Page 84 - 2025中醫藥與天然藥物聯合學術研討會-中醫藥與天然藥物的挑戰Ｘ機遇與未來大會手冊

Bridging Traditional Medicine and Modern Therapeutics: AI-Driven

Discovery of Bioactive Natural Compounds

,1
Kuan-Wen Chen*

1 Molecular Science and Digital Innovation Center, GGA Corp. Taipei, Taiwan

* E-mail: Genechen@gga.asia

Abstract
Traditional herbal medicine provides a rich reservoir of therapeutic compounds, yet its

clinical translation remains challenging due to structural complexity and limited mechanistic
understanding. In our recent studies, we applied in silico molecular simulations and artificial
intelligence (AI)-driven models to systematically evaluate natural products and traditional
Chinese medicine (TCM) formulations. Using approaches such as Molecular Dynamics,
ADMET prediction, and multi-omic integration, we examined compounds including baicalin,

pterostilbene, cannabidiol, and Jing-Si herbal tea in different studies. These analyses revealed
potential therapeutic effects in antiviral defense, anti-inflammatory regulation, metabolic
control, and cancer sensitization. Furthermore, AI-based prediction identified novel targets such

as IL-6 related targets and SARS-CoV-2 proteases, supporting applications in COVID-19,
psoriasis, and gastric cancer. Our findings demonstrate how combining traditional medicine
with computational technologies enables a predictive and evidence-based framework for
therapeutic innovation. This integrative strategy highlights the value of merging centuries-old
knowledge with digital medicine to accelerate next-generation drug discovery.

Keywords: AI Drug Design; TCM; Multi-Omics

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