SATxGNN - Saudi Arabia: Drug Repurposing

本專案由 藥提醒科技有限公司（yao.care）開發與維運。 SATxGNN 站台：https://satxgnn.yao.care/｜產品總覽：https://www.yao.care/medical/txgnn/

Drug repurposing predictions for Saudi Arabia SFDA-approved drugs using the TxGNN model.

Disclaimer

• The results of this project are for research purposes only and do not constitute medical advice.
• Drug repurposing candidates require clinical validation before application.

Project Overview

Report Statistics

Item	Count
Drug Reports	882
Total Predictions	12,123,340
Unique Drugs	1,102
Unique Indications	17,041
DDI Data	160,235
FHIR Resources	1,062 MK / 5,706 CUD

Evidence Level Distribution

Evidence Level	Report Count	Description
L1	13	Multiple Phase 3 RCTs
L2	39	Single RCT or multiple Phase 2
L3	33	Observational studies
L4	76	Preclinical / mechanistic studies
L5	692	Computational prediction only
N/A	29	Known uses only (no new prediction)

By Source

Source	Predictions
DL	12,117,681
KG + DL	4,874
KG	785

By Confidence

Confidence	Predictions
very_high	3,627
high	527,905
medium	1,008,316
low	10,583,492

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Prediction Methods

Method	Speed	Accuracy	Requirements
Knowledge Graph	Fast (seconds)	Lower	No special requirements
Deep Learning	Slow (hours)	Higher	Conda + PyTorch + DGL

Knowledge Graph Method

uv run python scripts/run_kg_prediction.py

Metric	Value
SFDA Total Drugs	11,641
Mapped to DrugBank	10,666 (91.6%)
Repurposing Candidates	5,659

Deep Learning Method

conda activate txgnn
PYTHONPATH=src python -m satxgnn.predict.txgnn_model

Metric	Value
Total DL Predictions	2,530,612
Unique Drugs	1,102
Unique Indications	17,041

Score Interpretation

The TxGNN score represents the model's confidence in a drug-disease pair, ranging from 0 to 1.

Threshold	Meaning
>= 0.9	Very high confidence
>= 0.7	High confidence
>= 0.5	Moderate confidence

Score Distribution

Threshold	Meaning
≥ 0.9999	Extremely high confidence, model's most confident predictions
≥ 0.99	Very high confidence, worth prioritizing for validation
≥ 0.9	High confidence
≥ 0.5	Moderate confidence (sigmoid decision boundary)

Evidence Level Definitions

Level	Definition	Clinical Significance
L1	Phase 3 RCT or systematic review	Can support clinical use
L2	Phase 2 RCT	Can consider for use
L3	Phase 1 or observational study	Requires further evaluation
L4	Case report or preclinical research	Not recommended yet
L5	Computational prediction only, no clinical evidence	Requires further research

Important Reminders

1. High scores do not guarantee clinical efficacy: TxGNN scores are knowledge graph-based predictions that require clinical trial validation.
2. Low scores do not mean ineffective: The model may not have learned certain associations.
3. Recommended to use with validation pipeline: Use this project's tools to review clinical trials, literature, and other evidence.

Validation Pipeline

flowchart TD
    A["TxGNN Prediction Results"] --> B
    subgraph B["Step 1: DrugBundle Collector"]
        B1["Drug-level: SFDA, DDI, DrugBank"]
        B2["Indication-level: ClinicalTrials, PubMed, ICTRP"]
    end
    B --> |"drug_bundle.json"| C
    subgraph C["Step 2: Evidence Pack Generator"]
        C1["Programmatic data transfer (100%)"]
        C2["+ LLM Analysis (L1-L5)"]
    end
    C --> |"drug_evidence_pack.json/md"| D
    subgraph D["Step 3: Notes Writer"]
        D1["drug_pharmacist_notes.md"]
        D2["drug_sponsor_notes.md"]
    end

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Quick Start

Step 1: Download Data

File	Download
SFDA Data	SFDA Open Data - Registered Human Drugs List
node.csv	Harvard Dataverse
kg.csv	Harvard Dataverse
edges.csv	Harvard Dataverse
model_ckpt.zip	Google Drive

Step 2: Install Dependencies

uv sync

Step 3: Process Drug Data

uv run python scripts/process_fda_data.py

Step 4: Prepare Vocabulary Data

uv run python scripts/prepare_external_data.py

Step 5: Run Knowledge Graph Prediction

uv run python scripts/run_kg_prediction.py

Step 6: Set Up Deep Learning Environment

conda create -n txgnn python=3.11 -y
conda activate txgnn
pip install torch==2.2.2 torchvision==0.17.2
pip install dgl==1.1.3
pip install git+https://github.com/mims-harvard/TxGNN.git
pip install pandas tqdm pyyaml pydantic ogb

Step 7: Run Deep Learning Prediction

conda activate txgnn
PYTHONPATH=src python -m satxgnn.predict.txgnn_model

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Resources

TxGNN Core

• TxGNN Paper - Nature Medicine, 2024
• TxGNN GitHub
• TxGNN Explorer

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