This is the PyTorch implementation of "Property-Aware Relation Networks (PAR) for Few-Shot Molecular Property Prediction (spotlight)" published in NeurIPS 2021 as a spotlight paper. The PaddlePaddle implementation is a part of PaddleHelix, which can be reached here.

Please cite our paper if you find it helpful. Thanks.

@InProceedings{wang2021property,
  title={Property-Aware Relation Networks for Few-Shot Molecular Property Prediction},
  author={Wang, Yaqing and Abuduweili, Abulikemu and Yao, Quanming and Dou, Dejing},
  booktitle = {Advances in Neural Information Processing Systems},
  year={2021},
}

Environment

We used the following Python packages for core development. We tested on Python 3.7.

- pytorch 1.7.0
- torch-geometric 1.7.0

Datasets

Tox21, SIDER, MUV and ToxCast are previously downloaded from SNAP. You can download the data here, unzip the file and put the resultant ``muv, sider, tox21, and toxcast" in the data folder.

Experiments

To run the experiments, use the command (please check and tune the hyper-parameters in parser.py:

python main.py

If you want to quickly run PAR method on tox21 dataset, please use the command:

bash script_train.sh