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| title: 🧪 HOMO‑LUMO Gap Predictor | |
| emoji: 🧬 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: docker | |
| app_file: app.py | |
| pinned: false | |
| # HOMO‑LUMO Gap Predictor — Streamlit + Hybrid GNN | |
| > **Live demo:** [huggingface.co/spaces/MooseML/homo-lumo-gap-predictor](https://huggingface.co/spaces/MooseML/homo-lumo-gap-predictor) | |
| > **Code:** <https://github.com/MooseML/homo-lumo-gap-predictor> | |
| This web app predicts HOMO–LUMO energy gaps from molecular **SMILES** using a trained hybrid Graph Neural Network (PyTorch Geometric and RDKit descriptors). | |
| It runs on Hugging Face Spaces via Docker and on any local machine with Docker or a Python ≥ 3.10 environment. | |
| --- | |
| ## Features | |
| * Predict gaps for **single or batch** inputs (comma / newline SMILES or CSV upload) | |
| * Shows **up to 20 molecules** per run with RDKit 2‑D depictions | |
| * Download full predictions as CSV | |
| * Logs all predictions to a lightweight SQLite DB (`/data/predictions.db`) | |
| * Containerised environment identical to the public Space | |
| --- | |
| ## Quick start | |
| ### 1 Use the hosted Space | |
| 1. Open the [live URL](https://huggingface.co/spaces/MooseML/homo-lumo-gap-predictor). | |
| 2. Paste SMILES *or* upload a CSV (1 column, no header). | |
| 3. Click **Run Prediction** → results and structures appear; a CSV is downloadable. | |
| > **Heads‑up:** on the free HF tier large files (> ~5 MB) can take 10–30 s to upload because of proxy buffering. | |
| > Local Docker runs are instant, see below: | |
| ### 2 Run locally with Docker (mirrors the Space) | |
| ```bash | |
| git clone https://github.com/MooseML/homo-lumo-gap-predictor.git | |
| cd homo-lumo-gap-predictor | |
| docker build -t homolumo . | |
| docker run -p 7860:7860 homolumo | |
| # open http://localhost:7860 | |
| ```` | |
| ### 3 Run locally with Python (no Docker) | |
| ```bash | |
| git clone https://github.com/MooseML/homo-lumo-gap-predictor.git | |
| cd homo-lumo-gap-predictor | |
| pip install -r requirements.txt | |
| streamlit run app.py | |
| # app on http://localhost:8501 | |
| ``` | |
| --- | |
| ## Input guidelines | |
| | Format | Example | | |
| | ------------ | ---------------------------------------------- | | |
| | **Text area** | `O=C(C)Oc1ccccc1C(=O)O` | | |
| | **CSV** | One column, no header:<br>`CCO`<br>`Cc1ccccc1` | | |
| Invalid or exotic SMILES are skipped and listed in the terminal log (RDKit warnings) | |
| --- | |
| ## Project files | |
| ``` | |
| ├── app.py – Streamlit front‑end | |
| ├── model.py – Hybrid GNN loader (PyTorch Geometric) | |
| ├── utils.py – RDKit helpers & SMILES→graph | |
| ├── Dockerfile – identical to the Hugging Face Space | |
| └── requirements.txt | |
| ``` | |
| The Docker image creates `/data` (writable, 775) for the persistent SQLite DB when a volume is attached. | |
| --- | |
| ## Model in brief | |
| * **Architecture:** AtomEncoder and BondEncoder → GINEConv layers → global mean pooling → dense head | |
| * **Descriptors:** six RDKit physico‑chemical features per molecule | |
| * **Training set:** [OGB PCQM4Mv2](https://ogb.stanford.edu/docs/lsc/pcqm4mv2/) | |
| * **Optimiser / search:** Optuna hyperparameter sweep | |
| --- | |
| ## Roadmap | |
| * Stream chunked CSV parsing to improve upload speed on the public Space | |
| * Toggle between 2‑D and 3‑D (3Dmol.js) molecule renderings | |
| * Serve the model weights from the HF Hub instead of bundling in the image | |
| --- | |
| ## Author | |
| **Matthew Graham** — [@MooseML](https://github.com/MooseML) | |
| Feel free to open issues or contact me for collaborations. | |