Evidence map›Paper›PMID 36615351›Full record

ReviewMolecules (Basel, Switzerland)2022

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication.

Guo-Fei Qin, Xiao Zhang, Feng Zhu, Zong-Qing Huo, Qing-Qiang Yao, Qun Feng, Zhong Liu, Gui-Min Zhang, Jing-Chun Yao, Hong-Bao Liang

Open access · goldAbstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 6% 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.

  1. Pooled it
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  3. Exploration of Phytochemical Constituents inAntioxidants (Basel, Switzerland) · 2026
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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

10 authors at 2 institutions in 1 country.

Guo-Fei QinState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.ORCID 0000-0002-0291-256X
Xiao ZhangCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
Feng ZhuState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.ORCID 0000-0003-3523-7078
Zong-Qing HuoState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.
Qing-Qiang YaoJining Medical University, Jining 272067, China.
Qun FengState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.ORCID 0000-0002-5329-4941
Zhong LiuState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.
Gui-Min ZhangState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.
Jing-Chun YaoState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.
Hong-Bao LiangState Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China.
Shandong University of Traditional Chinese Medicine · CNJining Medical University · CN

Funding

Independent Innovation Major Project of Linyi City, Shandong Province 2019ZDZX001Shandong Provincial Key Research and Development Program (Major Technological Innovation Project) 2021CXGC010508Shandong Provincial Natural Science Foundation ZR2021QH289Taishan Industrial Leading Talents Program the grant number is not available
6 · The paper itself

Abstract

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Indexed as

Biological ProductsTandem Mass SpectrometryArtificial IntelligenceDrug DiscoveryPlant ExtractsBiological ProductsPlant Extractsbioactivity-based molecular networking (BMN)building blocks-based molecular network (BBMN)classical MN (CLMN)feature-based molecular networking (FBMN)ion identity molecular networking (IIMN)MS/MS-based molecular networkingnatural products dereplicationsubstructure-based MN (MS2LDA)

Identifiers

PMID36615351
PMCPMC9822519
OpenAlexW4313244517

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.