Few-Shot Graph and SMILES Learning for Molecular Property Prediction

Introduction

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

Few-Shot Graph and SMILES Learning for Molecular Property Prediction

Contact Dan Sun (2201793@s.hlju.edu.cn,), 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	74.15/74.27		5	561/585	Si-T1	75.16/75.52
			Si-T2	68.34/68.85				Si-T2	68.90/70.06
			Si-T3	69.90/70.13				Si-T3	72.03/72.04
			Si-T4	71.78/71.88				Si-T4	72.40/72.51
			Si-T5	78.40/78.72				Si-T5	79.71/79.86
			Si-T6	69.59/70.44				Si-T6	71.90/72.33
			Ave.	72.03/72.38				Ave.	73.35/73.72
Tox21	1	1271/1415	SR-HS	74.27/74.86		5	1061/882	SR-HS	74.85/75.24
			SR-MMP	79.62/80.06				SR-MMP	80.10/80.15
			SR-p53	77.91/78.87				SR-p53	78.86/79.33
			Ave.	77.27/77.93				Ave.	77.94/78.24

Acknowledgements

The code is implemented based on Few-shot Graph Learning for Molecular Property Prediction.

Reference

@article{
  title={Few-Shot Graph and SMILES Learning for Molecular Property Prediction},
  author={Dan, Sun and Yong, Liu and Zhang, Wei},
  year={2022}
}