Evidence map›Paper›PMID 40206633›Full record

ArticleFrontiers in bioinformatics2025

Inhibition potential of margolonone and isomargolonone against the dengue virus protease using molecular modeling approaches.

Gourav Choudhir, Faiza Iram, Israil, Mohammad Shahid, Anas Shamsi, Md Imtaiyaz Hassan, Asimul Islam

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 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. 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

7 authors.

Gourav ChoudhirCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
Faiza IramCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
IsrailCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
Mohammad ShahidDepartment of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Anas ShamsiCenter for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
Md Imtaiyaz HassanCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
Asimul IslamCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dengue is a mosquito-borne viral disease with no cure. Inhibiting key enzymes vital in replication could manage the dengue virus infection. This study investigated the potential of margolonone and isomargolonone from Methods: The 3D structure of margolonone and isomargolonone were obtained from the PubChem database. The drug-likeness properties of these molecules were performed using a Swiss-ADME server. The molecular docking and molecular dynamics simulation assessed binding affinity and interactions. Results: The drug-likeness of parameters showed that Margolonone and isoMargolonone showed zero violation of Lipinski rules. Docking simulations showed that both compounds bind to the active site of a critical enzyme (NS3 protease) essential for viral replication. Molecular dynamics simulations suggested that isomargolonone may bind more stably to NS3 than margolonone. Additionally, MMPBSA analysis showed that Margolonone does not show favorable binding energy. Conclusion: These findings warrant further investigation of isomargolonone as a potential anti-dengue drug. Further

Indexed as

binding energyintegrating modelingnatural productnontoxicNS3 protease

Identifiers

PMID40206633
PMCPMC11979248

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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.