Project Introduction
SgTxGNN: Drug Repurposing Predictions for Singapore
Background
Drug repurposing (also known as drug repositioning) is a strategy for identifying new uses for existing, approved drugs. This approach offers several advantages over traditional drug discovery:
- Reduced development time: Existing drugs have known safety profiles
- Lower costs: Much of the clinical work has already been done
- Higher success rates: Known compounds reduce technical risk
The TxGNN Model
SgTxGNN is built on TxGNN (Therapeutic Target Prediction using Graph Neural Networks), developed by Harvard Medical School's Zitnik Lab and published in Nature Medicine (2023).
Key Features
- Knowledge Graph: Integrates biomedical knowledge from multiple sources
- Deep Learning: Uses graph neural networks for prediction
- Clinical Focus: Designed specifically for drug repurposing
Citation
Huang, K., Chandak, P., Wang, Q. et al. A foundation model for clinician-centered drug repurposing. Nat Med (2023). https://doi.org/10.1038/s41591-023-02233-x
Singapore Adaptation
SgTxGNN adapts TxGNN specifically for Singapore:
Data Sources
| Source | Description |
|---|---|
| HSA | Health Sciences Authority drug registry |
| DrugBank | Drug-target relationships |
| TxGNN KG | Biomedical knowledge graph |
Coverage
- 745 drugs mapped to DrugBank
- 31,543 predictions generated
- 4,589 diseases covered
Methodology
1. Drug Mapping
Singapore HSA drug data is mapped to international identifiers:
HSA Drug Registry → Ingredient Normalisation → DrugBank ID
Mapping rate: 73.87% of HSA ingredients successfully mapped
2. Prediction Methods
Two complementary approaches:
| Method | Description | Count |
|---|---|---|
| Knowledge Graph (KG) | Direct relationship queries | 22,136 |
| Deep Learning (DL) | Neural network predictions | 29,100 |
| Unified (KG+DL) | Dual-validated predictions | 1,217 |
3. Evidence Classification
Predictions are classified by evidence level:
| Level | Description |
|---|---|
| L1 | Multiple Phase 3 RCTs |
| L2 | Single RCT or Phase 2 trials |
| L3 | Observational studies |
| L4 | Preclinical/mechanistic evidence |
| L5 | Model prediction only |
Use Cases
For Researchers
- Identify promising drug repurposing candidates
- Prioritise experimental validation studies
- Explore drug-disease relationships
For Clinicians
- Research potential off-label uses
- Understand drug mechanisms
- Access structured evidence summaries
For Healthcare Institutions
- Support translational research
- Enable clinical decision support research
- Facilitate EHR integration via SMART on FHIR
Limitations
Important considerations:
- Research use only: Predictions have not been clinically validated
- No clinical guidance: Not intended for treatment decisions
- Model uncertainty: AI predictions may be incorrect
- Singapore focus: Based on HSA-approved medications
Future Directions
Planned enhancements:
- Integration of clinical trial evidence
- Literature-based validation scoring
- Drug interaction analysis
- Periodic model updates
Contact
- Website: sgtxgnn.yao.care
- GitHub: yao-care/SgTxGNN
- Issues: Report bugs or suggest features