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.
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.
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
Datasets and k (for k-shot) can be changed in the last line of main.py.
python main.py
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 |
The code is implemented based on Strategies for Pre-training Graph Neural Networks.
@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}
}