TCM network pharmacology: new perspective integrating network target with artificial intelligence and multi-modal...

Area of Science:

• Integrative medicine and computational biology.
• Exploration of Traditional Chinese Medicine (TCM) mechanisms.

Background:

• Traditional Chinese Medicine (TCM) offers unique disease prevention and treatment benefits.
• The reductionist 'single drug, single target' model struggles with TCM's holistic nature.
• Network pharmacology provides a systems-level approach to understand complex drug-disease interactions.

Purpose of the Study:

• To review advancements in network target methodologies for TCM research.
• To explore the integration of artificial intelligence (AI) and multi-modal multi-omics in network pharmacology.
• To highlight the application of these integrated methods in decoding TCM principles, formulae, and toxicity.

Main Methods:

• Network pharmacology and network target theory as core frameworks.
• Computational and experimental approaches combined with AI for predicting disease mechanisms and drug-target relationships.
• Application of multi-modal multi-omics technologies for analyzing TCM characteristics.

Main Results:

• Network target theory effectively bridges the gap between TCM's holistic principles and modern scientific inquiry.
• AI and multi-omics significantly enhance the predictive power and analytical depth of network pharmacology.
• These integrated methods show promise in elucidating TCM formulae, syndromes, and toxicity profiles.

Conclusions:

• Network target theory, augmented by AI and multi-omics, is crucial for advancing TCM research.
• Future integration of emerging technologies will further refine network target approaches for TCM.
• This synergy promises significant breakthroughs in understanding and innovating TCM.