MolGraphEval

This repository is the official implementation of paper: "Evaluating Self-supervised Learning for Molecular Graph Embeddings”, NeurIPS 2023, Datasets and Benchmarks Track.

Citation

@inproceedings{GraphEval,
  title = {Evaluating Self-supervised Learning for Molecular Graph Embeddings},
  author = {Hanchen Wang* and Jean Kaddour* and Shengchao Liu and Jian Tang and Joan Lasenby and Qi Liu},
  booktitle = {NeurIPS 2023, Datasets and Benchmarks Track},
  year = 2023
}

Usage

We include scripts for pre-training, probing and fine-tuning for GraphSSL on molecules, see script folder. We use conda to set up the environment:

conda env create -f env.yaml