Evidence map›Paper›PMID 35132518›Full record

ArticleMolecular diversity2022

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations.

Tejas M Dhameliya, Prinsa R Nagar, Normi D Gajjar

Abstract read
In one paragraph

Article in Molecular diversity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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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

7 citing papers in PubMed.

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  6. ChemistrySelect · 2022
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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

3 authors.

Tejas M DhameliyaL. M. College of Pharmacy, Navrangpura, Ahmedabad, Gujarat, 380009, India. tejas.dhameliya@lmcp.ac.in.ORCID http://orcid.org/0000-0001-5336-9918
Prinsa R NagarL. M. College of Pharmacy, Navrangpura, Ahmedabad, Gujarat, 380009, India.
Normi D GajjarL. M. College of Pharmacy, Navrangpura, Ahmedabad, Gujarat, 380009, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than - 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.

Indexed as

COVID-19 Drug TreatmentNucleic AcidsAmino AcidsAntiviral AgentsGlycoproteinsHumansMolecular Docking SimulationMolecular Dynamics SimulationSARS-CoV-2Spike Glycoprotein, CoronavirusAmino AcidsAntiviral AgentsGlycoproteinsNucleic AcidsSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2COVID-19MD simulationsMolecular dockingSARS CoV-2Spike glycoprotein

Identifiers

PMID35132518
PMCPMC8821869

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.