Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is...

Full description

Saved in:
Bibliographic Details
Published inBriefings in bioinformatics Vol. 25; no. 1
Main Authors Zhang, Peng, Zhang, Dingfan, Zhou, Wuai, Wang, Lan, Wang, Boyang, Zhang, Tingyu, Li, Shao
Format Journal Article
LanguageEnglish
Published England Oxford Publishing Limited (England) 01.01.2024
Oxford University Press
Subjects
Artificial Intelligence
Data mining
Herbal medicine
Humans
Medicine, Chinese Traditional
Network Pharmacology
Pharmacology
Research Personnel
Review
Traditional Chinese medicine
deep learning
traditional Chinese medicine
network target
network pharmacology
artificial intelligence
Online AccessGet full text

Cover

More Information
Summary:Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
Peng Zhang, Dingfan Zhang and Wuai Zhou should be regarded as joint First Authors.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbad518