To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Optimizing AlphaFold Training and Inference on GPU Clusters

Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design

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

Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module

Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design

🧬 ManyFold: An efficient and flexible library for training and validating protein folding models

Protein 3D structure prediction pipeline

FrameDiPT: an SE(3) diffusion model for protein structure inpainting

Example to fit parameters and run CG simulations using TorchMD and Schnet

Singularity recipe for AlphaFold

Deep learning for protein science

Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

An interactive visual simulator for distance-based protein folding

Infrastructure template and Jupyter notebooks for running RoseTTAFold on AWS Batch.

OPUS-Fold: An Open-Source Protein Folding Framework Based on Torsion-Angle Sampling

Python package for generating Markov state models

Prospr is a universal toolbox for protein structure prediction within the HP-model. The Python package is based on a C++ core, which gives Prospr its high performance. The C++ core is made available as a separate zip file to facilitate high-performance computing applications. The package comes with many prediction algorithms and datasets to use.

Decentralized Protein Folding Bittensor Subnet