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Abstract

Introduction

Breast cancer is a leading cause of cancer-related mortality in women. Although the traditional Chinese medicine Codonopsis Pilosula (CP) is empirically used in its treatment the underlying mechanisms of action remain elusive. This study aimed to apply a novel integrative network pharmacology and machine learning approach to identify bioactive compounds in CP and elucidate their anti-breast cancer mechanisms.

Methods

The analysis utilized a comprehensive and innovative workflow that combined network pharmacology machine learning-based target prediction bioinformatics analyses and molecular docking and molecular dynamics simulations. Publicly available datasets were mined for CP constituents and putative targets and integrated with breast cancer-associated gene profiles. Key compound-target interactions were prioritized machine learning algorithms.

Results

Machine learning highlighted EGFR and PTGS2 as primary targets. Molecular docking and dynamics demonstrated stable binding of Taraxerol and Stigmasterol to these proteins with EGFR–Taraxerol EGFR–Spinasterol PTGS2–Stigmasterol and PTGS2–Taraxerol complexes exhibiting robust affinity and stability.

Discussion

The findings are significant as they reveal previously unreported interactions between CP’s bioactive compounds and critical breast cancer targets. This provides a molecular-level explanation for the traditional use of CP bridging the gap between TCM and modern pharmacology. These results offer a solid foundation for further experimental validation.

Conclusion

This multidisciplinary predictive strategy successfully identified key bioactive compounds in CP and their molecular targets in breast cancer. The study provides crucial mechanistic evidence for CP’s therapeutic potential and highlights the power of this integrated approach for drug discovery from TCM (Traditional Chinese Medicine).

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2026-01-08
2026-01-19

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Unraveling Multi-target Mechanisms of Codonopsis pilosula in Breast Cancer: A Synergistic Approach Combining Network Pharmacology, Molecular Docking, and Machine Learning Techniques
Bentham Science Publishers ; https://doi.org/10.2174/0115734099391503251119090622
/content/journals/cad/10.2174/0115734099391503251119090622
/content/journals/cad/10.2174/0115734099391503251119090622
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  • Article Type:     Research Article
Keywords: Codonopsis pilosula ; network pharmacology ; mechanism ; breast cancer ; machine learning technique ; molecular dynamics

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