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

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design

Official repository for the ProteinGym benchmarks

The Rosetta Bio-macromolecule modeling package. Available through license with the University of Washington.

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.

De Novo Protein Design by Equivariantly Diffusing Oriented Residue Clouds

MaSIF-neosurf: surface-based protein design for ternary complexes.

Fitness landscape exploration sandbox for biological sequence design.

Open source biolab

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

A Unified Evaluation Suite for Protein Design

BindCraft modified to make PyRosetta use and installation optional: no license needed

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

Claude Code skills for protein design

OVO, an open-source ecosystem for de novo protein design

Codes for our paper "Full-Atom Peptide Design with Geometric Latent Diffusion" (NeurIPS 2024)

A Text-guided Protein Design Framework, Nat Mach Intell 2025 (https://www.nature.com/articles/s42256-025-01011-z)

Official repository for the ProteinDJ protein design pipeline

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.

The first large protein language model trained follows structure instructions.

Text-guided protein design

[ICML 2025] 🧬 ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

Geometric deep learning of protein–DNA binding specificity

Model for predicting micro-millisecond motions from protein sequence and/or structure

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

Protein Sequence Design with Deep Learning and Tooling like Monte Carlo Sampling and Analysis