List of papers about Proteins Design using Deep Learning

Protein Graph Library

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

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

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Versatile computational pipeline for processing protein structure data for deep learning applications.

Official repository for the ProteinGym benchmarks

The Rosetta Bio-macromolecule modeling package.

De Novo Protein Design by Equivariantly Diffusing Oriented Residue Clouds

Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch

Fitness landscape exploration sandbox for biological sequence design.

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

Official code repository for the paper "ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers"

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

The first large protein language model trained follows structure instructions.

SaprotHub: Making Protein Modeling Accessible to All Biologists

Code to reproduce experiments in "Accelerating Bayesian Optimization for Protein Design with Denoising Autoencoders" (Stanton et al 2022)

Implementation of Denoising Diffusion for protein design, but using the new Equiformer (successor to SE3 Transformers) with some additional improvements

Graph neural network for generating novel amino acid sequences that fold into proteins with predetermined topologies.