Network pharmacology


Network pharmacology is a rapidly evolving field in big data and AI era. It explores the intricate mechanisms underlying traditional Chinese medicine (TCM) through the lens of biomolecular network analysis. Unlike the conventional approach of 'one drug, one target,' network pharmacology focuses on comprehending the synergistic interactions of multiple compounds within TCM formulae on a holistic "network target". This innovative perspective showcases the advantages of TCM, where the combination of multiple compounds in a formula often leads to multifaceted therapeutic effects.

To unlock the potential of TCM and expedite drug development, our researchers have embraced bioinformatic technologies, which are essential in comprehending the complexities of molecular interactions within the human body. One of our achievements is the development of the UNIQ system, a cutting-edge network-pharmacology-based TCM drug development platform. This platform integrates vast amounts of biological and pharmacological data, enabling scientists to gain valuable insights into the intricate workings of TCM formulae.

The UNIQ system has proven to be an invaluable asset in the real-world context, as it facilitates TCM drug development in various applications. By leveraging the power of network pharmacology and bioinformatics, the platform aids researchers and drug developers in identifying potential therapeutic targets, predicting the interactions between TCM compounds and these targets, and gauging the overall efficacy and safety of the TCM formulas.

Network pharmacology and the UNIQ system have revolutionized the exploration and development of TCM drugs. By providing a more comprehensive understanding of the complex interactions within TCM formulae, these advancements have enhanced the prospects of TCM in modern medicine and introduced new possibilities for the development of novel and effective drugs inspired by traditional Chinese medicine.


Related articles:

· Shao Li, Jianxin Chen, Yuanjia Hu, Min Ye. Editorial: Network pharmacology and AI. Journal of Ethnopharmacology. 2023 May;307:116260.

https://doi.org/10.1016/j.jep.2023.116260

· Mingzhu Y, Zhang Y, Wang W, et al. Network-based molecular subtyping of acral melanoma. bioRxiv; 2023.

https://doi.org/10.1101/2023.02.04.527155

· Wang X, Hu Y, Zhou X, Li S. Editorial: Network pharmacology and traditional medicine: Setting the new standards by combining In silico and experimental work. Frontiers in Pharmacology. 2022 Oct 21;13:1002537.

https://doi.org/10.3389/fphar.2022.1002537

· Li S*. Mapping ancient remedies: applying a network approach to traditional Chinese medicine. Science 2015;350(6262 Suppl):S72-S74

· Wu X, Jiang R, Zhang MQ, Li S*. Network-based global inference of human disease genes. Molecular Systems Biology 2008;4:189

https://doi.org/10.1038/msb.2008.27