Drug Repurposing: From AI to Evidence
DkTxGNN uses the Harvard TxGNN model to predict drug repurposing candidates for 501 DKMA-approved drugs, identifying potential new therapeutic uses.
Browse Drug Reports Learn Methodology
Drug Search
Enter a drug name or disease name to find repurposing predictions. Supports generic names, brand names, and disease keywords.
Evidence Level:
Key Features
From Prediction to Evidence
Each report integrates clinical trial IDs (NCT), literature references (PMID), and DKMA approval information for complete traceability.
Each report integrates clinical trial IDs (NCT), literature references (PMID), and DKMA approval information for complete traceability.
Five-Level Evidence Classification
L1 (Multiple Phase 3 RCTs) to L5 (AI prediction only) classification helps prioritize candidates for validation.
L1 (Multiple Phase 3 RCTs) to L5 (AI prediction only) classification helps prioritize candidates for validation.
Danish Drug Coverage
Focused on 501 DKMA-approved medicines with repurposing predictions ready for research.
Focused on 501 DKMA-approved medicines with repurposing predictions ready for research.
FHIR Integration
FHIR R4 compliant API and SMART on FHIR app for seamless EHR integration.
FHIR R4 compliant API and SMART on FHIR app for seamless EHR integration.
Quick Navigation
| Category | Description | Link |
|---|---|---|
| High Evidence | L1-L2, priority for clinical evaluation | View drugs |
| Medium Evidence | L3-L4, requires additional validation | View drugs |
| AI Predictions | L5, research direction reference | View drugs |
| Full Drug List | All 501 drugs (searchable) | Drug List |
| Health News | Automated health news monitoring | View News |
| FHIR API | Integration endpoints | FHIR Metadata |
About This Project
DkTxGNN uses the TxGNN deep learning model published by Harvard’s Zitnik Lab in Nature Medicine to predict potential new therapeutic uses for DKMA-approved medications.
“TxGNN is the first foundation model designed for clinician-centered drug repurposing, integrating knowledge graphs with deep learning to predict drug efficacy for rare diseases.” — Huang et al., Nature Medicine (2023)
Statistics
| Item | Count |
|---|---|
| Drug Reports | 501 |
| Regulatory Agency | Danish Medicines Agency (DKMA) |
Data Sources
TxGNN
Harvard Zitnik Lab
ClinicalTrials.gov
NIH Clinical Trials
PubMed
Biomedical Literature
DrugBank
Drug Database
DKMA
Danish Medicines Agency
Disclaimer
This report is for research purposes only and does not constitute medical advice. Drug use should follow physician guidance. Any drug repurposing decisions require complete clinical validation and regulatory review.
Last updated: 2026-03-10 | Maintainer: DkTxGNN Research Team
This report is for research purposes only and does not constitute medical advice. Drug use should follow physician guidance. Any drug repurposing decisions require complete clinical validation and regulatory review.
Last updated: 2026-03-10 | Maintainer: DkTxGNN Research Team