HerbSyner_Finder: a network community-based model for identifying synergistic combinations from herbal medicines and complex systems

Abstract

Herbal medicine is a valuable resource for disease treatment, with enhanced synergistic efficacy and fewer side effects through combined herbal formulations. However, the synergistic mechanisms of action (MOAs) of these herbal medicines remain largely unexplored. Given the complexity of herbal systems, it is impractical to evaluate all possible drug/ingredient pairs experimentally. In this study, we propose a network-based model, HerbSyner_Finder, to prioritize synergistic ingredients in herbal medicine. By integrating network proximity and community analyses, HerbSyner_Finder could construct a multidimensional combinatorial atlas for complex biological systems to quantify herb-disease, ingredient-disease, herb-herb, and ingredient-ingredient interactions. Using cough variant asthma (CVA)-related herbal formulae as examples, kaempferol-quercetin and berberine-luteolin were successfully prioritized as synergistic for CVA among thousands of potential pairs. Further network analysis revealed that berberine and luteolin synergistically modulate the NLRP3/NF-κB signaling pathway, thereby alleviating CVA-associated inflammation. In summary, HerbSyner_Finder offers a tailored computational framework that efficiently identifies synergistic compounds from complex systems, and herbal medicines through high-throughput screening.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (82474065), Jiangsu Province Science Foundation for Youths (BK20231024), and Young Scientists Fund of the National Natural Science Foundation of China Grants (8240142940).

Ethical statements

All animal experiments received formal approval from the Institutional Animal Ethics Committee of China Pharmaceutical University (Approval No. 2025-01-021).

Author contributions

The authors confirm their contributions to the study as follows: methodology: Wang Y, Yao J, Sui Y; data curation: Wang Y, Yao J, Jiang H, Gao Z; writing, review, editing, funding acquisition, and supervision: Wang Y, Tan N; conceptualization: Wang Y; investigation and writing original draft: Yao J, Wang Y; formal analysis: Yao J, Lai S, Xu X, Ma B. All authors reviewed the results and approved the final version of the manuscript.

Data availability

All data, methodologies, and implementation details described in this study, including network construction, Louvain community detection, and synergy score calculations, are available on GitHub: https://github.com/19900321/HerbSyner_Finder. An interface website has also been developed to support task submission for HerbSyner_Finder calculations at https://herbcomb.com/#/main/finder_synergy.

Conflict of interest

The authors declare that they have no conflict of interest.

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