Evidence map›Paper›PMID 36177201›Full record

ArticleRoyal Society open science2022

Identification of promising anti-EBOV inhibitors:

Eslam A R Mohamed, Sayed F Abdelwahab, Ahmad M Alqaisi, Amaal Mohammed Salih Nasr, Heba Ali Hassan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Identification of promising anti-EBOV inhibitors:Royal Society open science · 2022
    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.

Eslam A R MohamedDepartment of Chemistry, Faculty of Science, Minia University, Minia 61511, Egypt.ORCID 0000-0002-0314-0725
Sayed F AbdelwahabDepartment of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif 21944, Saudi Arabia.
Ahmad M AlqaisiChemistry Department, University of Jordan, Amman 11942, Jordan.
Amaal Mohammed Salih NasrDepartment of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Malaysia.ORCID 0000-0002-0867-3950
Heba Ali HassanDepartment of Pharmacognosy, Faculty of Pharmacy, Sohag University, Sohag 82524, Egypt.ORCID 0000-0002-2631-5831

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Ebola virus (EBOV) outbreak was recorded as the largest in history and caused many fatalities. As seen in previous studies, drug repurposing and database filtration were the two major pathways to searching for potent compounds against EBOV. In this study, a deep learning (DL) approach via the LigDream tool was employed to obtain novel and effective anti-EBOV inhibitors. Based on the galidesivir (BCX4430) chemical structure, 100 compounds were collected and inspected using various

Indexed as

deep learningEbola virusLigDreammolecular dockingmolecular dynamics

Identifiers

PMID36177201
PMCPMC9515638

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.