This is a fork of the molnet-gometric-lightning repository modified to add possible H-donor-acceptor interactions as edges to molecular graphs. Large parts of this code are borrowed from PyTorch Geometric and OGB examples, therefore this package is available under the same license (MIT).
Molecular graphs used for training graph neural networks typically use covalent bonds as graph edges. However, for tasks such as solubility, intra-molecular forces can play a role.
After cloning this repo, you should be able to install with:
conda env create
Note: depending on your hardware, you may need to install the CUDA toolkit as well.
For instance, you might have to add a line - cudatoolkit=10.2 to environment.yml.
The following will train 5 models on the bbbp dataset with the default parameters.
The models will be stored in example_models/, and the data will be downloaded to datasets/.
python hbond_benchmark/train.py --default_root_dir=example_model/ --dataset_name=bbbp --dataset_root=datasets/ --gpus=1 --max_epochs=100 --n_runs=5 Replace the directories to your liking, and bbbp with any name from MoleculeNet, for example tox21, muv, hiv, pcba, bace, esol.
Validation curves and test set performance are logged to default_root_dir.
Start a Tensorboard server with default_root_dir as the log directory.
From the above example, something like:
tensorboard --logdir=/full/path/to/example_model/