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Updated: May 25, 2025

A Hormone-responsive 3D Culture Model of the Human Mammary Gland Epithelium

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Deep Learning-Based Drug Compounds Discovery for Gynecomastia.

Yeheng Lu¹, Byeong Seop Kim², Junhao Zeng¹

¹Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

|February 26, 2025

Summary

This study used AI to find new drug candidates for gynecomastia, an estrogen-testosterone imbalance. Deep learning identified 12 potential compounds, offering hope for novel treatments for this condition.

Keywords:

DeepPurpose deep learning (DL)drug therapy drug–target interactions (DTIs)gynecomastia

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Area of Science:

Computational biology
Pharmacology
Artificial intelligence in drug discovery

Background:

Gynecomastia results from an estrogen-testosterone imbalance, lacking approved treatments.
The unclear mechanisms of gynecomastia necessitate innovative therapeutic strategies.

Purpose of the Study:

To discover potential drug compounds for gynecomastia using deep learning.
To identify key genes and pathways involved in gynecomastia pathogenesis.

Main Methods:

Text mining and pathway enrichment analyses to identify gynecomastia-associated genes and pathways.
Protein-protein interaction (PPI) network construction to pinpoint crucial genes.
Deep learning (DeepPurpose toolkit) for drug-target interaction (DTI) prediction and compound prioritization based on binding affinity.

Main Results:

177 gynecomastia-associated genes were identified via text mining.
Key pathways included signal transduction, cell proliferation, and steroid hormone biosynthesis.
12 potential therapeutic compounds, including conteltinib and vosilasarm, were predicted with high binding affinities to target genes like IGF1, TGFB1, and AR.

Conclusions:

Deep learning effectively identified potential drug compounds for gynecomastia.
Combining text mining and AI accelerates drug discovery for complex conditions.
Further experimental validation is crucial for developing novel gynecomastia treatments.