Skip to content

chipinzhen/SSHGR

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

A-Self-Supervised-Based-Hierarchical-Graph-Representation-Learning-for-DDI-Prediction

The pretraining code is adapted from pretrain-gnns repo: https://github.com/Dinxin/pretrain-gnns

And we also followed the DDI network (GCN) setting from MIRACLE repo: https://github.com/isjakewong/MIRACLE

Installation

We used the conda environment with several python packagess. The details of environment and python packages we used has been put in the file "environment.yaml" and "requirements.txt".

Dataset download

In the pre-traning with SSL, we use a dataset named ZINC15 which can be downloaded from chem data The dataset of DDI task have been provided.

Reference

D. E. Shaw, J. Grossman, J. A. Bank, B. Batson, J. A. Butts, J. C. Chao, M. M. Deneroff, R. O. Dror, A. Even, C. H. Fenton, et al. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In SC’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 41–53. IEEE, 2014

W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265, 2019.

Y. Wang, Y. Min, X. Chen, and J. Wu. Multi-view graph contrastive representation learning for drug-drug interaction prediction. In Proceedings of the Web Conference 2021, pages 2921–2933, 2021.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages