Repository associated with the manuscript “Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction”.
Repository structure:
- Main
- Databases: graph and property database files.
- HyperparameterSearch: hyperparameter tuning results of each GCN model.
- Models: GCN models, train/val/test splits of final fitting, and CNN-related files.
- Python: main Python code used throughout the manuscript. Files are numbered in chronological order. Details and instructions included. A Jupyer Notebook is also included, allowing users to predict sigma profiles by providing a SMILES string.