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# HOMO–LUMO Gap Predictor
This web app uses a trained Graph Neural Network (GNN) to predict HOMO–LUMO energy gaps from molecular SMILES strings. Built with [Streamlit](https://streamlit.io), it enables fast single or batch predictions with visualization.
### Live App
[Click here to launch the app](https://www.willfillinoncedeployed.com)
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## Features
- Predict HOMO–LUMO gap for one or many molecules
- Accepts comma-separated SMILES or CSV uploads
- RDKit rendering of molecule structures
- Downloadable CSV of predictions
- Powered by a trained hybrid GNN model with RDKit descriptors
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## Usage
1. **Input Options**:
- Type one or more SMILES strings separated by commas
- OR upload a `.csv` file with a single column of SMILES
2. **Example SMILES**: CC(=O)Oc1ccccc1C(=O)O, C1=CC=CC=C1
3. **CSV Format**:
- One column
- No header
- Each row contains a SMILES string
4. **Output**:
- Predictions displayed in-browser (up to 10 molecules shown)
- Full results available for download as CSV
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## Project Structure
streamlit-app/
│
├── app.py # Main Streamlit app
├── model.py # Hybrid GNN architecture and model loader
├── utils.py # RDKit and SMILES processing
├── requirements.txt # Python dependencies
└── predictions.db # SQLite log of predictions
---
## Requirements
To run locally:
```
pip install -r requirements.txt
streamlit run app.py
```
## Model Info
The app uses a trained hybrid GNN model combining:
* AtomEncoder and BondEncoder from OGB
* GINEConv layers from PyTorch Geometric
* Global mean pooling
* RDKit-based physicochemical descriptors
Trained on the [OGB PCQM4Mv2 dataset](https://ogb.stanford.edu/docs/lsc/pcqm4mv2/), optimized using Optuna
## Author
Developed by [Matthew Graham](https://github.com/MooseML)
For inquiries, collaborations, or ideas, feel free to reach out!
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