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Synthesis and Regulation of Thyroid Hormones

Synthesis and Regulation of Thyroid Hormones

Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...

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Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

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MAMGN-HTI: A Graph Neural Network Model with Metapath and Attention Mechanisms for Hyperthyroidism Herb-Target

Yanqin Zhou^1,2, Xiaona Yang^1,2, Ru Lv^1,2

¹School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.

Bioengineering (Basel, Switzerland)

|October 29, 2025

Summary

This study introduces MAMGN-HTI, a novel graph neural network model for predicting herb-target interactions. The model enhances traditional Chinese medicine research and drug discovery by improving accuracy and efficiency.

Keywords:

attention mechanism graph neural network herb–target prediction hyperthyroidism metapath

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Area of Science:

Computational biology
Pharmacology
Bioinformatics

Background:

Predicting herb-target interactions (HTI) is crucial for modernizing traditional Chinese medicine (TCM) and accelerating drug discovery.
Experimental validation of HTI is resource-intensive due to the complexity of herbal compounds and target diversity.

Purpose of the Study:

To develop an accurate and efficient computational model for predicting herb-target interactions.
To leverage graph neural networks and attention mechanisms for enhanced HTI prediction.

Main Methods:

Constructed a heterogeneous graph integrating herbs, efficacies, ingredients, and targets.
Employed metapaths and attention mechanisms within a graph neural network (MAMGN-HTI).
Integrated ResGCN and DenseGCN with cross-layer skip connections for improved feature propagation.

Main Results:

MAMGN-HTI demonstrated superior performance over existing methods in HTI prediction and candidate drug screening.
The model exhibited high accuracy, robustness, and generalizability.
Validated predictions against existing literature and databases, confirming reliability.

Conclusions:

MAMGN-HTI offers a reliable computational framework for HTI prediction, advancing TCM modernization and drug discovery.
The model provides mechanistic insights and improves research efficiency.
Identified potential hyperthyroidism treatments, including specific herbs like Vinegar-processed Bupleuri Radix.