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Decoding Herbal Medicine: Machine Learning-Driven Insights into Structural Identification and Pharmacological
Yumo Bie1, Yang Yang2, Pan Wang1
1Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen, P. R. China.
The American Journal of Chinese Medicine
|June 11, 2026
Summary
Artificial intelligence (AI) and machine learning (ML) are revolutionizing herbal medicine research by identifying bioactive compounds and their mechanisms. These computational tools enhance precision and efficiency in understanding complex plant-based therapies.
Area of Science:
- Pharmacology
- Computational Biology
- Cheminformatics
Background:
- Herbal medicine is vital in global healthcare, offering therapeutic benefits from plant-derived compounds.
- Understanding the pharmacological mechanisms and bioactive constituents of herbal medicines is complex.
- Traditional methods for elucidating these mechanisms are often time-consuming and lack precision.
Purpose of the Study:
- To review recent advancements in applying artificial intelligence (AI) and machine learning (ML) to herbal medicine research.
- To focus on AI's role in identifying bioactive compounds and elucidating pharmacological actions.
- To highlight how AI enhances the precision and efficiency of mechanism elucidation for herbal medicines.
Main Methods:
- A comprehensive literature search was conducted in major scientific databases (Google Scholar, PubMed, Scopus, Web of Science, etc.).
- Keywords included "herbal medicine", "machine learning", "deep learning", "natural compounds", "docking", "QSAR", "toxicity", "mass spectrometry", "nuclear magnetic resonance", and ADME terms.
- The review focused on studies applying AI/ML for mechanism elucidation, compound identification, and target prediction.
Main Results:
- AI, particularly ML and deep learning, effectively identifies bioactive compounds and predicts their targets and pathways.
- AI facilitates the discovery of hidden patterns within complex chemical-biological datasets related to herbal medicines.
- Recent AI applications have significantly improved the understanding of herbal medicine components and their biological effects.
Conclusions:
- AI and ML are powerful tools for advancing the scientific understanding of herbal medicines.
- Combining experimental data with computational analysis shows significant promise for future research.
- Challenges remain, including plant material variability and the need for systematic pharmacological studies.
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