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Paper

A rapid selection strategy for umami peptide screening based on machine learning and molecular docking.

by Independent / Community 0017faad50593d4cd2d1ffd23521113ab1fd3ee3

Free2AITools Nexus Index

69.9

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 86

P: Popularity 63

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Umami peptides have been the focus of umami studies in recent years because of their high nutritional value and flavor activity. However, the existing screening methods of umami peptides were cumbersome, complex, time-consuming and laborious, and it was difficult to achieve high-throughput screening. In this study, a novel umami peptide rapid screening model was designed and by using lamb bone aqueous extract as raw material, through the step-by-step screening of peptidomics, machine learning...

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Semantic Scholar 92 Citations

Paper Information Summary
Entity Passport
Registry ID	0017faad50593d4cd2d1ffd23521113ab1fd3ee3
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0017faad50593d4cd2d1ffd23521113ab1fd3ee3,
  author = {Unknown},
  title = {A rapid selection strategy for umami peptide screening based on machine learning and molecular docking. Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0017faad50593d4cd2d1ffd23521113ab1fd3ee3}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). A rapid selection strategy for umami peptide screening based on machine learning and molecular docking. [Paper]. Free2AITools. https://api.semanticscholar.org/0017faad50593d4cd2d1ffd23521113ab1fd3ee3

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 86

Popularity (P) 63

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for A rapid selection strategy for umami peptide screening based on machine learning and molecular docking.: Authority (A:86), Popularity (P:63), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Umami peptides have been the focus of umami studies in recent years because of their high nutritional value and flavor activity. However, the existing screening methods of umami peptides were cumbersome, complex, time-consuming and laborious, and it was difficult to achieve high-throughput screening. In this study, a novel umami peptide rapid screening model was designed and by using lamb bone aqueous extract as raw material, through the step-by-step screening of peptidomics, machine learning..."

❝ Cite Node

@article{Unknown2026A,
  title={A rapid selection strategy for umami peptide screening based on machine learning and molecular docking.},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈92CitationsSemantic Scholar

🏛️86AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 92

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