About This Project
About the Developer
This platform is developed and operated by 藥提醒科技有限公司 (yao.care, company registration number 83620786, 12F, No. 220, Sec. 2, Taiwan Blvd., West Dist., Taichung City, Taiwan).
SgTxGNN is the Singapore site of the company's "TxGNN Drug Repurposing" product line.
The same system is deployed across 30 countries and regions, each named {CC}TxGNN
(JpTxGNN, UsTxGNN, DETxGNN, and so on) at {cc}txgnn.yao.care.
Product overview: https://www.yao.care/medical/txgnn/.
The TxGNN model itself was developed by the Zitnik Lab at Harvard Medical School and published in Nature Medicine. This platform is the production system 藥提醒科技有限公司 built on top of that model, covering national drug-registration data integration, dual knowledge-graph and deep-learning prediction, PubMed / ClinicalTrials evidence grading, and SMART on FHIR electronic health record integration.
Project Background
SgTxGNN is a drug repurposing research platform based on Harvard's TxGNN model published in Nature Medicine. Unlike other prediction tools, this platform not only provides AI prediction scores but also integrates clinical evidence from ClinicalTrials.gov, PubMed, and other sources, enabling researchers to quickly assess prediction credibility.
Team
| Item | Information |
|---|---|
| Project Maintainer | 藥提醒科技有限公司 (yao.care) |
| Model Basis | Harvard TxGNN (Zitnik Lab) |
| Last Updated | March 2026 |
Academic Foundation
This project's AI prediction model is based on:
Huang, K., et al. (2023). A foundation model for clinician-centered drug repurposing. Nature Medicine. DOI: 10.1038/s41591-023-02233-x
What is Drug Repurposing?
Drug Repurposing is discovering new therapeutic uses for existing drugs. Compared to developing new drugs (10-15 years, $1-2 billion), drug repurposing takes only 3-5 years and $100-300 million, with existing human safety data and lower failure risk.
| Comparison | New Drug Development | Drug Repurposing |
|---|---|---|
| Development Time | 10-15 years | 3-5 years |
| Development Cost | $1-2 billion | $100-300 million |
| Safety Data | Must establish new | Existing human data |
| Failure Risk | Very high (>90%) | Lower |
What is TxGNN?
TxGNN is a deep learning model developed by Harvard Medical School's Zitnik Lab team, published in Nature Medicine. It is the first foundation model designed for clinician-centered drug repurposing.
"TxGNN integrates a knowledge graph of 17,080 biomedical entities, using graph neural networks to learn complex relationships between nodes and predict drug efficacy for rare diseases." — Huang et al., Nature Medicine (2023)
Technical Features
- Knowledge Graph: Integrates 17,080 nodes including drugs, diseases, genes, and proteins
- Graph Neural Network: Learns complex relationships between nodes
- Prediction Capability: Predicts which diseases a drug might be effective for
Data Sources
This platform integrates multiple authoritative public data sources including AI predictions, clinical trials, academic literature, drug information, Singapore market information, and drug interaction data.
| Data Type | Source | Description |
|---|---|---|
| AI Prediction | TxGNN | Harvard knowledge graph prediction model |
| Clinical Trials | ClinicalTrials.gov | Global clinical trial registry |
| Academic Literature | PubMed | Biomedical literature database |
| Drug Information | DrugBank | Drug and target database |
| Singapore Market | HSA | Health Sciences Authority |
Project Scale
| Item | Count |
|---|---|
| Drug Reports | 745 |
| Repurposing Candidates | 31,543 |
| Diseases Covered | 4,589 |
| Dual Validated (KG+DL) | 1,217 |
How to Cite
If you use data from this platform, please use the following format:
APA Format
yao.care. (2026). SgTxGNN: Drug Repurposing Validation Reports for Singapore HSA Drugs (v1.0.0). https://sgtxgnn.yao.care/
Citing the Original Model
If using TxGNN prediction results, please also cite the original paper:
@article{huang2023txgnn,
title={A foundation model for clinician-centered drug repurposing},
author={Huang, Kexin and others},
journal={Nature Medicine},
year={2023},
doi={10.1038/s41591-023-02233-x}
}
Contact & Feedback
For questions or suggestions, please contact us through:
- GitHub Issues: https://github.com/yao-care/SgTxGNN/issues
- Project Homepage: https://sgtxgnn.yao.care/
This report is for academic research purposes only and does not constitute medical advice. Please follow physician instructions for medication use. Any drug repurposing decisions require complete clinical validation and regulatory approval.
Last Review: 2026-03-03 | Reviewer: 藥提醒科技有限公司 (yao.care)