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Synergizing Algorithms and Appetite: A Computational Leap Toward Precision Nutrition in Diabetes Management
Commentary | Published: 29 December 2025
Agricultural Science and Food Processing Volume 2, Issue 4, 2025: 173-175
Volume 2, Issue 4, Agricultural Science and Food Processing
Volume 2, Issue 4, 2025
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Agricultural Science and Food Processing, Volume 2, Issue 4, 2025: 173-175

Open Access | Commentary | 29 December 2025
Synergizing Algorithms and Appetite: A Computational Leap Toward Precision Nutrition in Diabetes Management
by
Chunming Xu Chunming Xu 1,2 *
1 School of Light Industry, Beijing Technology and Business University, Beijing 100048, China
2 Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Chunming Xu, [email protected]
DOI: 10.62762/ASFP.2025.185047
ARK: ark:/57805/asfp.2025.185047
Received: 22 December 2025, Accepted: 27 December 2025, Published: 29 December 2025  
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Abstract
This commentary evaluates the study by Liu et al. (2025), which employs deep learning models for large-scale virtual screening of dietary-derived α-glucosidase inhibitors and prediction of nutrient-nutrient synergistic interactions, commending its methodological rigor and advancement toward computational food science while highlighting translational challenges including food matrix effects, dosage feasibility, and gut microbiome influences, thereby providing a significant proof-of-concept for precision nutrition in diabetes management.
Keywords
virtual screening
dietary synergy
precision nutrition
diabetes management
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declares no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Liu, R., Gan, J. W., Sun, M. J., Chen, H. X., Zou, W. Y., Zou, S. D., & Liu, S. (2025). Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of $\alpha$-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro. Journal of Agricultural and Food Chemistry, 73(24), 15165–15177.
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APA Style
Xu, C. (2025). Synergizing Algorithms and Appetite: A Computational Leap Toward Precision Nutrition in Diabetes Management. Agricultural Science and Food Processing, 2(4), 173–175. https://doi.org/10.62762/ASFP.2025.185047
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TY  - JOUR
AU  - Xu, Chunming
PY  - 2025
DA  - 2025/12/29
TI  - Synergizing Algorithms and Appetite: A Computational Leap Toward Precision Nutrition in Diabetes Management
JO  - Agricultural Science and Food Processing
T2  - Agricultural Science and Food Processing
JF  - Agricultural Science and Food Processing
VL  - 2
IS  - 4
SP  - 173
EP  - 175
DO  - 10.62762/ASFP.2025.185047
UR  - https://www.icck.org/article/abs/ASFP.2025.185047
KW  - virtual screening
KW  - dietary synergy
KW  - precision nutrition
KW  - diabetes management
AB  - This commentary evaluates the study by Liu et al. (2025), which employs deep learning models for large-scale virtual screening of dietary-derived α-glucosidase inhibitors and prediction of nutrient-nutrient synergistic interactions, commending its methodological rigor and advancement toward computational food science while highlighting translational challenges including food matrix effects, dosage feasibility, and gut microbiome influences, thereby providing a significant proof-of-concept for precision nutrition in diabetes management.
SN  - 3066-1579
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Xu2025Synergizin,
  author = {Chunming Xu},
  title = {Synergizing Algorithms and Appetite: A Computational Leap Toward Precision Nutrition in Diabetes Management},
  journal = {Agricultural Science and Food Processing},
  year = {2025},
  volume = {2},
  number = {4},
  pages = {173-175},
  doi = {10.62762/ASFP.2025.185047},
  url = {https://www.icck.org/article/abs/ASFP.2025.185047},
  abstract = {This commentary evaluates the study by Liu et al. (2025), which employs deep learning models for large-scale virtual screening of dietary-derived α-glucosidase inhibitors and prediction of nutrient-nutrient synergistic interactions, commending its methodological rigor and advancement toward computational food science while highlighting translational challenges including food matrix effects, dosage feasibility, and gut microbiome influences, thereby providing a significant proof-of-concept for precision nutrition in diabetes management.},
  keywords = {virtual screening, dietary synergy, precision nutrition, diabetes management},
  issn = {3066-1579},
  publisher = {Institute of Central Computation and Knowledge}
}
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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