Drug Repurposing: From Data to Evidence
MyTxGNN is a drug repurposing prediction platform based on Harvard's TxGNN model. We predicted 41,560 potential new indications using knowledge graph methods, and 176,021 high-confidence predictions (score ≥ 0.7) using deep learning for 508 drugs approved by Malaysia's NPRA.
Browse Drugs Learn Methodology
Key Statistics
Our Approach
MyTxGNN uses two complementary approaches for drug repurposing prediction: Knowledge Graph (KG) methods leverage existing drug-disease relationships, while Deep Learning (DL) models learn complex patterns from biomedical data.
Based on TxGNN's biomedical knowledge graph containing drug-disease relationships from DrugBank, clinical trials, and scientific literature. 41,560 predictions for 508 drugs.
TxGNN's neural network model provides confidence scores for each drug-disease pair. 176,021 predictions with score ≥ 0.7, indicating high confidence.
Focused on drugs registered with Malaysia's National Pharmaceutical Regulatory Agency (NPRA), ensuring relevance to local healthcare needs.
Automated collection of supporting evidence from ClinicalTrials.gov, PubMed, and other authoritative sources to validate predictions.
Top Predictions by Disease Category
| Disease Category | Predictions | Top Drug |
|---|---|---|
| Allergic Rhinitis | 392 | Prednisolone, Betamethasone |
| Hypertension | 366 | Multiple antihypertensives |
| Rheumatoid Arthritis | 316 | Corticosteroids |
| Seborrheic Dermatitis | 288 | Ketoconazole, Fusidic Acid |
| Asthma | 259 | Bronchodilators, Steroids |
Quick Navigation
| Section | Description | Link |
|---|---|---|
| Drug List | Browse all 508 analyzed drugs | View Drugs |
| Methodology | Learn about our prediction approach | Read More |
| Data Sources | Explore our data sources | View Sources |
| About | Learn about this project | About Us |
| Downloads | Get prediction data | Download |
| FHIR API | Access via FHIR R4 | API Docs |
About This Project
This platform uses the TxGNN deep learning model published in Nature Medicine by Harvard University's Zitnik Lab to predict potential new indications for Malaysia NPRA-approved drugs.
"TxGNN is the first foundation model for drug repurposing designed specifically for clinicians, integrating knowledge graphs with deep learning to predict drug efficacy for rare diseases." — Huang et al., Nature Medicine (2023)
Data Scale
| Item | Count |
|---|---|
| NPRA Registered Products | 27,938 |
| Drugs with DrugBank Mapping | 508 |
| KG Predictions | 41,560 |
| DL High-Score Predictions | 176,021 |
| Unique Diseases | 17,041 |
Data Sources
This platform integrates multiple authoritative public data sources to ensure prediction traceability and academic value.
This report is for academic research purposes only and does not constitute medical advice. Drug use should follow physician guidance. Do not self-adjust medications. Any drug repurposing decisions require complete clinical validation and regulatory review.
Last updated: 2026-03-03 | MyTxGNN Research Team