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, it enables fast single or batch predictions with visualization.

Live App

Click here to launch the app

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

Usage

Input Options:
- Type one or more SMILES strings separated by commas
- OR upload a .csv file with a single column of SMILES
Example SMILES: CC(=O)Oc1ccccc1C(=O)O, C1=CC=CC=C1
CSV Format:

One column
No header
Each row contains a SMILES string

Output:

Predictions displayed in-browser (up to 10 molecules shown)
Full results available for download as CSV

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, optimized using Optuna

Author

Developed by Matthew Graham For inquiries, collaborations, or ideas, feel free to reach out!