recent advances in computational methodologies and resources have greatly enhanced our ability to identify and characterize such elusive pockets. This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence-driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41604887 · DOI · PMC12959236
Dynamic architecture of mycobacterial outer membranes revealed by all-atom simulations [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Tuberculosis remains a global health crisis due to the resilience of Mycobacterium tuberculosis (Mtb), largely attributed to its unique cell envelope.
- Closing excerpt
- Our work provides a foundational framework for targeting the mycobacterial outer membrane in future drug development.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (176 words)
Tuberculosis remains a global health crisis due to the resilience of Mycobacterium tuberculosis (Mtb), largely attributed to its unique cell envelope. The impermeability and structural complexity of the outer membrane of this envelope, driven by mycolic acids and glycolipids, pose significant challenges for therapeutic intervention. Here, we present the first all-atom models of an Mtb outer membrane using molecular dynamics simulations. We demonstrate that α-mycolic acids adopt extended conformations to stabilize bilayers, with a phase transition near 338 K that underscores their thermal resilience. Lipids in the outer leaflet, such as PDIM and PAT, induce membrane heterogeneity, migrating to the interleaflet space and reducing lipid order. The simulated mycobacterial outer membrane has ordered inner leaflets and disordered outer leaflets, which contrasts with the outer membrane of Gram-negative bacteria. These findings reveal that PDIM- and PAT-driven lipid redistribution, reduced lipid order, and asymmetric fluidity gradients enable Mtb's outer membrane to resist host-derived stresses and limit antibiotic penetration, thereby promoting bacterial survival. Our work provides a foundational framework for targeting the mycobacterial outer membrane in future drug development.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42090459 · DOI · PMC13148821
Destabilization of Structured RNAs by OPC and TIP4PD Water Models [3]
Abstract reading summary — source excerpts
- Opening excerpt
- The four-point OPC water model has recently gained a reputation as the preferred choice for molecular dynamics (MD) simulations of nucleic acids and proteins, providing more realistic reproduction of bulk physical properties of water than the older three-point models.
- Closing excerpt
- For at least some structured RNA systems, the three-point water models may provide more stable behavior in combination with current AMBER RNA force fields.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (351 words)
The four-point OPC water model has recently gained a reputation as the preferred choice for molecular dynamics (MD) simulations of nucleic acids and proteins, providing more realistic reproduction of bulk physical properties of water than the older three-point models. It has been shown to improve, for example, simulations of unstructured biomolecules such as RNA tetranucleotides or intrinsically disordered proteins. However, the performance for folded RNA structures was not specifically explored. Here we present extensive testing of the OPC water model on three different RNAs with intricate tertiary structures - the ribosomal L1 stalk RNA-protein protuberance, the mini tetraloop-tetraloop receptor (miniTTR-6) folded RNA, and the GAAA tetraloop-tetraloop receptor homodimer. The OPC performance is directly compared with SPC/E, TIP3P, and OPC3 water models using the OL3 AMBER RNA force field (FF). We found substantial effect of the water model on simulation behavior. For all three systems, we observe large-scale unfolding of the RNA, and even loss of the L1 stalk protein-RNA interface, when simulated with the OPC. In contrast, the simulations are entirely stable with the three-point water models. The underlying cause seems to be the higher affinity of the OPC waters to H-bond donor and acceptor groups of the RNA, which weakens the native solute-solute interactions. An identical issue is observed also for the similar and widely used TIP4PD water model combined with the DES-Amber RNA FF. Importantly, the structural consequences of these issues may range from significant structural perturbations to minimal or undetectable effects, depending on the RNA system. Accordingly, we do not claim that the imbalance in water-RNA interactions identified here is a general feature of all RNA molecules. Indeed, simulations of three additional, less structurally complex RNA systems revealed more balanced performance, with no observable differences between water models for the noncanonical 5S rRNA Loop E double helix. However, certain caution is warranted when using the four-point OPC and TIP4PD water models for simulations of structured RNAs, particularly those rich in 2'-OH hydroxyl-based tertiary interactions. For at least some structured RNA systems, the three-point water models may provide more stable behavior in combination with current AMBER RNA force fields.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41665624 · DOI · PMC12937104
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Molecular Dynamics Simulation for All [4]
- Research article Markov State Models: From an Art to a Science [5]
- Research article A Review on Applications of Computational Methods in Drug Screening and Design [6]
- Research article Key Topics in Molecular Docking for Drug Design [7]
- Research article Biophysical experiments and biomolecular simulations: A perfect match? [8]
- Research article Bottom-up Coarse-Graining: Principles and Perspectives [9]
References
- Zhang S, Bowman GR. Decrypting cryptic pockets with physics-based simulations and artificial intelligence. . 2026. PMID 41604887 · DOI · PMC12959236
- Brown TP, Chavent M, Im W. Dynamic architecture of mycobacterial outer membranes revealed by all-atom simulations. . 2026. PMID 42090459 · DOI · PMC13148821
- Krepl M, Mlýnský V, Rusnáková A, Banáš P, Otyepka M, Šponer J. Destabilization of Structured RNAs by OPC and TIP4PD Water Models. . 2026. PMID 41665624 · DOI · PMC12937104
- Hollingsworth SA, Dror RO. Molecular Dynamics Simulation for All. Neuron. 2018. PMID 30236283 · DOI · PMC6209097
- Husic BE, Pande VS. Markov State Models: From an Art to a Science. J Am Chem Soc. 2018. PMID 29323881 · DOI
- Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules. 2020. PMID 32197324 · DOI · PMC7144386
- Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key Topics in Molecular Docking for Drug Design. Int J Mol Sci. 2019. PMID 31540192 · DOI · PMC6769580
- Bottaro S, Lindorff-Larsen K. Biophysical experiments and biomolecular simulations: A perfect match?. Science. 2018. PMID 30049874 · DOI
- Jin J, Pak AJ, Durumeric AEP, Loose TD, Voth GA. Bottom-up Coarse-Graining: Principles and Perspectives. J Chem Theory Comput. 2022. PMID 36070494 · DOI · PMC9558379
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.