Special Issue Editors

Leading Editors

Hongjun Yang, Researcher, China Academy of Chinese Medical Sciences, hongjun.yang@cacms.ac.cn

Co-editors

Xianyu Li, Researcher, China Academy of Chinese Medical Sciences, phd_xianyuli@foxmail.com

Boyang Ji, Researcher, BioInnovation Institute, boyangji@gmail.com

Hongwei Wu, Researcher, China Academy of Chinese Medical Sciences,
hwwu@icmm.ac.cn

Gang Wang, Professor, Jinan University, wangguang7453@126.com

Yang Zhao, Researcher, National Institute of Metrology, China, zynellj@126.com

Yong Zhang, Researcher, Sichuan University, nankai1989@foxmail.com

Honghe Xiao, Associate Professor, Liaoning University Of Traditional Chinese Medicine, xiaohh89@163.com

Huan Yu, Professor, Jiangxi University of Chinese Medicine, 20151022@jxutcm.edu.cn

Qing Wang, Associate Professor, Beijing University of Chinese Medicine,
phd_qingw@foxmail.com

Liufeng Mao, Professor, The First Affiliated Hospital of Guangdong Pharmaceutical University, mlf_9295@126.com

Xingyue Xu, Postdoctoral, The First Affiliated Hospital of Guangdong Pharmaceutical University, xingyue5125@126.com

Yanmiao Ma, Professor, Shanxi University of Chinese Medicine, mymsxtcm@sxtcm.edu.cn

Yiwen Li, Assistant Professor, China Academy of Chinese Medical Sciences, liyiwen-@outlook.com

Description:

The rapid evolution of Artificial Intelligence (AI) is fundamentally transforming paradigms in life sciences and medicine. In Food and Medicine Homology (FMH), AI offers powerful toolkits that bridge traditional herbal wisdom with modern nutritional science. From establishing standardized databases and machine-learning-driven bioactive discovery to deep-learning-based target identification and multi-modal, personalized precision nutrition, AI is accelerating the shift of FMH research from empirical observation to data-driven, quantitative prediction.

Driven by global aging, chronic disease burdens, and an escalating demand for preventive healthcare, the FMH industry urgently requires systematic AI integration to elucidate its scientific mechanisms and propel commercial applications. Food & Medicine Homology Resources invites submissions for this special issue, "AI for Food & Medicine Homology," aiming to showcase cutting-edge interdisciplinary advances and lead the field into an intelligent, precision-driven era.

Topics:

• Creation of novel datasets to support systematic investigation of TCM, FMH, functional food, nutrition and food-as-medicine strategies
• Development of computational algorithms and AI-driven tools for TCM mining, dietary interventions, personalized nutritional recommendations, and clinical decision support.
• Integration of multiple data for TCM mining, substance evaluation, disease prevention, nutritional outcome quantification and individual responses prediction for TCM, FMH and food-as-medicine interventions

Key words:

Food-medicine homology, Artificial intelligence, Machine learning, Deep learning, Dataset, Precision nutrition, Traditional Chinese medicine, Functional food, Network pharmacology, Multi-omics integration, Personalized dietary intervention, Knowledge graph

Submit Type:

• Perspective
• Research Articles
• Review Articles
• Resource
• Data & Tool Updates
• Instructions for submission:
• The Editorial Manager System is ready for article submission towards the Virtual special issue: AI for Food & Medicine Homology 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-FMH 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