Evidence map›Paper›PMID 37268742›Full record

ArticleMolecular diversity2024

A novel compound to overcome influenza drug resistance in endonuclease inhibitors.

Yixin Ren, Li Wan, Shuang Cao

Open access · bronzeAbstract read
In one paragraph

Article in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Green synthesis of MnOVirology journal · 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

3 authors at 1 institution in 1 country.

Yixin Ren *Key Laboratory of Green Chemical Engineering Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, People's Republic of China.
Li Wan *Key Laboratory of Green Chemical Engineering Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, People's Republic of China.
Shuang CaoKey Laboratory of Green Chemical Engineering Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, People's Republic of China. caoshuang@wit.edu.cn.
Wuhan Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza is a seasonal respiratory illness that kills hundreds of thousands of people every year. Currently, neuraminidase inhibitors and endonuclease inhibitors are used in antiviral therapy. However, both drug types have encountered drug-resistant influenza strains in the human body. Fortunately, there is currently no resistance to endonuclease inhibitors in wild strains of influenza. We obtained the molecules with endonuclease inhibitor activity independent of the existing drug-resistant strains through computer-aided drug design, and we hope the obtained results can lay a theoretical foundation for the development of high-activity endonucleases. Combining a traditional fragment-based drug discovery approach with AI-directed fragment growth, we selected and designed a compound that achieved antiviral activity on drug-resistant strains by avoiding mutable residues and drug-resistant residues. We predicted the related properties using an ADMET model. Finally, we obtained a compound similar to baloxavir in terms of binding free energy but not affected by baloxavir resistance.

Indexed as

Antiviral AgentsDibenzothiepinsDrug Resistance, ViralEndonucleasesEnzyme InhibitorsDrug DesignHumansInfluenza, HumanMolecular Docking SimulationMorpholinesOrthomyxoviridaePyridinesPyridonesTriazinesAntiviral AgentsbaloxavirDibenzothiepinsEndonucleasesEnzyme InhibitorsMorpholinesPyridinesPyridonesTriazinesFBDDFragment growthMolecular dockingVirtual screening

Identifiers

PMID37268742
PMCPMC10237527
OpenAlexW4379114779

What OpenQuestion holds

Textmetadata
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.