Package: graphormer-pretrained
Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material discovery, drug discovery, etc.Project website.This forkThe purpose of this fork is to package Graphormer for systematic uses and easier installation.To install, runmamba install -c conda-forge 'graphormer-pretrained'Please refer to the original Graphormer github repo:https://github.com/microsoft/Graphormerfor any issues related to Graphormer.What's different ?We removed support for dgl due to packaging constraintsMore information on the Graphormer project is available below !Highlights in Graphormer v2.0The model, code, and script used in theOpen Catalyst Challengeare available.Pre-trained models on PCQM4M and PCQM4Mv2 are available, more pre-trained models are comming soon.Supports interface and datasets of PyG, DGL, OGB, and OCP.Supports fairseq backbone.Document is online!What's New:12/22/2021Graphormer v2.0 is released. Enjoy!12/10/2021Graphormerhas won theOpen Catalyst Challenge. The technical talk could be found through thislink.Theslidesof NeurIPS 2021 could be found through thislink.Thenew releaseof Graphormer is comming soon, as a general molecule modeling toolkit, with models used in OC dataset, completed pre-trained model zoo, flexible data interface, and higher effiency of training.09/30/2021Graphormerhas been accepted byNeurIPS 2021.We're hiring! Please contactshuz[at]microsoft.comfor more information.08/03/2021Codes and scripts are released.06/16/2021Graphormer has won the1st placeof quantum prediction track of Open Graph Benchmark Large-Scale Challenge (KDD CUP 2021)[Competition Description][Competition Result][Technical Report][Blog (English)][Blog (Chinese)]HiringWe are hiring at all levels (including FTE researchers and interns)! If you are interested in working with us on AI for Molecule Science, please send your resume toshuz@microsoft.com.Get StartedOur primary documentation is athttps://graphormer.readthedocs.io/and is generated from this repository, which contains instructions for getting started, training new models and extending Graphormer with new model types and tasks.Next you may want to read:Examplesshowing command line usage of common tasks.Requirements and InstallationSetup with Condabash install.shCitationPlease kindly cite this paper if you use the code:@inproceedings{
ying2021do,
title={Do Transformers Really Perform Badly for Graph Representation?},
author={Chengxuan Ying and Tianle Cai and Shengjie Luo and Shuxin Zheng and Guolin Ke and Di He and Yanming Shen and Tie-Yan Liu},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=OeWooOxFwDa}
}ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
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