Evidence map›Paper›PMID 40443526›Full record

ArticleFrontiers in molecular biosciences2025

Broad-spectrum coronavirus inhibitors discovered by modeling viral fusion dynamics.

Charles B Reilly, Joel Moore, Shanda Lightbown, Austin Paul, Sylvie G Bernier, Kenneth E Carlson, Donald E Ingber

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Charles B ReillyWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Joel MooreWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Shanda LightbownWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Austin PaulWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Sylvie G BernierWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Kenneth E CarlsonWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.
Donald E IngberWyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Development of oral, broad-spectrum therapeutics targeting SARS-CoV-2, its variants, and related coronaviruses could curb the spread of COVID-19 and avert future pandemics. We created a novel computational discovery pipeline that employed molecular dynamics simulation (MDS), artificial intelligence (AI)-based docking predictions, and medicinal chemistry to design viral entry inhibitors that target a conserved region in the SARS-CoV-2 spike (S) protein that mediates membrane fusion. DrugBank library screening identified the orally available, FDA-approved AXL kinase inhibitor bemcentinib as binding this site and we demonstrated that it inhibits viral entry in a kinase-independent manner. Novel analogs predicted to bind to the same region and disrupt S protein conformational changes were designed using MDS and medicinal chemistry. These compounds significantly suppressed SARS-CoV-2 infection and blocked the entry of S protein-bearing pseudotyped α,β,γ,δ,

Indexed as

antiviralartificial intelligence (AI)broad spectrum antiviralCOVID-19molecular dynamic (MD)small molecule

Identifiers

PMID40443526
PMCPMC12119275

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.