Evidence map›Paper›PMID 42011233›Full record

ReviewCurrent research in food science2026

In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic insight.

Xiaolong Li, Xinwei Luo, Sijia Xie, Yijie Wei, Feitong Hong, Yuqing Jiang, Xinrui Zhong, Xueqin Xie, Caiyi Ma, Yuduo Hao and 5 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current research in food science, 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

15 authors.

Xiaolong LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xinwei LuoThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Sijia XieThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Yijie WeiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Feitong HongThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Yuqing JiangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xinrui ZhongThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xueqin XieThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Caiyi MaSchool of Computer Science and Technology, Aba Teachers College, Aba, 623002, China.
Yuduo HaoThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Fuying DaoSchool of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore.
Kun YangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Hao LinThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Hongyan LaiChongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Hao LyuThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Umami peptides are naturally occurring bioactive peptides with distinctive umami taste and promising applications in the food industry. However, conventional identification methods based on sensory evaluation, chromatography, and mass spectrometry are time-consuming, costly, and low-throughput. Recent advances in artificial intelligence (AI) have enabled efficient computational frameworks for large-scale umami peptide screening. This review summarizes recent progress in AI-driven umami peptide prediction and mechanistic insights from molecular docking analyses. We overview available umami peptide databases, feature representation strategies, and state-of-the-art AI models, including machine learning, deep learning, and multi-model fusion approaches. In addition, molecular docking studies elucidating the interactions between umami peptides and taste receptors, particularly T1R1/T1R3, are discussed to support rational peptide design. Finally, current challenges and future perspectives in AI-assisted umami peptide research are highlighted, providing guidance for the intelligent development and application of umami peptides in the food industry.

Indexed as

Deep learningHomology modelingMachine learningMolecular dockingUmami peptides

Identifiers

PMID42011233
PMCPMC13092066

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.