Evidence map›Paper›PMID 39259409›Full record

ArticleCell biochemistry and biophysics2025

Molecular Modeling Studies of Similar Molecules to Selective Estrogen Receptor Degrader Elacestrant as Inhibitors of SARS-COV-2.

Oluwadamilare D Omotoso, Funsho Oyetunde Joshua, Abel Kolawole Oyebamiji, Oluwakemi Ebenezer

Erratum issuedAbstract read
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In one paragraph

Article in Cell biochemistry and biophysics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

4 authors.

Oluwadamilare D OmotosoDepartment of Bioinformatics, Faculty of Chemistry, Wybrzeze Stanislawa Wyspianskiego,, Waclow University of Science and Technology, Wroclaw, Poland.
Funsho Oyetunde JoshuaCenter of excellence for pharmaceutical sciences Northwest University, Potchefstroom, South Africa.
Abel Kolawole OyebamijiDepartment of Chemistry and Industrial Chemistry, Bowen University, Iwo, Osun State, Nigeria.
Oluwakemi EbenezerDepartment of Physics, University of Alberta, Edmonton, AB, Canada. oebeneze@ualberta.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) strain. Many anticancer compounds have been repurposed as effective anti-coronavirus agents and are currently in a clinical trial to be evaluated for treatment. Elacestrant is a novel selective estrogen receptor degrader (SERD). A fingerprint Tanimoto-based 2-dimensional similarity search was performed in the PubChem database using elacestrant as a prototype. The chemical compounds were downloaded, and virtual screening, molecular docking, and molecular dynamics were further used to identify the most active molecules in the binding pocket SARS-COV-2 main protease. Eight compounds with superior docking score, gscore, and glide binding energy were identified. Molecular dynamic simulations (MD) were performed at 100 ns to remove the false interactions between the receptor and the active ligands. The results showed that all the compounds displayed good stability. Further, the ADMET results showed that compounds CID58023104 was observed to be deemed a hit compound; hence, CID58023104 and could be optimize, derivatize, and explore for further development as an anti-coronavirus agent targeting SARS-COV-2 main protease.

Indexed as

SARS-CoV-2TetrahydronaphthalenesMolecular Docking SimulationMolecular Dynamics SimulationSelective Estrogen Receptor ModulatorselacestrantSelective Estrogen Receptor ModulatorsTetrahydronaphthalenesCoronavirusElacestrantMolecular dockingMolecular dynamicsVirtual screening

Identifiers

PMID39259409

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.