Evidence map›Paper›PMID 37037848›Full record

ArticleScientific reports2023

Deep learning-based network pharmacology for exploring the mechanism of licorice for the treatment of COVID-19.

Yu Fu, Yangyue Fang, Shuai Gong, Tao Xue, Peng Wang, Li She, Jianping Huang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
5.2field-weighted citation impact, top 4% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Yu FuAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Yangyue FangAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Shuai GongAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Tao XueAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Peng WangAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Li SheAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China.
Jianping HuangAlibaba Business School, Hangzhou Normal University, Hangzhou, 310000, China. hjp@hznu.edu.cn.
Hangzhou Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach to identify more potential active compounds and targets of licorice. 4 compounds (quercetin, naringenin, liquiritigenin, and licoisoflavanone), 2 targets (SYK and JAK2) and the relevant pathways (P53, cAMP, and NF-kB) were predicted, which were confirmed by previous studies to be associated with SARS-CoV-2-infection. In addition, 2 new active compounds (glabrone and vestitol) and 2 new targets (PTEN and MAP3K8) were further validated by molecular docking and molecular dynamics simulations (simultaneous molecular dynamics), as well as the results showed that these active compounds bound well to COVID-19 related targets, including the main protease (Mpro), the spike protein (S-protein) and the angiotensin-converting enzyme 2 (ACE2). Overall, in this study, glabrone and vestitol from licorice were found to inhibit viral replication by inhibiting the activation of Mpro, S-protein and ACE2; related compounds in licorice may reduce the inflammatory response and inhibit apoptosis by acting on PTEN and MAP3K8. Therefore, licorice has been proposed as an effective candidate for the treatment of COVID-19 through PTEN, MAP3K8, Mpro, S-protein and ACE2.

Indexed as

COVID-19Deep LearningGlycyrrhizaAngiotensin-Converting Enzyme 2Molecular Docking SimulationNetwork PharmacologySARS-CoV-2Angiotensin-Converting Enzyme 2

Identifiers

PMID37037848
PMCPMC10086012
OpenAlexW4363678850

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.