Molecular Property Prediction using Contrastive Learning and Graph Isomorphism

Running the Scripts

Follow the procedure in the documentation in our graph creation repository here to save all the molecular graphs for all datasets.
Install the requirements by running:
```
pip install -r requirements.txt
```
The weights for the pre-trained spectral backbone can be downloaded from our release. The weights are saved as .pt files which can directly be loaded in the training scripts. The procedures used for pre-training the backbone can be found here
Load the pre-trained weights in the train.py file of each dataset folder. Each train file is named as per the dataset, change the name of the script accordingly before running.
Before running the train scripts, insert the paths for the location of the saved graph tensors and the pre-trained backbone. A detailed list of keys used in the .env file is given below.
Change the path for saving the model based on your convenience. Ensure that the folder is created before saving the models.
The saved models can be loaded in the test.py of each dataset for inference. The primary metric is ROC-AUC for classification and RMSE for regression.
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"

Key	Value
zinc_weights	/path/to/zinc/pretrained.pt
graph_files	path/to/graphs/data/processed/

We will release the weights for the trained models soon. You may send an email or raise an issue if there are any bugs. Have fun!!!