[Lecture] Artificial Intelligence Technology Aids in the Efficient Analysis of Active Substances in Traditional Chinese Medicine

Speaker: Ge Guangbo (Shanghai University of Traditional Chinese Medicine)

Date: July 5, 2024(Friday)

Time: 16:00-17:30

Venue: Room 225, Zhang Heng Building

Lecture content: The clinical efficacy of traditional Chinese medicine (TCM) must be based on its material foundation. These efficacy components are not only the cornerstone of the pharmacological activity of herbal medicines but also an important source for the development of innovative drugs. However, for a long time, due to the variety and often trace amounts of compounds in natural medicinal resources (such as animals, plants, and microorganisms), discovering specific pharmacological effect substances from complex extracts of animals, plants, or microorganisms has always been a major challenge in the field of TCM. In recent years, with the continuous innovation and integration of modern separation and analysis techniques, omics, chemical biology, bioinformatics, artificial intelligence, and other technologies, a series of new strategies and methods suitable for the efficient discovery of active components in TCM have emerged, greatly facilitating researchers in efficiently discovering efficacy substances from complex herbal medicines. This report will focus on the application progress of technologies such as artificial intelligence and big data analysis in the field of TCM, and by combining specific research cases, it will share how to efficiently discover small molecules with specific efficacy from herbal medicines (such as inhibitors of target proteins), as well as how to use bio-medical big data to analyze the key targets and pharmacological mechanisms of TCM effect components. The related work provides new ideas and key technologies for the scientific interpretation of the clinical efficacy of TCM and the development of innovative drugs derived from TCM.

Introduction to the speaker: Ge Guangbo, researcher, doctoral supervisor, currently the executive vice president of the Interdisciplinary Research Institute at Shanghai University of Traditional Chinese Medicine. Main research directions: 1) Research on new technologies and methods for drug screening; 2) Research on efficient discovery and optimization of Chinese medicine efficacy substances based on AI. He has presided over more than 10 major national scientific research projects (including 8 National Natural Science Foundation projects).

Source: NYNU Academic Activities (Chinese)

https://www.nynu.edu.cn/info/1048/28099.htm