Toward a new industry 5.0 paradigm for human-centered food manufacturing: AI-enabled digitization of nano-scale smart nutrient carriers

IF 15.4 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology Pub Date : 2025-08-18 DOI:10.1016/j.tifs.2025.105241

Sana Yakoubi

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引用次数: 0

Abstract

Background

In the era of precision food engineering, the convergence of AI-powered protein structure prediction, advanced molecular docking, and digital twin technologies represents a paradigm shift in the development of adaptive delivery systems. The precision design of nanoscale food delivery systems increasingly depends on the ability to accurately predict interactions between proteins and complex food matrices, which is crucial for optimizing encapsulation, stability, and controlled nutrient release.

Scope and approach

This review explores recent advances in computational tools for modeling protein structures and predicting protein-matrix interactions (PMIs). It covers template-based, de novo, hybrid, and deep learning approaches that have redefined molecular modeling capabilities. The review also assesses the role of artificial intelligence in enhancing molecular docking algorithms, particularly through graph neural networks, natural language processing, and transformer-based models. Furthermore, it examines the potential of digital twin technologies to virtualize and simulate molecular behaviors in real time, creating opportunities for dynamic, smart food system design.

Key findings and conclusions

Computational and AI-driven approaches are transforming the ability to model protein structures and predict PMIs with unprecedented accuracy. These advancements facilitate the design of more effective biointelligent encapsulation systems, tailored for enhanced nutrient stability and targeted delivery. Digital twin technologies complement these developments by enabling real-time simulation and optimization of delivery system performance. Taken together, this review integrates these interdisciplinary tools to present a roadmap for the next generation of data-driven, personalized, and sustainable biointelligent food systems for controlled release, thereby advancing the frontiers of precision food engineering.

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面向以人为中心的食品制造的新工业5.0范式：纳米级智能营养载体的人工智能数字化

在精准食品工程时代，人工智能驱动的蛋白质结构预测、先进分子对接和数字孪生技术的融合代表了自适应递送系统发展的范式转变。纳米级食物递送系统的精确设计越来越依赖于准确预测蛋白质和复杂食物基质之间相互作用的能力，这对于优化封装、稳定性和控制营养物质释放至关重要。本文综述了用于蛋白质结构建模和预测蛋白质-基质相互作用（pmi）的计算工具的最新进展。它涵盖了基于模板、从头开始、混合和深度学习的方法，这些方法重新定义了分子建模能力。该综述还评估了人工智能在增强分子对接算法方面的作用，特别是通过图神经网络、自然语言处理和基于变压器的模型。此外，它还研究了数字孪生技术在实时虚拟化和模拟分子行为方面的潜力，为动态智能食品系统设计创造了机会。计算和人工智能驱动的方法正在以前所未有的精度改变蛋白质结构建模和预测pmi的能力。这些进步有助于设计更有效的生物智能封装系统，为增强营养稳定性和靶向递送量身定制。数字孪生技术通过实现实时仿真和优化交付系统性能来补充这些发展。综上所述，本综述整合了这些跨学科的工具，为下一代数据驱动、个性化和可持续的生物智能食品控制释放系统提供了路线图，从而推进了精密食品工程的前沿。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Trends in Food Science & Technology 工程技术-食品科技

CiteScore

32.50

自引率

2.60%

发文量

322

审稿时长

37 days

期刊介绍： Trends in Food Science & Technology is a prestigious international journal that specializes in peer-reviewed articles covering the latest advancements in technology, food science, and human nutrition. It serves as a bridge between specialized primary journals and general trade magazines, providing readable and scientifically rigorous reviews and commentaries on current research developments and their potential applications in the food industry. Unlike traditional journals, Trends in Food Science & Technology does not publish original research papers. Instead, it focuses on critical and comprehensive reviews to offer valuable insights for professionals in the field. By bringing together cutting-edge research and industry applications, this journal plays a vital role in disseminating knowledge and facilitating advancements in the food science and technology sector.