There are 14 repositories under protein-design topic.
List of papers about Proteins Design using Deep Learning
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.
Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch
De Novo Protein Design by Equivariantly Diffusing Oriented Residue Clouds
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design
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
Official code repository for the paper "ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers"
Graph neural network for generating novel amino acid sequences that fold into proteins with predetermined topologies.
Preforms De novo protein design using machine learning and PyRosetta to generate a novel protein structure
Protein Sequence Design with Deep Learning and Tooling like Monte Carlo Sampling and Analysis
Deep Critical Learning. Implementation of ProSelfLC, IMAE, DM, etc.
Learning to design protein-protein interactions with enhanced generalization (ICLR24)
PDBench is a dataset and software package for evaluating fixed-backbone sequence design algorithms.
Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm
SLIP is a sandbox environment for engineering protein sequences with synthetic fitness functions.
Rosetta FunFolDes – a general framework for the computational design of functional proteins.
Design platform for creating single-chain polyhedral cages made from coiled-coil building modules
Protein Design by Machine Learning guided Directed Evolution
Benchmarking uncertainty quantification methods on proteins.
Codebase for our preprint using trRosetta to design proteins with discontinuous functional sites, found here: https://www.biorxiv.org/content/10.1101/2020.11.29.402743v1.abstract
DE-STRESS is a model evaluation pipeline that aims to make protein design more reliable and accessible.
A bare metal Python library for building and manipulating protein molecular structures