Evidence map›Paper›PMID 40728751›Full record

ArticleJournal of molecular modeling2025

Targeting SARS-CoV-2 main protease: a pharmacophore and molecular modeling approach.

Nitchakan Darai, Piyatida Pojtanadithee, Kamonpan Sanachai, Thierry Langer, Peter Wolschann, Thanyada Rungrotmongkol

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Article in Journal of molecular modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Nitchakan DaraiCenter of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
Piyatida PojtanaditheeProgram in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand.
Kamonpan SanachaiDepartment of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand.
Thierry LangerDepartment of Pharmaceutical Chemistry, Faculty of Chemistry, University of Vienna, 1090, Vienna, Austria.
Peter WolschannDepartment of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, 1090, Vienna, Austria. karl.peter.wolschann@univie.ac.at.
Thanyada RungrotmongkolCenter of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand. t.rungrotmongkol@gmail.com.

Funding

National Research Council of Thailand N42A650231
6 · The paper itself

Abstract

contextThe COVID-19 pandemic, driven by SARS-CoV-2, has had a profound impact on global health, with severe respiratory complications being a primary concern. The main protease (Mpro) of SARS-CoV-2 plays a critical role in viral replication, making it an attractive target for therapeutic intervention. This study aimed to identify potential Mpro inhibitors using an integrated computational approach. From an initial pool of 89,200 compounds in the ChemDiv database, a systematic screening process reduced the candidates to 735 through drug-like property predictions and pharmacophore-based virtual screening. Molecular docking against four co-crystal structures of the inhibitor/Mpro complex, followed by molecular dynamics (MD) simulations and binding free energy calculations, identified E912-0363 and G740-1003 as promising candidates with binding affinities comparable to nirmatrelvir. Extended 500-ns MD simulations further established E912-0363 as a highly promising Mpro inhibitor, supporting its potential for therapeutic development as a complementary or alternative treatment to nirmatrelvir.

methodsPharmacophore modeling and virtual screening were conducted using the ChemDiv database, reducing 89,200 compounds to 735 candidates based on drug-like property predictions. Molecular docking was performed against four SARS-CoV-2 Mpro co-crystal structures using AutoDock VinaXB and GOLD docking programs. The top five candidates (E912-0363, P635-0261, G740-1003, G069-0804, and 8602-0428) were subjected to 100-ns molecular dynamics (MD) simulations using the AMBER force field. Binding free energy calculations were performed using the MM/GBSA method. Extended 500-ns MD simulations were carried out for the most promising candidate, E912-0363, to evaluate its long-term stability and interaction with the Mpro binding site.

Indexed as

Antiviral AgentsCoronavirus 3C ProteasesCOVID-19 Drug TreatmentProtease InhibitorsSARS-CoV-2Binding SitesCOVID-19HumansMolecular Docking SimulationMolecular Dynamics SimulationPharmacophoreProtein Binding3C-like proteinase, SARS-CoV-2Antiviral AgentsCoronavirus 3C ProteasesProtease InhibitorsMain protease inhibitorsMolecular dockingMolecular dynamics simulationPharmacophore modellingSARS-CoV-2

Identifiers

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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 Socratic graph.