Defending Graph Neural Networks against Adversarial Attacks

This repository was used in the Molecular Property Prediction Project. The pre-trained backbone can be extended for several other uses cases as well.

Running the Scripts

• Follow the procedure detailed here to save the tensors of all molecular graphs.
• You may use the SMILE strings from either Zinc or TwoSides. Note that TwoSides has 2 molecules per instance and different pairing and batching strategies need to be used to successfully run the scripts. The procedure for such a usecase is detailed here
• Run the following command:

  pip install -r requirements.txt

• Set the values for the paths in a .env file. The keys are detailed below.
• Run train.py for the dataset of your choice. Further, you may be required to re-set the environment path. For such cases, linux/macOS users may use:

  export PYTHONPATH="/path/to/project/root"

• Windows users are suggested to use bash terminal and run the above command.

Datasets Used

• ZINC
• TwoSIDES

Environment Variables

Key	Value
graph_files	path/to/graphs/data/processed/

You may send an email or raise an issue if there are any bugs.