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]

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

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,