README.md

metadata

title: StereoGNN Transporter Predictor
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: mit

StereoGNN Transporter Substrate Predictor

Predict monoamine transporter activity for drug molecules using a stereochemistry-aware graph neural network.

Targets

• DAT - Dopamine Transporter
• NET - Norepinephrine Transporter
• SERT - Serotonin Transporter

Predictions

Type	Description
🟢 Substrate	Actively transported by the transporter
🟡 Blocker	Inhibits transporter without being transported
⚪ Inactive	No significant interaction

Model Performance

Metric	Value
Overall ROC-AUC	0.968
DAT AUC	0.982
NET AUC	0.953
SERT AUC	0.969
Stereo Sensitivity	83.3%

Key Features

• Stereochemistry-Aware: Correctly distinguishes between enantiomers (e.g., d- vs l-amphetamine)
• Multi-Target: Simultaneous prediction for all three monoamine transporters
• Interpretable: Provides substrate probability scores and molecular property analysis

Example Molecules

SMILES	Name	Expected
C[C@H](N)Cc1ccccc1	d-Amphetamine	DAT/NET substrate
C[C@@H](N)Cc1ccccc1	l-Amphetamine	Less active
NCCc1ccc(O)c(O)c1	Dopamine	DAT substrate
NCCc1c[nH]c2ccc(O)cc12	Serotonin	SERT substrate

Usage

Enter a SMILES string in the input box and click "Predict" to get:

1. Transporter predictions with probabilities
2. Molecular structure visualization
3. Molecular properties analysis
4. Interpretation and warnings

Citation

If you use this tool in your research, please cite:

@software{stereognn_transporter,
  title={StereoGNN: Stereochemistry-Aware Graph Neural Network for Transporter Substrate Prediction},
  year={2025},
  url={https://huggingface.co/spaces/nabilyasini/stereognn-transporter}
}

Disclaimer

This is a research tool for educational and scientific purposes. Predictions should be validated experimentally before use in any clinical or pharmaceutical context.

License

MIT License