Evidence map›Paper›PMID 37773489›Full record

ArticleScientific reports2023

Multi-structural molecular docking (MOD) combined with molecular dynamics reveal the structural requirements of designing broad-spectrum inhibitors of SARS-CoV-2 entry to host cells.

Anqi Da, Meritxell Wu-Lu, Jovan Dragelj, Maria Andrea Mroginski, Kourosh H Ebrahimi

Abstract read
In one paragraph

Article in Scientific reports, 2023. 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

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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026
    Review
  5. Review
  6. Design, Synthesis,ACS omega · 2025
    Article
  7. 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

5 authors.

Anqi Da *Institute of Pharmaceutical Science, King's College London, London, UK.
Meritxell Wu-Lu *Institute of Chemistry, Technische Universität Berlin, Berlin, Germany.
Jovan DrageljInstitute of Chemistry, Technische Universität Berlin, Berlin, Germany.
Maria Andrea MroginskiInstitute of Chemistry, Technische Universität Berlin, Berlin, Germany. andrea.mroginski@tu-berlin.de.
Kourosh H EbrahimiInstitute of Pharmaceutical Science, King's College London, London, UK. Kourosh.Ebrahimi@kcl.ac.uk.

Funding

ECOG OPERATIONS OFFICE (IBCSG)U10CA021115 · NCI · UNIVERSITY OF WISCONSIN MADISON · PI COMIS, ROBERT L · 1985 to 2013
$132.6M
NCI NIH HHS U10 CA021115
6 · The paper itself

Abstract

New variants of SARS-CoV-2 that can escape immune response continue to emerge. Consequently, there is an urgent demand to design small molecule therapeutics inhibiting viral entry to host cells to reduce infectivity rate. Despite numerous in silico and in situ studies, the structural requirement of designing viral-entry inhibitors effective against multiple variants of SARS-CoV-2 has yet to be described. Here we systematically screened the binding of various natural products (NPs) to six different SARS-CoV-2 receptor-binding domain (RBD) structures. We demonstrate that Multi-structural Molecular Docking (MOD) combined with molecular dynamics calculations allowed us to predict a vulnerable site of RBD and the structural requirement of ligands binding to this vulnerable site. We expect that our findings lay the foundation for in silico screening and identification of lead molecules to guide drug discovery into designing new broad-spectrum lead molecules to counter the threat of future variants of SARS-CoV-2.

Indexed as

Biological ProductsCOVID-19HumansMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingSARS-CoV-2Biological Products

Identifiers

PMID37773489
PMCPMC10541870

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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.