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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP
Lulu Qi, Jialuo Du, Yue Sun, et al.
Food Chemistry (2022) Vol. 405, pp. 134935-134935
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Virtual screening and characteristics of novel umami peptides from porcine type I collagen
Yuxiang Gu, Jingcheng Zhang, Yajie Niu, et al.
Food Chemistry (2023) Vol. 434, pp. 137386-137386
Closed Access | Times Cited: 42
Screening and Characterization of novel umami peptides in Cheddar cheese using peptidomics and bioinformatics approaches
Yuxiang Gu, Jingcheng Zhang, Yajie Niu, et al.
LWT (2024) Vol. 194, pp. 115780-115780
Open Access | Times Cited: 19
Characteristics of saltiness-enhancing peptides derived from yeast proteins and elucidation of their mechanism of action by molecular docking
Yajie Niu, Yuxiang Gu, Jingcheng Zhang, et al.
Food Chemistry (2024) Vol. 449, pp. 139216-139216
Closed Access | Times Cited: 17
In-depth discovery and taste presentation mechanism studies on umami peptides derived from fermented sea bass based on peptidomics and machine learning
Chunxin Wang, Yanyan Wu, Huan Xiang, et al.
Food Chemistry (2024) Vol. 448, pp. 138999-138999
Closed Access | Times Cited: 16
Combining molecular docking and molecular dynamics simulation to discover four novel umami peptides from tuna skeletal myosin with sensory evaluation validation
Shuai Zhao, Shuang Ma, Yuanyue Zhang, et al.
Food Chemistry (2023) Vol. 433, pp. 137331-137331
Closed Access | Times Cited: 31
A screening strategy for identifying umami peptides with multiple bioactivities from Stropharia rugosoannulata using in silico approaches and SPR sensing
Jialin Li, Xiaofeng Liu, Wen Li, et al.
Food Chemistry (2023) Vol. 431, pp. 137057-137057
Closed Access | Times Cited: 25
Current progress in umami peptides from blue foods: Preparation, screening, evaluation, synergistic effects, and taste mechanism
Hao Li, Yan Fan, Qiaoji Tian, et al.
Trends in Food Science & Technology (2024) Vol. 149, pp. 104567-104567
Closed Access | Times Cited: 12
Recent advances and applications of deep learning, electroencephalography, and modern analysis techniques in screening, evaluation, and mechanistic analysis of taste peptides
Lijun Su, Huizhuo Ji, Jianlei Kong, et al.
Trends in Food Science & Technology (2024) Vol. 150, pp. 104607-104607
Closed Access | Times Cited: 10
Combination of virtual screening, machine learning, molecular dynamics simulations and sensory evaluation to discover novel umami peptides from fermented Atlantic cod
Hao Zou, Hao Wang, Jinlong Zhao, et al.
Food Bioscience (2024) Vol. 59, pp. 104250-104250
Closed Access | Times Cited: 9
Characterization and functional properties of umami peptides from douchi: Taste mechanism and antioxidant activity
Shiyu Zhang, Feng Chen, Jianfei Li, et al.
Food Bioscience (2024) Vol. 60, pp. 104303-104303
Closed Access | Times Cited: 8
Simulation screening of umami peptides in the microbiota of soy sauce based on molecular docking and molecular dynamics
Qi Wu, Chunming Xu, Yuan Tian, et al.
Food Bioscience (2024) Vol. 61, pp. 104722-104722
Closed Access | Times Cited: 8
Unraveling novel umami peptides from yeast extract (Saccharomyces cerevisiae) using peptidomics and molecular interaction modeling
Chunyu Gao, Rilei Yu, Xiaomei Zhang, et al.
Food Chemistry (2024) Vol. 453, pp. 139691-139691
Closed Access | Times Cited: 6
Umami-BERT: An interpretable BERT-based model for umami peptides prediction
Jingcheng Zhang, Wenjing Yan, Qingchuan Zhang, et al.
Food Research International (2023) Vol. 172, pp. 113142-113142
Closed Access | Times Cited: 14
Screening and identification of novel umami peptides from yeast proteins: Insights into their mechanism of action on receptors T1R1/T1R3
Yuxiang Gu, Xuewei Zhou, Yajie Niu, et al.
Food Chemistry (2024) Vol. 463, pp. 141138-141138
Closed Access | Times Cited: 4
Artificial intelligence in food bioactive peptides screening: Recent advances and future prospects
Ji Yoon Chang, Haitao Wang, Wentao Su, et al.
Trends in Food Science & Technology (2024), pp. 104845-104845
Closed Access | Times Cited: 4
Analysis of the mechanism of difference in umami peptides from oysters (Crassostrea ariakensis) prepared by trypsin hydrolysis and boiling through hydrogen bond interactions
Yikun Fang, Min Lv, Chuanyan Pan, et al.
Food Chemistry (2025) Vol. 476, pp. 143367-143367
Closed Access
Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance
Muhammad Nabeel Asim, Tayyaba Asif, Faiza Mehmood, et al.
Computers in Biology and Medicine (2025) Vol. 188, pp. 109821-109821
Closed Access
Machine learning-based exploration of Umami peptides in Pixian douban: Insights from virtual screening, molecular docking, and post-translational modifications
Sen Mei, Liangyu Zhang, Yajie Li, et al.
Food Chemistry (2025) Vol. 478, pp. 143672-143672
Closed Access
Integration of cloud-based molecular networking and docking for enhanced umami peptide screening from Pixian douban
Sen Mei, Shanshan Yao, Jingjing Mo, et al.
Food Chemistry X (2023) Vol. 21, pp. 101098-101098
Open Access | Times Cited: 11
Generation, Characterisation and Identification of Bioactive Peptides from Mesopelagic Fish Protein Hydrolysates Using In Silico and In Vitro Approaches
María Hayes, Azza Silotry Naik, Leticia Mora, et al.
Marine Drugs (2024) Vol. 22, Iss. 7, pp. 297-297
Open Access | Times Cited: 3
Contemporary bioinformatics and cheminformatics support for food peptidomics
Piotr Mińkiewicz, Anna Iwaniak, Małgorzata Darewicz
Current Opinion in Food Science (2024) Vol. 56, pp. 101125-101125
Closed Access | Times Cited: 3
Predicting multiple taste sensations with a multiobjective machine learning method
Lampros Androutsos, Lorenzo Pallante, Agorakis Bompotas, et al.
npj Science of Food (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 3
Bioinformatics and bioactive peptides from foods: Do they work together?
Anna Iwaniak, Piotr Mińkiewicz, Małgorzata Darewicz
Advances in food and nutrition research (2024), pp. 35-111
Closed Access | Times Cited: 2
UmamiPreDL: Deep Learning model for umami taste prediction of peptides using BERT and CNN
Arun Pandiyan Indiran, Humaira Fatima, S. Chattopadhyay, et al.
Computational Biology and Chemistry (2024) Vol. 111, pp. 108116-108116
Closed Access | Times Cited: 2
Identification of Novel Umami Peptides from Yeast Protein through Enzymatic, Sensory, and In Silico Approaches
Yuxiang Gu, Yajie Niu, Jingcheng Zhang, et al.
Journal of Agricultural and Food Chemistry (2024) Vol. 72, Iss. 36, pp. 20014-20027
Closed Access | Times Cited: 2
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