About
Background
AuTxGNN is a research-support platform for drug repurposing, built on the TxGNN model published in Nature Medicine by the Zitnik Lab at Harvard University. It predicts indication expansion for medicines listed on the PBS Schedule in Australia. Beyond AI prediction scores, the platform integrates clinical evidence from ClinicalTrials.gov and PubMed so researchers can quickly assess how credible each prediction is.
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).
AuTxGNN is the Australia 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 local drug-list data integration, deep-learning prediction that excludes uses already recorded in the knowledge graph, PubMed / ClinicalTrials evidence grading, and SMART on FHIR electronic health record integration.
What is drug repurposing?
Drug repurposing means finding new therapeutic uses for existing medicines. Compared with developing a new drug from scratch — 10 to 15 years and USD 1–2 billion — repurposing takes 3 to 5 years and USD 100–300 million, and human safety data already exists, so the risk of failure is lower.
| Aspect | New drug development | Drug repurposing |
|---|---|---|
| Time | 10–15 years | 3–5 years |
| Cost | USD 1–2 billion | USD 100–300 million |
| Safety data | Must be established | Human data already available |
| Risk of failure | Very high (>90%) | Lower |
What is TxGNN?
TxGNN is a deep learning model developed by the Zitnik Lab at Harvard Medical School and published in Nature Medicine. It predicts novel drug–disease associations and is the first foundation model for drug repurposing designed specifically for clinicians.
"TxGNN integrates a knowledge graph covering 17,080 diseases and uses graph neural networks to learn complex relationships between nodes, predicting potential efficacy of drugs against rare diseases." — Huang et al., Nature Medicine (2024)
Data sources
The platform’s data sources (TxGNN prediction, clinical trials, literature, DrugBank and the local medicines list), their licences and update frequency are listed on the Data Sources page.
Academic basis
Huang, K., et al. (2024). A foundation model for clinician-centered drug repurposing. Nature Medicine. DOI: 10.1038/s41591-024-03233-x
Scale
| Item | Value |
|---|---|
| Drug reports | 749 |
| Local medicines list | PBS Schedule |
| Deployed sites | 30 countries / regions |
Contact
- GitHub Issues: https://github.com/yao-care/AuTxGNN/issues
- Developer: 藥提醒科技有限公司 (https://www.yao.care, service@yao.care)
- Product overview: https://www.yao.care/medical/txgnn/
This report is for academic research reference only and does not constitute medical advice. Always follow your physician's instructions; never adjust medication on your own. Any drug repurposing decision requires full clinical validation and regulatory review.
Reviewed by: 藥提醒科技有限公司 (yao.care)