Graphormer is a general-purpose deep learning backbone for molecular modeling.

Research code of ICCV 2021 paper "Mesh Graphormer"

Microsoft Graphormer (https://arxiv.org/abs/2106.05234) rewritten in Pytorch-Geometric

[ICLR 2023 notable top-5%] Rethinking the Expressive Power of GNNs via Graph Biconnectivity (official implementation)

Do Transformers Really Perform Bad for Graph Representation? [NIPS-2021]

Graphormer Based Protein Sequence Design

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VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections, ICLR 2024

A graph transformer-based approach for drug-target interaction prediction

Graphormer-IR is an extension of the Graphormer framework, specifically to perform predictions of infrared spectra using only chemical structure

The first model to perform generalizable, first principles predictions of RTs for an arbitrary RP or HILIC chromatographic method.

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A prototype of Graphormer

A three-channel deep attribute graph clustering network combining an innovative integrated coding approach for Graphormer networks and a new pre-training module for contrast learning

Applying Deep Learning Methods to LC-MS Metabolomics Data to Improve Metabolite Identification

Packaging of Graphormer, 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.

TensorFlow implementation of the Graphormer architecture

[ICCV 2023] Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images

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This project provides modular implementation of Interpretable Graphormer for molecular property prediction (ESOL, FreeSolv, Lipophilicity, etc.) using your own CSV data.

Geometry-Aware Graph Transformer with Unified EEG Representation Enables Cross-Topology Emotion Recognition

Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?

PyTorch implementation of Graphormer for graph representation learning. Based on the paper : "? Do Transformers Really Perform Bad for Graph Representation?"

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Implementation of Sign Invariant Positional Encoded Graphomer using deep graph library and pytorch

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