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

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.