Special Issue Editors

Leading Editors：

• Song-Hong Yang, Associate Professor, Jiangxi University of Chinese Medicine, yangsonghong@jxutcm.edu.cn
• Ye Qiu, Associate Professor, Changchun University of Chinese Medicine, ccyeqiu@163.com

Co-editors：

• Yang Lu, Professor, Beijing University of Chinese Medicine, landocean28@163.com
• Gang Cao, Professor, Zhejiang Chinese Medical University, caogang33@163.com
• Cheng-Guo Ju, Professor, Liaoning University of Traditional Chinese Medicine, jcg7092357@163.com
• Hai Jiang, Professor, Heilongjiang University of Chinese Medicine, 25424533@qq.com
• Tao Su, Researcher, Guangzhou University of Chinese Medicine, sutao@gzucm.edu.cn
• Jun-Qing Huang, Professor, Jinan University, jqhuang@jnu.edu.cn
• Zhi-Fei Xu, Researcher, Zhejiang University, xzfzjut@zju.edu.cn
• Bing Yang, Associate Professor, China Pharmaceutical University, 15751151582@163.com
• Yi-Qi Yan, Senior Engineer, Shaanxi University of Technology, yyq993523132@163.com

Description

Processing is a distinctive core technology of Traditional Chinese Medicine, sharing a deep origin with culinary arts—both transform the physicochemical properties and functional orientations of materials through techniques such as heat control, aqueous processing, adjuvants, and fermentation. Food-medicine homologous substances serve as important objects of herbal processing: as medicines, their processing must achieve toxicity reduction and efficacy enhancement; as foods, they must also satisfy edibility quality (taste, color, safety, bioavailability). However, traditional processing research has largely relied on empirical descriptions and single-factor analyses, making it difficult to systematically reveal the multidimensional patterns of dynamic chemical composition changes, detoxification and loss of toxic components, enrichment and generation of active components, and their interactions with the organism during processing. In recent years, the rapid advancement of artificial intelligence (AI) technologies has provided a new paradigm for processing research: based on machine learning, deep learning, multi-omics data integration, process analytical technology, and intelligent sensory evaluation, it enables real-time monitoring of processing procedures, dynamic modeling of efficacy and toxicity changes, causal inference of toxicity-reduction and efficacy-enhancement mechanisms, and intelligent optimization of processing parameters. Meanwhile, the emergence of organ-on-a-chip technology offers a high-throughput platform that more closely mimics human physiology for safety and activity evaluation of processed products, which can be deeply integrated with AI to accelerate mechanistic elucidation and translational application.

This special issue aims to build an interdisciplinary research platform of "AI+organoids+processing," integrating cutting-edge technologies including medicinal chemistry, multi-omics, culinary science, organ-on-a-chip, and gut microbiome analysis, to systematically elucidate the scientific connotation of toxicity reduction and efficacy enhancement in processing, to promote the transformation of processing technology from experience-driven to data-driven and from qualitative to quantitative intelligence, and to provide theoretical support and safety assurance for the development of high-quality health products from the perspective of food-medicine homology.

The topics include but are not limited to the following:

1.AI-enabled dynamic profiling of chemical components and identification of toxicity/efficacy biomarkers during processing (component-toxicity-efficacy association modeling based on mass spectrometry big data, network pharmacology, and deep learning).

2.AI-based mining of molecular mechanisms underlying toxicity reduction and efficacy enhancement in processing (multi-omics integration, target and pathway prediction, and gut microbiome-mediated toxicity metabolism regulation).

3.Intelligent optimization and process control of processing technologies (real-time decision-making of processing parameters driven by in-line sensors, computer vision, knowledge graphs, and digital twin technologies).

4.Processing for edibility and dual-objective optimization of food-medicine homologous substances (AI-assisted process design balancing toxicity reduction, efficacy enhancement, and sensory quality, and its effects on bioavailability and in vivo behavior).

5.Novel technologies for safety and activity evaluation of processing-induced toxicity reduction and efficacy enhancement based on organ-on-a-chip models (construction of intestinal, hepatic, renal and other organoid and organ-on-a-chip models, combined with AI-based image recognition and multimodal data analysis, for rapid assessment of cytotoxicity, metabolic activation, and pharmacological activity before and after processing, enabling high-throughput in vitro safety and efficacy screening).

Keywords: Artificial intelligence; Herbal processing; Toxicity reduction and efficacy enhancement; Food-medicine homology; Health function

Instructions for submission:

The Editorial Manager System is ready for article submission towards the Virtual special issue: AI-Empowered Herbal Processing: Technological Upgrading, Toxicity Reduction and Efficacy Enhancement, and Food-Medicine Homology Research

The submission website for this journal is located at: https://mc03.manuscriptcentral.com/fmhr

To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important that authors select Special issue: AI-Empowered HP when they reach the “Article Type” step in the submission process.

Benefits of publishing with Food & Medicine Homology Resources:

Open access (unlimited and free access for readers, and high visibility on SciOpen).

https://www.sciopen.com/journal/3136-103X

Free of any charge.

Precise push, good reference effect.

Submission Deadline: December 30, 2026

Publication Date: May, 2027