User Guide
Looking up a drug
- Use the search box at the top of the page (generic ingredient names match better than brand names).
- Or browse the full list on All Drugs.
- You can also browse by evidence level: high, moderate, model prediction only.
Reading a report
Each drug report lists predicted new indications, and each indication carries an L1–L5 evidence level. L1 means multiple Phase 3 randomised controlled trials already support it; L5 means model prediction only, with no human evidence. Full criteria are on the Methodology page.
| If you see | It means | Suggested action |
|---|---|---|
| L1 / L2 | Clinical trial evidence exists | Review the source NCT and PMID records |
| L3 / L4 | Observational or preclinical evidence | Treat as a research lead |
| L5 | Model prediction only | Hypothesis generation only; not for clinical reference |
Citation and traceability
Every piece of evidence in a report carries a traceable identifier:
- NCT number: links to the ClinicalTrials.gov registration
- PMID: links to the PubMed record
- DrugBank ID: links to drug and target data
Please read the source literature to confirm context before citing any conclusion from this platform.
Frequently asked questions
No. Predictions on this platform are research leads, not clinical advice. Any clinical application of drug repurposing must go through full clinical trial validation and regulatory review.
An ingredient must map to the DrugBank vocabulary to be included in prediction. Herbal extracts, vaccines, excipients and other items not catalogued by DrugBank do not appear on this platform.
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).
InTxGNN is the India 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.
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)