List of papers about Protein Design using Deep Learning

This repository is inspired by the remarkable work of Kevin Kaichuang Yang and their outstanding project Machine-learning-for-proteins. We have established this repository to provide a specialized and focused platform for the field of Deep Learning for Protein Design, a rapidly advancing domain in computational biology.

Contributions and suggestions are warmly welcome! Community Values, Guiding Principles, and Commitments for the Responsible Development of AI for Protein Design: details

Papers last week, updated on 2026.08.02:

• Generative artificial intelligence for enzyme design and biocatalysis
  • [Current Opinion in Chemical Biology]
• The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates
  • [bioRxiv 2026.07.30.741542] • [code] • [website]

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0) Benchmarks and datasets
Sequence dataset/benchmarks • Structure datasets/benchmarks • Public database • Similar list • Guides
1) Reviews and surveys
De novo design • Antibody design • Peptide design • Binder design • Enzyme design
2) Model-based design
Structure Prediction Model-based • CM-Align • MSA transformer-based • LLM-based • Sampling-algorithms
3) Function to Scaffold
GAN-based • AutoEncoder-based • MLP-based • Diffusion-based • RL-based • Flow-based • Score-based • Autoregressive
4) Scaffold to Sequence
Review • MLP-based • VAE-based • LSTM-based • CNN-based • GNN-based • GAN-based • Transformer-based • ResNet-based • Diffusion-based • Bayesian method • Flow-based • RL-based • Train method
5) Function to Sequence
CNN-based • VAE-based • GAN-based • Transformer-based • Bayesian method • Reinforcement Learning • Flow-based • RNN-based • LSTM-based • Autoregressive • Boltzmann machine • Diffusion-based • GNN-based • Score-based
6) Function to Structure
Review • LSTM-based • Diffusion-based • RoseTTAFold-based • CNN-based • GNN-based • Transformer-based • MLP-based • Flow-based • AlphaFold-based
7) Other
Effects of mutations & Fitness Landscape • Protein Language Model & Representation Learning • Molecular Design Model • Framework • Unclassified

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0. Benchmarks and datasets

0.1 Sequence Datasets, Benchmarks

FLIP: Benchmark tasks in fitness landscape inference for proteins Christian Dallago, Jody Mou, Kadina E Johnston, Bruce Wittmann, Nick Bhattacharya, Samuel Goldman, Ali Madani, Kevin K Yang NeurIPS 2021 Datasets and Benchmarks Track/bioRxiv 2021 • website • code • Supplementary

A Benchmark Framework for Evaluating Structure-to-Sequence Models for Protein Design Jeffrey Chan, Seyone Chithrananda, David Brookes, Sam Sinai Paper unavailable at Machine Learning in Structural Biology Workshop 2022

PDBench: Evaluating Computational Methods for Protein-Sequence Design Leonardo V Castorina, Rokas Petrenas, Kartic Subr, Christopher W Wood Bioinformatics, 2023;, btad027 • code

Benchmarking deep generative models for diverse antibody sequence design Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie Lozano arXiv:2111.06801

The Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design Chase Armer, Hassan Kane, Dana Cortade, Dave Estell, Adil Yusuf, Radhakrishna Sanka, Henning Redestig, TJ Brunette, Pete Kelly, Erika DeBenedictis arXiv:2309.09955

Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks Sean R.Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, Kevin K. Yang bioRxiv (2023) • code

FLOP: Tasks for Fitness Landscapes Of Protein Wildtypes Peter Mørch Groth, Richard Michael, Jesper Salomon, Pengfei Tian, Wouter Boomsma bioRxiv 2023.06.21.545880 • code

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction Pascal Notin, Aaron W Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Hansen Spinner, Nathan Rollins, Ada Shaw, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Rose Orenbuch, Yarin Gal, Debora S Marks bioRxiv 2023.12.07.570727 • code

Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design Chase Armer, Hassan Kane, Dana L. Cortade, Henning Redestig, David A. Estell, Adil Yusuf, Nathan Rollins, Hansen Spinner, Debora Marks, TJ Brunette, Peter J. Kelly, Erika DeBenedictis bioRxiv 2024.08.12.606135/Proteins: Structure, Function, and Bioinformatics (2025) • code • Supplementary

Generative AI Models for the Protein Scaffold Filling Problem Letu Qingge, Kushal Badal, Richard Annan, Jordan Sturtz, Xiaowen Liu, and Binhai Zhu Journal of Computational Biology

Benchmarking Inverse Folding Models for Antibody CDR Sequence Design Per Junior Greisen, Yifan Li, Yuxiang Lang, Chenrui Xu, Yi Zhou, Ziwei Pang bioRxiv 2024.12.16.628614

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants Moritz Ertelt, Rocco Moretti, Jens Meiler, and Clara T. Schoeder Science Advances 11.7 (2025) • code

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition Tudor-Stefan Cotet, Igor Krawczuk, Filippo Stocco, Noelia Ferruz, Anthony Gitter, Yoichi Kurumida, Lucas de Almeida Machado, Francesco Paesani, Cianna N. Calia, Chance A. Challacombe, Nikhil Haas, Ahmad Qamar, Bruno E. Correia, Martin Pacesa, Lennart Nickel, Kartic Subr, Leonardo V. Castorina, Maxwell J. Campbell, Constance Ferragu, Patrick Kidger, Logan Hallee, Christopher W. Wood, Michael J. Stam, Tadas Kluonis, Suleyman Mert Unal, Elian Belot, Alexander Naka, Adaptyv Competition Organizers bioRxiv 2025.04.17.648362 • github

Experimental Evaluation of AI-Driven Protein Design Risks Using Safe Biological Proxies
Svetlana P. Ikonomova, Bruce J. Wittmann, Fernanda Piorino, David J. Ross, Samuel W. Schaffter, Olga Vasilyeva, Eric Horvitz, James Diggans, Elizabeth A. Strychalski, Sheng Lin-Gibson, Geoffrey J. Taghon
bioRxiv 2025.05.15.654077 • code

Benchmark for Antibody Binding Affinity Maturation and Design
Xinyan Zhao, Yi-Ching Tang, Akshita Singh, Victor J Cantu, KwanHo An, Junseok Lee, Adam E Stogsdill, Ashwin Kumar Ramesh, Zhiqiang An, Xiaoqian Jiang, Yejin Kim
arXiv:2506.04235 • dataset • code

The Dayhoff Atlas: scaling sequence diversity for improved protein generation
Kevin K. Yang, Sarah Alamdari, Alex J. Lee, Kaeli Kaymak-Loveless, Samir Char, Garyk Brixi, Carles Domingo-Enrich, Chentong Wang, Suyue Lyu, Nicolo Fusi, Neil Tenenholtz, Ava P. Amini
bioRxiv 2025.07.21.665991 • code • dataset

Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
Danny Reidenbach, Zhonglin Cao, Zuobai Zhang, Kieran Didi, Tomas Geffner, Guoqing Zhou, Jian Tang, Christian Dallago, Arash Vahdat, Emine Kucukbenli, Karsten Kreis
arXiv:2512.01976

Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches
Alexander J Barnett , Rajendra KC , Pratikshya Pandey , Pamodha Somasiri , Kirsten A Fairfax , Sandy Hung , Alex W Hewitt
Genomics, Proteomics & Bioinformatics (2026)

AFD-INSTRUCTION: A Comprehensive Antibody Instruction Dataset with Functional Annotations for LLM-Based Understanding and Design
Ling Luo, Wenbin Jiang, Hongyuan Chang, Xinkang Wang, Xushi Zhang, Yueting Xiong, Mengsha Tong, Rongshan Yu
arXiv:2602.04916 • code • dataset • website

Benchmarking and behavioral characterization of LLM agents for protein design
Jeonghyeon Kim, Philip Romero
bioRxiv 2026.05.06.723381 • code

0.2 Structure Datasets, Benchmarks

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB Zhangyang Gao, Cheng Tan, Stan Z. Li arxiv (2022)

SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning Jonathan E. King, David Ryan Koes arxiv • github::sidechainnet

TDC maintains a resource list that currently contains 22 tasks (and its datasets) related to small molecules and macromolecules, including PPI, DDI and so on. MoleculeNet published a small molecule related benchmark four years ago.

In terms of datasets and benchmarks, protein design is far less mature than drug discovery (paperwithcode drug discovery benchmarks). (Maybe should add the evaluation of protein design for deep learning method (especially deep generative model)) Difficulties and opportunities always coexist. Happy to see the work of Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang and Zhangyang Gao, Cheng Tan, Stan Z. Li.

Sampling of structure and sequence space of small protein folds Thomas W. Linsky, Kyle Noble, Autumn R. Tobin, Rachel Crow, Lauren Carter, Jeffrey L. Urbauer, David Baker & Eva-Maria Strauch Nat Commun 13, 7151 (2022) • code • Supplementary

OpenProteinSet: Training data for structural biology at scale Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi arXiv:2308.05326 • OpenFold

ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Stan Z. Li GitHub

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, Jian Tang arXiv preprint arXiv:2312.00080 (2023) • code

Scaffold-Lab: Critical Evaluation and Ranking of Protein Backbone Generation Methods in A Unified Framework Zhuoqi Zheng, Bo Zhang, Bozitao Zhong, Kexin Liu, Jinyu Yu, Zhengxin Li, JunJie Zhu, Ting Wei, Hai-Feng Chen bioRxiv 2024.02.10.579743 • code • Supplementary

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer, Nathan C. Frey, Andrew Martin Watkins, Aya Abdelsalam Ismail, Arian Rokkum Jamasb, Edith Lee, Tyler Bryson, Stephen Ra, Kyunghyun Cho arXiv:2407.21028 • code • dataset

Large protein databases reveal structural complementarity and functional locality Paweł Szczerbiak, Lukasz Szydlowski, Witold Wydmański, P. Douglas Renfrew, Julia Koehler Leman, Tomasz Kosciolek bioRxiv 2024.08.14.607935 • code • Supplementary • website

The Protein Design Archive (PDA): insights from 40 years of protein design Marta Chronowska, Michael J. Stam, Derek N. Woolfson, Luigi F. Di Constanzo, Christopher W. Wood bioRxiv 2024.09.05.611465/Nat Biotechnol (2025) • code • Supplementary • website

ProteinBench: A Holistic Evaluation of Protein Foundation Models Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu arXiv:2409.06744 • code

Benchmarking Generative Models for Antibody Design & Exploring Log-Likelihood for Sequence Ranking Talip Uçar, Cedric Malherbe, Ferran Gonzalez bioRxiv 2024.10.07.617023 • code

Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of Metrics Pavel Strashnov, Andrey Shevtsov, Viacheslav Meshchaninov, Maria Ivanova, Fedor Nikolaev, Olga Kardymon, Dmitry Vetrov bioRxiv 2024.10.25.620213

MotifBench: A standardized protein design benchmark for motif-scaffolding problems Zhuoqi Zheng, Bo Zhang, Kieran Didi, Kevin K. Yang, Jason Yim, Joseph L. Watson, Hai-Feng Chen, Brian L. Trippe arXiv:2502.12479 • code

Systematic comparison of Generative AI-Protein Models reveals fundamental differences between structural and sequence-based approaches Alexander J Barnett, KC Rajendra, Pratikshya Pandey, Pamodha Somasiri, Kirsten A Fairfax, Sandy Hung, Alex W Hewitt bioRxiv 2025.03.23.644844 • code • Supplementary

Conformation-specific Design: a New Benchmark and Algorithm with Application to Engineer a Constitutively Active Map Kinase
Jacob A. Stern, Siba Alharbi, Anandsukeerthi Sandholu, Stefan T. Arold, Dennis Della Corte
bioRxiv 2025.04.23.650138 • code • dataset

PRIDE-New-Benchmark-Dataset-For-Protein-Structural-Design
Hanqun CAO, dchenhe
github

Protein FID: Improved Evaluation of Protein Structure Generative Models
Felix Faltings, Hannes Stark, Tommi Jaakkola, Regina Barzilay
arXiv:2505.08041

PDFBench: A Benchmark for De novo Protein Design from Function
Jiahao Kuang, Nuowei Liu, Changzhi Sun, Tao Ji, Yuanbin Wu
arXiv:2505.20346 • website • code

An improved model for prediction of de novo designed proteins with diverse geometries
Benjamin Orr, Stephanie E Crilly, Deniz Akpinaroglu, Eleanor Zhu, Michael J. Keiser, Tanja Kortemme
bioRxiv 2025.06.02.657515

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design
Lang Yu, Zhangyang Gao, Cheng Tan, Qin Chen, Jie Zhou, Liang He arXiv:2507.20243

Evaluating zero-shot prediction of protein design success by AlphaFold, ESMFold, and ProteinMPNN
Mario Garcia, Gabriel Jacob Rocklin, Sugyan Dixit
bioRxiv 2025.07.29.667290

Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised Binders
Max Daniel Overath, Andreas Rygaard, Christian Peder Jacobsen, Valentas Brasas, Oliver Morell, Pietro Sormanni, Timothy Patrick Jenkins
bioRxiv 2025.08.14.670059 • dataset

Limitations of the refolding pipeline for de novo protein design
Kerlen T. Korbeld, Vsevolod Viliuga, Maximilian J.L.J. Fürst
bioRxiv 2025.12.09.693122 • Supplementary • data

Assessment of Generative De Novo Peptide Design Methods for G Protein-Coupled Receptors
Hannes Junker, Clara T. Schoeder
bioRxiv 2026.02.26.708415

ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
Cong Liu, Milong Ren, Jiaqi Guan, Chengyue Gong, Jinyuan Sun, Xinshi Chen, Wenzhi Xiao
arXiv:2605.04118

0.3 Databases

A list of suggested protein databases, more lists at CNCB.

0.3.1 Sequence Database

1. UniProt
2. DisProt
3. MobiDB
4. Peptipedia

0.3.2 Structure Database

0.4 Similar List

Some similar GitHub lists that include papers about protein design using deep learning:

1. design_tools
2. awesome-AI-based-protein-design
3. ProteinStructureWithDL
4. List of available bioinformatic tools and services

0.5 Guides

Guides/Tutorials for beginners on GitHub:

1. how_to_create_a_protein
2. protein-design-tutorials
3. AI-driven-protein-design
4. Deep Learning for Proteins Notebook Series Teaches AI for Biomolecular Structure Prediction and Design

Collection of Protein Design Labs:

• ProteinDesignLabs
• proteindesigngroups

1. Reviews

1.1 De novo protein design

Protein design: from computer models to artificial intelligence Antonella Paladino, Filippo Marchetti, Silvia Rinaldi, Giorgio Colombo Wiley Interdisciplinary Reviews: Computational Molecular Science 7.5 (2017): e1318

Advances in protein structure prediction and design Brian Kuhlman, Philip Bradley Nat Rev Mol Cell Biol 20, 681-697 (2019)

Deep learning in protein structural modeling and design Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray Patterns 1.9 • 2020

100th anniversary of macromolecular science viewpoint: Data-driven protein design Ferguson, Andrew L., and Rama Ranganathan ACS Macro Letters 10.3 (2021)

Artificial intelligence in early drug discovery enabling precision medicine Fabio Bonioloa, Emilio Dorigattia, Alexander J. Ohnmachta, Dieter Saurb, Benjamin Schuberta, and Michael P. Menden Expert Opinion on Drug Discovery 16.9 (2021)

Protein design with deep learning Defresne, Marianne, Sophie Barbe, and Thomas Schiex International Journal of Molecular Sciences 22.21 (2021)

Protein sequence design with deep generative models Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang Current Opinion in Chemical Biology 65 • note • 2021

Structure-based protein design with deep learning Ovchinnikov, Sergey, and Po-Ssu Huang Current opinion in chemical biology 65 • note • 2021

Deep learning techniques have significantly impacted protein structure prediction and protein design Pearce, Robin, and Yang Zhang Current opinion in structural biology 68 (2021)

Recent advances in de novo protein design: Principles, methods, and applications Pan, Xingjie, and Tanja Kortemme Journal of Biological Chemistry 296 (2021)

Protein design via deep learning Wenze Ding, Kenta Nakai, Haipeng Gong Briefings in Bioinformatics • 25 March 2022

Deep generative modeling for protein design Strokach, Alexey, and Philip M. Kim Current Opinion in Structural Biology • 2022

Dawn of a new era for membrane protein design Sowlati-Hashjin, Shahin, Aanshi Gandhi, and Michael Garton BioDesign Research (2022)

Deep learning approaches for conformational flexibility and switching properties in protein design Rudden, Lucas SP, Mahdi Hijazi, and Patrick Barth Frontiers in Molecular Biosciences

Computational protein design with evolutionary-based and physics-inspired modeling: current and future synergies Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana arXiv:2208.13616v2

From sequence to function through structure: deep learning for protein design Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago bioRxiv 2022.08.31.505981/Computational and Structural Biotechnology Journal Volume 21, 2023 • Supplementary • accompanying list

Computational protein design with data-driven approaches: Recent developments and perspectives Haiyan Liu, Quan Chen WIREs Comput Mol Sci. 2022. e1646

Understanding by design: Implementing deep learning from protein structure prediction to protein design Gao, Yuanxu, Jiangshan Zhan, and Albert CH Yu MedComm-Future Medicine 1.2 (2022): e22

Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng arXiv:2302.10907

Machine learning for evolutionary-based and physicsinspired protein design: Current and future synergies Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana Current Opinion in Structural Biology

De novo design of polyhedral protein assemblies: before and after the AI revolution Bhoomika Basu Mallik, Jenna Stanislaw, Tharindu Madhusankha Alawathurage, and Alena Khmelinskaia ChemBioChem 2023, e202300117

Research progress of artificial intelligence in protein design CHEN Zhihang, JI Menglin, QI Yifei Synthetic Biology Journal (2023)

A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang https://arxiv.org/abs/2304.01565

Exploring the Protein Sequence Space with Global Generative Models Sergio Romero-Romero, Sebastian Lindner, Noelia Ferruz arXiv:2305.01941

The Era of Machine Learning for Protein Design, Summarized in Four Key Methods LucianoSphere Towards Data Science

Is novelty predictable? Clara Fannjiang, Jennifer Listgarten arXiv:2306.00872

Computational protein design - where it goes? Xu Binbin, Chen Yingjun and Xue Weiwei Current Medicinal Chemistry 2023

How can the protein design community best support biologists who want to harness AI tools for protein structure prediction and design? Birte Höcker, Peilong Lu, Anum Glasgow, Debora S. Marks Pranam Chatterjee, Joanna S.G. Slusky, Ora Schueler-Furman, Possu Huang Cell Systems 14.8 (2023)

De novo 設計ナノポアの創製 新津藍 生物工学会誌 101.8 (2023)

Generative artificial intelligence for de novo protein design Adam Winnifrith, Carlos Outeiral, Brian Hie arXiv:2310.09685

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review Jingjing Wang, Chang Chen, Ge Yao, Junjie Ding, Liangliang Wang and Hui Jiang Molecules 28.23 (2023)

Generative models for protein sequence modeling: recent advances and future directions Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, Daniel Woldring Briefings in Bioinformatics

A new age in protein design empowered by deep learning Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde, Michael Bronstein, Bruno Correia Cell Systems, Volume 14, Issue 11

Deep learning for protein structure prediction and design—progress and applications Jürgen Jänes and Pedro Beltrao Mol Syst Biol(2024)

De novo protein design—From new structures to programmable functions Tanja Kortemme Cell 187.3 (2024)

Generative models for protein structures and sequences Chloe Hsu, Clara Fannjiang & Jennifer Listgarten Nat Biotechnol 42, 196–199 (2024)

What does it take for an ‘AlphaFold Moment’ in functional protein engineering and design? Roberto A. Chica & Noelia Ferruz Nat Biotechnol 42, 173–174 (2024)

Protein design: the experts speak Anne Doerr Nat Biotechnol 42, 175–178 (2024)

Machine learning for functional protein design Pascal Notin, Nathan Rollins, Yarin Gal, Chris Sander & Debora Marks Nat Biotechnol 42, 216–228 (2024)

Sparks of function by de novo protein design Alexander E. Chu, Tianyu Lu & Po-Ssu Huang Nat Biotechnol 42, 203–215 (2024) • poster

A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein arXiv:2402.08703

Security challenges by AI-assisted protein design Philip Hunter EMBO Rep(2024)

Opportunities and challenges in design and optimization of protein function Dina Listov, Casper A. Goverde, Bruno E. Correia & Sarel Jacob Fleishman Nat Rev Mol Cell Biol (2024)

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction Saber Saharkhiz, Mehrnaz Mostafavi, Amin Birashk, Shiva Karimian, Shayan Khalilollah, Sohrab Jaferian, Yalda Yazdani, Iraj Alipourfard, Yun Suk Huh, Marzieh Ramezani Farani & Reza Akhavan-Sigari Top Curr Chem (Z) 382, 23 (2024)

Computational methods for protein design Noelia Ferruz, Amelie Stein Protein Engineering, Design and Selection, Volume 37, 2024

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review Farzan Soleymani, Eric Paquet, Herna Lydia Viktor, Wojtek Michalowski Computational and Structural Biotechnology Journal (2024)

Machine learning in biological physics: From biomolecular prediction to design Jonathan Martin, Marcos Lequerica Mateos, José N. Onuchic, and Faruck Morcos Proceedings of the National Academy of Sciences 121.27 (2024)

AI has dreamt up a blizzard of new proteins. Do any of them actually work? Ewen Callaway Nature 634.8034 (2024)

Five protein-design questions that still challenge AI Sara Reardon Nature 635.8037 (2024)

De novo protein design in the age of artificial intelligence Nan Liu, Xiaocheng Jin, Chongzhou Yang, Ziyang Wang, Xiaoping Min, Shengxiang Ge Sheng Wu Gong Cheng Xue Bao

Generative Models in Protein Engineering: A Comprehensive Survey Chen Xinhui, Yiwen Yuan, Joseph Liu, Chak Tou Leong, Xiaoye Zhu, Jiaqi Chen Neurips 2024 Workshop

A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design Weihang Dai arXiv:2501.01477

The Promise of Protein Design: A Q&A with Nobel Laureate David Baker David Baker and Fay Lin GEN Biotechnology (2025)

Protein design and structure solution for drug discovery Petra Bombicz Crystallography Reviews (2024)

A Model-Centric Review of Deep Learning for Protein Design Gregory W. Kyro, Tianyin Qiu, Victor S. Batista arXiv:2502.19173

Computational protein design Katherine I. Albanese, Sophie Barbe, Shunsuke Tagami, Derek N. Woolfson & Thomas Schiex Nature Reviews Methods Primers 5.1 (2025)

Exploring the Blueprint of Life: The Innovation in Antibody and Protein Design Yang, Zhiwei, and Gerald H. Lushington Combinatorial chemistry & high throughput screening

Advanced Deep Learning Methods for Protein Structure Prediction and Design Yichao Zhang, Ningyuan Deng, Xinyuan Song, Ziqian Bi, Tianyang Wang, Zheyu Yao, Keyu Chen, Ming Li, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Li Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Lawrence KQ Yan, Hongming Tseng, Yan Zhong, Yunze Wang, Ziyuan Qin, Bowen Jing, Junjie Yang, Jun Zhou, Chia Xin Liang, Junhao Song arXiv:2503.13522

Deep Learning-Driven Protein Structure Prediction and Design: Key Model Developments by Nobel Laureates and Multi-Domain Applications Wanqing Yang, Yanwei Wang, Yang Wang arXiv:2504.01490

Intelligent mining, engineering, and de novo design of proteins LIU Cui, SHI Zhenkun, MA Hongwu, LIAO Xiaoping Sheng wu gong cheng xue bao= Chinese journal of biotechnology 41.3 (2025)

Protein-based Materials: Applications, Modification and Molecular Design
Alitenai Tunuhe, Ze Zheng, Xinran Rao, Hongbo Yu, Fuying Ma, Yaxian Zhou, Shangxian Xie
BioDesign Research (2025)

Artificial intelligence is transforming the study of proteins: Structures and beyond
Haiyan Liu, Quan Chen, and Yufeng Liu
hLife (2025)

Artificial intelligence methods for protein folding and design
Zhidian Zhang, Chenxi Ou, Yehlin Cho, Yo Akiyama, Sergey Ovchinnikov
Current Opinion in Structural Biology 93 (2025)

AI4Protein: transforming the future of protein design
Dequan Wang, Zheling Tan, Jin Gao, Shaoting Zhang, Jiaqi Shen & Yuming Lu
Science China Life Sciences (2025)

Comment on the Use of AI-based Protein Design for Autoimmune Encephalitis: Exciting Possibilities and Practical Considerations
Zengwei Kou
Multiple Sclerosis and Related Disorders (2025)

Computational Protein Design: Advancing Biotechnology through In Silico Engineering
Ranjit Ranbhor, Ruthvik Venkatesan, Amay Sanjay Redkar, Vibin Ramakrishnan
Progress in Biophysics and Molecular Biology (2025)

Advances of computational protein design: Principles, strategies and applications in nutrition and health
Ziling Zhao, Qiyang Qu, Fuwei Sun, Jiachen Zang, Bowen Zheng, Tuo Zhang, Guanghua Zhao, Chenyan Lv, Zhongjiang Wang
Biotechnology Advances (2025)

Artificial intelligence in de novo protein design
Yao, Jiawei, and Xiaogang Wang
Medicine in Novel Technology and Devices (2025)

AI-driven protein design
Huan Yee Koh, Yizhen Zheng, Madeleine Yang, Rohit Arora, Geoffrey I. Webb, Shirui Pan, Li Li & George M. Church
Nat Rev Bioeng (2025)

DL4Proteins Jupyter Notebooks Teach how to use Artificial Intelligence for Biomolecular Structure Prediction and Design
Michael Chungyoun, Gabe Au, Britnie Carpentier, Sreevarsha Puvada, Courtney Thomas, Jeffrey J. Gray
arXiv:2511.02128

Guiding Generative Models for Protein Design: Prompting, Steering and Aligning
Filippo Stocco, Michele Garibbo, Noelia Ferruz
arXiv:2511.21476

RFDiffusion: Revolutionizing Protein Design with Generative AI
Zhangzhi(Fred) Peng
Serican Journal of Medicine 2.4 (2025)

Protein design drives synthetic biology research of plant natural products
Xiaopeng Zhang, Yinying Yao, Ye Wang, Yongshuo Ma, Yi Shang
BioDesign Research (2025)

The role of ai-driven de novo protein design in the exploration of the protein functional universe
Guohao Zhang,Chuanyang Liu, Jiajie Lu, Shaowei Zhang and Lingyun Zhu
Biology 14.9 (2025): 1268

Protein design and RNA design: Perspectives
Xi Chen, Xu Dai, Peilong Lu
Quantitative Biology 14.2 (2026)

Transformation of Protein Design: From Traditional Approaches to AI-Driven Precision Engineering
Xin Fang
MedScien 1.1 (2025)

Harnessing advances in artificial intelligence for protein design
Russell Johnson
Nature Chemical Biology (2025): 1-4

De novo protein design: a transformative frontier in clinical protein applications
Jie Gao, Zaiyong Zheng, Xueting Yu, Yamei Luo, Yang Yu & Chunxiang Zhang
J Transl Med (2026)

Designing de novo TIM barrels: insights into stabilization, diversification, and functionalization strategies
Julian Beck, Sergio Romero-Romero
Biochem Soc Trans

AI-enabled protein design facilitates future plant research and crop breeding
Yuxuan Lou, Tianhao Wu, Fan Xia, Anwen Zhao, Xiangfeng Wang
Plant Physiology

Frontiers and challenges in the design of binders for intrinsically disordered proteins
Chentong Wang, Yanzhe Zhang, Minchao Fang, Zhangzhi Peng, Longxing Cao
Current Opinion in Structural Biology

Closing the loop: Experimentally validated methods in artificial intelligence–driven protein design
Clayton W. Kosonocky, Sarah Alamdari, Kevin K. Yang, Ava P. Amini
Current Opinion in Structural Biology 98 (2026)

Protein design, generative AI and biological security
Maximilian Brackmann, Sophie Reiners, Masja Hoogendoorn, Michel Moser
Frontiers in Microbiology 17 (2026)

The past, present and future of de novo protein design
Wei Yang, Shunzhi Wang, Gyu Rie Lee, Jason Z. Zhang, Alexis Courbet, David Juergens, Xinru Wang, Thomas Schlichthaerle, Mohamad Abedi, Robert Ragotte, Linna An, Indrek Kalvet, Sam Pellock, Ljubica Mihaljevic, Cameron Glasscock, Arvind Pillai, Adam Broerman, Nathan Ennist, Ella Haefner, Nora McNamara-Bordewick, Ian Haydon, Lance Stewart, Gaurav Bhardwaj & David Baker
Nature 652, 1139–1152 (2026)

Generative Protein Design: From Deep Learning Algorithms to Translational Applications
Shaotong Luo, Bo Zhou
International Journal of Molecular Sciences 27.9 (2026)

Machine-guided design for bioengineering gene therapy vectors: Where are we and what lies ahead?
Ana F. Rodrigues, Lucas Ferraz, Catia Pesquita
Biotechnology Advances (2026)

Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms
Yanlin Mi, Arpit Shukla, Mark Tangney, Sabin Tabirca, and Venkata VB Yallapragada
Comput Struct Biotechnol J. 2026

Membrane Protein Design: From Reprogramming Functions to AI-Guided De Novo Design Approaches
Robert E. Jefferson, Patrick Barth
Chem. Rev. 2026

Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives
Wenjuan Zhao, Xiuwu Pan, Wei Yang, Zichang Liu, Xiyi Wei, Anqi Lin, Bufu Tang, Lin Zhang, Mingjia Xiao, Qing Zeng, Quan Cheng, Weiming Mou, Xiaofan Lu, Kai Miao, Peng Luo, Xin-gang Cui & Wen-jin Chen
Probiotics & Antimicro. Prot. (2026)

De Novo Design of Protein Nanopores: From Minimal Peptides to AI-Driven Design
Anastassia A. Vorobieva, Rituparna Samanta, Toon Van Thillo
Chem. Rev. (2026)

1.2 Antibody design

A review of deep learning methods for antibodies Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati , S. Vince Parish, Brenda Medellin and Monica Berrondo Antibodies 9.2 (2020)

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies Rahmad Akbar, Habib Bashour, Puneet Rawat, Philippe A. Robert, Eva Smorodina, Tudor-Stefan Cotet, Karine Flem-Karlsen, Robert Frank, Brij Bhushan Mehta, Mai Ha Vu, Talip Zengin, Jose Gutierrez-Marcos, Fridtjof Lund-Johansen, Jan Terje Andersen, and Victor Greif Mabs. Vol. 14. No. 1. Taylor & Francis, 2022

Advances in computational structure-based antibody design Hummer, Alissa M., Brennan Abanades, and Charlotte M. Deane Current Opinion in Structural Biology 74 (2022)

Computational and artificial intelligence-based methods for antibody development Jisun Kim, Matthew McFee, Qiao Fang, Osama Abdin, Philip M. Kim Trends in Pharmacological Sciences (2023)

Leveraging deep learning to improve vaccine design Andrew P. Hederman, Margaret E. Ackerman Trends in immunology (2023)

In Silico Approaches to Deliver Better Antibodies by Design: The Past, the Present and the Future Andreas Evers, Shipra Malhotra, Vanita D. Sood arXiv:2305.07488

AI Models for Protein Design are Driving Antibody Engineering Michael Chungyoun, Jeffrey J. Gray Current Opinion in Biomedical Engineering (2023): 100473

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens Federica Guarra and Giorgio Colombo Journal of Chemical Theory and Computation (2023)

Simplifying complex antibody engineering using machine learning Makowski, Emily K., Hsin-Ting Chen, and Peter M. Tessier Cell Systems 14.8 (2023)/2022 AIChE Annual Meeting. AIChE, 2022.

AI driven B-cell Immunotherapy Design Bruna Moreira da Silva, David B. Ascher, Nicholas Geard, Douglas E. V. Pires arXiv:2309.01122

Best practices for machine learning in antibody discovery and development Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar, Victor Greiff arXiv:2312.08470/Drug Discovery Today (2024)

Next generation of multispecific antibody engineering Daniel Keri, Matt Walker, Isha Singh, Kyle Nishikawa, Fernando Garces Antibody Therapeutics (2023): tbad027

A primer on ML in antibody engineering ABHISHAIKE MAHAJAN Substack • blog

Antibody design using deep learning: from sequence and structure design to affinity maturation Sara Joubbi, Alessio Micheli, Paolo Milazzo, Giuseppe Maccari, Giorgio Ciano, Dario Cardamone, Duccio Medini Briefings in Bioinformatics, Volume 25, Issue 4, July 2024, bbae307

AI-accelerated therapeutic antibody development: practical insights Luca Santuari, Marianne Bachmann Salvy, Ioannis Xenarios, Bulak Arpat Frontiers in Drug Discovery 4 (2024)

AI-driven antibody design with generative diffusion models: current insights and future directions Xin-heng He, Jun-rui Li, James Xu, Hong Shan, Shi-yi Shen, Si-han Gao & H. Eric Xu Acta Pharmacologica Sinica (2024)

Applying computational protein design to therapeutic antibody discovery -- current state and perspectives Weronika Bielska, Igor Jaszczyszyn, Pawel Dudzic, Bartosz Janusz, Dawid Chomicz, Sonia Wrobel, Victor Greiff, Ryan Feehan, Jared Adolf-Bryfogle, Konrad Krawczyk arXiv:2503.00913/Frontiers in Immunology 16 (2025)

Artificial intelligence-driven computational methods for antibody design and optimization
Luiz Felipe Vecchietti, Bryan Nathanael Wijaya, Azamat Armanuly,Begench Hangeldiyev, Hyunkyu Jung, Sooyeon Lee, Meeyoung Cha & Ho Min Kim
mAbs, 2025

In Silico Peptide Design: Methods, Resources, and Role of AI
Priyanka Ray Choudhury, Sai Kumar Mishra, Siddharth Yadav, Shubhi Singh, Puniti Mathur
Journal of Peptide Science 31.12 (2025)

Artificial intelligence in antibody design and development: harnessing the power of computational approaches
Soudabeh Kavousipour, Mahdi Barazesh, Shiva Mohammadi
Medical & Biological Engineering & Computing (2025)

Harnessing deep learning to accelerate the development of antibodies and aptamers Pan Tan, Song Li, Jin Huang, Ziyi Zhou, Liang Hong
Acta Pharmaceutica Sinica B (2025)

Artificial intelligence advancements in monoclonal antibody development technology
Ammar M, Samsonov M, Gurylina E and Bayzigitov D
Front. Immunol

1.3 Peptide design

Deep generative models for peptide design Wan, Fangping, Daphne Kontogiorgos-Heintz, and Cesar de la Fuente-Nunez Digital Discovery (2022)

Design of protein segments and peptides for binding to protein targets Gupta, Suchetana, Noora Azadvari, and Parisa Hosseinzadeh BioDesign Research 2022 (2022)

Revolutionizing peptide-based drug discovery: Advances in the post-AlphaFold era Liwei Chang, Arup Mondal, Bhumika Singh, Yisel Martínez-Noa, Alberto Perez Wiley Interdisciplinary Reviews: Computational Molecular Science

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides Montserrat Goles, Anamaría Daza, Gabriel Cabas-Mora, Lindybeth Sarmiento-Varón, Julieta Sepúlveda-Yañez, Hoda Anvari-Kazemabad, Mehdi D Davari, Roberto Uribe-Paredes, Álvaro Olivera-Nappa, Marcelo A Navarrete, David Medina-Ortiz Briefings in Bioinformatics 25.4 (2024)

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery Ahmad M. Al-Omari ,Yazan H. Akkam,Ala’a Zyout,Shayma’a Younis,Shefa M. Tawalbeh,Khaled Al-Sawalmeh,Amjed Al Fahoum ,Jonathan Arnold PloS one 19.12 (2024): e0315477

Trends in the Research and Development of Peptide Drug Conjugates: Artificial Intelligence Aided Design Dong-E Zhang, Dong-E Zhang, Tong He, Tong He, Tianyi Shi, Tianyi Shi, Kun Huang, Kun Huang, Anlin Peng, Anlin Peng Frontiers in Pharmacology 16

Generative models for antimicrobial peptide design: auto-encoders and beyond
Lukas Beierle, Julian Hahnfeld, Alexander Goesmann, Reihaneh Mostolizadeh, Franz Cemič
bioRxiv 2025.10.29.685317/BioData Mining (2026) • Supplementary • code

Contemporary data-driven innovations in peptide-based therapeutic design
Lipsa Priyadarsinee, Vyacheslav Kungurtsev, Vibhor Kumar, Bapi Chatterjee, Garikapati Narahari Sastry, Natarajan Arul Murugan
Briefings in Bioinformatics

1.4 Binder design

Improving de novo Protein Binder Design with Deep Learning Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas Savvides, David Baker bioRxiv 2022.06.15.495993/Nat Commun 14, 2625 (2023) • code • news

Data and AI-driven synthetic binding protein discovery Yanlin Li, Zixin Duan, Zhenwen Li, Weiwei Xue Trends in Pharmacological Sciences (2025)

Code to complex: AI-driven de novo binder design
Daniel R. Fox, Cyntia Taveneau, Janik Clement, Rhys Grinter, Gavin J. Knott
Structure (2025)

The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction
Luciano A. Abriata
Commun Biol 9, 627 (2026)

1.5 Enzyme design

A review of enzyme design in catalytic stability by artificial intelligence Yongfan Ming, Wenkang Wang, Rui Yin, Min Zeng, Li Tang, Shizhe Tang, Min Li Briefings in Bioinformatics, 2023

Application of "foldability" in the intelligent of enzymes engineering and design: take AlphaFold2 for example MENG Qiaozhen, GUO Fei Synthetic Biology Journal (2023)

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design Casadevall, Guillem, Cristina Duran, and Sí­lvia Osuna JACS Au (2023)

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design Braun Markus, Gruber Christian C, Krassnigg Andreas, Kummer Arkadij, Lutz Stefan, Oberdorfer Gustav, Siirola Elina, and Snajdrova Radka ACS Catal. 2023

Building Enzymes through Design and Evolution Hossack, Euan J., Florence J. Hardy, and Anthony P. Green ACS Catalysis 13.19 (2023)

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels Rana A Barghout, Zhiqing Xu, Siddharth Betala, Radhakrishnan Mahadevan Current Opinion in Biotechnology, Volume 84, 2023

Opportunites and Challenges for Machine Learning-Assisted Enzyme Engineering Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold ACS Central Science (2024)

Navigating the landscape of enzyme design: from molecular simulations to machine learning Jiahui Zhoua, Meilan Huang Chemical Society Reviews (2024)

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins Rebecca Buller, Jiri Damborsky, Donald Hilvert, Uwe Bornscheuer Angewandte Chemie (International ed. in English)

Generative AI for Enzyme Design and Biocatalysis
Lasse Middendorf, Noelia Ferruz
arXiv:2602.03779

Generative artificial intelligence for enzyme design and biocatalysis
Lasse Middendorf, Noelia Ferruz
Current Opinion in Chemical Biology

2. Model-based design

Invert trained models with optimize algorithms through iterations for sequence design. Inverted structure prediction models are known as Hallucination.

2.1 Structure Prediction Model-based

2.1.1 trRosetta-based

Design of proteins presenting discontinuous functional sites using deep learning Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, View ORCID ProfileIvan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker bioRxiv (2020)

Fast differentiable DNA and protein sequence optimization for molecular design Linder, Johannes, and Georg Seelig arXiv preprint arXiv:2005.11275 (2020)

De novo protein design by deep network hallucination Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker Nature (2021) • code • trRosetta

Protein sequence design by conformational landscape optimization Christoffer Norn, Basile I. M. Wicky, David Juergens, and Sergey Ovchinnikov Proceedings of the National Academy of Sciences 118.11 (2021) • code

De novo design of small beta barrel proteins David E. Kim, Davin R. Jensen, David Feldman, Doug Tischer and Ayesha Saleem, Cameron M. Chow, Xinting Li, Lauren Carter, Lukas Milles, Hannah Nguyen, Alex Kang, Asim K. Bera, Francis C. Peterson, Brian F. Volkman, Sergey Ovchinnikov, David Baker PNAS(2023),e2207974120 • code

Exploring "dark matter" protein folds using deep learning Zander Harteveld, Alexandra Van Hall-Beauvais, Irina Morozova, Joshua Southern, Casper Alexander Goverde, Sandrine Georgeon, Stephane Rosset, Andreas Loukas, Pierre Vandergheynst, Michael Bronstein, Bruno Correia bioRxiv 2023.08.30.555621/Cell Systems • Suppplymentary • code

Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination Anders Lønstrup Hansen, Frederik Friis Theisen, Ramon Crehuet, Enrique Marcos, Nushin Aghajari, and Martin Willemoës ACS Synth. Biol. 2024

Implicit modeling of the conformational landscape and sequence allows scoring and generation of stable proteins Yehlin Cho, Justas Dauparas, Kotaro Tsuboyama, Gabriel Rocklin, Sergey Ovchinnikov bioRxiv 2024.12.20.629706/Nat Commun (2025) • code • Supplementary

2.1.2 AlphaFold-based

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter K, Ovchinnikov, Sergey bioRxiv (2021)/Bioinformatics, 2022;, btac724 • ColabDesign, SMURF, AF2 back propagation • our notes1, notes2 • lecture1, lecture2 • Discord

AlphaDesign: A de novo protein design framework based on AlphaFold Jendrusch, Michael, Jan O. Korbel, and S. Kashif Sadiq bioRxiv (2021)/Molecular Systems Biology (2025)

Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design Moffat, Lewis, Joe G. Greener, and David T. Jones bioRxiv (2021)

State-of-the-art estimation of protein model accuracy using AlphaFold James P. Roney, Sergey Ovchinnikov bioRxiv 2022.03.11.484043/Physical Review Letters 129.23 (2022) • code

Solubility-aware protein binding peptide design using AlphaFold Takatsugu Kosugi, Masahito Ohue bioRxiv 2022.05.14.491955/Biomedicines 10.7 (2022) • Supplemental Materials • code

Hallucinating protein assemblies Basile I M Wicky, Lukas F Milles, Alexis Courbet, Robert J Ragotte, Justas Dauparas, Elias Kinfu, Sam Tipps, Ryan D Kibler, Minkyung Baek, Frank DiMaio, Xinting Li, Lauren Carter, Alex Kang, Hannah Nguyen, Asim K Bera, David Baker bioRxiv 2022.06.09.493773/Science (2022) • related slides • our notes • news

EvoBind: in silico directed evolution of peptide binders with AlphaFold Patrick Bryant, Arne Elofsson bioRxiv 2022.07.23.501214 • code

Hallucination of closed repeat proteins containing central pockets Linna An, Derrick R Hicks, Dmitri Zorine, Justas Dauparas, Basile I. M. Wicky, Lukas F Milles, Alexis Courbet, Asim K. Bera, Hannah Nguyen, Alex Kang, Lauren Carter, David Baker bioRxiv 2022.09.01.506251/Nat Struct Mol Biol 30, 1755-1760 (2023) • Supplementary data

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search Patrick Bryant, Gabriele Pozzati, Wensi Zhu, Aditi Shenoy, Petras Kundrotas & Arne Elofsson Nature communications 13.1 (2022) • gitlba, github • Supplementary data1, Supplementary data2

De novo protein design by inversion of the AlphaFold structure prediction network Casper Goverde, Benedict Wolf, Hamed Khakzad, Stephane Rosset, Bruno E Correia bioRxiv 2022.12.13.520346 • code • lecture1 • lecture2

Code of OpenComplex Jingcheng, Yu and Zhaoming, Chen and Zhaoqun, Li and Mingliang, Zeng and Wenjun, Lin and He, Huang and Qiwei, Ye code

Efficient and scalable de novo protein design using a relaxed sequence space Christopher Josef Frank, Ali Khoshouei, Yosta de Stigter, Dominik Schiewitz, Shihao Feng, Sergey Ovchinnikov, Hendrik Dietz bioRxiv 2023.02.24.529906 • code

Cyclic peptide structure prediction and design using AlphaFold Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Joshmyn De La Cruz, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj bioRxiv/Nat Commun 16, 4730 (2025) • Code • Supplementary

De novo design of luciferases using deep learning Andy Hsien-Wei Yeh, Christoffer Norn, Yakov Kipnis, Doug Tischer, Samuel J. Pellock, Declan Evans, Pengchen Ma, Gyu Rie Lee, Jason Z. Zhang, Ivan Anishchenko, Brian Coventry, Longxing Cao, Justas Dauparas, Samer Halabiya, Michelle DeWitt, Lauren Carter, K. N. Houk & David Baker Nature • Code • Supplementary Materials

In silico evolution of protein binders with deep learning models for structure prediction and sequence design Odessa J Goudy, Amrita Nallathambi, Tomoaki Kinjo, Nicholas Randolph, Brian Kuhlman bioRxiv 2023.05.03.539278 • Supplementary • code

Computational design of soluble analogues of integral membrane protein structures Casper Alexander Goverde, Martin Pacesa, Lars Jeremy Dornfeld, Sandrine Georgeon, Stephane Rosset, Justas Dauparas, Christian Shellhaas, Simon Kozlov, David Baker, Sergey Ovchinnikov, Bruno Correia bioRxiv 2023.05.09.540044/Nature (2024) • code • Supplementary

Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 and Binding Affinity Prediction Model Takafumi Ueki, Masahito Ohue bioRxiv 2023.06.02.543382

Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes Lior Zimmerman, Noga Alon, Itay Levin, Anna Koganitsky, Nufar Shpigel, Chen Brestel, Gideon David Lapidoth bioRxiv 2023.07.27.550799 • Supplementary

Highly accurate and robust protein sequence design with CarbonDesign/Accurate and robust protein sequence design with CarbonDesign Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang bioRxiv 2023.08.07.552204/Nat Mach Intell 6, 536–547 (2024) • code

Design of Cyclic Peptides Targeting Protein-Protein Interactions using AlphaFold Takatsugu Kosugi, Masahito Ohue bioRxiv 2023.08.20.554056 • Supplementary • code

MetaPPI: In Silico Screen for Novel CRBN-based Substrates neoxbio website • news • masif-based • commercial

AlphaFold Distillation for Protein Design Anonymous ICLR 2024 • code

High-throughput computational discovery of inhibitory protein fragments with AlphaFold Andrew Savinov, Sebastian Swanson, Amy E. Keating, Gene-Wei Li bioRxiv 2023.12.19.572389 • code

An integrative approach to protein sequence design through multiobjective optimization Lu Hong, Tanja Kortemme bioRxiv 2024.03.01.582670/PLOS Computational Biology 20(7) • code • Supplementary

Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models Jue Wang, Joseph L. Watson and Sidney L. Lisanza Cold Spring Harbor Perspectives in Biology(2024)

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes Lior Zimmerman, Noga Alon, Itay Levin, and Gideon D. Lapidoth Proceedings of the National Academy of Sciences 121.11(2024)

Design of Repeat Alpha-Beta Proteins with Capping Helices Dmitri Zorine, David Baker bioRxiv 2024.06.15.590358 • code

Design of linear and cyclic peptide binders of different lengths only from a protein target sequence Qiuzhen Li, Efstathios Nikolaos Vlachos, Patrick Bryant bioRxiv 2024.06.20.599739 • code • Supplementary

BindCraft: one-shot design of functional protein binders Martin Pacesa, Lennart Nickel, Joseph Schmidt, Ekaterina Pyatova, Christian Schellhaas, Lucas Kissling, Ana Alcaraz-Serna, Yehlin Cho, Kourosh H. Ghamary, Laura Vinue, Brahm J. Yachnin, Andrew M. Wollacott, Stephen Buckley, Sandrine Georgeon, Casper A. Goverde, Georgios N. Hatzopoulos, Pierre Gonczy, Yannick D. Muller, Gerald Schwank, Sergey Ovchinnikov, Bruno E. Correia bioRxiv 2024.09.30.615802/Nature (2025) • code

Design of linear and cyclic peptide binders of different lengths from protein sequence information Qiuzhen Li, Efstathios Nikolaos Vlachos, Patrick Bryant bioRxiv 2024.06.20.599739 • code

Scalable protein design using optimization in a relaxed sequence space Christopher Frank, Ali Khoshouei , Lara Fub , Dominik Schiwietz , Dominik Putz, Lara Weber, Zhixuan Zhao, Motoyuki Hattori, Shihao Feng, Yosta de Stigter, Sergey Ovchinnikov, Hendrik Dietz Science386,439-445(2024) • code

Alphafold2 refinement improves designability of large de novo proteins Christopher Josef Frank, Dominik Schiwietz, Lara Fuss, Sergey Ovchinnikov, Hendrik Dietz bioRxiv 2024.11.21.624687 • colab

Low-N OpenFold fine-tuning improves peptide design without additional structures Theodore Sternlieb, Jakub Otwinowski, Sam Sinai, Jeffrey Chan Machine Learning for Structural Biology Workshop, NeurIPS 2024

HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction Huitian Lin, Cheng Zhu, Tianfeng Shang, Ning Zhu, Kang Lin, Xiang Shao, Xudong Wang, Hongliang Duan bioRxiv 2025.03.17.643626

Designing Novel Solenoid Proteins with In Silico Evolution Daniella Pretorius, Georgi Ivanov Nikov, Kono Washio, Steve-William Florent, Henry Taunt, Sergey Ovchinnikov, James William Murray bioRxiv 2025.04.23.646631 • Supplementary

Single-Shot Design of a Cyclic Peptide Inhibitor of HIV-1 Membrane Fusion with EvoBind
Diandra Daumiller, Federica Giammarino, Qiuzhen Li, Anders Sönnerborg, Rafael Ceña Diez, Patrick Bryant
bioRxiv 2025.04.30.651413

BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design
Divya Nori, Anisha Parsan, Caroline Uhler, Wengong Jin
arXiv:2505.21241

Blind De Novo Design of Dual Cyclic Peptide Agonists Targeting GCGR and GLP1R
Qiuzhen Li, Elisee Wiita, Thomas Helleday, Patrick Bryant
bioRxiv 2025.06.06.658268 • code

AlphaFold distillation for inverse protein design
Igor Melnyk, Aurélie Lozano, Payel Das & Vijil Chenthamarakshan
Sci Rep 15, 21743 (2025) • code

Fold-Conditioned De Novo Binder Design via AlphaFold2-Multimer Hallucination
Khondamir. R. Rustamov, Artyom Y. Baev
bioRxiv 2025.07.02.662497 • Supplementary • code

Design of linear and cyclic peptide binders from protein sequence information
Qiuzhen Li, Efstathios Nikolaos Vlachos & Patrick Bryant
Commun Chem 8, 211 (2025)

Generative Design of High-Affinity Peptides Using BindCraft
Mike Filius, Thanasis Patsos, Hugo Minee, Gianluca Turco, Jingming Liu, Monika Gnatzy, Ramon S.M. Rooth, Andy C. H. Liu, Rosa D.T. Ta, Isa H. A. Rijk, Safiya Ziani, Femke J. Boxman, Sebastian J. Pomplun
bioRxiv 2025.07.23.666285 • Supplementary

Computational Design of Soluble CCR8 Analogues with Preserved Antibody Binding
Trang Nguyen, Songming Liu, Yifan Li, Longfei Cong, Roger Shek, Tek Hyang Lee, Li Yi, Per Greisen
bioRxiv 2025.08.18.670068

De novo design of a peptide modulator to reverse sodium channel dysfunction linked to cardiac arrhythmias and epilepsy
Ryan Mahling, Bence Hegyi, Erin R. Cullen, Timothy M. Cho, Aaron R. Rodriques, Lucile Fossier, Marc Yehya, Lin Yang, Bi-Xing Chen, Alexander N. Katchman, Nourdine Chakouri, Ruiping Ji, Elaine Y. Wan, Jared Kushner, Steven O. Marx, Sergey Ovchinnikov, Christopher D. Makinson, Donald M. Bers, Manu Ben-Johny
Cell (2025)

Efficient generation of epitope-targeted de novo antibodies with Germinal
Luis Santiago Mille-Fragoso, John N Wang, Claudia L Driscoll, Haoyu Dai, Talal M Widatalla, Xiaowei Zhang, Brian L Hie, Xiaojing J Gao
bioRxiv 2025.09.19.677421 • Supplementary • code

mBER: Controllable de novo antibody design with million-scale experimental screening
Erik Swanson, Michael Nichols, Supriya Ravichandran, Pierce Ogden
bioRxiv 2025.09.26.678877

Automated Deep Learning-Based Pipelines for Multi-Objective De Novo Protein Design
Amrita Nallathambi, Brian Kuhlman
Current protocols 5.10 (2025)

Protein Hunter: exploiting structure hallucination within diffusion for protein design
Yehlin Cho, Griffin Rangel, Gaurav Bhardwaj, Sergey Ovchinnikov
bioRxiv 2025.10.10.681530 • code

De novo protein design enables targeting of intractable oncogenic interfaces
Varshika Ram Prakash, Yusuf Najy, Kalel Garrett, Brian F.P. Edwards, Benjamin L Kidder
bioRxiv 2025.10.22.683953 • Supplementary

HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence design
Minchao Fang, Chentong Wang, Jungang Shi, Fangbai Lian, Qihan Jin, Zhe Wang, Yanzhe Zhang, Zhanyuan Cui, YanJun Wang, Yitao Ke, Qingzheng Han, Longxing Cao
bioRxiv 2025.11.08.686881 • code

Sequence and structural determinants of efficacious de novo chimeric antigen receptors
Arthur Chow, Hoyin Chu, Ruofan Li, Benan Nalbant, Abdul Dozic, Laura Kida, Caleb Lareau
bioRxiv 2025.12.12.694033 • code

De novo design of protein competitors for small molecule immunosensing
Yosta de Stigter, Tallie Godschalk, Maarten Merkx
bioRxiv 2025.12.16.694474 • Supplementary

A Rapid and Universal Pipeline for High-Resolution GPCR Structure Determination through In Silico Construct Optimization and de novo Protein Design
Asato Kojima, Kouki Kawakami, Naoya Kobayashi, Kazuhiro Kobayashi, Toshiki E. Matsui, Kohei Uemoto, Yuzhong Gu, Masahiro Fukuda, Hideaki E. Kato
bioRxiv 2026.04.02.716066 • Supplementary

Computational design of an ultrapotent deltacoronavirus miniprotein inhibitor
Nathan G. Avery, Courtney N. Yoshiyama, Ashley L. Taylor, Young-Jun Park, Daniel Asarnow, Lisa Perruzza, Jack T. Brown, Davide Corti, Fabio Benigni, Tyler N. Starr, and David Veesler
Proceedings of the National Academy of Sciences 123.18 (2026)

AI-Guided De Novo Design of a Caffeine-Induced Protein Dissociation System。 Tatsuki Nonomura, Brendan McKee, Anna Price, Mingguang Cui, Zaynah Yousuf, Faith Tran, Lian He, Tianlu Wang, Yubin Zhou
J. Am. Chem. Soc. 2026

SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
Qianxun Xu, Chenxi Song, Yujun Cai, Chi Zhang
arXiv:2602.23956

ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteins
Jing Xu, Milong Ren, Ning Qi, Xinru Zhang, Zaikai He, Chungong Yu, Dongbo Bu
bioRxiv 2026.06.21.733655 • Supplementary

Efficient generation of epitope-targeted antibodies with Germinal
Luis S. Mille-Fragoso, Claudia L. Driscoll, John N. Wang, Haoyu Dai, Talal Widatalla, Jim L. Zhang, Xiaowei Zhang, Bing Rao, Liang Feng, Brian L. Hie & Xiaojing J. Gao
Nat Biotechnol (2026) • code

Programmable design of synthetic plant immune receptors for pathogen protein recognition
Haocheng Zhu, Dandan Jiang, Qiao Zhang, Kang Zhang, Kevin Tianmeng Zhao, Jin-Long Qiu, and Caixia Gao
Science

The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates
Julius Wenckstern, Anna M. Diaz-Rovira, Julia Kuhn, Arvid Ban, Rahma Hamdani, Roser Pruano-Milla, Evgenia Elizarova, Sandrine Georgeon, Kaiden Thompson, Matthias Hinterndorfer, Devanarayanan Siva Sankar, Maximilian Dunnebacke, David Desscan, Sreenath Nair, Marcelo Querino Lima Afonso, Jennifer Fleming, Sameer Velankar, Andrea Ablasser, Paola Picotti, Georg Winter, Mikko Taipale, Bruno E. Correia
bioRxiv 2026.07.30.741542 • code • website

2.1.3 DMPfold2-based

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design Moffat, Lewis, Shaun M. Kandathil, and David T. Jones bioRxiv (2022) • DMPfold2

2.1.4 DeepAb-based

Towards deep learning models for target-specific antibody design Sai Pooja Mahajan, Jeffrey Ruffolo, Rahel Frick, Jeffrey J. Gray Biophysical Journal 121.3 (2022) • DeepAb • lecture

Hallucinating structure-conditioned antibody libraries for target-specific binders Sai Pooja Mahajan, Jeffrey A Ruffolo, Rahel Frick, Jeffrey J. Gray bioRxiv 2022.06.06.494991/Front. Immunol. 13:999034 • Supplementary • code

2.1.5 TRFold2-based

News of TRDesign TIANRANG XLab paper unavailable • slides • website • commercial • news

2.1.6 Boltz-based

Boltzdesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design Yehlin Cho, Martin Pacesa, Zhidian Zhang, Bruno E. Correia, Sergey Ovchinnikov bioRxiv 2025.04.06.647261 • code

BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability
Talip Uçar, Jack Bates, Yunguan Fu, Wenxian Shi, Hannes Stark, Demitri Nava, Luca Cavalleri, Jeremy Wohlwend, Gabriele Corso, Saro Passaro
technical report/bioRxiv 2026.06.23.733997

2.1.7 RareFold-based

RareFold: Structure prediction and design of proteins with noncanonical amino acids
Qiuzhen Li, Diandra Daumiller, Patrick Bryant
bioRxiv 2025.05.19.654846 • code

2.1.8 HelixFold-based

HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3
Jie Gao, Jun Li, Jing Hu, Shanzhuo Zhang, Kunrui Zhu, Yueyang Huang, Xiaonan Zhang, Xiaomin Fang
arXiv:2505.21873 • ESM-IF-based

HelixDesign-Antibody: A Scalable Production-Grade Platform for Antibody Design Built on HelixFold3
Jie Gao, Jing Hu, Shanzhuo Zhang, Kunrui Zhu, Sheng Qian, Yueyang Huang, Xiaonan Zhang, Xiaomin Fang
arXiv:2507.02345 • website

2.1.9 ESMfold-based

Design of proteins by parallel tempering in the sequence space
Preet Kalani, Vojtěch Spiwok
Protein Science 34.10 (2025)

2.1.10 tFold-based

De novo design of epitope-specific antibodies via a structure-driven computational workflow
Fandi Wu, Yu Zhao, JiaXiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin, Yang Xiao, Fan Yang, Rubo Wang, Ningqiao Huang, Huaxian Jia, Yuyi Liu, Houtim Lai, Tingyang Xu, Fang Wang, Zihan Wu, Yidong Song, Shaoning Li, Wei Liu, Yu Rong, Peilin Zhao & Jianhua Yao
Nat Commun (2025) • code

2.1.11 Chai-based

De novo protein ligand design including protein flexibility and conformational adaptation
Jakob Agamia, Martin Zacharias
bioRxiv 2026.01.08.698398 • code • Supplementary

2.2 CM-Align

AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design Shuhao Zhang, Youjun Xu, Jianfeng Pei, Luhua Lai NeurIPS 2021

2.3 MSA-transformer-based

Protein language models trained on multiple sequence alignments learn phylogenetic relationships Damiano Sgarbossa, Umberto Lupo, Anne-Florence Bitbol arXiv preprint arXiv:2203.15465 (2022)/bioRxiv 2022.04.14.488405

EvoOpt: an MSA-guided, fully unsupervised sequence optimization pipeline for protein design Hideki Yamaguchi, Yutaka Saito NeurIPS 2022

Generative power of a protein language model trained on multiple sequence alignments Sgarbossa, Damiano, Umberto Lupo, and Anne-Florence Bitbol Elife 12 (2023): e79854 • code

2.4 LLM-based

2.4.1 GPT-based

Multi-segment preserving sampling for deep manifold sampler Daniel Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Vladimir Gligorijević, Richard Bonneau, Stephen Ra, Kyunghyun Cho arXiv preprint arXiv:2205.04259 (2022)

Preference optimization of protein language models as a multi-objective binder design paradigm Pouria Mistani, Venkatesh Mysore arXiv:2403.04187

HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design Li Wang, Yiping Li, Xiangzheng Fu, Xiucai Ye, Junfeng Shi, Gary G. Yen, Xiangxiang Zeng arXiv:2405.00753

2.4.2 ESM-based

Generating novel protein sequences using Gibbs sampling of masked language models Sean R. Johnson, Sarah Monaco, Kenneth Massie, Zaid Syed bioRxiv 2021.01.26.428322 • code

A high-level programming language for generative protein design Brian Hie, Salvatore Candido, Zeming Lin, Ori Kabeli, Roshan Rao, Nikita Smetanin, Tom Sercu, Alexander Rives bioRxiv 2022.12.21.521526

Language models generalize beyond natural proteins Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, Alexander Rives bioRxiv 2022.12.21.521521

ESMFold Hallucinates Native-Like Protein Sequences Jeliazko R Jeliazkov, Diego del Alamo, Joel D Karpiak bioRxiv 2023.05.23.541774

Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method Bo Zhang, Kexin Liu, Zhuoqi Zheng, Yunfeiyang Liu, Junxi Mu, Ting Wei, Hai-Feng Chen bioRxiv 2023.10.26.564121/preprint • code • Supplementary

Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models Arjuna M. Subramanian, Matt Thomson bioRxiv 2023.12.22.573145

Computational scoring and experimental evaluation of enzymes generated by neural networks Sean R. Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak & Kevin K. Yang Nature Biotechnology (2024) • code

Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models Po-Yu Liang, Xueting Huang, Tibo Duran, Andrew J. Wiemer, Jun Bai arXiv:2408.08341 • code

Designing diverse and high-performance proteins with a large language model in the loop Carlos A. Gomez-Uribe, Japheth Gado, Meiirbek Islamov bioRxiv 2024.10.25.620340

Key-cutting machine: A novel optimization framework for tailored protein and peptide design Yan C. Leyva, Marcelo D. T. Torres, Carlos A. Oliva, Cesar de la Fuente-Nunez, Carlos A. Brizuela bioRxiv 2025.01.05.631393 • code

Improving functional protein generation via foundation model-derived latent space likelihood optimization Changge Guan, Fangping Wan, Marcelo D. T. Torres, Cesar de la Fuente-Nunez bioRxiv 2025.01.07.631724 • Supplementary

DPAC: Prediction and Design of Protein-DNA Interactions via Sequence-Based Contrastive Learning
Leo Tianlai Chen, Rishab Pulugurta, Pranay Vure, Pranam Chatterjee
bioRxiv 2025.05.14.654102 • code

BAGEL: Protein Engineering via Exploration of an Energy Landscape
Jakub Lála, Ayham Al-Saffar, Stefano Angiolleti-Uberti
bioRxiv 2025.07.05.663138 • code

GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design
Jiale Liu, Zheng Guo and Luhua Lai
Proceedings of the National Academy of Sciences 122.41 (2025) • code

Harnessing protein-folding algorithms to drug intrinsically disordered epitopes
Jakub Lála, Stefano Angioletti-Uberti
bioRxiv 2025.11.11.687846

2.4.3 Antiberta-based

DyAb: sequence-based antibody design and property prediction in a low-data regime Joshua Yao-Yu Lin, Jennifer L. Hofmann, Andrew Leaver-Fay, Wei-Ching Liang, Stefania Vasilaki, Edith Lee, Pedro O. Pinheiro, Natasa Tagasovska, James R. Kiefer, Yan Wu, Franziska Seeger, Richard Bonneau, Vladimir Gligorijevic, Andrew Watkins, Kyunghyun Cho, Nathan C. Frey bioRxiv 2025.01.28.635353 • code • Supplementary

An Energy Landscape Approach to Miniaturizing Enzymes using Protein Language Model Embeddings
Jakub Lála, Harsh Agrawal, Fanfei Dong, Jude Wells, Stefano Angioletti-Uberti
bioRxiv 2026.03.04.709378

2.5 Sampling-algorithms

AdaLead: A simple and robust adaptive greedy search algorithm for sequence design Sam Sinai, Richard Wang, Alexander Whatley, Stewart Slocum, Elina Locane, Eric D. Kelsic arXiv preprint arXiv:2010.02141 (2020) • code

Autofocused oracles for model-based design Fannjiang, Clara, and Jennifer Listgarten Advances in Neural Information Processing Systems 33 (2020)

An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta arXiv:2211.03193

Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter St. Joh NeurIPS 2022/arXiv:2212.09925

Importance Weighted Expectation-Maximization for Protein Sequence Design Zhenqiao Song, Lei Li arXiv:2305.00386 • Supplementary

Simultaneous enhancement of multiple functional properties using evolution-informed protein design Benjamin Fram, Ian Truebridge, Yang Su, Adam J. Riesselman, John B. Ingraham, Alessandro Passera, Eve Napier, Nicole N. Thadani, Samuel Lim, Kristen Roberts, Gurleen Kaur, Michael Stiffler, Debora S. Marks, Christopher D. Bahl, Amir R. Khan, Chris Sander, Nicholas P. Gauthier bioRxiv (2023): 2023-05

Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing Andrew Kirjner, Jason Yim, Raman Samusevich, Tommi Jaakkola, Regina Barzilay, Ila Fiete arXiv:2307.00494 • code

Sampling Protein Language Models for Functional Protein Design
Jeremie Theddy Darmawan, Yarin Gal, Pascal Notin
ICLR 2025 Workshop LMRL

Reliable algorithm selection for machine learning-guided design Clara Fannjiang, Ji Won Park arXiv:2503.20767

Why risk matters for protein binder design Tudor-Stefan Cotet, Igor Krawczuk arXiv:2504.00146

Guide your favorite protein sequence generative model
Junhao Xiong, Hunter Nisonoff, Ishan Gaur, Jennifer Listgarten
arXiv:2505.04823

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling
Lei Wang, Xiaoming He, Gaoxing Guo, Xinzhou Qian, Qiang Huang
bioRxiv 2025.06.08.658414 • code

Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Xuefeng Liu, Mingxuan Cao, Songhao Jiang, Xiao Luo, Xiaotian Duan, Mengdi Wang, Tobin R. Sosnick, Jinbo Xu, Rick Stevens
arXiv:2509.15796

Relaxed Sequence Sampling for Diverse Protein Design
Joohwan Ko, Aristofanis Rontogiannis, Yih-En Andrew Ban, Axel Elaldi, Nicholas Franklin
arXiv:2510.23786

Controllable protein design with particle-based Feynman-Kac steering
Erik Hartman, Jonas Wallin, Johan Malmström, Jimmy Olsson
arXiv:2511.09216

Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization
Mingming Zhu, Jiahua Rao, Xiaoyu Chen, Qianmu Yuan, Yuedong Yang
bioRxiv 2026.01.13.699365

Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
Ashima Khanna, Dominik Grimm
arXiv:2605.26690 • code

3. Function to Scaffold

These models design backbone/scaffold/template in Cartesian coordinates, contact maps, distance maps and φ & ψ angles. Including conditional/unconditional generative models.

3.1 GAN-based

Generative modeling for protein structures Anand, Namrata, and Possu Huang NeurIPS 2018

Fully differentiable full-atom protein backbone generation Anand Namrata, Raphael Eguchi, and Po-Ssu Huang OpenReview ICLR 2019 workshop DeepGenStruct • without code

RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network Sabban, Sari, and Mikhail Markovsky F1000Research 9 (2020) • code • pyRosetta • tensorflow • maximizaing the fluorescence of a protein

A Generative Model for Creating Path Delineated Helical Proteins Nicholas B. Woodall, Ryan Kibler, Basile Wicky, Brian Coventry bioRxiv 2023.05.24.542095 • code

3.2 AutoEncoder-based

Conditioning by adaptive sampling for robust design Brookes, David, Hahnbeom Park, and Jennifer Listgarten International conference on machine learning. PMLR, 2019 • without code

IG-VAE: generative modeling of immunoglobulin proteins by direct 3D coordinate generation Raphael R. Eguchi, Christian A. Choe, Po-Ssu Huang Biorxiv (2020) • without code

Generating tertiary protein structures via an interpretative variational autoencoder Xiaojie Guo, Yuanqi Du, Sivani Tadepalli, Liang Zhao, Amarda Shehu arXiv preprint arXiv:2004.07119 (2020) • code not available

Function-guided protein design by deep manifold sampling Vladimir Gligorijevic, Stephen Ra, Daniel Berenberg, Richard Bonneau, Kyunghyun Cho NeurIPS 2021 • without code

Deep sharpening of topological features for de novo protein design Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, Pierre Vandergheynst, Micheal Bronstein, Bruno Correia ICLR2022 Machine Learning for Drug Discovery. 2022 • code not available

End-to-End deep structure generative model for protein design Boqiao Lai, matthew McPartlon, Jinbo Xu bioRxiv 2022.07.09.499440

Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization Raphael R Eguchi, Christian A Choe, Udit Parekh, Irene S Khalek, Michael D Ward, Neha Vithani, Gregory R Bowman, Joseph G Jardine, Possu Huang bioRxiv 2022.12.22.521698

Leveraging Deep Generative Model For Computational Protein Design And Optimization Boqiao Lai arXiv:2408.17241 • PhD thesis

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Backbone Sampling Andrew C. Powers, P. Douglas Renfrew, Parisa Hosseinzadeh, Vikram Khipple Mulligan bioRxiv 2025.02.21.639569

3.3 MLP-based

A backbone-centred energy function of neural networks for protein design Bin Huang, Yang Xu, Xiuhong Hu, Yongrui Liu, Shanhui Liao, Jiahai Zhang, Chengdong Huang, Jingjun Hong, Quan Chen & Haiyan Liu Nature (2022) • code

De novo Design of Cavity-Containing Proteins with a Backbone-Centered Neural Network Energy Function Yang Xu, Xiuhong Hu, Chenchen Wang, Yongrui Liu, Quan Chen Haiyan Liu Structure (2024)

3.4 Diffusion-based

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem Brian L. Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola arXiv:2206.04119/NeurIPS 2022/ICLR 2023 • poster • Supplementary • code

ProteinSGM: Score-based generative modeling for de novo protein design Jin Sub Lee, Philip M Kim bioRxiv 2022.07.13.499967/Nat Comput Sci (2023) • code

Protein structure generation via folding diffusion Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini arXiv:2209.15611/Nat Commun 15, 1059 (2024) • code

Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds Yeqing Lin, Mohammed AlQuraishi arXiv:2301.12485v3 • code • news

SE(3) diffusion model with application to protein backbone generation Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola arXiv:2302.02277/ICLR 2023 • code • Supplementary

A Latent Diffusion Model for Protein Structure Generation Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji arXiv:2305.04120

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham arXiv:2306.17775 • code

Dynamics-Informed Protein Design with Structure Conditioning Simon V. Mathis, Urszula Julia Komorowska, Mateja Jamnik, Pietro Lió WCBICML2023/ICLR 2024

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model Bo Ni and David L. Kaplan and M. Buehler arXiv:2310.10605/Science Advances 10.6 (2024) • Supplementary • code

DiffSDS: A geometric sequence diffusion model for protein backbone inpainting Anonymous ICLR 2024/arXiv:2301.09642

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, Pietro Lio arXiv:2312.09236

Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding Yuyang Zhang, Yuhang Liu, Zinnia Ma, Min Li, Chunfu Xu & Haipeng Gong
bioRxiv 2023.12.13.571602/Nat Mach Intell (2025)• code

Improved motif-scaffolding with SE(3) flow matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola arXiv:2401.04082/TMLR • code1,code2

DiffTopo: Fold exploration using coarse grained protein topology representations Yangyang Miao, Bruno Correia bioRxiv 2024.02.01.578456/ICLR 2024

Diffusion models in protein structure and docking Jason Yim, Hannes Stärk, Gabriele Corso, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola Wiley Interdisciplinary Reviews: Computational Molecular Science 14.2 (2024) • review

De novo antibody design with SE(3) diffusion Daniel Cutting, Frédéric A. Dreyer, David Errington, Constantin Schneider, Charlotte M. Deane arXiv:2405.07622

Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2 Yeqing Lin, Minji Lee, Zhao Zhang, Mohammed AlQuraishi arXiv:2405.15489 • code • news

DSG2-mini DiffuseBio technical appendix • website • commercial

Floating Anchor Diffusion Model for Multi-motif Scaffolding Ke Liu, Weian Mao, Shuaike Shen, Xiaoran Jiao, Zheng Sun, Hao Chen, Chunhua Shen ICML 2024/arXiv:2406.03141 • code • poster

De novo Design of A Fusion Protein Tool for GPCR Research Kaixuan Gao, Xin Zhang, Jia Nie, Hengyu Meng, Weishe Zhang, Boxue Tian, Xiangyu Liu bioRxiv 2024.09.14.613090/Proceedings of the National Academy of Sciences 122.29 (2025) • Supplementary • RFdiffusion-based

Text2Protein: A Generative Model for Designated Protein Design on Given Description Ramtin Hosseini, Siyang Zhang, Pengtao Xie PREPRINT (Version 1) available at Research Square • code

Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs Young, James Matthew Uygongco, and Omer Deniz Akyildiz Imperial CollegeofScience, Technology and Medicine, 2024 • code • Master thesis • Genie-based

Protein A-like Peptide Design Based on Diffusion and ESM2 Models Long Zhao, Qiang He, Huijia Song, Huijia Song,Tianqian Zhou, An Luo, Zhenguo Wen,Teng Wang, and Xiaozhu Lin Molecules 29.20 (2024) • code

FoldMark: Protecting Protein Generative Models with Watermarking Zaixi Zhang, Ruofan Jin, Kaidi Fu, Le Cong, Marinka Zitnik, Mengdi Wang arXiv:2410.20354 • code

ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design Yiming Ma, Fei Ye, Yi Zhou, Zaixiang Zheng, Dongyu Xue, Quanquan Gu arXiv:2411.16686

On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs James Matthew Young, O. Deniz Akyildiz arXiv:2412.05788 • code

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering Wen-ran Li, Xavier F. Cadet, David Medina-Ortiz, Mehdi D. Davari, Ramanathan Sowdhamini, Cedric Damour, Yu Li, Alain Miranville, Frederic Cadet arXiv:2501.02680 • review

RFdiffusion Exhibits Low Success Rate in De Novo Design of Functional Protein Binders for Biochemical Detection Bruce Jiang, Xiaoxiao Li, Amber Guo, Moris Wei, Jonny Wu bioRxiv 2025.02.07.636769

From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design Leonardo V Castorina, Christopher W Wood, Kartic Subr bioRxiv 2025.03.19.644162 • Supplementary • RFdiffusion-based

Hierarchical Protein Backbone Generation with Latent and Structure Diffusion Jason Yim, Marouane Jaakik, Ge Liu, Jacob Gershon, Karsten Kreis, David Baker, Regina Barzilay, Tommi Jaakkola ICLR 2025

The Dance of Atoms-De Novo Protein Design with Diffusion Model Yujie Qin, Ming He, Changyong Yu, Ming Ni, Xian Liu, Xiaochen Bo arXiv:2504.16479 • review

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation
Xiao Liang, Wentao Ma, Eric Paquet, Herna Lydia Viktor, Wojtek Michalowski arXiv:2504.21092

NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones
Urszula Julia Komorowska, Francisco Vargas, Alessandro Rondina, Pietro Lio, Mateja Jamnik
ICML 2025 poster

AIDO.StructureDiffusion: The AIDO Module for Molecular Design
GenBio Team
website

De novo design of phosphorylation-induced protein switches for synthetic signaling in cells
Stephen Buckley, Yangyang Miao, Mubarak Idris, Pao-Wan Lee, Leo Scheller, Roland Riek, Sebastian J. Maerkl, Luciano A. Abriata, Bruno E. Correia
bioRxiv 2025.09.10.675034

Constrained Diffusion for Protein Design with Hard Structural Constraints
Jacob K Christopher, Austin Seamann, Jingyi Cui, Sagar Khare, Ferdinando Fioretto
bioRxiv 2025.10.15.682365

Controllable protein design through Feynman-Kac steering
Erik Hartman, Jonas Wallin, Johan Malmström, Jimmy Olsson
arXiv:2511.09216 • code

Protein generation with embedding learning for motif diversification
Kevin Michalewicz, Chen Jin, Philip Alexander Teare, Tom Diethe, Mauricio Barahona, Barbara Bravi, Asher Mullokandov
arXiv:2510.18790

Torsion-Space Diffusion for Protein Backbone Generation with Geometric Refinement
Lakshaditya Singh, Adwait Shelke, Divyansh Agrawal
arXiv:2511.19184

SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers
Shentong Mo, Lanqing Li
arXiv:2602.06706

AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
Hanqun Cao, Zachary Quinn, Aastha Pal, Sumi Kimura, Jingjie Zhang, Pheng Ann Heng, Pranam Chatterjee
arXiv:2606.05474

De Novo Design of Protein Switches with Diffusion-Based Ensemble Sampling
Alireza Omidi, Jiajun He, Jennifer M. Bui, Jörg Gsponer, Saifuddin Syed
bioRxiv 2026.07.20.739027

3.5 RL-based

Top-down design of protein nanomaterials with reinforcement learning Isaac D Lutz, Shunzhi Wang, Christoffer Norn, Andrew J Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Zhe Li, Minkyung Baek, Neil P King, Hannele Ruohola-Baker, David Baker bioRxiv 2022.09.25.509419/Science380, 266-273(2023) • code,code2

Model-based reinforcement learning for protein backbone design Frederic Renard, Cyprien Courtot, Alfredo Reichlin, Oliver Bent arXiv:2405.01983

Target-based de novo design of cyclic peptide binders Fanhao Wang, Tiantian Zhang, Jintao Zhu, Xiaoling Zhang, Changsheng Zhang, Luhua Lai bioRxiv 2025.01.18.633746 • Supplementary

Symmetric Self-play Online Preference Optimization for Protein Inverse Folding
Wenwu Zeng, Xiaoyu Li, Haitao Zou, Yutao Dou, Xiongjun Zhao, Shaoliang Peng
bioRxiv 2026.03.26.714453 • code

3.6 Flow-based

SE(3)-Stochastic Flow Matching for Protein Backbone Generation Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong arXiv:2310.02391/ICLR 2024

Fast protein backbone generation with SE(3) flow matching Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé arXiv:2310.05297 • code

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose arXiv:2405.20313/NeurIPS 2024 • website • lecture

Design of Ligand-Binding Proteins with Atomic Flow Matching Junqi Liu, Shaoning Li, Chence Shi, Zhi Yang, Jian Tang arXiv:2409.12080

ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation
Angxiao Yue, Zichong Wang, Hongteng Xu
arXiv:2502.14637 • code

Proteina: Scaling Flow-based Protein Structure Generative Models Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Kucukbenli, Arash Vahdat, Karsten Kreis ICLR 2025 Oral • code • website • lecture

ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, Karsten Kreis ICLR 2025 Oral • code • lecture

Robust and Reliable de novo Protein Design: A Flow-Matching-Based Protein Generative Model Achieves Remarkably High Success Rates
Junyu Yan, Zibo Cui, Wenqing Yan, Yuhang Chen, Mengchen Pu, Shuai Li, Sheng Ye
bioRxiv 2025.04.29.651154 • code

Flexibility-conditioned protein structure design with flow matching
Vsevolod Viliuga, Leif Seute, Nicolas Wolf, Simon Wagner, Arne Elofsson, Jan Stühmer, Frauke Gräter
ICML 2025

Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies
Tianyuan Zheng, Alessandro Rondina, Gos Micklem, Pietro Lio FM4LS 2025

Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Yogesh Verma, Markus Heinonen, Vikas Garg
arXiv:2509.25379

Distilled Protein Backbone Generation
Liyang Xie, Haoran Zhang, Zhendong Wang, Wesley Tansey, Mingyuan Zhou
arXiv:2510.03095

Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design
Junhua Chen, Simon Mathis, Charles Harris, Kieran Didi, Pietro Lio
arXiv:2510.24732

High-Affinity Protein Binder Design via Flow Matching and In Silico Maturation
Qilin Yu, Liangyue Guo, Xiayan Qin, Xikun Huang, Baihui Tian, Hongzhun Wang, Yu Liu, Yunzhi Lang, Di Wang, Zhouhanyu Shen, Jie Lin, and Mingchen Chen
preprint • code

3.7 Score-based

Score-Based Generative Models for Designing Binding Peptide Backbones John D Boom, Matthew Greenig, Pietro Sormanni, Pietro Liò arXiv:2310.07051 • code

Building Confidence in Deep Generative Protein Design Tianyuan Zheng, Alessandro Rondina, Pietro Liò arXiv:2411.18568 • code

3.8 Autoregressive

Protein Autoregressive Modeling via Multiscale Structure Generation
Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu, Quanquan Gu arXiv:2602.04883

4.Scaffold to Sequence

Identify amino sequence from given backbone/scaffold/template constrains: torsion angles(φ & ψ), backbone angles(θ and τ), backbone dihedrals (φ, ψ & ω), backbone atoms (Cα, N, C, & O), Cα − Cα distance, unit direction vectors of Cα−Cα, Cα−N & Cα−C, etc(aka. inverse folding). Referred from here. Energy-based models are also inculded for task of rotamer conformation(χ angles or atom coordinates) recovery.

4.0 Review

Protein sequence design on given backbones with deep learning Yufeng Liu, Haiyan Liu Protein Engineering, Design and Selection, 2023

Multi-indicator comparative evaluation for deep Learning-Based protein sequence design methods Jinyu Yu, Junxi Mu, Ting Wei, Hai-Feng Chen Bioinformatics, 2024;, btae037

Generative AI for Controllable Protein Sequence Design: A Survey Yiheng Zhu, Zitai Kong, Jialu Wu, Weize Liu, Yuqiang Han, Mingze Yin, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou arXiv:2402.10516

Backbone Conditional Protein Sequence Design
Justas Dauparas
Cold Spring Harbor Perspectives in Biology (2025)

Zero-shot protein stability prediction by inverse folding models: a free energy interpretation
Jes Frellsen, Maher M. Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg, Wouter Boomsma
arXiv:2506.05596

4.1 MLP-based

3D representations of amino acids-applications to protein sequence comparison and classification Li, Jie, and Patrice Koehl Computational and structural biotechnology journal 11.18 (2014) • 2014

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, Yaoqi Zhou Proteins: Structure, Function, and Bioinformatics 82.10 (2014) • code unavailable

SPIN2: Predicting sequence profiles from protein structures using deep neural networks James O'Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou Proteins: Structure, Function, and Bioinformatics 86.6 (2018) • code unavailable

Computational protein design with deep learning neural networks Jingxue Wang, Huali Cao, John Z. H. Zhang & Yifei Qi Scientific reports 8.1 (2018) • code unavailable

Ligand-aware protein sequence design using protein self contacts Jody Mou, Benjamin Fry, Chun-Chen Yao, Nicholas Polizzi NeurIPS 2022

SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures Lategan, F. Adriaan, Caroline Schreiber, and Hugh G. Patterton BMC bioinformatics 24.1 (2023) • code

A Generative Neuro-Symbolic AI for Protein Sequence Design
Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar-Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis Garcia-Alles, Thomas Schiex, Sophie Barbe
bioRxiv 2026.03.31.715526/Advanced Science (2026) • code

4.2 VAE-based

Design of metalloproteins and novel protein folds using variational autoencoders Greener, Joe G., Lewis Moffat, and David T. Jones Scientific reports 8.1 (2018)

AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Siyuan Li, Yufei Huang, Stan Z. Li
arXiv:2506.08365

DivPro: diverse protein sequence design with direct structure recovery guidance
Xinyi Zhou, Guibao Shen, Yingcong Chen, Guangyong Chen, Pheng Ann Heng
Bioinformatics (2025) • code

4.3 LSTM-based

To improve protein sequence profile prediction through image captioning on pairwise residue distance map Sheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, and Yuedong Yang Journal of chemical information and modeling 60.1 (2019) • SPROF

Deep learning of Protein Sequence Design of Protein-protein Interactions Syrlybaeva, Raulia, and Eva-Maria Strauch bioRxiv (2022)/Bioinformatics, 2022;, btac733 • Supplementary • code

Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets
Sean M. Brown, Ashley B. Cohen, Scott N. Dean bioRxiv 2026.04.29.721096

4.4 CNN-based

A structure-based deep learning framework for protein engineering Raghav Shroff, Austin W. Cole, Barrett R. Morrow, Daniel J. Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer bioRxiv (2019)

ProDCoNN: Protein design using a convolutional neural network Yuan Zhang, Yang Chen, Chenran Wang, Chun-Chao Lo, Xiuwen Liu, Wei Wu, Jinfeng Zhang Proteins: Structure, Function, and Bioinformatics 88.7 (2020) • code unavailable

Protein sequence design with a learned potential Namrata Anand, Raphael Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman & Po-Ssu Huang Nacture Communications (2022) • code

TIMED-Design: Flexible and Accessible Protein Sequence Design with Convolutional Neural Networks Leonardo V Castorina, Suleyman Mert Ünal, Kartic Subr, Christopher W Wood Protein Engineering, Design and Selection, 2024) • code • website

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme Simon d’Oelsnitz, Daniel J. Diaz, Wantae Kim, Daniel J. Acosta, Tyler L. Dangerfield, Mason W. Schechter, Matthew B. Minus, James R. Howard, Hannah Do, James M. Loy, Hal S. Alper, Y. Jessie Zhang & Andrew D. Ellington Nature Communications 15.1 (2024) • code1, code2

OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model Gang Xu, Yulu Yang, Yiqiu Zhang, Qinghua Wang, Jianpeng Ma bioRxiv 2024.08.20.608889 • code

ProBID-Net: A Deep Learning Model for Protein-Protein Binding Interface Design Zhihang Chen, Menglin Ji, Jie Qiana, Zhe Zhang, Xiangying Zhang, Haotian Gao, Haojie Wang, Renxiao Wang, Yifei Qi Chemical Science (2024) • code

4.5 GNN-based

Learning from protein structure with geometric vector perceptrons Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J.L. Townshend, Ron Dror arXiv preprint arXiv:2009.01411 (2020)/ICLR(2021) • GVP

Fast and flexible protein design using deep graph neural networks Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, Philip M. Kim Cell Systems (2020) • code::ProteinSolver

Mimetic Neural Networks: A unified framework for Protein Design and Folding Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister arXiv:2102.03881/Front. Bioinform. 2:715006

TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs Alex J. Li, Vikram Sundar, Gevorg Grigoryan, Amy E. Keating NeurIPS 2021 / arXiv (2022)

A neural network model for prediction of amino-acid probability from a protein backbone structure Shintaro Minami, Koya Sakuma, Naoya Kobayashi Unpublished yet (June 2021)• GCNdesgin

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers Jack B Maguire, Daniele Grattarola, Vikram Khipple Mulligan, Eugene Klyshko, Hans Melo PLoS computational biology 17.9 (2021)

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB Gao, Zhangyang, Cheng Tan, and Stan Li arXiv preprint arXiv:2202.01079 (2022) • code

Generative De Novo Protein Design with Global Context Cheng Tan, Zhangyao Gao, Jun Xia and Stan Z. Li arXiv • Apr 2022 • code

Masked inverse folding with sequence transfer for protein representation learning Kevin K Yang, Hugh Yeh, Niccolò Zanichelli bioRxiv 2022.05.25.493516/Protein Engineering, Design and Selection 36 (2023) • code • model

Robust deep learning based protein sequence design using ProteinMPNN Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker bioRxiv 2022.06.03.494563/Science (2022) • code • hugging face • lecture • colab(in_jax) • ProteinMPNN+ESMFold

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement Jin, Wengong, Regina Barzilay, and Tommi Jaakkola arXiv preprint arXiv:2207.06616 (2022)/International Conference on Machine Learning. PMLR, 2022 • code • poster

Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs Alex J. Li, Mindren Lu, Israel Desta, Vikram Sundar, Gevorg Grigoryan, and Amy E. Keating bioRxiv 2022.08.02.501736/Protein Science, 32(2)

SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation Deqin Liu, Sheng Chen, Shuangjia Zheng, Sen Zhang, Yuedong Yang bioRxiv 2022.09.05.506704 • code

PiFold: Toward effective and efficient protein inverse folding Zhangyang Gao, Cheng Tan, Stan Z. Li arXiv:2209.12643v2/ICLR 2023 • github

Protein Sequence Design by Entropy-based Iterative Refinement Xinyi Zhou, Guangyong Chen, Junjie Ye, Ercheng Wang, Jun Zhang, Cong Mao, Zhanwei Li, Jianye Hao, Xingxu Huang, Jin Tang, Pheng Ann Heng bioRxiv 2023.02.04.527099

Lightweight Contrastive Protein Structure-Sequence Transformation Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li arXiv:2303.11783

Modeling Protein Structure Using Geometric Vector Field Networks Weian Mao, Muzhi Zhu, Hao Chen, Chunhua Shen bioRxiv 2023.05.07.539736

Knowledge-Design: Pushing the Limit of Protein Deign via Knowledge Refinement Zhangyang Gao, Cheng Tan, Stan Z. Li arXiv:2305.15151/ICLR • code

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network Xing Zhang, Hongmei Yin, Fei Ling, Jian Zhan, Yaoqi Zhou bioRxiv 2023.07.07.548080/PLOS Computational Biology • code

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing Junyu Yan and others Briefings in Bioinformatics, 2023 • code

Contextual protein encodings from equivariant graph transformers Sai Pooja Mahajan, Jeffrey A. Ruffolo, Jeffrey J. Gray bioRxiv 2023.07.15.549154 • code

Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN Hsi-Wen Kao, Wei-Lin Lu, Meng-Ru Ho, Yu-Fong Lin, Yun-Jung Hsieh, Tzu-Ping Ko, Shang-Te Danny Hsu, and Kuen-Phon Wu ACS Synthetic Biology (2023) • Supplementary

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN Robbert J. de Haas, Natalie Brunette, Alex Goodson, Justas Dauparas, Sue Y. Yi, Erin C. Yang, Quinton Dowling, Hannah Nguyen, Alex Kang, Asim K. Bera, Banumathi Sankaran, Renko de Vries, David Baker, Neil P. King bioRxiv 2023.08.04.551935/Proceedings of the National Academy of Sciences 121.(13) • Supplementary • data

Rationally seeded computational protein design Katherine I. Albanese, Rokas Petrenas, Fabio Pirro, Elise A. Naudin, Ufuk Borucu, William M. Dawson, D. Arne Scott, Graham J. Leggett, Orion D. Weiner, Thomas A. A. Oliver, Derek N. Woolfson bioRxiv 2023.08.25.554789 • code

Computational design of sequence-specific DNA-binding proteins Cameron J Glasscock, Robert Pecoraro, Ryan McHugh, Lindsey A. Doyle, Wei Chen, Olivier Boivin, Beau Lonnquist, Emily Na, Yuliya Politanska, Hugh K Haddox, David Cox, Christoffer Norn, Brian Coventry, Inna Goreshnik, Dionne Vafeados, Gyu Rie Lee, Raluca Gordan, Barry L Stoddard, Frank DiMaio, David Baker bioRxiv 2023.09.20.558720/Nat Struct Mol Biol (2025) • code • Supplementary

Improving protein expression, stability, and function with ProteinMPNN Kiera H. Sumida, Reyes Núñez Franco, Indrek Kalvet, Samuel J. Pellock, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, Jue Wang, Yakov Kipnis, Noel Jameson, Alex Kang, Joshmyn De La Cruz, Banumathi Sankaran, Asim K Bera, Gonzalo Jimenez Oses, David Baker bioRxiv 2023.10.03.560713/J. Am. Chem. Soc. 2024 • Supplementary

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols Kyle Meador, Roger Castells-Graells, Roman Aguirre, Michael R. Sawaya, Mark A. Arbing, Trent Sherman, Chethaka Senarathne, Todd O. Yeates bioRxiv 2023.10.09.561468 • code • colab

PROTEIN DESIGNER BASED ON SEQUENCE PROFILE USING ULTRAFAST SHAPE RECOGNITION Anonymous ICLR 2024

Inverse folding for antibody sequence design using deep learning Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M. Deane arXiv:2310.19513

Small-molecule binding and sensing with a designed protein family
Gyu Rie Lee, Samuel J. Pellock, Christoffer Norn, Doug Tischer, Justas Dauparas, Ivan Anischenko, Jaron A. M. Mercer, Alex Kang, Asim Bera, Hannah Nguyen, Inna Goreshnik, Dionne Vafeados, Nicole Roullier, Hannah L. Han, Brian Coventry, Hugh K. Haddox, David R. Liu, Andy Hsien-Wei Yeh, David Baker
bioRxiv 2023.11.01.565201/Nat Commun (2026)

De novo design of allosterically switchable protein assemblies Arvind Pillai, Abbas Idris, Annika Philomin, Connor Weidle, Rebecca Skotheim, Philip J. Y. Leung, Adam Broerman, Cullen Demakis, Andrew J. Borst, Florian Praetorius, David Baker bioRxiv 2023.11.01.565167/Nature (2024) • code • data

ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention Xinyi Zhou, Guangyong Chen, Junjie Ye, Ercheng Wang, Jun Zhang, Cong Mao, Zhanwei Li, Jianye Hao, Xingxu Huang, Jin Tang, Pheng Ann Heng Nature Communications • Supplementary • code

Engineered immunogens to elicit antibodies against conserved coronavirus epitopes A. Brenda Kapingidza, Daniel J. Marston, Caitlin Harris, Daniel Wrapp, Kaitlyn Winters, Dieter Mielke, Lu Xiaozhi, Qi Yin, Andrew Foulger, Rob Parks, Maggie Barr, Amanda Newman, Alexandra Schäfer, Amanda Eaton, Justine Mae Flores, Austin Harner, Nicholas J. Catanzaro Jr., Michael L. Mallory, Melissa D. Mattocks, Christopher Beverly, Brianna Rhodes, Katayoun Mansouri, Elizabeth Van Itallie, Pranay Vure, Brooke Dunn, Taylor Keyes, Sherry Stanfield-Oakley, Christopher W. Woods, Elizabeth A. Petzold, Emmanuel B. Walter, Kevin Wiehe, Robert J. Edwards, David C. Montefiori, Guido Ferrari, Ralph Baric, Derek W. Cain, Kevin O. Saunders, Barton F. Haynes & Mihai L. Azoitei Nat Commun 14, 7897 (2023) • code

DNDesign: Enhancing Physical Understanding of Protein Inverse Folding Model via Denoising Youhan Lee, Jaehoon Kim bioRxiv 2023.12.05.570298

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding Amir Shanehsazzadeh, Julian Alverio, George Kasun, Simon Levine, Jibran A Khan, Chelsea Chung, Nicolas Diaz, Breanna K Luton, Ysis Tarter, Cailen McCloskey, Katherine B Bateman, Hayley Carter, Dalton Chapman, Rebecca Consbruck, Alec Jaeger, Christa Kohnert, Gaelin Kopec-Belliveau, John M Sutton, Zheyuan Guo, Gustavo Canales, Kai Ejan, Emily Marsh, Alyssa Ruelos, Rylee Ripley, Brooke Stoddard, Rodante Caguiat, Kyra Chapman, Matthew Saunders, Jared Sharp, Douglas Ganini da Silva, Audree Feltner, Jake Ripley, Megan E Bryant, Danni Castillo, Joshua Meier, Christian M Stegmann, Katherine Moran, Christine Lemke, Shaheed Abdulhaqq, Lillian R Klug, Sharrol Bachas bioRxiv 2023.12.08.570889

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition Hui Wang, Dong Liu, Kailong Zhao, Yajun Wang, Guijun Zhang bioRxiv 2023.12.14.571651/Briefings in Bioinformatics 25.3 (2024): bbae146 • website

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles Linna An, Meerit Said, Long Tran, Sagardip Majumder, Inna Goreshnik, Gyu Rie Lee, David Juergens, Justas Dauparas, Ivan Anishchenko, Brian Coventry, Asim K Bera, Alex Kang, Paul M Levine, Valentina Alvarez, Arvindd Pillai, Christoffer Norn, David Feldman, Dmitri Zorine, Derrick R Hicks, Xinting Li, Mariana Garcia Sanchez, Dionne K Vafeados, Patrick J Salveson, Anastassia A Vorobieva, David Baker bioRxiv 2023.12.20.572602/Science385,276-282(2024) • code1, code2, code3

Atomic context-conditioned protein sequence design using LigandMPNN Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, Linna An, Ivan Anishchenko, Cameron Glasscock, D. Baker bioRxiv 2023.12.22.573103/Nat Methods (2025) • code

Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space Deniz Akpinaroglu, Kosuke Seki, Amy Guo, Eleanor Zhu, Mark J. S. Kelly, Tanja Kortemme bioRxiv 2023.12.15.571823 • code

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-Barrels Marissa D Dolorfino, Anastassia A Vorobieva bioRxiv 2024.01.16.575764 • code

DIProT: A deep learning based interactive toolkit for efficient and effective Protein design He, Jieling, Wenxu Wu, and Xiaowo Wang Synthetic and Systems Biotechnology (2024)

Blueprinting extendable nanomaterials with standardized protein blocks Timothy F. Huddy, Yang Hsia, Ryan D. Kibler, Jinwei Xu, Neville Bethel, Deepesh Nagarajan, Rachel Redler, Philip J. Y. Leung, Connor Weidle, Alexis Courbet, Erin C. Yang, Asim K. Bera, Nicolas Coudray, S. John Calise, Fatima A. Davila-Hernandez, Hannah L. Han, Kenneth D. Carr, Zhe Li, Ryan McHugh, Gabriella Reggiano, Alex Kang, Banumathi Sankaran, Miles S. Dickinson, Brian Coventry, T. J. Brunette, Yulai Liu, Justas Dauparas, Andrew J. Borst, Damian Ekiert, Justin M. Kollman, Gira Bhabha & David Baker Nature (2024) • RosettaScripts

All-atom protein sequence design based on geometric deep learning Jiale Liu, Zheng Guo, Changsheng Zhang, Luhua Lai bioRxiv 2024.03.18.585651/Angew. Chem. Int. Ed. 2024 • code

Graphormer supervised de novo protein design method and function validation Junxi Mu, Zhengxin Li, Bo Zhang, Qi Zhang, Jamshed Iqbal, Abdul Wadood, Ting Wei, Yan Feng, Hai-Feng Chen Briefings in Bioinformatics 25.3 (2024): bbae135 • code

The Damietta Server: a comprehensive protein design toolkit Iwan Grin, Kateryna Maksymenko, Tobias Wörtwein, Mohammad ElGamacy Nucleic Acids Research, 2024;, gkae297 • website • ProteinMPNN-based • news, news2

Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design Helder V. Ribeiro-Filho, Gabriel E. Jara, João V. S. Guerra, Melyssa Cheung, Nathaniel R. Felbinger, José G. C. Pereira, Brian G. Pierce, Paulo S. Lopes-de-Oliveira bioRxiv 2024.04.19.590222 • code, code2

SurfPro: Functional Protein Design Based on Continuous Surface Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin arXiv:2405.06693 • ProteinMPNN-based

Computational Design of Myoglobin-based Carbene Transferases for Monoterpene Derivatization Yiyang Sun, Yinian Tang, Jing Zhou, Bingchen Guo, Feiyan Yuan, Bo Yao, Yang Yu, Chun Li Biochemical and Biophysical Research Communications (2024) • code • LigandMPNN-based

UniIF: Unified Molecule Inverse Folding Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li arXiv:2405.18968

Integrating MHC Class I visibility targets into the ProteinMPNN protein design process Hans-Christof Gasser, Diego A. Oyarzún, Javier Antonio Alfaro, Ajitha Rajan bioRxiv 2024.06.04.597365

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy Bohan Ma, Donghua Liu, Zhe Wang, Dize Zhang, Yanlin Jian, Kun Zhang, Tianyang Zhou, Yibo Gao, Yizeng Fan, Jian Ma, Yang Gao, Yule Chen, Si Chen, Jing Liu, Xiang Li, and Lei Li Journal of Medicinal Chemistry (2024)

Improving Inverse Folding models at Protein Stability Prediction without additional Training or Data Oliver Dutton, Sandro Bottaro, Michele Invernizzi, Istvan Redl, Albert Chung, Carlo Fisicaro, Fabio Airoldi, Stefano Ruschetta, Louie Henderson, Benjamin MJ Owens, Patrik Foerch, Kamil Tamiola bioRxiv 2024.06.15.599145 • ProteinMPNN/ESMIF-based

Kernel-Based Evaluation of Conditional Biological Sequence Models Pierre Glaser, Steffanie Paul, Alissa M Hummer, Charlotte Deane, Debora Susan Marks, Alan Nawzad Amin Proceedings of the 41st International Conference on Machine Learning, PMLR 235:15678-15705, 2024 • ProteinMPNN-based

Design of intrinsically disordered region binding proteins
Kejia Wu, Hanlun Jiang, Derrick R. Hicks, Caixuan Liu, Edin Muratspahić, Theresa A. Ramelot, Yuexuan Liu, Kerrie McNally, Sebastian Kenny, Andrei Mihut, Amit Gaur, Brian Coventry, Wei Chen, Asim K. Bera, Alex Kang, Stacey Gerben, Mila Ya-Lan Lamb, Analisa Murray, Xinting Li, Madison A. Kennedy, Wei Yang, Zihao Song, Gudrun Schober, Stuart M. Brierley, John O’Neill, Michael H. Gelb, Gaetano T. Montelione, Emmanuel Derivery, David Baker
bioRxiv 2024.07.15.603480/Science389,eadr8063(2025)

Deep learning guided design of dynamic proteins Amy B. Guo, Deniz Akpinaroglu, Mark J.S. Kelly, Tanja Kortemme bioRxiv 2024.07.17.603962/Science388,eadr7094(2025) • code • Supplementary

CodonMPNN for Organism Specific and Codon Optimal Inverse Folding Hannes Stark, Umesh Padia, Julia Balla, Cameron Diao, George Church arXiv:2409.17265 • ProteinMPNN-based • code

Exploring the potential of structure-based deep learning approaches for T cell receptor design Helder V. Ribeiro-Filho, Gabriel E. Jara, João V. S. Guerra, Melyssa Cheung,Nathaniel R. Felbinger, José G. C. Pereira, Brian G. Pierce, Paulo S. Lopes-de-Oliveira PLoS Comput Biol 20(9) • ProteinMPNN-based • ESM-based

ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering Jonathan Funk, Laura Machado, Samuel A. Bradley, Marta Napiorkowska, Rodrigo Gallegos-Dextre, Liubov Pashkova, Niklas G. Madsen, Henry Webel, Patrick Victor Phaneuf, Timothy P. Jenkins, Carlos G. Acevedo-Rocha Sr bioRxiv 2024.10.01.616114 • ProteinMPNN-based • ESM-based

Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization Ryan Park, Darren J. Hsu, C. Brian Roland, Maria Korshunova, Chen Tessler, Shie Mannor, Olivia Viessmann, Bruno Trentini arXiv:2410.19471

Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations Frédéric A. Dreyer, Constantin Schneider, Aleksandr Kovaltsuk, Daniel Cutting, Matthew J. Byrne, Daniel A. Nissley, Newton Wahome, Henry Kenlay, Claire Marks, David Errington, Richard J. Gildea, David Damerell, Pedro Tizei, Wilawan Bunjobpol, John F. Darby, Ieva Drulyte, Daniel L. Hurdiss, Sachin Surade, Douglas E. V. Pires, Charlotte M. Deane bioRxiv 2024.10.03.616038 • code • AbMPNN-based

BC-Design: A Biochemistry-Aware Framework for Inverse Protein Design
Xiangru Tang, Xinwu Ye, Fang Wu, Yimeng Liu, Anna Su, Antonia Panescu, Guanlue Li, Daniel Shao, Dong Xu, Mark Gerstein
bioRxiv 2024.10.28.620755

State-specific Peptide Design Targeting G Protein-coupled Receptors Yang Xue, Jun Li, Hong Wang, Jianguo Hu, Zhi Zheng, Jingzhou He, Huanzhang Gong, Xiangqun Li, Xiaonan Zhang, Xiaomin Fang bioRxiv 2024.11.27.625792/Journal of Chemical Information and Modeling (2025) • ProteinMPNN-based

Computer-guided design of Z domain peptides with improved inhibition of VEGF Carsten Geist, Abibe Useini, Aleksandr Kazimir, Richy Kümpfel, Jens Meiler, Christina Lamers, Stefan Kalkhof, Georg Künze bioRxiv 2024.11.29.626075 Supplementary • ProteinMPNN-based

HyperMPNN – A general strategy to design thermostable proteins learned from hyperthermophiles Moritz Ertelt, Phillip Schlegel, Max Beining, Leonard Kaysser, Jens Meiler, Clara T. Schoeder bioRxiv 2024.11.26.625397 • code • Supplementary

IgDesign: In vitro validated antibody design against multiple therapeutic antigens using inverse folding Amir Shanehsazzadeh, Julian Alverio, George Kasun, Simon Levine, Ido Calman, Jibran A. Khan, Chelsea Chung, Nicolas Diaz, Breanna K. Luton, Ysis Tarter, Cailen McCloskey, Katherine B. Bateman, Hayley Carter, Dalton Chapman, Rebecca Consbruck, Alec Jaeger, Christa Kohnert, Gaelin Kopec-Belliveau, John M. Sutton, Zheyuan Guo, Gustavo Canales, Kai Ejan, Emily Marsh, Alyssa Ruelos, Rylee Ripley, Brooke Stoddard, Rodante Caguiat, Kyra Chapman, Matthew Saunders, Jared Sharp, Douglas Ganini da Silva, Audree Feltner, Jake Ripley, Megan E. Bryant, Danni Castillo, Joshua Meier, Christian M. Stegmann, Katherine Moran, Christine Lemke, Shaheed Abdulhaqq, Lillian R. Klug, Sharrol Bachas bioRxiv 2023.12.08.570889 • code

Learning to engineer protein flexibility Petr Kouba, Joan Planas-Iglesias, Jiri Damborsky, Jiri Sedlar, Stanislav Mazurenko, Josef Sivic arXiv:2412.18275 • code

AI.zymes – A modular platform for evolutionary enzyme design Lucas P. Merlicek, Jannik Neumann, Abbie Lear, Vivian Degiorgi, Moor de Waal, Tudor-Stefan Cotet, Adrian J. Mulholland, H. Adrian Bunzel bioRxiv 2025.01.18.633707 • Supplementary

AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications Gabriel Galindo, Daiki Maejima, Jacob DeRoo, Scott R. Burlingham, Gretchen Fixen, Tatsuya Morisaki, Hallie P. Febvre, Ryan Hasbrook, Ning Zhao, Soham Ghosh, E. Handly Mayton, Christopher D. Snow, Brian J. Geiss, Yasuyuki Ohkawa, Yuko Sato, Hiroshi Kimura, Timothy J. Stasevich bioRxiv 2025.02.06.636921/Sci. Adv.12,eadx8352(2026) • code • Supplementary • ProteinMPNN-based

Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN Richard W. Shuai, Talal Widatalla, Po-Ssu Huang, Brian L. Hie bioRxiv 2025.02.13.637498 • code

Fast and Accurate Antibody Sequence Design via Structure Retrieval Xingyi Zhang, Kun Xie, Ningqiao Huang, Wei Liu, Peilin Zhao, Sibo Wang, Kangfei Zhao, Biaobin Jiang arXiv:2502.19395

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti‐Inflammatory and Gene Therapy Applications Patat, Ayşenur Soytürk, and Özkan Ufuk Nalbantoğlu Proteins: Structure, Function, and Bioinformatics (2025) • code

ProDualNet: Dual-Target Protein Sequence Design Method Based on Protein Language Model and Structure Model Liu Cheng, Ting Wei, Xiaochen Cui, Haifeng Chen, Zhangsheng Yu bioRxiv 2025.02.28.640919/Briefings in Bioinformatics, July 2025, bbaf391 • code

CHIEF: An Attention-based Ensemble Learning Framework for Functional Protein Design Zilong Geng, Yuze Wang, Tingting Liu, Ao Tan, Shuo Wu, Xiaoling Guo, Ruogu Li, Xumin Hou, Kun Sun, LianPin Wu, Qinghua Cui, Lintai Da, Zhiyuan Ma, Honglin Li, Bing Zhang bioRxiv 2025.03.07.641005 • ProteinMPNN-based • ESM-IF-based • Frame2seq-based • PiFold-based

Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization Hans-Christof Gasser, Diego A Oyarzún, Javier Alfaro, Ajitha Rajan Protein Engineering, Design and Selection (2025) • code • ProteinMPNN-based

AI-Driven Efficient De Novo design of GLP-1RAs with Extended Half-Life and Enhanced Efficacy Ting Wei, Xiaochen Cui, Jiahui Lin, Zhuoqi Zheng, Taiying Cui, Liu Cheng, Xiaoqian Lin, Junjie Zhu, Xuyang Ran, Xiaohun Hong, Zhangsheng Yu, Haifeng Chen bioRxiv 2025.03.26.645438 • ProteinMPNN-based

A novel decoding strategy for ProteinMPNN to design with less MHC Class I immune-visibility Hans-Christof Gasser, Ajitha Rajan, Javier A. Alfaro bioRxiv 2025.04.14.648837 • ProteinMPNN-based

Zero-shot design of drug-binding proteins via neural selection-expansion
Benjamin Fry, Kaia Slaw, Nicholas F. Polizzi
bioRxiv 2025.04.22.649862 • code

Conformation-specific Design: a New Benchmark and Algorithm with Application to Engineer a Constitutively Active Map Kinase
Jacob A. Stern, Siba Alharbi, Anandsukeerthi Sandholu, Stefan T. Arold, Dennis Della Corte
bioRxiv 2025.04.23.650138 • code • dataset

AI. zymes–A modular platform for evolutionary enzyme design
Lucas P. Merlicek, Jannik Neumann, Abbie Lear, Vivian Degiorgi, Moor M. de Waal, Tudor-Stefan Cotet, Adrian J. Mulholland, Adrian Bunzel
Angewandte Chemie International Edition (2025) • code • ProteinMPNN-based

Design of overlapping genes using deep generative models of protein sequences
Gun Woo Byeon, Marc Expòsit, David Baker, Georg Seelig
bioRxiv 2025.05.06.652464 • code • ProteinMPNN-based

De novo design of porphyrin-containing proteins as efficient and stereoselective catalysts
Kaipeng Hou, Wei Huang, Miao Qi, Thomas H. Tugwell, Turki M. Alturaifi, Yuda Chen, Xingjie Zhang, Lei Lu, Samuel I. Mann, Peng Liu, Yang Yang, and William F. DeGrado
Science 388.6747 (2025) • LigandMPNN-based

Adapting ProteinMPNN for antibody design without retraining
Diego del Alamo, Rahel Frick, Daphne Truan, Joel D Karpiak
bioRxiv 2025.05.09.653228

HighMPNN: A Graph Neural Network Approach for Structure-Constrained Cyclic Peptide Sequence Design
Wen Xu, Chengyun Zhang ,Tianfeng Shang ,Qingyi Mao ,Hongliang Duan
ChemRxiv. 2025

Computational design of dynamic biosensors for emerging synthetic opioids
Alison C. Leonard, Chase Lenert-Mondou, Rachel Chayer, Samuel Swift, Zachary T. Baumer, Ryan Delaney, Anika J. Friedman, Nicholas R. Robertson, Norman Seder, Jordan Wells, Lindsey M. Whitmore, Sean R. Cutler, Michael R. Shirts, Ian Wheeldon, Timothy A. Whitehead
bioRxiv 2025.05.15.654300 • LigandMPNN-based

Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling
Xiangxin Zhou, Mingyu Li, Yi Xiao, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu
arXiv:2505.21452

De novo design of high-affinity miniprotein binders targeting Francisella tularensis virulence factor
Gizem Gokce-Alpkilic, Buwei Huang, Andi Liu, Lieselotte S.M. Kreuk, Yaxi Wang, Victor Adebomi, Yensi Flores Bueso, Asim K. Bera, Alex Kang, Stacey R. Gerben, Stephen Rettie, Dionne K. Vafeados, Nicole Roullier, Inna Goreshnik, Xinting Li, David Baker, Joshua J. Woodward, Joseph D. Mougous, Gaurav Bhardwaj
bioRxiv 2025.07.02.662053 • Supplementary • code • ProteinMPNN-based

A Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates
Abeeb A. Yekeen, Cynthia J. Meyer, Melissa McCoy, Bruce Posner, Kenneth D. Westover
bioRxiv 2025.07.04.663216 • ProteinMPNN-based

Fully functional AAV viral vectors with highly altered structural cores and subunit interfaces using ProteinMPNN
Ziyu Jiang, Sirimar Laosinwattana, Paul A. Dalby
bioRxiv 2025.07.24.666527

Computational design of bifaceted protein nanomaterials
Sanela Rankovic, Kenneth D. Carr, Justin Decarreau, Rebecca Skotheim, Ryan D. Kibler, Sebastian Ols, Sangmin Lee, Jung-Ho Chun, Marti R. Tooley, Justas Dauparas, Helen E. Eisenach, Matthias Glögl, Connor Weidle, Andrew J. Borst, David Baker & Neil P. King
Nat. Mater. (2025)

Multi-state Protein Design with DynamicMPNN
Alex Abrudan, Sebastian Pujalte Ojeda, Chaitanya K. Joshi, Matthew Greenig, Felipe Engelberger, Alena Khmelinskaia, Jens Meiler, Michele Vendruscolo, Tuomas P. J. Knowles
arXiv:2507.21938 • code

De Novo Design of High-Performance Cortisol Luminescent Biosensors Julie Yi-Hsuan Chen, Xue Peng, Chenggang Xi, Gyu Rie Lee, David Baker, Andy Hsien-Wei Yeh
J. Am. Chem. Soc

Accelerating protein design by scaling experimental characterization
Jason Qian, Lukas F. Milles, Basile I. M. Wicky, Amir Motmaen, Xinting Li, Ryan D. Kibler, Lance Stewart, David Baker
bioRxiv 2025.08.05.668824 • code

AI-Driven De Novo Design of Ultra Long-Acting GLP-1 Receptor Agonists
Ting Wei, Jiating Ma, Xiaochen Cui, Jiahui Lin, Zhuoqi Zheng, Liu Cheng, Taiying Cui, Xiaoqian Lin, Junjie Zhu, Xuyang Ran, Xiaokun Hong, Luke Johnston, Zhangsheng Yu, Haifeng Chen
Advanced science (Weinheim, Baden-Wurttemberg, Germany) • ProteinMPNN-based

From sequence to scaffold: computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks
Cyrus M. Haas, Naveen Jasti, Annie Dosey, Joel D. Allen, Rebecca Gillespie, Jackson McGowan, Elizabeth M. Leaf, Max Crispin, Cole A. DeForest, Masaru Kanekiyo, Neil P. King
bioRxiv 2025.08.20.671178/Proceedings of the National Academy of Sciences 122.45 (2025) • Supplementary

De novo design of light-responsive protein–protein interactions enables reversible formation of protein assemblies
Bowen Yu, Jiao Liu, Zhanyuan Cui, Chu Wang, Peipei Chen, Chentong Wang, Yanzhe Zhang, Xingxing Zhu, Ze Zhang, Shichao Li, Jinheng Pan, Mingqi Xie, Huaizong Shen & Longxing Cao
Nat. Chem. (2025) • code

Multi-objective optimization for designing structurally similar proteins with diverse sequences
Ryo Akiba, Yoshitaka Moriwaki, Ryuichiro Ishitani, Naruki Yoshikawa
bioRxiv 2025.09.13.676063 • ProteinMPNN-based

Ensemble-conditioned protein sequence design with Caliby
Richard W. Shuai, Tianyu Lu, Subhang Bhatti, Petr Kouba, Po-Ssu Huang
bioRxiv 2025.09.30.679633 • code

De novo designed voltage-gated anion channels suppress neuron firing
Chen Zhou, Huican Li, Jiaxing Wang, Cheng Qian, Hui Xiong, Zhilin Chu, Qiming Shao, Xuan Li, Shijin Sun, Ke Sun, Aiqin Zhu, Jiawei Wang, Xueqin Jin, Fan Yang, Tamer M. Gamal El-Din, Bo Li, Jing Huang, Kun Wu, Peilong Lu
Cell(2025)

AI-Guided Hydrophobic Core Design of Robust Six-Helix Bundle Proteins
Yinying Meng, Guojin Tang, Ruishi Wang, Bin Zheng, Yuanhao Liu, Hantian Zhang, Peng Zheng
ACS Nano .5c13783 • ProteinMPNN-based

De Novo Design of High‐Affinity Miniprotein Binders Targeting Francisella Tularensis Virulence Factor
Gizem Gokce-Alpkilic, Buwei Huang, Andi Liu, Lieselotte S.M. Kreuk, Yaxi Wang, Victor Adebomi, Yensi Flores Bueso, Asim K. Bera, Alex Kang, Stacey R. Gerben, Stephen Rettie, Dionne K. Vafeados, Nicole Roullier, Inna Goreshnik, Xinting Li, David Baker, Joshua J. Woodward, Joseph D. Mougous, Gaurav Bhardwaj
Angewandte Chemie International Edition (2025) • ProteinMPNN-based

Targeting peptide-MHC complexes with designed T cell receptors and antibodies
Amir Motmaen, Kevin M Jude, Nan Wang, Anastasia Minervina, David Feldman, Mauriz A Lichtenstein, Abishai Ebenezer, Colin Correnti, Paul G Thomas, K. Christopher Garcia, David Baker, Philip Bradley
bioRxiv 2025.11.19.689381 • ProteinMPNN-based

Computational Design and Glycoengineering of Interferon-Lambda for Nasal Prophylaxis Against Respiratory Viruses
Jeongwon Yun, Seungju Yang, Jae Hyuk Kwon, Luiz Felipe Vecchietti, Ji Hyun Choi, Mi-ra Choi, Keun Bon Ku, Hyun-Joo Ro, Kyun-Do Kim, Meeyoung Cha, Hyun Jung Chung, Ji Eun Oh, Ho Min Kim
Adv. Sci. (2025) • ProteinMPNN-based

De novo design of small molecule–regulated protein oligomers
Qihan Jin, Yukai Wang, Dachuan Chen, Jinyang Liao, Zhanyuan Cui, Yuxuan Fan, Anping Zeng, Mingqi Xie, Longxing Cao
Science391,eady6017(2026) • code

De novo design of dual-topology membrane transporters
Xi Chen, Xiaofeng Zhou, Jiawei Zhou, Tengyu Xie, Yaning Li, Yuxuan Yan, Jing Huang, Zibo Chen, Dan Ma, Peilong Lu
LangTaoSha Preprint Server

Origin-1: a generative AI platform for de novo antibody design against novel epitopes
Simon Levine, Jonathan Edward King, Jacob Stern, David Grayson, Raymond Wang, Rui Yin, Umberto Lupo, Paulina Kulyte, Ryan Matthew Brand, Tristan Bertin, Robert Pfingsten, Jovan Cejovic, Chelsea Chung, Breanna K Luton, Andrew Hagemann, Robel Haile, Elliot Medina, Pankaj Panwar, Oleksii Dubrovskyi, Chase LaCombe, Zahra Anderson, Derrik Mildh, Scott Benjamin, Joe Kaiser, Joseph Ferron, Marta Sarrico, Alexandria Kershner, Apurva Mishra, Kai R Ejan, Emily K Marsh, Paul Bringas, Phetsamay Vilaychack, Kyra Chapman, Jacob Ripley, Muttappa Gowda, Kathryn M Collins, Cailen M McCloskey, Jeremiah S Joseph, Rylee Ripley, Shaheed A Abdulhaqq, Audree Feltner, Michael Guerin, Jeffrey Goby, Jesse Hendricks, Danielle Castillo, Sean McClain, Douglas Ganini, Derek Shpiel, James Mategko, Eder Cruz Garcia, Masoud Zabet-Moghaddam, John M Sutton, Zheyuan Guo, Sean M West, Janani S Iyer, Amir Shanehsazzadeh
bioRxiv 2026.01.14.699389 • code

Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy
Yuanying Qian, Weikang Yan, Fan Xu, Yali Liu, Fabao Chen, Yue Lu, Zihan Zhang, Ao Gu, Ruobing Yu, Zhen Fang, Yang Yu, Maolan Li, Longxing Cao, Yingbin Liu, Yongning He
bioRxiv 2026.01.28.702218

CyclicMPNN: Stable Cyclic Peptide Sequence Generation
Andrew C. Powers, Yanapat Janthana, Parisa Hosseinzadeh
bioRxiv 2026.01.31.702993 • Supplementary • code

Mesostructured Water Enhances Stability of ProteinMPNN-Designed Ubiquitin-Fold
Lu-Yi Chen, Wei-Lin Lu, Tanvi Pathania, I-Hsuan Chu, Meng-Ru Ho, Wei-Chen Chuang, Yuan-Chao Lou, Ta I. Hung, Yohei Miyanoiri, Chia-en A. Chang, Kuen-Phon Wu
J. Am. Chem. Soc. 2026 • ProteinMPNN-based

A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins
Connor V. Galvin, Amy B. Guo, Maple N. Chen, Isabella L. Alfonso, Dominic Grisingher, Simon Kretschmer, Divya Kranthi, Mark J. S. Kelly, Tanja Kortemme
bioRxiv 2026.04.12.717919 • code

De novo and scaffold-based design of GDF15 binders for cancer cachexia diagnostics and therapeutics
Jinsook Ahn, Ryeongeun Cho, Sohyun Kim, Dong Sun Lee & Ho Min Kim
Exp Mol Med (2026)

ProtSeqGen: a novel deep learning model for protein sequence design
Qiang Gao, Zhijin Li, Yang Deng, Zhiwei Ji
BMC Bioinformatics (2026)

Generative AI-driven enzyme engineering unlocks sustainable valorization of broccoli waste into sulforaphane
Hao Luo, Siyuan Guo, Yihong Bao, Shan Lu, Fengxia Lv, Ping Zhu
Bioresource Technology (2026)

Discriminator-Guided Inverse Folding for Multi-Property Protein Design
Yuchuan Zheng, Chuyi Liu, Zhaoming Liu, Mao Su, Chenyu Tang, Xiang Zheng, Hao Zhang, Jingyuan Li
Advanced Science (2026) • code

Deep learning based design of buried hydrogen bond networks with HBDesigner
Henry Dieckhaus, Brock T. Harvey, Tomiris Mulikova, Jessica T. Horenstein, Nathan I. Nicely, Nicholas Z. Randolph, Brian Kuhlman
bioRxiv 2026.06.08.730848 • code

Computational Redesign of an Antifreeze Protein Using Deep Learning
Cianna Calia, Arthur J. Altunc, Rosemary J. Eufemio, Benjamin O. Alvarado, Jake D. Huynh, Eunkeu Oh, Michael D. Burkart, Konrad Meister, Francesco Paesani
bioRxiv 2026.06.21.733612 • Supplementary

Zero-shot design of drug-binding proteins via neural iterative selection−expansion
Benjamin Fry, Kaia Slaw & Nicholas F. Polizzi
Nature (2026) • code

Integrating Machine-learning and Ultra-high-throughput Screening for Enzyme spaces exploration
Yitao Ke, Yanzhe Zhang, Minchao Fang, Jingyang Zhao, Hongli Zhu, Zehui Xu, Longxing Cao
bioRxiv 2026.06.23.733994

OpenGerminal: an open-source implementation of the Germinal antibody design pipeline
Bing Han, Sheng Li
bioRxiv 2026.06.25.734527 • Supplementary • code

De novo design of ligand binding and sensing with a physics based generative approach
Yanzhe Zhang, Yitao Ke, Rui Zhi, Qihan Jin, Yiqing Feng, Chentong Wang, Minchao Fang, Jinyang Liao, Dachuan Chen, Jiao Liu, Longxing Cao
bioRxiv 2026.07.13.738243

4.6 GAN-based

De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen Journal of chemical information and modeling 60.12 (2020) • gcWGAN

HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints Xuezhi Xie, Philip M. Kim Machine Learning for Structural Biology Workshop, NeurIPS 2021/Bioinformatics, 2023;, btad036 • code

4.7 Transformer-based

Generative models for graph-based protein design John Ingraham, Vikas K Garg, Dr.Regina Barzilay, Tommi Jaakkola NeurIPS 2019 • GraphTrans

Fold2Seq: A Joint Sequence (1D)-Fold (3D) Embedding-based Generative Model for Protein Design Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen International Conference on Machine Learning. PMLR, 2021

Rotamer-Free Protein Sequence Design Based on Deep Learning and Self-Consistency Yufeng Liu, Lu Zhang, Weilun Wang, Min Zhu, Chenchen Wang, Fudong Li, Jiahai Zhang, Houqiang Li, Quan Chen& Haiyan Liu Nature portfolio (2022)/Nature computational science(2022) • Supplementary • Comment • code

A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding Mmatthew McPartlon, Ben Lai, Jinbo Xu bioRxiv (2022)

Learning inverse folding from millions of predicted structures Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives bioRxiv (2022) • esm

Breaking boundaries in protein design with a new AI model that understands interactions with any kind of molecule LucianoSphere Towards Data Science

Accurate and efficient protein sequence design through learning concise local environment of residues Bin Huang, Tingwen Fan, Kaiyue Wang, Haicang Zhang, Chungong Yu, Shuyu Nie, Yangshuo Qi, Wei-Mou Zheng, Jian Han, Zheng Fan, Shiwei Sun, Sheng Ye, Huaiyi Yang, Dongbo Bu bioRxiv (2022)/Bioinformatics 39.3 (2023) • Supplementary • website • code

PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design Baldwin Dumortier, Antoine Liutkus, Clément Carré, Gabriel Krouk bioRxiv 2022.08.10.503344

Evolutionary-scale prediction of atomic level protein structure with a language model Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives bioRxiv 2022.07.20.500902 • blog • github

Structure-informed Language Models Are Protein Designers Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei YE, Quanquan Gu arXiv:2302.01649 • code::ByProt

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu arXiv:2211.08406 • code

A Text-guided Protein Design Framework Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar arXiv:2302.04611/Nat Mach Intell (2025) • code

An end-to-end deep learning method for protein side-chain packing and inverse folding McPartlon, Matthew, and Jinbo Xu Proceedings of the National Academy of Sciences 120.23 (2023) • code • Supplementary

Context-aware geometric deep learning for protein sequence design Lucien Krapp, Fernado Meireles, Luciano Abriata, Matteo Dal Peraro bioRxiv 2023.06.19.545381/Nature Communications, 2024 • code • News

De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone Suhaas Bhat, Kalyan Palepu, Vivian Yudistyra, Lauren Hong, Venkata Srikar Kavirayuni, Tianlai Chen, Lin Zhao, Tian Wang, Sophia Vincoff, Pranam Chatterjee bioRxiv 2023.06.26.546591 • code • colab • Supplementary

ProstT5: Bilingual Language Model for Protein Sequence and Structure Michael Heinzinger Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost bioRxiv 2023.07.23.550085 • Supplementary • code

De novo Protein Sequence Design Based on Deep Learning and Validation on CalB Hydrolase Junxi Mu, ZhengXin Li, Bo Zhang, Qi Zhang, Jamshed Iqbal, Abdul Wadood, Ting Wei, Yan Feng, Haifeng Chen bioRxiv 2023.08.01.551444 • code

Invariant point message passing for protein side chain packing and design Nicholas Z Randolph, Brian Kuhlman bioRxiv 2023.08.03.551328 • code

Atom-by-atom protein generation and beyond with language models Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik arXiv:2308.09482

SaProt: Protein Language Modeling with Structure-aware Vocabulary Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan bioRxiv 2023.10.01.560349 • code

SaprotHub: Making Protein Modeling Accessible to All Biologists Jin Su, Zhikai Li, Chenchen Han, Yuyang Zhou, Yan He, Junjie Shan, Xibin Zhou, Xing Chang, Shiyu Jiang, Dacheng Ma, The OPMC, Martin Steinegger, Sergey Ovchinnikov, Fajie Yuan bioRxiv 2024.05.24.595648 • code • colab

AntiFold: Improved antibody structure design using inverse folding Magnus Høie, Alissa Hummer, Tobias Olsen, Morten Nielsen, Charlotte Deane GenBio@NeurIPS2023 Spotlight/Bioinformatics Advances (2025) • code, github • colab, website

MMDesign: Multi-Modality Transfer Learning for Generative Protein Design Jiangbin Zheng, Siyuan Li, Yufei Huang, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jun Xia, Ge Wang, Stan Z. Li arXiv preprint arXiv:2312.06297 (2023)

ShapeProt: Top-down Protein Design with 3D Protein Shape Generative Model Lee, Youhan, and Jaehoon Kim bioRxiv (2023): 2023-12

X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design Eric L. Buehler, Markus J. Buehler arXiv:2402.07148 • code • Model & weights

AntiFold: Improved antibody structure-based design using inverse folding Magnus Haraldson Høie, Alissa Hummer, Tobias H. Olsen, Broncio Aguilar-Sanjuan, Morten Nielsen, Charlotte M. Deane arXiv:2405.03370 • code • website • ESM-IF-based

Protein Design with StructureGPT: a Deep Learning Model for Protein Structure-to-Sequence Translation Nicanor Zalba Sr., Pablo Ursua-Medrano Sr., Humberto Bustince Sr bioRxiv 2024.06.03.597105 • code • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/06/07/2024.06.03.59