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Few-shot Graph Learning for Molecular Property Prediction

Introduction

This is the source code and dataset for the following paper:

Few-shot Graph Learning for Molecular Property Prediction. In WWW 2021.

Contact Zhichun Guo (zguo5@nd.edu), if you have any questions.

Datasets

The datasets uploaded can be downloaded to train our model directly.

The original datasets are downloaded from Data. We utilize Original_datasets/splitdata.py to split the datasets according to the molecular properties and save them in different files in the Original_datasets/[DatasetName]/new. Then run main.py, the datasets will be automatically preprocessed by loader.py and the preprocessed results will be saved in the Original_datasets/[DatasetName]/new/[PropertyNumber]/propcessed.

Usage

Installation

We used the following Python packages for the development by python 3.6.

- torch = 1.4.0
- torch-geometric = 1.6.1
- torch-scatter = 2.0.4
- torch-sparse = 0.6.1
- scikit-learn = 0.23.2
- tqdm = 4.50.0
- rdkit

Run code

Datasets and k (for k-shot) can be changed in the last line of main.py.

python main.py

Performance

The performance of meta-learning is not stable for some properties. We report two times results and the number of the iteration where we obtain the best results here for your reference.

Dataset k Iteration Property Results k Iteration Property Results
Sider 1 307/599 Si-T1 75.08/75.74 5 561/585 Si-T1 76.16/76.47
Si-T2 69.44/69.34 Si-T2 68.90/69.77
Si-T3 69.90/71.39 Si-T3 72.23/72.35
Si-T4 71.78/73.60 Si-T4 74.40/74.51
Si-T5 79.40/80.50 Si-T5 81.71/81.87
Si-T6 71.59/72.35 Si-T6 74.90/73.34
Ave. 72.87/73.82 Ave. 74.74/74.70
Tox21 1 1271/1415 SR-HS 73.72/73.90 5 1061/882 SR-HS 74.85/74.74
SR-MMP 78.56/79.62 SR-MMP 80.25/80.27
SR-p53 77.50/77.91 SR-p53 78.86/79.14
Ave. 76.59/77.14 Ave. 77.99/78.05

Acknowledgements

The code is implemented based on Strategies for Pre-training Graph Neural Networks.

Reference

@article{guo2021few,
  title={Few-Shot Graph Learning for Molecular Property Prediction},
  author={Guo, Zhichun and Zhang, Chuxu and Yu, Wenhao and Herr, John and Wiest, Olaf and Jiang, Meng and Chawla, Nitesh V},
  journal={arXiv preprint arXiv:2102.07916},
  year={2021}
}

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