Evidence map›Paper›PMID 41226607›Full record

ArticleInternational journal of molecular sciences2025

In Silico Identification of the NLRP3 Inhibitors from Traditional Chinese Medicine.

Shunjiang Jia, Huanling Lai, Xinyu Chen, Jiajie Lu, Wei Ding, Dongxiao Cui, Peng Zhao, Qiao Zhang, Yuwei Wang, Chunsong Cheng

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Shunjiang JiaCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Huanling LaiGuangzhou National Laboratory, Guangzhou 510000, China.ORCID 0000-0001-5821-7817
Xinyu ChenCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Jiajie LuCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Wei DingState Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau 999078, China.
Dongxiao CuiCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Peng ZhaoCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Qiao ZhangCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.
Yuwei WangCollege of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave, Xi'an-Xianyang New Economic Zone, Xianyang 712046, China.ORCID 0000-0001-5782-1107
Chunsong ChengJiangxi Key Laboratory for Sustainable Utilization of Chinese Materia Medica Resources, Lushan Botanical Garden, Chinese Academy of Sciences, Jiujiang 332900, China.ORCID 0000-0002-5863-6162

Funding

Natural Science Foundation of Shaanxi Province 2024SF2-GJHX-67Science and Technology Association Youth Talent Lifting Project of Shaanxi Province 20240325Science and Technology Innovative Talent Program of Shaanxi University of Chinese Medicine 2023-KJXX-05The Key Research Projects in Jiangxi Province 20223BBH8007 & 20232BBG70014This research was funded by Natural Science Foundation of Shaanxi Province 2024SF-YBXM-482
6 · The paper itself

Abstract

NOD-like receptor protein 3 (NLRP3) inflammasome is a key mediator of inflammation and a promising therapeutic target. However, the discovery of novel and effective inhibitors of NLRP3 remains limited. A combined docking-based virtual screening (DBVS) and shape-based screening approach was applied to eight traditional Chinese medicine (TCM) databases to identify potential NLRP3 inhibitors. Structural similarity analysis, ADMET prediction, and molecular dynamics (MD) simulations were performed to evaluate structural novelty, pharmacokinetic properties, and binding stability. A total of 25 potential NLRP3 inhibitors were identified, each exhibiting docking scores higher than those of the reference inhibitor XE3. Structural similarity analysis revealed that the screened compounds exhibited low similarity to previously reported NLRP3 inhibitors, demonstrating their structural novelty. ADMET evaluation indicated that compounds C2, C3, and C4 exhibited favorable physicochemical and pharmacokinetic properties. Molecular dynamics (MD) simulations demonstrated that the complexes of compounds C2, C3, and C4 with NLRP3 remained stable throughout the simulations, exhibiting limited backbone fluctuations and compact conformations, as indicated by Rg values of approximately 6 Å. Solvent-accessible surface area (SASA) and polar surface area (PSA) analyses suggested that compounds C3 and C4 were tightly solvated and maintained favorable membrane permeability. Notably, binding free energy calculations revealed that all three compounds exhibited stronger binding than XE3, with compound C3 showing the most favorable energy (-48.81 ± 3.89 kcal/mol), indicating a highly stable and energetically preferred interaction with NLRP3. This study identified promising TCM-derived compounds as potential NLRP3 inhibitors, offering new directions for anti-inflammatory drug development.

Indexed as

Drugs, Chinese HerbalMedicine, Chinese TraditionalNLR Family, Pyrin Domain-Containing 3 ProteinComputer SimulationHumansMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingDrugs, Chinese HerbalNLR Family, Pyrin Domain-Containing 3 ProteinNLRP3 protein, humanmolecular dynamics simulationNLRP3TCM databasevirtual screening

Identifiers

PMID41226607
PMCPMC12607448

What Socratic 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 Socratic graph.