Evidence map›Paper›PMID 35479689›Full record

ArticleRSC advances2021

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2.

Nguyen Minh Tam, Minh Quan Pham, Nguyen Xuan Ha, Pham Cam Nam, Huong Thi Thu Phung

Abstract read
In one paragraph

Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Natural Phosphodiesterase-4 Inhibitors with PotentialMolecules (Basel, Switzerland) · 2023
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Marine derivatives preventRoyal Society open science · 2021
    Article
  12. Article
  13. 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.

Nguyen Minh TamComputational Chemistry Research Group, Ton Duc Thang University Ho Chi Minh City Vietnam nguyenminhtam@tdtu.edu.vn.ORCID https://orcid.org/0000-0003-3153-4606
Minh Quan PhamInstitute of Natural Products Chemistry, Vietnam Academy of Science and Technology Hanoi Vietnam.ORCID https://orcid.org/0000-0001-6922-1627
Nguyen Xuan HaFaculty of Chemistry and Environment, Thuyloi University, Ministry of Agriculture and Rural Development Hanoi Vietnam.
Pham Cam NamDepartment of Chemical Engineering, The University of Da Nang, University of Science and Technology Da Nang City Vietnam.ORCID https://orcid.org/0000-0002-7257-544X
Huong Thi Thu PhungNTT Hi-Tech Institute, Nguyen Tat Thanh University Ho Chi Minh City Vietnam ptthuong@ntt.edu.vn.ORCID https://orcid.org/0000-0001-5702-4597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19

Identifiers

PMID35479689
PMCPMC9032918

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.