Graph Neural Network Library for PyTorch

Open source code for AlphaFold 2.

Imaging, analysis, and simulation software for radio interferometry

Official repository for the Boltz biomolecular interaction models

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

Message Passing Neural Networks for Molecule Property Prediction

Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

TGN: Temporal Graph Networks

Code for NeurIPS 2022 Paper, "Poisson Flow Generative Models" (PFGM)

graph wavenet

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

AlphaFold Meets Flow Matching for Generating Protein Ensembles

Code and resources on scalable and efficient Graph Neural Networks (TNNLS 2023)

cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural n…

Implementation for SE(3) diffusion model with application to protein backbone generation

DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

Fast protein backbone generation with SE(3) flow matching.

Official implementation of All Atom Diffusion Transformers (ICML 2025)

Protein Ligand INteraction Dataset and Evaluation Resource

Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

Implementation of DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models in PyTorch (ICLR 2023 - MLDD Workshop)

Hyperbolic Hierarchical Clustering.

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

Message Passing Neural Networks for Simplicial and Cell Complexes

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch