Evidence map›Paper›PMID 36263438›Full record

ArticleJournal of tropical medicine2022

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (M

Harrison Onyango, Patrick Odhiambo, David Angwenyi, Patrick Okoth

Abstract read
In one paragraph

Article in Journal of tropical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. In-silico analysis of potent Mosquirix vaccine adjuvant leads.Journal, genetic engineering & biotechnology · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2023
    Review
  11. 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.

Harrison OnyangoDepartment of Biological Sciences (Molecular Biology, Computational Biology and Bioinformatics Section), School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.ORCID https://orcid.org/0000-0001-7990-5897
Patrick OdhiamboDepartment of Biological Sciences (Molecular Biology, Computational Biology and Bioinformatics Section), School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.ORCID https://orcid.org/0000-0001-9288-3952
David AngwenyiDepartment of Mathematics, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.ORCID https://orcid.org/0000-0002-6958-2817
Patrick OkothDepartment of Biological Sciences (Molecular Biology, Computational Biology and Bioinformatics Section), School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.ORCID https://orcid.org/0000-0002-0241-8799

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 M

Identifiers

PMID36263438
PMCPMC9576439

What OpenQuestion holds

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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.