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.

Highlights in Graphormer v2.0

  • The model, code, and script used in the Open Catalyst Challenge are 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:


  1. Graphormer v2.0 is released. Enjoy!


  1. Graphormer has won the Open Catalyst Challenge. The technical talk could be found through this link.
  2. The slides of NeurIPS 2021 could be found through this link.
  3. The new release of 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.


  1. Graphormer has been accepted by NeurIPS 2021.
  2. We’re hiring! Please contact shuz[at] for more information.


  1. Codes and scripts are released.


  1. Graphormer has won the 1st place of quantum prediction track of Open Graph Benchmark Large-Scale Challenge (KDD CUP 2021) [Competition Description] [Competition Result] [Technical Report] [Blog (English)] [Blog (Chinese)]


We 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 to [email protected].

Get Started

Our primary documentation is at 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:

  • Examples showing command line usage of common tasks.

Requirements and Installation

Setup with Conda



Please kindly cite this paper if you use the code:

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},


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