This is a PyTorch implementation.
Install pytorch and torchvision.
Train EAGCN model
Four benchmark datasets (Tox21, HIV, Freesolv and Lipophilicity) are utilized in this study to evaluate the predictive performance of built graph convolutional networks. They are all downloaded from the MoleculeNet that hold various benchmark datasets for molecular machine learning.
Datasets are also provided in folder "Data".
Train the model
Open the folder "eagcn_pytorch".
When you train the model, you can use:
EAGCN_dataset.py: pre-processing data
neural_fp.py: from smiles to graph
layers.py: define layers
models.py: define models
utils.py: other tools
check_model.py: check parameters (edge attention for each layer).
mol_to_vec.py: visualize the molecule in 2D space, compare with other molecules which have similiar SMILEs.
plot.py: show model training process.
tsnes.py: tsne visualization about atom subtype, also provide umap option.
kmeans_atomrep.py: kmeans clustering for atom subtype.
plot_molecule.py: plot single molecule.