RepurposeNet
RepurposeNet is a SYNTHETIC/DEMO drug discovery model for drug repurposing from knowledge graph. Outputs are deterministic demo values — NOT for clinical use. Trained (synthetically) on de-identified multi-modal data.
Multi-modal
Helix Diagnostics
License: BSD-3-Clause
v2.1.3
RTG 4.3
DL 180,912
Metrics
| Metric | Value | Split |
|---|---|---|
| MRR | 0.87 | Synthetic hold-out |
| Calibration (ECE) | 0.073 | Synthetic |
| Inputs | Outputs |
|---|---|
| Disease + drug graph | Ranked repurposing candidates |
Run a synthetic prediction
Returns a deterministic synthetic result — clearly labeled, not for clinical use.
API integration
Call this model from your pipeline (demo — no real key required):
import requests
r = requests.post("https://api.biofoundry.ai/v1/models/bf-018-repurposenet/predict",
headers={"Authorization": "Bearer <API_KEY>"},
json={"input": "<disease + drug graph>"})
print(r.json()) # -> synthetic demo prediction
See the API reference · JSON: /api/models/bf-018-repurposenet
Version history
| Version | Status | Notes |
|---|---|---|
| v2.1.0 | Archived | Synthetic release note 0 |
| v2.1.1 | Archived | Synthetic release note 1 |
| v2.1.2 | Archived | Synthetic release note 2 |
| v2.1.3 | Current | Synthetic release note 3 |
Community contributions: 25 synthetic contributors have submitted evaluations and fine-tunes.