Methodology
Overall pipeline
The platform uses a four-stage pipeline: the TxGNN knowledge graph model predicts potential drug–disease associations, evidence is then collected automatically for each predicted pair, the evidence is graded from L1 to L5, and finally a decision recommendation is issued.
- TxGNN prediction: drug–disease relationships predicted with a knowledge graph combined with graph neural networks.
- Evidence collection: for each predicted pair, evidence is gathered from ClinicalTrials.gov, PubMed, DrugBank and CDSCO.
- Evidence grading: graded L1 to L5, where L1 is strongest (multiple Phase 3 RCTs) and L5 is model prediction only.
- Decision recommendation: Go, Proceed, Consider, Explore or Hold, based on the evidence level.
Evidence grading criteria
| Level | Definition | Clinical meaning |
|---|---|---|
| L1 | Multiple Phase 3 RCTs / systematic reviews | Strong support; clinical use may be considered |
| L2 | Single RCT or multiple Phase 2 trials | Moderate support; validation trials can be designed |
| L3 | Observational studies / large case series | Preliminary support; needs further validation |
| L4 | Preclinical / mechanistic studies | Theoretical support; far from clinical use |
| L5 | Model prediction only | Hypothesis stage; no human evidence yet |
Dual-engine prediction
Two methods run in parallel, and a confidence label records whether they agree:
| Method | Speed | Precision | Description |
|---|---|---|---|
| Knowledge graph (KG) | Fast | Lower | Inference over DrugBank relations and graph structure |
| Deep learning (DL) | Slow | Higher | TxGNN graph neural network model |
| Confidence | Source | Meaning |
|---|---|---|
| very_high | KG + DL | Both methods agree |
| high | DL only | High-scoring deep learning support |
| medium | KG only | Knowledge graph support |
Regulatory data integration
Drug approval data for India comes from CDSCO. Ingredient names are mapped to the DrugBank vocabulary; ingredients that cannot be mapped — herbal extracts, vaccines, excipients and others not catalogued by DrugBank — are excluded from prediction.
Limitations
- Predictions are statistical associations and do not imply causation or clinical efficacy.
- An L5 rating means model prediction only, with no supporting human evidence.
- Evidence collection depends on public databases; unpublished or unindexed studies are not captured.
- Ingredient mapping may miss items because of naming differences.
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)