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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and

Jiafang Li^1,2, Yifei Li^1,2, Siying Lin^1,3

¹Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.

Plos Computational Biology

|May 29, 2026

Summary

A new AI model, Multi-omics Graph Transformer Network (MOGT), effectively predicts disease-associated genes for brain disorders using multi-omics data. This approach aids in identifying potential drug candidates for conditions like Parkinson's disease.

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Area of Science:

Genomics
Computational Biology
Neuroscience

Background:

Genome-wide association studies (GWAS) have identified numerous genetic loci linked to neuropsychiatric and neurodegenerative disorders, but their functional impact remains unclear.
Deep learning and multi-omics data offer promising avenues to connect GWAS findings with underlying disease mechanisms.

Purpose of the Study:

To develop a novel computational framework, the Multi-omics Graph Transformer Network (MOGT), for predicting disease-associated genes by integrating multi-omics data and biological networks.
To evaluate MOGT's performance in identifying high-risk genes (HRGs) for psychiatric and neurodegenerative/neurological diseases.

Main Methods:

MOGT, a semi-supervised graph neural network, was employed to model biological networks derived from multi-omics data for gene prediction.
The model's predictive accuracy was assessed across two psychiatric and three neurodegenerative/neurological disease cohorts.
High-risk genes identified for Parkinson's disease (PD) were utilized for drug discovery through integration with the Connectivity Map (CMAP) database.

Main Results:

MOGT demonstrated superior performance in disease gene prediction compared to existing methods for the studied brain disorders.
Ten potential drug candidates were identified for Parkinson's disease (PD) by analyzing MOGT-predicted HRGs against the CMAP database.
Experimental validation confirmed that the drug UK-356618 could reverse abnormal PD-associated gene expression and improve cellular phenotypes in a primary neuron model.

Conclusions:

MOGT serves as a powerful tool for identifying high-risk genes associated with various brain disorders.
The predicted HRGs offer valuable insights into the molecular mechanisms of brain diseases and facilitate the discovery of novel therapeutic strategies.
The successful drug repurposing for PD highlights the clinical translational potential of MOGT-driven gene discovery.