Evidence map›Paper›PMID 38257788›Full record

ArticleViruses2024

Computer-Selected Antiviral Compounds: Assessing In Vitro Efficacies against Rift Valley Fever Virus.

Cigdem Alkan, Terrence O'Brien, Victor Kenyon, Tetsuro Ikegami

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2024. 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
2.3field-weighted citation impact, top 13% of its field
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, 6 citations in OpenAlex.

  1. Review
  2. Small Molecule Inhibitors ofInternational journal of molecular sciences · 2024
    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

4 authors at 2 institutions in 1 country.

Cigdem AlkanDepartment of Pathology, The University of Texas Medical Branch at Galveston, Galveston, TX 77555, USA.ORCID 0000-0002-1302-9270
Terrence O'BrienDiscovery Chemistry, Genentech, Inc., South San Francisco, CA 94080, USA.
Victor KenyonAtomwise Inc., San Francisco, CA 94103, USA.ORCID 0000-0001-8713-9440
Tetsuro IkegamiDepartment of Pathology, The University of Texas Medical Branch at Galveston, Galveston, TX 77555, USA.ORCID 0000-0001-8318-2783
The University of Texas Medical Branch at Galveston · USHealthwise · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rift Valley fever is a zoonotic viral disease transmitted by mosquitoes, impacting both humans and livestock. Currently, there are no approved vaccines or antiviral treatments for humans. This study aimed to evaluate the in vitro efficacy of chemical compounds targeting the Gc fusion mechanism. These compounds were identified through virtual screening of millions of commercially available small molecules using a structure-based artificial intelligence bioactivity predictor. In our experiments, a pretreatment with small molecule compounds revealed that 3 out of 94 selected compounds effectively inhibited the replication of the Rift Valley fever virus MP-12 strain in Vero cells. As anticipated, these compounds did not impede viral RNA replication when administered three hours after infection. However, significant inhibition of viral RNA replication occurred upon viral entry when cells were pretreated with these small molecules. Furthermore, these compounds exhibited significant inhibition against Arumowot virus, another phlebovirus, while showing no antiviral effects on tick-borne bandaviruses. Our study validates AI-based virtual high throughput screening as a rational approach for identifying effective antiviral candidates for Rift Valley fever virus and other bunyaviruses.

Indexed as

PhlebovirusRift Valley fever virusAnimalsAntiviral AgentsArtificial IntelligenceChlorocebus aethiopsComputersHumansRNA, ViralVero CellsAntiviral AgentsRNA, ViralAI-based computational drug discoveryArumowot virusDabie bandavirusGc fusion loopHeartland virusRift Valley feversmall molecule compound

Identifiers

PMID38257788
PMCPMC10818293
OpenAlexW4390614543

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.