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AI-assisted rational design of efficient nutrient delivery systems: Molecular interactions, physiological barrier
Jiabao Huang1, Wu Li1, Yilong Cao1
1College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian 361021, People's Republic of China.
None:
The rational design of efficient nutrient delivery systems (ENDSs) is pivotal for enhancing the stability, bioavailability, and targeted release of bioactive nutrients. This complex, multiscale endeavor requires the simultaneous optimization of molecular interactions, carrier compatibility, processing stability, and physiological barrier penetration. Conventional empirical approaches, often time-consuming and resource-intensive, struggle to navigate this high-dimensional design space efficiently, highlighting the urgent need for transformative methodologies. This review systematically examines how artificial intelligence (AI) is reshaping the rational design pipeline across each key stage, critically evaluate strategies for dataset generation and feature representation specific to food systems, assesses the suitability of various AI models under data-limited conditions, and explores emerging trends such as large language model-assisted knowledge extraction and automated molecular simulation workflows. AI is demonstrably permeating and enhancing multiple facets of ENDS design, contributions a significant reduction in experimental workload, acceleration of development timelines, and expansion of the explorable space of food-derived compounds. As a forward-looking synthesis, this review proposes a conceptual blueprint for a closed-loop, AI-driven intelligent platform. This framework underscores the transition toward next-generation, data-informed ENDSs, paving the way for more efficient, personalized, and intelligent nutrition delivery solutions aligned with industrial and consumer needs.
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