Evidence map›Paper›PMID 42072629›Full record

ReviewBiomolecules2026

Label-Free Target Discovery Strategy for Natural Active Products.

Lei Shan, Yujia Chen, Xiuling Cao, Xuejiao Jin, Beidong Liu

Abstract readReview
In one paragraph

Review in Biomolecules, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lei ShanState Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China.
Yujia ChenState Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China.
Xiuling CaoState Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China.ORCID 0000-0001-6940-4482
Xuejiao JinState Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China.
Beidong LiuState Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China.ORCID 0000-0001-6052-8411

Funding

National Natural Science Foundation of China 32370036Swedish Cancer Society-Cancerfonden CAN 19-0069 and 23-2769Swedish Natural Research Council VR 2019-03604 and 2023-03560
6 · The paper itself

Abstract

The growing interest in harnessing natural compounds for health and medical applications underscores the necessity for innovative approaches to decipher their mechanisms of action. Among diverse strategies, non-probe-based methods for identifying the molecular targets of compounds represent one of the frontiers in drug discovery. This review focuses on an array of non-probe techniques for unveiling interactions between natural molecules and biological targets. The advantages and limitations of label-free protein target identification schemes suitable for lysed cells and living cells are analyzed. High-throughput target screening methods and their role in facilitating a holistic understanding of compound-target interactions are summarized. Based on comprehensive evaluation and comparison, this review aims to provide guidelines for selecting appropriate non-probe strategies to accelerate the characterization of the therapeutic potential of natural compounds.

Indexed as

Biological ProductsDrug DiscoveryAnimalsHigh-Throughput Screening AssaysHumansProteomicsBiological Productshigh-throughputlysatesnatural compoundsnon-probe-based methodsproteomicstarget identification

Identifiers

PMID42072629
PMCPMC13113158

What OpenQuestion holds

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LicenceCC BY
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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.