equivariance

#计算机科学#Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

#Awesome#A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond

E(2)-Equivariant CNNs Library for Pytorch

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

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)

#计算机科学#Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

#计算机科学#Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/

#计算机科学#A Euclidean diffusion model for structure-based drug design.

[NeurIPS'22] Tokenized Graph Transformer (TokenGT), in PyTorch

#计算机科学#DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

#计算机科学#Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. This specific repository is geared towards integration with eventual Alphafold2 replication.

#计算机科学#A library for programmatically generating equivariant layers through constraint solving

#计算机科学#Implementation of the Equiformer, SE3/E3 equivariant attention network that reaches new SOTA, and adopted for use by EquiFold for protein folding

#计算机科学#Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

#计算机科学#Implementation of E(n)-Transformer, which incorporates attention mechanisms into Welling's E(n)-Equivariant Graph Neural Network

[ECCV 2022] Official PyTorch Code of DEVIANT: Depth Equivariant Network for Monocular 3D Object Detection

#计算机科学#A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks

Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023

Equivariant Transformer (ET) layers are image-to-image mappings that incorporate prior knowledge on invariances with respect to continuous transformations groups (ICML 2019). Paper: https://arxiv.org...