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Proteina is a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture.
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Evaluating Protein Binding Interfaces with Transformer Networks
[CVPR 2025] Official repository for "Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders"
List of papers about Proteins Design using Deep Learning
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
High-Resolution 3D Assets Generation with Large Scale Hunyuan3D Diffusion Models.
A trainable PyTorch reproduction of AlphaFold 3.
Simple Guidance Mechanisms for Discrete Diffusion Models
Implementation of DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
Code related to : O. Mendez-Lucio, M. Ahmad, E.A. del Rio-Chanona, J.K. Wegner, A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules
FoldFlow: SE(3)-Stochastic Flow Matching for Protein Backbone Generation
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
Boltzmann Generators and Normalizing Flows in PyTorch
The official implementation of the ICLR'23 paper PiFold: Toward effective and efficient protein inverse folding.
Plugin for folding sequences directly in PyMOL
Official implementation of NeurIPS'24 paper "Bridge-IF: Learning Inverse Protein Folding with Markov Bridges"
Public RFDiffusionAA repo
Joint sequence and structure generation with RoseTTAFold sequence space diffusion
[NeurIPS 2024] MSA generative pretraining for advancing protein structure prediction