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. 2025 Mar 5;20(1):31.
doi: 10.1186/s13020-025-01082-5.

Prediction of herbal compatibility for colorectal adenoma treatment based on graph neural networks

Limei Gu #  1 , Yinuo Ma #  2 , Shunji Liu  2 , Qinchang Zhang  3 , Qiang Zhang  1 , Ping Ma  1 , Dongfang Huang  1 , Haibo Cheng  4 , Yang Sun  5 , Tingsheng Ling  6
Affiliations

Prediction of herbal compatibility for colorectal adenoma treatment based on graph neural networks

Limei Gu et al. Chin Med. .

Abstract

Colorectal adenoma is a common precancerous lesion with a high risk of malignant transformation. Traditional Chinese medicine and its complex prescriptions have shown promising efficacy in the treatment of adenomas; however, there remains a lack of systematic understanding regarding the compatibility patterns within these prescriptions, as well as an effective model for predicting therapeutic outcomes. In this study, we collected numerous TCM prescriptions and their components, recommended by experts for the treatment of colorectal adenoma, and developed a heterogeneous graph neural network model to predict the compatibility strength and probability among the herbs within these prescriptions. This model delineates the complex relationships among herbs, active compounds, and molecular targets, allowing for a quantification of the interactions and compatibility potential among the herbs. Using this model, we identified high-potential therapeutic prescriptions from clinical prescription records and identified their active components through network pharmacology. Through this approach, we aim to provide a theoretical foundation for the clinical TCM treatment of colorectal adenoma, foster the discovery of new prescriptions to optimize the therapeutic efficacy of TCM, and ultimately advance the field of cancer prevention and treatment based on traditional Chinese medicine.

Keywords: Colorectal adenoma; Graph neural network; Traditional Chinese medicine.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All authors agreed to the publication of the manuscript. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Model workflow for herbal compatibility prediction
Fig. 2
Fig. 2
Analysis of herbal categories, properties, molecular composition, and target enrichment in colorectal adenoma treatments. A Distribution of Herbal Categories for Colorectal Adenoma Treatment. B Distribution of Herbal Properties (Nature and Flavor). C t-SNE Clustering of Small Molecule Components Based on MACCS Fingerprints. D GO Enrichment Analysis of Herb Targets. E Word Cloud of GO Enrichment Analysis Terms
Fig. 3
Fig. 3
Analysis of herb-herb pairing potential based on model predictions. A Comparison of Model Predictions and Actual Values. B High-Potential Herb Pair Screening. C Herb-Herb Pairing Network Structure. D Core Network of High-Frequency, High-Potential Herb Pairs
Fig. 4
Fig. 4
Suitability analysis of prescriptions based on HCPI. A Suitability distribution of prescriptions related to colonic polyps and intestinal tuberculosis. B Comparison of suitability between high-frequency and low-frequency prescriptions (student t-test, p > 0.05, ns; p < 0.05, *; p < 0.01, **; p < 0.001, ***). C Example of a representative high-frequency, high-suitability prescription. D Herbal network structure of a representative high-frequency prescription, with nodes representing herbs and edges indicating HCPI values between herbs
Fig. 5
Fig. 5
Enrichment analysis of target genes and protein–protein interaction (PPI) network study. A Venn diagram of colorectal adenoma targets and high-potential prescription targets. B, C GO enrichment analysis of overlapping genes. D PPI network analysis of overlapping genes
Fig. 6
Fig. 6
Active small molecules in high-potential prescription. A Volcano plot showing differential gene expression. B Target frequency analysis in high-potential prescriptions. C Molecular docking results of CHRM3. D, E CCK-8 cell viability assays demonstrating the anticancer potential of small molecules
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