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https://github.com/Peldom/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
https://github.com/Peldom/papers_for_protein_design_using_DL
deep-learning protein-design
Last synced: 15 days ago
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List of papers about Proteins Design using Deep Learning
- Host: GitHub
- URL: https://github.com/Peldom/papers_for_protein_design_using_DL
- Owner: Peldom
- License: gpl-3.0
- Created: 2021-11-25T15:01:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-16T14:05:04.000Z (18 days ago)
- Last Synced: 2024-06-16T15:41:08.406Z (18 days ago)
- Topics: deep-learning, protein-design
- Homepage:
- Size: 5.64 MB
- Stars: 1,165
- Watchers: 103
- Forks: 147
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-protein-design-software - List of papers about Proteins Design using Deep Learning (Peldom)
README
# List of papers about Proteins Design using Deep Learning
> This repository is inspired by the remarkable work of [Kevin Kaichuang Yang](https://github.com/yangkky) and their outstanding project [Machine-learning-for-proteins](https://github.com/yangkky/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](https://github.com/Peldom/papers_for_protein_design_using_DL/blob/main/CONTRIBUTING.md) and [suggestions](https://github.com/Peldom/papers_for_protein_design_using_DL/issues) are warmly welcome!
> Community Values, Guiding Principles, and Commitments for the Responsible Development of AI for Protein Design: [details](https://responsiblebiodesign.ai/)
*Papers last week, updated on 2024.06.16:*
+ Improving Antibody Design with Force-Guided Sampling in Diffusion Models
+ [[arXiv:2406.05832](https://arxiv.org/abs/2406.05832)]
+ Peptide Vaccine Design by Evolutionary Multi-Objective Optimization
+ [[arXiv:2406.05743](https://arxiv.org/abs/2406.05743)]
+ Topological Neural Networks go Persistent, Equivariant, and Continuous
+ [[arXiv:2406.03164](https://arxiv.org/abs/2406.03164)] • [[code](https://github.com/Aalto-QuML/TopNets)]
------
0) Benchmarks and datasets
Sequence datasets •
Structure datasets •
Public database •
Similar list
1) Reviews and surveys
De novo design •
Antibody design •
Peptide design •
Binder design •
Enzyme design
2) Model-based design
trRosetta-based •
AlphaFold2-based •
DMPfold2-based •
CM-Align •
MSA transformer-based •
DeepAb-based •
TRFold2-based •
GPT-based •
ESM-based •
Sampling-algorithms
3) Function to Scaffold
GAN-based •
VAE-based •
DAE-based •
MLP-based •
Diffusion-based •
RL-based •
Flow-based
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
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
LSTM-based •
Diffusion-based •
RoseTTAFold-based •
CNN-based •
GNN-based •
Transformer-based •
MLP-based •
Flow-based
7) Other
Effects of mutations & Fitness Landscape •
Protein Language Model & Representation Learning •
Molecular Design Model
------
## 0. Benchmarks and datasets
### 0.1 Sequence Datasets
**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](https://openreview.net/forum?id=p2dMLEwL8tF)/[bioRxiv 2021](https://www.biorxiv.org/content/10.1101/2021.11.09.467890v2) • [website](https://benchmark.protein.properties/) • [code](https://github.com/J-SNACKKB/FLIP) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/01/19/2021.11.09.467890/DC1/embed/media-1.pdf)
**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](https://nips.cc/Conferences/2022/ScheduleMultitrack?event=50005)
**PDBench: Evaluating Computational Methods for Protein-Sequence Design**
Leonardo V Castorina, Rokas Petrenas, Kartic Subr, Christopher W Wood
[Bioinformatics, 2023;, btad027](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad027/6986968) • [code](https://github.com/wells-wood-research/PDBench)
**Benchmarking deep generative models for diverse antibody sequence design**
Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie Lozano
[arXiv:2111.06801](https://arxiv.org/abs/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](https://arxiv.org/abs/2309.09955v2)
**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)](https://www.biorxiv.org/content/10.1101/2023.03.04.531015v2) • [code](https://github.com/seanrjohnson/protein_scoring)
**FLOP: Tasks for Fitness Landscapes Of Protein Wildtypes**
Peter Mørch Groth, Richard Michael, Jesper Salomon, Pengfei Tian, Wouter Boomsma
[bioRxiv 2023.06.21.545880](https://www.biorxiv.org/content/10.1101/2023.06.21.545880v2) • [code](https://github.com/petergroth/FLOP)
**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](https://biorxiv.org/content/10.1101/2023.12.07.570727v1) • [code](https://github.com/OATML-Markslab/ProteinGym)
### 0.2 Structure Datasets
**AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB**
Zhangyang Gao, Cheng Tan, Stan Z. Li
[arxiv (2022)](https://arxiv.org/abs/2202.01079)
**SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning**
Jonathan E. King, David Ryan Koes
[arxiv](https://arxiv.org/abs/2010.08162) • [github::sidechainnet](https://github.com/jonathanking/sidechainnet)
[TDC](https://tdcommons.ai/overview/) 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](https://github.com/GLambard/Molecules_Dataset_Collection) 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](https://paperswithcode.com/task/drug-discovery)). (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](https://www.biorxiv.org/content/10.1101/2021.11.09.467890v1) and [Zhangyang Gao, Cheng Tan, Stan Z. Li](https://arxiv.org/abs/2202.01079).
**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)](https://www.nature.com/articles/s41467-022-34937-8) • [code](https://github.com/strauchlab/scaffold_design) • [Supplementary](https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-022-34937-8/MediaObjects/41467_2022_34937_MOESM1_ESM.pdf)
**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](https://arxiv.org/abs/2308.05326) • [OpenFold](https://github.com/aqlaboratory/openfold)
**ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics**
Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Stan Z. Li
[GitHub](https://github.com/A4Bio/ProteinInvBench)
**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)](https://arxiv.org/abs/2312.00080) • [code](https://github.com/WANG-CR/PDB-Struct)
**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](https://www.biorxiv.org/content/10.1101/2024.02.10.579743v1) • [code](https://github.com/Immortals-33/Scaffold-Lab) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/02/12/2024.02.10.579743/DC1/embed/media-1.pdf)
### 0.3 Databases
> A list of suggested protein databases, more lists at [CNCB](https://ngdc.cncb.ac.cn/databasecommons/).
>
#### 0.3.1 Sequence Database
1. [UniProt](https://www.uniprot.org/downloads)
2. [DisProt](https://disprot.org)
3. [MobiDB](https://mobidb.bio.unipd.it/)
#### 0.3.2 Structure Database
Database | Description
---------|----------
[PDB](https://www.rcsb.org/) | The Protein Data Bank (PDB) is a database of 3D structural data of large biological molecules, such as proteins and nucleic acids. These data are gathered using experimental methods such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy.
[AlphaFoldDB](https://alphafold.ebi.ac.uk/) | AlphaFoldDB is a database of protein structure predictions produced by DeepMind's AlphaFold system. It provides highly accurate predictions of protein 3D structures.
[PDBbind](http://www.pdbbind.org.cn/download.php) | PDBbind is a comprehensive collection of the binding data of all types of biomolecular complexes in the PDB database. It is primarily used for the development and validation of computational methods for predicting molecular interactions.
[AB-Bind](https://github.com/sarahsirin/AB-Bind-Database) | AB-Bind is a database for antibody binding affinity data. It offers a curated set of experimental binding data and corresponding antibody-protein complex structures.
[AntigenDB](http://crdd.osdd.net/raghava/antigendb/) | AntigenDB is a manually curated database of experimentally verified antigens that includes detailed information about the antigen, the source organism, and the associated antibodies.
[CAMEO](https://www.cameo3d.org/) | CAMEO (Continuous Automated Model EvaluatiOn) is a project for the automated evaluation of methods predicting macromolecular structure. It continuously assesses the performance of automated protein structure prediction servers.
[CAPRI](https://www.ebi.ac.uk/msd-srv/capri/) | The Critical Assessment of PRediction of Interactions (CAPRI) is a community-wide experiment to evaluate protein-protein interaction prediction methods.
[PIFACE](http://prism.ccbb.ku.edu.tr/piface) | PIFACE is a web server for the prediction of protein-protein interactions. It identifies potential interaction interfaces on protein surfaces.
[SAbDab](http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/) | The Structural Antibody Database (SAbDab) is an automatically updated resource for the structural information of antibodies from the PDB. It allows for easy access to curated, annotated, and classified antibody structures.
[SKEMPI v2.0](https://life.bsc.es/pid/skempi2) | SKEMPI 2.0 is a database of experimental measurements of the change in binding free energy caused by mutations in protein-protein complexes.
[ProtCAD](http://dunbrack2.fccc.edu/protcad/) | ProtCAD is a suite of tools for the design and engineering of novel protein structures, sequences, and functions. It allows users to build and manipulate complex protein structures, generate and evaluate sequence libraries, and simulate mutational effects. ProtCAD is a suite of tools for the design and engineering of novel protein structures, sequences, and functions. It allows users to build and manipulate complex protein structures, generate and evaluate sequence libraries, and simulate mutational effects.
### 0.4 Similar List
> Some similar GitHub lists that include papers about protein design using deep learning:
1. [design_tools](https://github.com/hefeda/design_tools/blob/main/README.md)
2. [awesome-AI-based-protein-design](https://github.com/opendilab/awesome-AI-based-protein-design)
3. [ProteinStructureWithDL](https://github.com/Yang-J-LIN/ProteinStructureWithDL)
4. [List of available bioinformatic tools and services](https://neurosnap.ai/services)
## 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](https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1318)
**Advances in protein structure prediction and design**
Kuhlman B., Bradley P.
[Nat Rev Mol Cell Biol 20, 681-697 (2019)](https://www.nature.com/articles/s41580-019-0163-x)
**Deep learning in protein structural modeling and design**
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray
[Patterns 1.9](https://www.sciencedirect.com/science/article/pii/S2666389920301902) • 2020
**100th anniversary of macromolecular science viewpoint: Data-driven protein design**
Ferguson, Andrew L., and Rama Ranganathan.
[ACS Macro Letters 10.3 (2021)](https://pubs.acs.org/doi/abs/10.1021/acsmacrolett.0c00885)
**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)](https://www.tandfonline.com/doi/full/10.1080/17460441.2021.1918096)
**Protein design with deep learning**
Defresne, Marianne, Sophie Barbe, and Thomas Schiex.
[International Journal of Molecular Sciences 22.21 (2021)](https://www.mdpi.com/1422-0067/22/21/11741)
**Protein sequence design with deep generative models**
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang
[Current Opinion in Chemical Biology 65](https://www.sciencedirect.com/science/article/pii/S136759312100051X) • [note](https://zhuanlan.zhihu.com/p/466616309) • 2021
**Structure-based protein design with deep learning**
Ovchinnikov, Sergey, and Po-Ssu Huang.
[Current opinion in chemical biology 65](https://www.sciencedirect.com/science/article/pii/S1367593121001125) • [note](https://zhuanlan.zhihu.com/p/467001175) • 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)](https://www.sciencedirect.com/science/article/pii/S0959440X21000142)
**Recent advances in de novo protein design: Principles, methods, and applications**
Pan, Xingjie, and Tanja Kortemme.
[Journal of Biological Chemistry 296 (2021)](https://www.sciencedirect.com/science/article/pii/S0021925821003367)
**Protein design via deep learning**
Wenze Ding, Kenta Nakai, Haipeng Gong
[Briefings in Bioinformatics](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac102/6554124) • 25 March 2022
**Deep generative modeling for protein design**
Strokach, Alexey, and Philip M. Kim.
[Current Opinion in Structural Biology](https://www.sciencedirect.com/science/article/pii/S0959440X21001573) • 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](https://www.frontiersin.org/articles/10.3389/fmolb.2022.928534/full)
**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](https://arxiv.org/abs/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](https://www.biorxiv.org/content/10.1101/2022.08.31.505981v1)/[Computational and Structural Biotechnology Journal Volume 21, 2023](https://www.sciencedirect.com/science/article/pii/S2001037022005086) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/09/03/2022.08.31.505981/DC1/embed/media-1.pdf) • [accompanying list](https://github.com/hefeda/design_tools/blob/main/README.md)
**Computational protein design with data-driven approaches: Recent developments and perspectives**
Liu H, Chen Q.
[WIREs Comput Mol Sci. 2022. e1646](https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1646)
**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](https://onlinelibrary.wiley.com/doi/full/10.1002/mef2.22)
**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](https://arxiv.org/abs/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](https://www.sciencedirect.com/science/article/pii/S0959440X23000453)
**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](http://dx.doi.org/10.1002/cbic.202300117)
**Research progress of artificial intelligence in protein design**
CHEN Zhihang, JI Menglin, QI Yifei
[Synthetic Biology Journal (2023)](https://synbioj.cip.com.cn/article/2023/2096-8280/2023-008.shtml)
**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](https://arxiv.org/pdf/2304.01565.pdf)
**Exploring the Protein Sequence Space with Global Generative Models**
Sergio Romero-Romero, Sebastian Lindner, Noelia Ferruz
[arXiv:2305.01941](https://arxiv.org/abs/2305.01941)
**The Era of Machine Learning for Protein Design, Summarized in Four Key Methods**
LucianoSphere
[Towards Data Science](https://towardsdatascience.com/the-era-of-machine-learning-for-protein-design-summarized-in-four-key-methods-d6f1dac5de96)
**Is novelty predictable?**
Clara Fannjiang, Jennifer Listgarten
[arXiv:2306.00872](https://arxiv.org/abs/2306.00872)
**Computational protein design - where it goes?**
Xu Binbin, Chen Yingjun and Xue Weiwei
[Current Medicinal Chemistry 2023](https://www.eurekaselect.com/article/132267)
**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)](https://www.cell.com/cell-systems/fulltext/S2405-4712(23)00212-0)
**De novo 設計ナノポアの創製**
新津藍
[生物工学会誌 101.8 (2023)](https://www.jstage.jst.go.jp/article/seibutsukogaku/101/8/101_101.8_431/_article/-char/ja/)
**Generative artificial intelligence for de novo protein design**
Adam Winnifrith, Carlos Outeiral, Brian Hie
[arXiv:2310.09685](https://arxiv.org/abs/2310.09685)
**Generative models for protein sequence modeling: recent advances and future directions**
Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, Daniel Woldring
[Briefings in Bioinformatics](https://academic.oup.com/bib/article/24/6/bbad358/7325909)
**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](https://www.cell.com/cell-systems/fulltext/S2405-4712(23)00298-3)
**Deep learning for protein structure prediction and design—progress and applications**
Jürgen Jänes and Pedro Beltrao
[Mol Syst Biol(2024)](https://www.embopress.org/doi/full/10.1038/s44320-024-00016-x)
**De novo protein design—From new structures to programmable functions**
Kortemme, Tanja.
[Cell 187.3 (2024)](https://www.cell.com/cell/fulltext/S0092-8674(23)01402-2)
**Generative models for protein structures and sequences**
Hsu, C., Fannjiang, C. & Listgarten, J.
[Nat Biotechnol 42, 196–199 (2024)](https://www.nature.com/articles/s41587-023-02115-w)
**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)](https://www.nature.com/articles/s41587-023-02120-z)
**Protein design: the experts speak**
Doerr, A.
[Nat Biotechnol 42, 175–178 (2024)](https://www.nature.com/articles/s41587-023-02111-0)
**Machine learning for functional protein design**
Pascal Notin, Nathan Rollins, Yarin Gal, Chris Sander & Debora Marks
[Nat Biotechnol 42, 216–228 (2024)](https://www.nature.com/articles/s41587-024-02127-0)
**Sparks of function by de novo protein design**
Chu, A.E., Lu, T. & Huang, PS.
[Nat Biotechnol 42, 203–215 (2024)](https://www.nature.com/articles/s41587-024-02133-2) • [poster](https://drive.google.com/file/d/1sG3OlEWvhHcWAdtf7RTcCawAapDmyeEx/view)
**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](https://arxiv.org/abs/2402.08703)
**Security challenges by AI-assisted protein design**
Philip Hunter
[EMBO Rep(2024)](https://www.embopress.org/doi/full/10.1038/s44319-024-00124-7)
**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)](https://www.nature.com/articles/s41580-024-00718-y)
### 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)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344881/pdf/antibodies-09-00012.pdf)
**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](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928824/)
**Advances in computational structure-based antibody design**
Hummer, Alissa M., Brennan Abanades, and Charlotte M. Deane.
[Current Opinion in Structural Biology 74 (2022)](https://www.sciencedirect.com/science/article/pii/S0959440X22000586)
**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)](https://www.sciencedirect.com/science/article/pii/S0165614722002796)
**Leveraging deep learning to improve vaccine design**
Hederman AP, Ackerman ME
[Trends in immunology (2023)](https://www.cell.com/trends/immunology/fulltext/S1471-4906(23)00046-7)
**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](https://arxiv.org/abs/2305.07488)
**AI Models for Protein Design are Driving Antibody Engineering**
Michael Chungyoun, Jeffrey J. Gray
[Current Opinion in Biomedical Engineering (2023): 100473](https://www.sciencedirect.com/science/article/abs/pii/S2468451123000296)
**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)](https://pubs.acs.org/doi/10.1021/acs.jctc.3c00513)
**Simplifying complex antibody engineering using machine learning**
Makowski, Emily K., Hsin-Ting Chen, and Peter M. Tessier.
[Cell Systems 14.8 (2023)](https://www.cell.com/cell-systems/fulltext/S2405-4712(23)00118-7)/[2022 AIChE Annual Meeting. AIChE, 2022.](https://aiche.confex.com/aiche/2022/meetingapp.cgi/Paper/650993)
**AI driven B-cell Immunotherapy Design**
Bruna Moreira da Silva, David B. Ascher, Nicholas Geard, Douglas E. V. Pires
[arXiv:2309.01122](https://arxiv.org/abs/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](https://arxiv.org/abs/2312.08470)/[Drug Discovery Today (2024)](https://www.sciencedirect.com/science/article/pii/S1359644624001508)
**Next generation of multispecific antibody engineering**
Daniel Keri, Matt Walker, Isha Singh, Kyle Nishikawa, Fernando Garces
[Antibody Therapeutics (2023): tbad027](https://academic.oup.com/abt/article/7/1/37/7463325)
**A primer on ML in antibody engineering**
[ABHISHAIKE MAHAJAN](https://substack.com/@abhishaikemahajan)
[Substack](https://www.abhishaike.com/p/a-primer-on-ai-in-antibody-engineering) • blog
### 1.3 Peptide design
**Deep generative models for peptide design**
Wan, Fangping, Daphne Kontogiorgos-Heintz, and Cesar de la Fuente-Nunez
[Digital Discovery (2022)](https://pubs.rsc.org/en/content/articlehtml/2022/dd/d1dd00024a)
**Design of protein segments and peptides for binding to protein targets**
Gupta, Suchetana, Noora Azadvari, and Parisa Hosseinzadeh.
[BioDesign Research 2022 (2022)](https://spj.science.org/doi/10.34133/2022/9783197)
**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](https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1693)
### 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](https://www.biorxiv.org/content/10.1101/2022.06.15.495993v1)/[Nat Commun 14, 2625 (2023)](https://www.nature.com/articles/s41467-023-38328-5) • [code](https://github.com/nrbennet/dl_binder_design) • [news](https://phys.org/news/2023-08-deep-protein.html)
### 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](https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbad065/7086816)
**Application of "foldability" in the intelligent of enzymes engineering and design: take AlphaFold2 for example**
MENG Qiaozhen, GUO Fei
[Synthetic Biology Journal (2023)](https://synbioj.cip.com.cn/article/2023/2096-8280/2023-011.shtml)
**AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design**
Casadevall, Guillem, Cristina Duran, and Sílvia Osuna.
[JACS Au (2023)](https://pubs.acs.org/doi/10.1021/jacsau.3c00188)
**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](https://pubs.acs.org/doi/10.1021/acscatal.3c03417)
**Building Enzymes through Design and Evolution**
Hossack, Euan J., Florence J. Hardy, and Anthony P. Green.
[ACS Catalysis 13.19 (2023)](https://pubs.acs.org/doi/10.1021/acscatal.3c02746)
**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](https://www.sciencedirect.com/science/article/abs/pii/S0958166923001179)
**Opportunites and Challenges for Machine Learning-Assisted Enzyme Engineering**
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold
[ACS Central Science (2024)](https://pubs.acs.org/doi/10.1021/acscentsci.3c01275)
## 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 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)](https://www.biorxiv.org/content/10.1101/2020.11.29.402743v1)
**Fast differentiable DNA and protein sequence optimization for molecular design**
Linder, Johannes, and Georg Seelig.
[arXiv preprint arXiv:2005.11275 (2020)](https://arxiv.org/abs/2005.11275)
**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)](https://doi.org/10.1038/s41586-021-04184-w) • [code](https://github.com/gjoni/trDesign) • [trRosetta](https://yanglab.nankai.edu.cn/trRosetta/download/)
**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)](https://www.pnas.org/content/118/11/e2017228118) • [code](https://github.com/gjoni/trDesign)
**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](https://www.pnas.org/doi/10.1073/pnas.2207974120) • [code](https://github.com/sokrypton/TrDesign_partialhal)
**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](https://www.biorxiv.org/content/10.1101/2023.08.30.555621v1) • [Suppplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/09/01/2023.08.30.555621/DC1/embed/media-1.pdf) • [code](https://github.com/zanderharteveld/genesis)
**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](https://pubs.acs.org/doi/10.1021/acssynbio.3c00674)
### 2.2 AlphaFold2-based
**Solubility-aware protein binding peptide design using AlphaFold**
Takatsugu Kosugi, Masahito Ohue
[bioRxiv 2022.05.14.491955](https://doi.org/10.1101/2022.05.14.491955) • [Supplemental Materials](https://www.biorxiv.org/content/biorxiv/early/2022/05/15/2022.05.14.491955/DC1/embed/media-1.pdf)
**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)](http://repository.cshl.edu/id/eprint/40409/)/[Bioinformatics, 2022;, btac724](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac724/6820925) • [ColabDesign](https://github.com/sokrypton/ColabDesign), [SMURF](https://github.com/spetti/SMURF), [AF2 back propagation](https://github.com/sokrypton/af_backprop) • [our notes1](https://zhuanlan.zhihu.com/p/468219547), [notes2](https://zhuanlan.zhihu.com/p/472037977) • [lecture1](https://www.youtube.com/watch?v=2HmXwlKWMVs), [lecture2](https://www.youtube.com/watch?v=BJdRvODiDnk) • [Discord](https://discord.com/invite/FpYPneYB)
**AlphaDesign: A de novo protein design framework based on AlphaFold**
Jendrusch, Michael, Jan O. Korbel, and S. Kashif Sadiq.
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.10.11.463937v1)
**Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design**
Moffat, Lewis, Joe G. Greener, and David T. Jones.
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.08.24.457549v1)
**State-of-the-art estimation of protein model accuracy using AlphaFold**
James P. Roney, Sergey Ovchinnikov
[bioRxiv 2022.03.11.484043](https://www.biorxiv.org/content/10.1101/2022.03.11.484043v3)/[Physical Review Letters 129.23 (2022)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.129.238101) • [code](https://github.com/jproney/AF2Rank)
**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](https://www.biorxiv.org/content/10.1101/2022.06.09.493773v1)/[Science (2022)](https://www.science.org/doi/10.1126/science.add1964) • [related slides](https://docs.google.com/presentation/d/1_tvzLKks83sYOKemfFeImCPnWtCQ-CHqmKK_3IQI1so/) • [our notes](https://zhuanlan.zhihu.com/p/527152827) • [news](https://www.nature.com/articles/d41586-022-02947-7)
**EvoBind: in silico directed evolution of peptide binders with AlphaFold**
Patrick Bryant, Arne Elofsson
[bioRxiv 2022.07.23.501214](https://www.biorxiv.org/content/10.1101/2022.07.23.501214v1) • [code](https://github.com/patrickbryant1/EvoBind)
**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](https://www.biorxiv.org/content/10.1101/2022.09.01.506251v1)/[Nat Struct Mol Biol 30, 1755-1760 (2023)](https://www.nature.com/articles/s41594-023-01112-6) • [Supplementary data](https://static-content.springer.com/esm/art%3A10.1038%2Fs41594-023-01112-6/MediaObjects/41594_2023_1112_MOESM1_ESM.pdf)
**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)](https://www.nature.com/articles/s41467-022-33729-4) • [gitlba](https://gitlab.com/patrickbryant1/molpc), [github](https://github.com/patrickbryant1/MoLPC) • [Supplementary data1](https://doi.org/10.5281/zenodo.6367019), [Supplementary data2](https://doi.org/10.17044/scilifelab.19375172)
**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](https://www.biorxiv.org/content/10.1101/2022.12.13.520346v1) • [code](https://github.com/bene837/af_gradmcmc) • [lecture1](https://www.youtube.com/watch?v=aUMGuogMZCA) • [lecture2](https://www.youtube.com/watch?v=4S4J7gbhAa0)
**Code of OpenComplex**
Jingcheng, Yu and Zhaoming, Chen and Zhaoqun, Li and Mingliang, Zeng and Wenjun, Lin and He, Huang and Qiwei, Ye
[code](https://github.com/baaihealth/OpenComplex)
**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](https://www.biorxiv.org/content/10.1101/2023.02.24.529906v1) • [code](https://github.com/sokrypton/ColabDesign/blob/main/af/examples/af_relax_design.ipynb)
**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](https://www.biorxiv.org/content/10.1101/2023.02.25.529956v1.full.pdf) • [Code](https://github.com/sokrypton/ColabDesign/blob/main/af/examples/af_cyc_design.ipynb) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/02/26/2023.02.25.529956/DC1/embed/media-1.xlsx)
**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](https://www.nature.com/articles/s41586-023-05696-3) • [Code](https://files.ipd.uw.edu/pub/luxSit/scaffold_generation.tar.gz) • [Supplementary Materials](https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-023-05696-3/MediaObjects/41586_2023_5696_MOESM1_ESM.pdf)
**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](https://www.biorxiv.org/content/10.1101/2023.05.03.539278v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/05/03/2023.05.03.539278/DC1/embed/media-1.pdf) • [code](https://github.com/KuhlmanLab/evopro)
**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](https://www.biorxiv.org/content/10.1101/2023.05.09.540044v2) • [code](https://github.com/bene837/af2seq) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/05/09/2023.05.09.540044/DC1/embed/media-1.pdf)
**Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 and Binding Affinity Prediction Model**
Takafumi Ueki, Masahito Ohue
[bioRxiv 2023.06.02.543382](https://www.biorxiv.org/content/10.1101/2023.06.02.543382v1)
**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](https://www.biorxiv.org/content/10.1101/2023.07.27.550799v2) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/07/31/2023.07.27.550799/DC1/embed/media-1.xlsx)
**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](https://www.biorxiv.org/content/10.1101/2023.08.07.552204v1)/[Nat Mach Intell 6, 536–547 (2024)](https://www.nature.com/articles/s42256-024-00838-2) • [code](https://github.com/zhanghaicang/carbonmatrix_public)
**Design of Cyclic Peptides Targeting Protein-Protein Interactions using AlphaFold**
Takatsugu Kosugi, Masahito Ohue
[bioRxiv 2023.08.20.554056](https://www.biorxiv.org/content/10.1101/2023.08.20.554056v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/08/21/2023.08.20.554056/DC1/embed/media-1.pdf) • [code](https://github.com/YoshitakaMo/localcolabfold/)
**MetaPPI: In Silico Screen for Novel CRBN-based Substrates**
neoxbio
[website](https://www.neoxbio.com/platform-technology.html) • [news](https://mp.weixin.qq.com/s/Kb4EQ0YvYDvoLZ_cnAlUPw) • masif-based • commercial
**AlphaFold Distillation for Protein Design**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=3pgJNIx3gc) • [code](https://anonymous.4open.science/r/AFDistill-28C3)
**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](https://www.biorxiv.org/content/10.1101/2023.12.19.572389v1) • [code](https://github.com/swanss/FragFold)
**An integrative approach to protein sequence design through multiobjective optimization**
Lu Hong, Tanja Kortemme
[bioRxiv 2024.03.01.582670](https://www.biorxiv.org/content/10.1101/2024.03.01.582670v1) • [code](https://github.com/luhong88/int_seq_des) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2024/03/04/2024.03.01.582670/DC1/embed/media-1.pdf)
**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)](https://cshperspectives.cshlp.org/content/early/2024/03/01/cshperspect.a041472.short)
**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)](https://www.pnas.org/doi/10.1073/pnas.2313809121)
### 2.3 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)](https://www.biorxiv.org/content/10.1101/2022.01.27.478087v1) • [DMPfold2](https://github.com/psipred/DMPfold2)
### 2.4 CM-Align
**AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design**
Shuhao Zhang, Youjun Xu, Jianfeng Pei, Luhua Lai
[NeurIPS 2021](https://www.mlsb.io/papers_2021/MLSB2021_AutoFoldFinder.pdf)
### 2.5 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)](https://arxiv.org/abs/2203.15465)/[bioRxiv 2022.04.14.488405](https://www.biorxiv.org/content/10.1101/2022.04.14.488405v1)
**EvoOpt: an MSA-guided, fully unsupervised sequence optimization pipeline for protein design**
Hideki Yamaguchi, Yutaka Saito
[NeurIPS 2022](https://www.mlsb.io/papers_2022/EvoOpt_an_MSA_guided_fully_unsupervised_sequence_optimization_pipeline_for_protein_design.pdf)
**Generative power of a protein language model trained on multiple sequence alignments**
Sgarbossa, Damiano, Umberto Lupo, and Anne-Florence Bitbol
[Elife 12 (2023): e79854](https://elifesciences.org/articles/79854) • [code](https://github.com/Bitbol-Lab/Iterative_masking)
### 2.6 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)](https://www.cell.com/biophysj/pdf/S0006-3495(21)03758-9.pdf) • [DeepAb](https://github.com/RosettaCommons/DeepAb) • [lecture](https://www.youtube.com/watch?v=LIo-1jPfrns)
**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](https://www.biorxiv.org/content/10.1101/2022.06.06.494991v1)/[Front. Immunol. 13:999034](https://www.frontiersin.org/articles/10.3389/fimmu.2022.999034/full) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/06/06/2022.06.06.494991/DC1/embed/media-1.pdf) • [code](https://github.com/RosettaCommons/FvHallucinator)
### 2.7 TRFold2-based
[News of TRDesign](https://mp.weixin.qq.com/s/OQzKawtL9RdK9HzYsfu80g)
[TIANRANG XLab](https://xlab.tianrang.com/)
paper unavailable • [slides](https://pan.baidu.com/share/init?surl=4AOW_D9dwlvC7VGGZA2tmQ&pwd=ffui) • [website](https://xcreator.tianrang.com/auth/login) • commercial • [news](https://mp.weixin.qq.com/s/45Gz7GWOGxHl0i6LXxTUpw)
### 2.8 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)](https://arxiv.org/abs/2205.04259)
**Preference optimization of protein language models as a multi-objective binder design paradigm**
Pouria Mistani, Venkatesh Mysore
[arXiv:2403.04187](https://arxiv.org/abs/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](https://arxiv.org/abs/2405.00753)
### 2.9 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](https://www.biorxiv.org/content/10.1101/2021.01.26.428322v1) • [code](https://github.com/seanrjohnson/protein_gibbs_sampler)
**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](https://www.biorxiv.org/content/10.1101/2022.12.21.521526v1)
**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](https://www.biorxiv.org/content/10.1101/2022.12.21.521521v1)
**ESMFold Hallucinates Native-Like Protein Sequences**
Jeliazko R Jeliazkov, Diego del Alamo, Joel D Karpiak
[bioRxiv 2023.05.23.541774](https://www.biorxiv.org/content/10.1101/2023.05.23.541774v1)
**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](https://www.biorxiv.org/content/10.1101/2023.10.26.564121v1) • [code](https://github.com/sirius777coder/GPDL) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/10/30/2023.10.26.564121/DC1/embed/media-1.pdf)
**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](https://www.biorxiv.org/content/10.1101/2023.12.22.573145v1)
**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)](https://www.nature.com/articles/s41587-024-02214-2) • [code](https://github.com/seanrjohnson/protein_scoring)
### 2.10 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)](https://arxiv.org/abs/2010.02141) • [code](https://github.com/samsinai/FLEXS)
**Autofocused oracles for model-based design**
Fannjiang, Clara, and Jennifer Listgarten.
[Advances in Neural Information Processing Systems 33 (2020)](https://proceedings.neurips.cc/paper/2020/file/972cda1e62b72640cb7ac702714a115f-Paper.pdf)
**An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction**
Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta
[arXiv:2211.03193](https://arxiv.org/abs/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](https://www.mlsb.io/papers_2022/Plug_Play_Directed_Evolution_of_Proteins_with_Gradient_based_Discrete_MCMC.pdf)/[arXiv:2212.09925](https://arxiv.org/abs/2212.09925)
**Importance Weighted Expectation-Maximization for Protein Sequence Design**
Zhenqiao Song, Lei Li
[arXiv:2305.00386](https://arxiv.org/abs/2305.00386) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/05/09/2023.05.09.539914/DC1/embed/media-1.pdf?download=true)
**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](https://www.biorxiv.org/content/10.1101/2023.05.09.539914v1)
**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](https://arxiv.org/abs/2307.00494) • [code](https://github.com/kirjner/GGS)
## 3. Function to Scaffold
> These models design backbone/scaffold/template in Cartesian coordinates, contact maps, distance maps and φ & ψ angles.
### 3.1 GAN-based
**Generative modeling for protein structures**
Anand, Namrata, and Possu Huang.
[NeurIPS 2018](https://proceedings.neurips.cc/paper/2018/file/afa299a4d1d8c52e75dd8a24c3ce534f-Paper.pdf)
**Fully differentiable full-atom protein backbone generation**
Anand Namrata, Raphael Eguchi, and Po-Ssu Huang.
[OpenReview ICLR 2019 workshop DeepGenStruct](https://openreview.net/forum?id=SJxnVL8YOV) • 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)](http://f1000researchdata.s3.amazonaws.com/manuscripts/29106/f45e92eb-5d68-4da0-b918-91ded85d2e7d_22907_-_sari_sabban_v2.pdf) • [code](https://sarisabban.github.io/RamaNet/) • 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](https://www.biorxiv.org/content/10.1101/2023.05.24.542095v1) • [code](https://github.com/NickWoodall/HelixGen)
### 3.2 VAE-based
**Conditioning by adaptive sampling for robust design**
Brookes, David, Hahnbeom Park, and Jennifer Listgarten.
[International conference on machine learning. PMLR, 2019](http://proceedings.mlr.press/v97/brookes19a/brookes19a.pdf) • 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)](https://www.biorxiv.org/content/10.1101/2020.08.07.242347v2) • 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)](https://arxiv.org/abs/2004.07119) • code not available
**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](https://openreview.net/forum?id=DwN81YIXGQP) • code not available
**End-to-End deep structure generative model for protein design**
Boqiao Lai, matthew McPartlon, Jinbo Xu
[bioRxiv 2022.07.09.499440](https://www.biorxiv.org/content/10.1101/2022.07.09.499440v1)
**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](https://www.biorxiv.org/content/10.1101/2022.12.22.521698v1)
### 3.3 DAE-based
**Function-guided protein design by deep manifold sampling**
Vladimir Gligorijevic, Stephen Ra, Daniel Berenberg, Richard Bonneau, Kyunghyun Cho
[NeurIPS 2021](https://www.mlsb.io/papers_2021/MLSB2021_Function-guided_protein_design_by.pdf) • without code
### 3.4 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)](https://doi.org/10.1038/s41586-021-04383-5) • [code](https://zenodo.org/record/4533424#.YwP3UPFBwqs)
**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)](https://www.cell.com/structure/fulltext/S0969-2126(24)00007-8)
### 3.5 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](https://arxiv.org/abs/2206.04119v2)/[NeurIPS 2022](https://www.mlsb.io/papers_2022/Diffusion_probabilistic_modeling_of_protein_backbones_in_3D_for_the_motif_scaffolding_problem.pdf)/[ICLR 2023](https://openreview.net/forum?id=6TxBxqNME1Y) • [poster](https://nips.cc/media/PosterPDFs/NeurIPS%202022/d3d9446802a44259755d38e6d163e820.png?t=1667835607.0141048) • [Supplementary](https://openreview.net/attachment?id=6TxBxqNME1Y&name=supplementary_material) • [code](https://github.com/blt2114/ProtDiff_SMCDiff)
**ProteinSGM: Score-based generative modeling for de novo protein design**
Jin Sub Lee, Philip M Kim
[bioRxiv 2022.07.13.499967](https://www.biorxiv.org/content/10.1101/2022.07.13.499967v2)/[Nat Comput Sci (2023)](https://www.nature.com/articles/s43588-023-00440-3) • [code](https://gitlab.com/mjslee0921/proteinsgm)
**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](https://arxiv.org/abs/2209.15611v2)/[Nat Commun 15, 1059 (2024)](https://www.nature.com/articles/s41467-024-45051-2) • [code](https://github.com/microsoft/foldingdiff)
**Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds**
Yeqing Lin, Mohammed AlQuraishi
[arXiv:2301.12485v3](https://arxiv.org/abs/2301.12485v3) • [code](https://github.com/aqlaboratory/genie) • [news](https://www.dw.com/en/generative-ai-inventing-proteins-is-changing-medicine/a-66356415)
**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](https://arxiv.org/abs/2302.02277v2)/[ICLR 2023](https://openreview.net/forum?id=6TxBxqNME1Y) • [code](https://github.com/jasonkyuyim/se3_diffusion) • [Supplementary](https://openreview.net/attachment?id=6TxBxqNME1Y&name=supplementary_material)
**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](https://arxiv.org/abs/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](https://arxiv.org/abs/2306.17775) • [code](https://github.com/blt2114/twisted_diffusion_sampler)
**Dynamics-Informed Protein Design with Structure Conditioning**
Simon V. Mathis, Urszula Julia Komorowska, Mateja Jamnik, Pietro Lió
[WCBICML2023](https://icml-compbio.github.io/2023/papers/WCBICML2023_paper121.pdf)/[ICLR 2024 under review](https://openreview.net/forum?id=jZPqf2G9Sw)
**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](https://arxiv.org/abs/2310.10605)/[Science Advances 10.6 (2024)](https://www.science.org/doi/10.1126/sciadv.adl4000) • [Supplementary](https://www.dropbox.com/scl/fi/33tnpd6u2xwermlvj22y9/SI_3_unfolding_movies_from_dataset.zip?rlkey=qno7rcitcdree8t9cj8wzg9sf&dl=0) • [code](https://github.com/lamm-mit/ProteinMechanicsDiffusionDesign)
**DiffSDS: A geometric sequence diffusion model for protein backbone inpainting**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=2xYO9oxh0y)/[arXiv:2301.09642](https://arxiv.org/abs/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](https://arxiv.org/abs/2312.09236)
**TopoDiff: Improving Protein Backbone Generation with Topology-aware Latent Encoding**
Yuyang Zhang, Zihui (Zinnia) Ma, Haipeng Gong
[bioRxiv 2023.12.13.571602](https://www.biorxiv.org/content/10.1101/2023.12.13.571602v1)
**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](https://arxiv.org/abs/2401.04082) • [code](https://github.com/microsoft/frame-flow)
**DiffTopo: Fold exploration using coarse grained protein topology representations**
Yangyang Miao, Bruno Correia
[bioRxiv 2024.02.01.578456](https://www.biorxiv.org/content/10.1101/2024.02.01.578456v1)/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)](https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1711) • review
**De novo antibody design with SE(3) diffusion**
Daniel Cutting, Frédéric A. Dreyer, David Errington, Constantin Schneider, Charlotte M. Deane
[arXiv:2405.07622](https://arxiv.org/abs/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](https://arxiv.org/abs/2405.15489) • [code](https://github.com/aqlaboratory/genie2) • [news](https://www.marktechpost.com/2024/05/29/genie-2-transforming-protein-design-with-advanced-multi-motif-scaffolding-and-enhanced-structural-diversity/)
### 3.6 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](https://www.biorxiv.org/content/10.1101/2022.09.25.509419v1)/[Science380, 266-273(2023)](https://www.science.org/doi/10.1126/science.adf6591) • [code](https://github.com/idlutz/protein-backbone-MCTS),[code2](https://files.ipd.uw.edu/pub/2023_RL_capsid_design/sequence_design_pipeline.tar)
**Model-based reinforcement learning for protein backbone design**
Frederic Renard, Cyprien Courtot, Alfredo Reichlin, Oliver Bent
[arXiv:2405.01983](https://arxiv.org/abs/2405.01983)
### 3.7 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](https://arxiv.org/abs/2310.02391)/[ICLR 2024](https://openreview.net/forum?id=kJFIH23hXb)
**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](https://arxiv.org/abs/2310.05297) • [code](https://github.com/microsoft/frame-flow)
**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](https://arxiv.org/abs/2405.20313) • [website](https://www.dreamfold.ai/blog/foldflow-2)
## 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](https://arxiv.org/abs/2202.01079). 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](https://academic.oup.com/peds/advance-article-abstract/doi/10.1093/protein/gzad024/7503843)
**Multi-indicator comparative evaluation for deep Learning-Based protein sequence design methods**
Jinyu Yu, Junxi Mu, Ting Wei, Hai-Feng Chen
[Bioinformatics, 2024;, btae037](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btae037/7585533)
**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](https://arxiv.org/abs/2402.10516)
### 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)](https://www.sciencedirect.com/science/article/pii/S2001037014000270) • 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)](https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.24620) • 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)](https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25489) • 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)](https://www.nature.com/articles/s41598-018-24760-x.pdf) • code unavailable
**Ligand-aware protein sequence design using protein self contacts**
Jody Mou, Benjamin Fry, Chun-Chen Yao, Nicholas Polizzi
[NeurIPS 2022](https://www.dropbox.com/s/98ri2f9gverljcw/Ligand-aware_protein_sequence_design_using_protein_self_contacts.pdf?dl=0)
**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)](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05498-4) • [code](https://github.com/falategan/SeqPredNN)
### 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)](https://www.nature.com/articles/s41598-018-34533-1)
### 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)](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00438) • [SPROF](https://github.com/biomed-AI/SPROF)
**Deep learning of Protein Sequence Design of Protein-protein Interactions**
Syrlybaeva, Raulia, and Eva-Maria Strauch.
[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.01.28.478262v1)/[Bioinformatics, 2022;, btac733](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac733/6827796) • [Supplementary](https://www.biorxiv.org/content/10.1101/2022.01.28.478262v1.supplementary-material) • [code](https://github.com/strauchlab/iNNterfaceDesign)
### 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)](https://www.biorxiv.org/content/10.1101/833905v1)
**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)](https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25868) • 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)](https://www.nature.com/articles/s41467-022-28313-9) • [code](https://github.com/ProteinDesignLab/protein_seq_des)
**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]((https://academic.oup.com/peds/advance-article/doi/10.1093/protein/gzae002/7591701)) • [code](https://github.com/wells-wood-research/timed-design) • [website](https://pragmaticproteindesign.bio.ed.ac.uk/timed/)
**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)](https://www.nature.com/articles/s41467-024-46356-y) • [code1](https://github.com/danny305/MutComputeX), [code2](https://github.com/simonsnitz/plotting)
### 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)](https://arxiv.org/abs/2009.01411)/[ICLR(2021)](https://openreview.net/forum?id=1YLJDvSx6J4) • [GVP](https://github.com/drorlab/gvp-pytorch)
**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)](https://www.sciencedirect.com/science/article/pii/S2405471220303276) • [code::ProteinSolver](https://gitlab.com/ostrokach/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](https://arxiv.org/abs/2102.03881)/[Front. Bioinform. 2:715006](https://www.frontiersin.org/articles/10.3389/fbinf.2022.715006/full)
**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](https://www.mlsb.io/papers_2021/MLSB2021_TERMinator:_A_Neural_Framework.pdf) / [arXiv (2022)](https://arxiv.org/pdf/2204.13048.pdf)
**A neural network model for prediction of amino-acid probability from a protein backbone structure**
Koya Sakuma, Naoya Kobayashi
Unpublished yet (June 2021)• [GCNdesgin](https://github.com/ShintaroMinami/GCNdesign)
**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)](https://pdfs.semanticscholar.org/23bc/58424378d15fda91e9d427fb553728c38b8a.pdf)
**AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB**
Gao, Zhangyang, Cheng Tan, and Stan Li.
[arXiv preprint arXiv:2202.01079 (2022)](https://arxiv.org/abs/2202.01079) • [code](https://github.com/jonathanking/sidechainnet)
**Generative De Novo Protein Design with Global Context**
Cheng Tan, Zhangyao Gao, Jun Xia and Stan Z. Li
[arXiv](https://arxiv.org/abs/2204.10673) • Apr 2022 • [code](https://github.com/chengtan9907/gca-generative-protein-design)
**Masked inverse folding with sequence transfer for protein representation learning**
Kevin K Yang, Hugh Yeh, Niccolò Zanichelli
[bioRxiv 2022.05.25.493516](https://www.biorxiv.org/content/10.1101/2022.05.25.493516v1)/[Protein Engineering, Design and Selection 36 (2023)](https://academic.oup.com/peds/article/doi/10.1093/protein/gzad015/7330543) • [code](https://github.com/microsoft/protein-sequence-models) • [model](https://doi.org/10.1234/mifst)
**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](https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1.article-metrics)/[Science (2022)](https://www.science.org/doi/10.1126/science.add2187) • [code](https://github.com/dauparas/ProteinMPNN) • [hugging face](https://huggingface.co/spaces/simonduerr/ProteinMPNN) • [lecture](https://www.youtube.com/watch?v=aVQQuoToTJA) • [colab(in_jax)](https://colab.research.google.com/github/sokrypton/ColabDesign/blob/v1.1.0/mpnn/examples/proteinmpnn_in_jax.ipynb) • [ProteinMPNN+ESMFold](https://huggingface.co/spaces/simonduerr/ProteinMPNNESM/blob/main/README.md)
**Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement**
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.
[arXiv preprint arXiv:2207.06616 (2022)](https://arxiv.org/abs/2207.06616)/[International Conference on Machine Learning. PMLR, 2022](https://icml.cc/virtual/2022/poster/16625) • [code](https://github.com/wengong-jin/abdockgen) • [poster](https://icml.cc/media/PosterPDFs/ICML%202022/b7f520a55897b35e6eb462bbf80915c6.png)
**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](https://www.biorxiv.org/content/10.1101/2022.08.02.501736v1.full.pdf)/[Protein Science, 32(2)](https://onlinelibrary.wiley.com/doi/10.1002/pro.4554)
**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](https://www.biorxiv.org/content/10.1101/2022.09.05.506704v1) • [code](https://github.com/biomed-AI/GraphEBM)
**PiFold: Toward effective and efficient protein inverse folding**
Zhangyang Gao, Cheng Tan, Stan Z. Li
[arXiv:2209.12643v2](https://arxiv.org/abs/2209.12643v3)/[ICLR 2023](https://openreview.net/pdf?id=oMsN9TYwJ0j) • [github](https://github.com/A4Bio/PiFold)
**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](https://www.biorxiv.org/content/10.1101/2023.02.04.527099v1)
**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](https://arxiv.org/abs/2303.11783)
**Modeling Protein Structure Using Geometric Vector Field Networks**
Weian Mao, Muzhi Zhu, Hao Chen, Chunhua Shen
[bioRxiv 2023.05.07.539736](https://www.biorxiv.org/content/10.1101/2023.05.07.539736v1)
**Knowledge-Design: Pushing the Limit of Protein Deign via Knowledge Refinement**
Zhangyang Gao, Cheng Tan, Stan Z. Li
[arXiv:2305.15151](https://arxiv.org/abs/2305.15151)/[ICLR under review](https://openreview.net/forum?id=mpqMVWgqjn) • [code](https://github.com/A4Bio/ProteinInvBench)
**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](https://www.biorxiv.org/content/10.1101/2023.07.07.548080v1)/[PLOS Computational Biology](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011330) • [code](https://github.com/EricZhangSCUT/SPIN-CGNN)
**ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing**
Junyu Yan and others
[Briefings in Bioinformatics, 2023](https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbad257/7222295) • [code](https://github.com/JoreyYan/zetadesign)
**Contextual protein encodings from equivariant graph transformers**
Sai Pooja Mahajan, Jeffrey A. Ruffolo, Jeffrey J. Gray
[bioRxiv 2023.07.15.549154](https://www.biorxiv.org/content/10.1101/2023.07.15.549154v1) • [code](https://github.com/GrayLab/MaskedProteinEnT)
**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)](https://pubs.acs.org/doi/10.1021/acssynbio.3c00042) • [Supplymentary](https://pubs.acs.org/doi/suppl/10.1021/acssynbio.3c00042/suppl_file/sb3c00042_si_001.pdf)
**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](https://www.biorxiv.org/content/10.1101/2023.08.04.551935v1)/[Proceedings of the National Academy of Sciences 121.(13)
](https://www.pnas.org/doi/10.1073/pnas.2314646121) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/08/04/2023.08.04.551935/DC1/embed/media-1.pdf) • [data](https://zenodo.org/records/8278877)
**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](https://www.biorxiv.orxg/content/10.1101/2023.08.25.554789v1) • [code](https://github.com/polizzilab/design_tools)
**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](https://www.biorxiv.org/content/10.1101/2023.09.20.558720v1) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/09/21/2023.09.20.558720/DC1/embed/media-1.docx)
**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](https://www.biorxiv.org/content/10.1101/2023.10.03.560713v1)/[J. Am. Chem. Soc. 2024](https://pubs.acs.org/doi/full/10.1021/jacs.3c10941) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/10/03/2023.10.03.560713/DC1/embed/media-1.pdf)
**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](https://www.biorxiv.org/content/10.1101/2023.10.09.561468v1) • [code](https://github.com/kylemeador/symdesign) • [colab](https://bit.ly/symdesign-colab)
**PROTEIN DESIGNER BASED ON SEQUENCE PROFILE USING ULTRAFAST SHAPE RECOGNITION**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=s4mPCrSNUZ)
**Inverse folding for antibody sequence design using deep learning**
Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M. Deane
[arXiv:2310.19513](https://arxiv.org/abs/2310.19513)
**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](https://www.nature.com/articles/s41467-023-43166-6) • [Supplementary](https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43166-6/MediaObjects/41467_2023_43166_MOESM1_ESM.pdf) • [code](https://zenodo.org/records/10030882)
**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)](https://www.nature.com/articles/s41467-023-43638-9) • [code](https://github.com/AzoiteiLab/S2-scaffold-scripts)
**DNDesign: Enhancing Physical Understanding of Protein Inverse Folding Model via Denoising**
Youhan Lee, Jaehoon Kim
[bioRxiv 2023.12.05.570298](https://www.biorxiv.org/content/10.1101/2023.12.05.570298v1)
**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](https://www.biorxiv.org/content/10.1101/2023.12.08.570889v1)
**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](https://www.biorxiv.org/content/10.1101/2023.12.14.571651v1)/[Briefings in Bioinformatics 25.3 (2024): bbae146](https://academic.oup.com/bib/article/25/3/bbae146/7642672) • [website](http://zhanglab-bioinf.com/SPDesign/)
**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](https://www.biorxiv.org/content/10.1101/2023.12.20.572602v1) • [code1](https://github.com/LAnAlchemist/Pseudocycle_small_molecule_binder), [code2](https://github.com/iamlongtran/pseudocycle_paper), [code3](https://github.com/feldman4/ngs_app)
**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](https://www.biorxiv.org/content/10.1101/2023.12.22.573103v1) • [code](https://github.com/dauparas/LigandMPNN)
**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](https://www.biorxiv.org/content/10.1101/2023.12.15.571823v1) • [code](https://github.com/dakpinaroglu/Frame2seq)
**ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-Barrels**
Marissa D Dolorfino, Anastassia A Vorobieva
[bioRxiv 2024.01.16.575764](https://www.biorxiv.org/content/10.1101/2024.01.16.575764v1) • [code](https://github.com/marissadolorfino2024/ProteinMPNN-TMB-Design.git)
**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)](https://www.sciencedirect.com/science/article/pii/S2405805X24000115)
**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)](https://www.nature.com/articles/s41586-024-07188-4) • [RosettaScripts](https://github.com/tfhuddy/2023-manuscript-materials)
**All-atom protein sequence design based on geometric deep learning**
Jiale Liu, Zheng Guo, Changsheng Zhang, Luhua Lai
[bioRxiv 2024.03.18.585651](https://www.biorxiv.org/content/10.1101/2024.03.18.585651v1) • [code](https://github.com/PKUliujl/GesSeqBuilder)
**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](https://academic.oup.com/bib/article/25/3/bbae135/7638270) • [code](https://github.com/decodermu/GPD)
**The Damietta Server: a comprehensive protein design toolkit**
Iwan Grin, Kateryna Maksymenko, Tobias Wörtwein, Mohammad ElGamacy
[Nucleic Acids Research, 2024;, gkae297](https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae297/7658041) • [website](https://damietta.de/) • ProteinMPNN-based • [news](https://cbirt.net/protein-design-made-easy-with-damietta-server-a-comprehensive-toolkit/), [news2](https://www.innovations-report.com/life-sciences/toolkit-makes-protein-design-faster-and-more-accessible/)
**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](https://www.biorxiv.org/content/10.1101/2024.04.19.590222v1) • [code](https://github.com/LBC-LNBio/ESMIFDesign), [code2](https://github.com/piercelab/tcrmodel2/)
**SurfPro: Functional Protein Design Based on Continuous Surface**
Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin
[arXiv:2405.06693](https://arxiv.org/abs/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)](https://www.sciencedirect.com/science/article/pii/S0006291X2400696X) • [code](https://github.com/yangyu1-github/MbDesignMPNN) • 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](https://arxiv.org/abs/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](https://www.biorxiv.org/content/10.1101/2024.06.04.597365v1)
**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)](https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c00828)
### 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)](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00593) • [gcWGAN](https://github.com/Shen-Lab/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](https://www.mlsb.io/papers_2021/MLSB2021_HelixGAN:_A_bidirectional_Generative.pdf)/[Bioinformatics, 2023;, btad036](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad036/6991169) • [code](https://github.com/xxiexuezhi/helix_gan)
### 4.7 Transformer-based
**Generative models for graph-based protein design**
[John Ingraham](https://openreview.net/profile?email=ingraham%40csail.mit.edu), Vikas K Garg, Dr.Regina Barzilay, Tommi Jaakkola
[NeurIPS 2019](https://openreview.net/forum?id=ByMEAHrgLB) • [GraphTrans](https://github.com/jingraham/neurips19-graph-protein-design)
**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](https://arxiv.org/pdf/2106.13058)
**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)](https://www.researchsquare.com/article/rs-1209166/v1)/[Nature computational science(2022)](https://www.nature.com/articles/s43588-022-00273-6) • [Supplementary](https://static-content.springer.com/esm/art%3A10.1038%2Fs43588-022-00273-6/MediaObjects/43588_2022_273_MOESM1_ESM.pdf) • [Comment](https://www.nature.com/articles/s43588-022-00274-5) • [code](https://codeocean.com/capsule/6949436/tree/v1)
**A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding**
Mmatthew McPartlon, Ben Lai, Jinbo Xu
[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.04.15.488492v1)
**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)](https://doi.org/10.1101/2022.04.10.487779) • [esm](https://github.com/facebookresearch/esm)
**Breaking boundaries in protein design with a new AI model that understands interactions with any kind of molecule**
LucianoSphere
[Towards Data Science](https://towardsdatascience.com/breaking-boundaries-in-protein-design-with-a-new-ai-model-that-understands-interactions-with-any-388fd747ee40)
**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)](https://www.biorxiv.org/content/10.1101/2022.06.25.497605v4)/[Bioinformatics 39.3 (2023)](https://academic.oup.com/bioinformatics/article/39/3/btad122/7077134) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/06/30/2022.06.25.497605/DC1/embed/media-1.pdf) • [website](http://81.70.37.223) • [code](https://github.com/bigict/ProDESIGN-LE)
**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](https://www.biorxiv.org/content/10.1101/2022.08.10.503344v1)
**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](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2) • [blog](https://ai.facebook.com/blog/protein-folding-esmfold-metagenomics/) • [github](https://github.com/facebookresearch/esm)
**Structure-informed Language Models Are Protein Designers**
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei YE, Quanquan Gu
[arXiv:2302.01649](https://arxiv.org/abs/2302.01649) • [code::ByProt](https://github.com/BytedProtein/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](https://arxiv.org/abs/2211.08406) • [code](https://github.com/KyGao/ABGNN)
**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](https://arxiv.org/abs/2302.04611) • [code](https://github.com/chao1224/ProteinDT)
**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)](https://www.pnas.org/doi/10.1073/pnas.2216438120) • [code](https://github.com/MattMcPartlon/AttnPacker) • [Supplementary](https://www.pnas.org/doi/suppl/10.1073/pnas.2216438120/suppl_file/pnas.2216438120.sapp.pdf)
**Context-aware geometric deep learning for protein sequence design**
Lucien Krapp, Fernado Meireles, Luciano Abriata, Matteo Dal Peraro
[bioRxiv 2023.06.19.545381](https://www.biorxiv.org/content/10.1101/2023.06.19.545381v1) • [code](https://github.com/LBM-EPFL/CARBonARa)
**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](https://www.biorxiv.org/content/10.1101/2023.06.26.546591v1) • [code](https://github.com/programmablebio/pepprclip) • [colab](https://drive.google.com/drive/u/0/folders/1A4kQXjsG5j3OrO0XQtzBWWZu9Zm7c0ak) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/06/28/2023.06.26.546591/DC1/embed/media-1.pdf)
**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](https://www.biorxiv.org/content/10.1101/2023.07.23.550085v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/07/25/2023.07.23.550085/DC1/embed/media-1.pdf) • [code](https://github.com/mheinzinger/ProstT5)
**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](https://www.biorxiv.org/content/10.1101/2023.08.01.551444v1) • [code](https://github.com/weitinging/GPD)
**Invariant point message passing for protein side chain packing and design**
Nicholas Z Randolph, Brian Kuhlman
[bioRxiv 2023.08.03.551328](https://www.biorxiv.org/content/10.1101/2023.08.03.551328v1) • [code](https://github.com/Kuhlman-Lab/PIPPack)
**Atom-by-atom protein generation and beyond with language models**
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
[arXiv:2308.09482](https://arxiv.org/abs/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](https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5) • [code](https://github.com/westlake-repl/SaProt)
**AntiFold: Improved antibody structure design using inverse folding**
Magnus Høie, Alissa Hummer, Tobias Olsen, Morten Nielsen, Charlotte Deane
[GenBio@NeurIPS2023 Spotlight](https://openreview.net/forum?id=bxZMKHtlL6) • [code](https://opig.stats.ox.ac.uk/data/downloads/AntiFold/) • [colab](https://colab.research.google.com/drive/1TTfgjoZx3mzF5u4e9b4Un9Y7b_rqXc_4)
**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)](https://arxiv.org/abs/2312.06297)
**ShapeProt: Top-down Protein Design with 3D Protein Shape Generative Model**
Lee, Youhan, and Jaehoon Kim.
[bioRxiv (2023): 2023-12](https://www.biorxiv.org/content/10.1101/2023.12.03.567710v2)
**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](https://arxiv.org/abs/2402.07148) • [code](https://github.com/EricLBuehler/xlora) • [Model & weights](https://huggingface.co/lamm-mit/x-lora)
**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](https://arxiv.org/abs/2405.03370) • [code](https://github.com/oxpig/AntiFold) • [website](https://opig.stats.ox.ac.uk/webapps/antifold/) • 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](https://www.biorxiv.org/content/10.1101/2024.06.03.597105v1) • [code](https://github.com/StructureGPT) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2024/06/07/2024.06.03.597105/DC1/embed/media-1.pdf)
### 4.8 ResNet-based
**DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet**
Qi, Yifei, and John ZH Zhang.
[Journal of chemical information and modeling 60.3 (2020)](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00043) • code unavailable
### 4.9 Diffusion-based
**De novo protein backbone generation based on diffusion with structured priors and adversarial training**
Yufeng Liu, Linghui Chen, Haiyan Liu
[bioRxiv 2022.12.17.520847](https://www.biorxiv.org/content/10.1101/2022.12.17.520847v1)
**Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model**
Bo Ni, David L. Kaplan, Markus J. Buehler
[Chem,(2023)](https://www.cell.com/chem/fulltext/S2451-9294(23)00139-0) • [code](https://github.com/lamm-mit/ProteinDiffusionGenerator) • [news](https://news.mit.edu/2023/ai-system-can-generate-novel-proteins-structural-design-0420)
**Graph Denoising Diffusion for Inverse Protein Folding**
Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, Yu Guang Wang
[arXiv:2306.16819](https://arxiv.org/abs/2306.16819)
**Conditional Protein Denoising Diffusion Generates Programmable Endonucleases**
Bingxin Zhou, Lirong Zheng, Banghao Wu, Kai Yi, Bozitao Zhong, Pietro Lio, Liang Hong
[bioRxiv 2023.08.10.552783](https://www.biorxiv.org/content/10.1101/2023.08.10.552783v1)
**Diffusion in a quantized vector space generates non-idealized protein structures and predicts conformational distributions**
Liu Haiyan, Liu Yufeng, Chen Linghui
[bioRxiv 2023.11.18.567666](https://www.biorxiv.org/content/10.1101/2023.11.18.567666v1)
**Fast non-autoregressive inverse folding with discrete diffusion**
John J. Yang, Jason Yim, Regina Barzilay, Tommi Jaakkola
[arXiv:2312.02447](https://arxiv.org/abs/2312.02447) • [code](https://github.com/johnyang101/pmpnndiff)
**Diffusion Language Models Are Versatile Protein Learners**
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
[arXiv:2402.18567](https://arxiv.org/abs/2402.18567)
**LéxFusion**
Levinthal
paper not available • [news](https://mp.weixin.qq.com/s/Iex0YndimhLDM0mASp1MtA) • commercial
### 4.10 Bayesian-based
**Inverse Protein Folding Using Deep Bayesian Optimization**
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
[arXiv:2305.18089](https://arxiv.org/abs/2305.18089) • [code](https://github.com/nataliemaus/bo-if)
### 4.11 Flow-based
**Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design**
Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
[arXiv:2310.05764](https://arxiv.org/abs/2310.05764) • [code](https://github.com/HannesStark/FlowSite)
## 5.Function to Sequence
> These models generate sequences from expected function.
### 5.1 CNN-based
**Antibody complementarity determining region design using high-capacity machine learning**
Ge Liu, Haoyang Zeng, Jonas Mueller, Brandon Carter, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael E Birnbaum, Stefan Ewert, David K Gifford
[Bioinformatics 36.7 (2020): 2126-2133](https://academic.oup.com/bioinformatics/article/36/7/2126/5645171) • [code](https://github.com/gifford-lab/antibody-2019)
**Protein design and variant prediction using autoregressive generative models**
Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C. Kruse & Debora S. Marks
[Nature communications 12.1 (2021)](https://www.nature.com/articles/s41467-021-22732-w.pdf) • [code::SeqDesign](https://github.com/debbiemarkslab/SeqDesign) • mutation effect prediction • sequence generation • April 2021
**Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning**
Derek M. Mason, Simon Friedensohn, Cédric R. Weber, Christian Jordi, Bastian Wagner, Simon M. Meng, Roy A. Ehling, Lucia Bonati, Jan Dahinden, Pablo Gainza, Bruno E. Correia & Sai T. Reddy
[Nature Biomedical Engineering 5.6 (2021)](https://www.nature.com/articles/s41551-021-00699-9) • [code](https://github.com/dahjan/DMS_opt)
**Accelerated Engineering of ELP‐based Materials through Hybrid Biomimetic‐De Novo Predictive Molecular Design**
Timo Laakko, Antti Korkealaakso, Burcu Firatligil Yildirir, Piotr Batys, Ville Liljeström, Ari Hokkanen, Nonappa, Merja Penttilä, Anssi Laukkanen, Ali Miserez, Caj Södergård, Pezhman Mohammadi
[Advanced Materials (2024)](https://onlinelibrary.wiley.com/doi/10.1002/adma.202312299)
### 5.2 VAE-based
**Machine learning-aided design and screening of an emergent protein function in synthetic cells**
Shunshi Kohyama, Béla P. Frohn, Leon Babl & Petra Schwille
[Nature Communications 15, 2010 (2024)](https://doi.org/10.1038/s41467-024-46203-0) • [code](https://github.com/BelaFrohn/synMinE)
**Variational auto-encoding of protein sequences**
Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
[arXiv preprint arXiv:1712.03346 (2017)](https://arxiv.org/abs/1712.03346)
**Design by adaptive sampling**
Brookes, David H., and Jennifer Listgarten.
[arXiv preprint arXiv:1810.03714 (2018)](https://arxiv.org/abs/1810.03714)
**Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences**
Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero Dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic
[arXiv preprint arXiv:1810.07743 (2018)](https://arxiv.org/abs/1810.07743)
**Deep generative models for T cell receptor protein sequences**
Kristian Davidsen, Branden J Olson, William S DeWitt III, Jean Feng, Elias Harkins, Philip Bradley, Frederick A Matsen IV
[Elife 8 (2019)](https://elifesciences.org/articles/46935)
**How to hallucinate functional proteins**
Costello, Zak, and Hector Garcia Martin.
[arXiv preprint arXiv:1903.00458 (2019)](https://arxiv.org/abs/1903.00458)
**Convergent selection in antibody repertoires is revealed by deep learning**
Simon Friedensohn, Daniel Neumeier, Tarik A Khan, Lucia Csepregi, Cristina Parola, Arthur R Gorter de Vries, Lena Erlach, Derek M Mason, Sai T Reddy
[BioRxiv (2020)](https://www.biorxiv.org/content/10.1101/2020.02.25.965673v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2020/02/26/2020.02.25.965673/DC1/embed/media-1.pdf) • code available after publication
**Variational autoencoder for generation of antimicrobial peptides**
Dean, Scott N., and Scott A. Walper.
[ACS omega 5.33 (2020)](https://pubs.acs.org/doi/abs/10.1021/acsomega.0c00442)
**Generating functional protein variants with variational autoencoders**
Alex Hawkins-Hooker, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, David Bikard
[PLoS computational biology 17.2 (2021)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008736)
**Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations**
Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy P. K. Tan, James Hedrick, Jason Crain & Aleksandra Mojsilovic
[Nature Biomedical Engineering 5.6 (2021)](https://www.nature.com/articles/s41551-021-00689-x)
**Deep generative models create new and diverse protein structures**
Zeming, Tom, Yann and Alexander.
[NeurIPS 2021](https://www.mlsb.io/papers_2021/MLSB2021_Deep_generative_models_create.pdf)
**PepVAE: variational autoencoder framework for antimicrobial peptide generation and activity prediction**
Scott N. Dean, Jerome Anthony E. Alvarez, Dan Zabetakis, Scott A. Walper, and Anthony P. Malanoski
[Frontiers in microbiology 12 (2021)](https://www.frontiersin.org/articles/10.3389/fmicb.2021.725727/full) • [code](https://github.com/zswitten/Antimicrobial-Peptides) • [Supplementary](https://www.frontiersin.org/articles/10.3389/fmicb.2021.725727/full#supplementary-material)
**HydrAMP: a deep generative model for antimicrobial peptide discovery**
Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, Marta Bauer, Damian Neubauer, Michał Michalski, Jacek Sroka, Piotr Setny, Wojciech Kamysz, Ewa Szczurek
[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.01.27.478054v2) • [code](https://github.com/szczurek-lab/hydramp)
**Therapeutic enzyme engineering using a generative neural network**
Andrew Giessel, Athanasios Dousis, Kanchana Ravichandran, Kevin Smith, Sreyoshi Sur, Iain McFadyen, Wei Zheng & Stuart Licht
[Scientific Reports 12.1 (2022)](https://www.nature.com/articles/s41598-022-05195-x)
**GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences**
Qushuo Chen, Changyan Yang, Yihao Xie, Yuqiang Wang, Xiaoxu Li, Kairong Wang, Jinqi Huang, and Wenjin Yan
[Journal of Chemical Information and Modeling (2022)](https://pubs.acs.org/doi/10.1021/acs.jcim.2c00089) • [code](https://github.com/TimothyChen225/GM-Pep)
**Mean Dimension of Generative Models for Protein Sequences**
Christoph Feinauer, Emanuele Borgonovo
[bioRxiv 2022.12.12.520028](https://www.biorxiv.org/content/10.1101/2022.12.12.520028v1) • [code](https://github.com/christophfeinauer/ProteinMeanDimension)
**Prediction of designer-recombinases for DNA editing with generative deep learning**
Lukas Theo Schmitt, Maciej Paszkowski-Rogacz, Florian Jug & Frank Buchholz
[Nat Commun 13, 7966 (2022)](https://www.nature.com/articles/s41467-022-35614-6) • [code](https://github.com/ltschmitt/RecGen) • [Supplementary](https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-022-35614-6/MediaObjects/41467_2022_35614_MOESM1_ESM.pdf)
**ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design**
Emre Sevgen, Joshua Moller, Adrian Lange, John Parker, Sean Quigley, Jeff Mayer, Poonam Srivastava, Sitaram Gayatri, David Hosfield, Maria Korshunova, Micha Livne, Michelle Gill, Rama Ranganathan, Anthony B Costa, Andrew L Ferguson
[bioRxiv 2023.01.23.525232](https://www.biorxiv.org/content/10.1101/2023.01.23.525232v1)
**Target specific peptide design using latent space approximate trajectory collector**
Tong Lin, Sijie Chen, Ruchira Basu, Dehu Pei, Xiaolin Cheng, Levent Burak Kara
[arXiv:2302.01435](https://arxiv.org/abs/2302.01435)
**Deep-learning generative models enable design of synthetic orthologs of a signaling protein**
Xinran Lian, Niksa Praljak, Andrew L. Ferguson, Rama Ranganathan
[Biophysical Journal 122.3 (2023): 311a](https://www.cell.com/biophysj/fulltext/S0006-3495(22)02664-9)
**Designing a protein with emergent function by combined in silico, in vitro and in vivo screening**
Shunshi Kohyama, Bela Paul Frohn, Leon Babl, Petra Schwille
[bioRxiv 2023.02.16.528840](https://www.biorxiv.org/content/10.1101/2023.02.16.528840v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/02/19/2023.02.16.528840/DC1/embed/media-1.pdf)
**ProteinVAE: Variational AutoEncoder for Translational Protein Design**
Suyue Lyu, Shahin Sowlati-Hashjin, Michael Garton
[bioRxiv 2023.03.04.531110](https://www.biorxiv.org/content/10.1101/2023.03.04.531110v1)/[Nat Mach Intell (2024)](https://www.nature.com/articles/s42256-023-00787-2) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/03/05/2023.03.04.531110/DC1/embed/media-1.pdf) • [code](https://huggingface.co/Rostlab/prot_bert)
**ProtWave-VAE: Integrating autoregressive sampling with latent-based inference for data-driven protein design**
Niksa Praljak, Xinran Lian, Rama Ranganathan, Andrew Ferguson
[bioRxiv 2023.04.23.537971](https://www.biorxiv.org/content/10.1101/2023.04.23.537971v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/04/23/2023.04.23.537971/DC1/embed/media-1.pdf?download=true) • [code](https://github.com/PraljakReps/ProtWaveVAE)
**Designing meaningful continuous representations of T cell receptor sequences with deep generative models**
Allen Y. Leary, Darius Scott, Namita T. Gupta, Janelle C. Waite, Dimitris Skokos, Gurinder S. Atwal, Peter G. Hawkins
[bioRxiv 2023.06.17.545423](https://www.biorxiv.org/content/10.1101/2023.06.17.545423v1) • [code](https://github.com/peterghawkins-regn/tcrvalid)
**Utility of language model and physics-based approaches in modifying MHC Class-I immune-visibility for the design of vaccines and therapeutics**
Hans-Christof Gasser, Diego Oyarzun, Ajitha Rajan, Javier Alfaro
[bioRxiv 2023.07.10.548300](https://www.biorxiv.org/content/10.1101/2023.07.10.548300v1)
**Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides**
Amir Pandi, David Adam, Amir Zare, Van Tuan Trinh, Stefan L. Schaefer, Marie Burt, Björn Klabunde, Elizaveta Bobkova, Manish Kushwaha, Yeganeh Foroughijabbari, Peter Braun, Christoph Spahn, Christian Preußer, Elke Pogge von Strandmann, Helge B. Bode, Heiner von Buttlar, Wilhelm Bertrams, Anna Lena Jung, Frank Abendroth, Bernd Schmeck, Gerhard Hummer, Olalla Vázquez & Tobias J. Erb
[Nature Communications 14.1 (2023)](https://www.nature.com/articles/s41467-023-42434-9) • [code](https://github.com/amirpandi/Deep_AMP)
**Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations**
Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara & Xiaolin Cheng
[Nat Commun 15, 1611 (2024)](https://www.nature.com/articles/s41467-024-45766-2) • [code](https://doi.org/10.5281/zenodo.10587692)
### 5.3 GAN-based
**Feedback GAN for DNA optimizes protein functions**
Gupta, Anvita, and James Zou.
[Nature Machine Intelligence 1.2 (2019)](https://www.nature.com/articles/s42256-019-0017-4) • [code](https://github.com/av1659/fbgan)
**Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks**
Chhibbar, Prabal, and Arpit Joshi.
[arXiv preprint arXiv:1904.13240 (2019)](https://arxiv.org/abs/1904.13240)
**ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework**
Xi Han, Liheng Zhang, Kang Zhou, Xiaonan Wang
[Computers & Chemical Engineering 131 (2019)](https://www.sciencedirect.com/science/article/abs/pii/S0098135419304922)
**GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks**
Rossetto, Allison, and Wenjin Zhou.
[Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2020](https://dl.acm.org/doi/abs/10.1145/3388440.3412487)
**Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks**
Tileli Amimeur, Jeremy M. Shaver, Randal R. Ketchem, J. Alex Taylor, Rutilio H. Clark, Josh Smith, Danielle Van Citters, Christine C. Siska, Pauline Smidt, Megan Sprague, Bruce A. Kerwin, Dean Pettit
[BioRxiv (2020)](https://www.biorxiv.org/content/10.1101/2020.04.12.024844v2)
**Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks**
Andrejs Tucs, Duy Phuoc Tran, Akiko Yumoto, Yoshihiro Ito, Takanori Uzawa, and Koji Tsuda
[ACS omega 5.36 (2020)](https://pubs.acs.org/doi/10.1021/acsomega.0c02088) • [code](https://github.com/tsudalab/PepGAN)
**Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions**
Kucera, Tim, Matteo Togninalli, and Laetitia Meng-Papaxanthos
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.11.10.467885v1)/[Bioinformatics 38.13 (2022)](https://academic.oup.com/bioinformatics/article/38/13/3454/6593486) • [code](https://github.com/timkucera/proteogan)
**Expanding functional protein sequence spaces using generative adversarial networks**
Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist & Aleksej Zelezniak
[Nature Machine Intelligence 3.4 (2021)](https://www.nature.com/articles/s42256-021-00310-5) • [code](https://github.com/Biomatter-Designs/ProteinGAN)
**A Generative Approach toward Precision Antimicrobial Peptide Design.**
Jonathon B. Ferrell, Jacob M. Remington, Colin M. Van Oort, Mona Sharafi, Reem Aboushousha, Yvonne Janssen-Heininger, Severin T. Schneebeli, Matthew J. Wargo, Safwan Wshah, Jianing Li
[BioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2020.10.02.324087v2) • [code](https://gitlab.com/vail-uvm/amp-gan/-/tree/test_samples/)
**AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides**
Colin M. Van Oort, Jonathon B. Ferrell, Jacob M. Remington, Safwan Wshah, and Jianing Li
[Journal of chemical information and modeling 61.5 (2021)](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c01441)
**DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity**
Guangyuan Li, Balaji Iyer, V B Surya Prasath, Yizhao Ni, Nathan Salomonis
[Briefings in bioinformatics 22.6 (2021)](https://academic.oup.com/bib/article-abstract/22/6/bbab160/6261914) • [code](https://github.com/frankligy/DeepImmuno) • [web](https://deepimmuno.research.cchmc.org/)
**PandoraGAN: Generating antiviral peptides using Generative Adversarial Network**
Shraddha Surana, Pooja Arora, Divye Singh, Deepti Sahasrabuddhe, Jayaraman Valadi
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.02.15.431193v2)
**Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides**
Kano Hasegawa, Yoshitaka Moriwaki, Tohru Terada, Cao Wei, and Kentaro Shimizu
[Journal of Bioinformatics and Computational Biology (2022)](https://www.worldscientific.com/doi/10.1142/S0219720022500263) • [code](https://github.com/KanoHase/AVP-Generator)
**Designing antimicrobial peptides using deep learning and molecular dynamic simulations**
Qiushi Cao, Cheng Ge, Xuejie Wang, Peta J Harvey, Zixuan Zhang, Yuan Ma, Xianghong Wang, Xinying Jia, Mehdi Mobli, David J Craik, Tao Jiang, Jinbo Yang, Zhiqiang Wei, Yan Wang, Shan Chang, Rilei Yu
[Briefings in Bioinformatics (2023)](https://academic.oup.com/bib/article-abstract/24/2/bbad058/7066348)
**Generative β-Hairpin Design Using a Residue-Based Physicochemical Property Landscape**
Vardhan Satalkar and Gemechis D. Degaga and Wei Li and Yui Tik Pang and Andrew C. McShan and James C. Gumbart and Julie C. Mitchell and Matthew P. Torres
[Biophysical Journal(2024)](https://www.sciencedirect.com/science/article/pii/S0006349524000705) • [code](https://github.com/juliecmitchell/beGAN)
### 5.4 Transformer-based
> Including protein large language models(pLLM) and autoregressive language models.
**Progen: Language modeling for protein generation** / **Large language models generate functional protein sequences across diverse families**
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher
[arXiv preprint arXiv:2004.03497 (2020)](https://arxiv.org/abs/2004.03497)/[Nat Biotechnol (2023)](https://www.nature.com/articles/s41587-022-01618-2) • [ProGen](https://github.com/salesforce/progen), [CTRL](https://github.com/salesforce/ctrl)
**Signal peptides generated by attention-based neural networks**
Zachary Wu, Kevin K. Yang, Michael J. Liszka, Alycia Lee, Alina Batzilla, David Wernick, David P. Weiner, and Frances H. Arnold
[ACS Synthetic Biology 9.8 (2020)](https://pubs.acs.org/doi/full/10.1021/acssynbio.0c00219)
**ProtTrans: towards cracking the language of Life's code through self-supervised deep learning and high performance computing**
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger,Debsindhu Bhowmik, and Burkhard Rost
[arXiv preprint arXiv:2007.06225 (2020)](https://ieeexplore.ieee.org/document/9477085) • [code](https://github.com/agemagician/ProtTrans)
**Generative Language Modeling for Antibody Design**
Shuai, Richard W., Jeffrey A. Ruffolo, and Jeffrey J. Gray.
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.12.13.472419v2)/[Cell Systems](https://www.cell.com/cell-systems/pdf/S2405-4712(23)00271-5.pdf) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/12/20/2021.12.13.472419/DC1/embed/media-1.pdf) • [code](https://github.com/Graylab/IgLM)
**Deep neural language modeling enables functional protein generation across families**
Ali Madani, Ben Krause, Eric R. Greene, Subu Subramanian, Benjamin P. Mohr, James M. Holton, Jose Luis Olmos Jr., Caiming Xiong, Zachary Z. Sun, Richard Socher, James S. Fraser, Nikhil Naik
[bioRxiv (2021)](https://www.biorxiv.org/content/10.1101/2021.07.18.452833v1)
**Protein sequence sampling and prediction from structural data**
Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez
[bioRxiv 2021.09.06.459171](https://www.biorxiv.org/content/10.1101/2021.09.06.459171v3)
**Transformer-based protein generation with regularized latent space optimization**
Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin Givechian, Dhananjay Bhaskar & Smita Krishnaswamy
[Nat Mach Intell (2022)](https://www.nature.com/articles/s42256-022-00532-1)/[arXiv:2201.09948](https://arxiv.org/abs/2201.09948) • [code](https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers)
**BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning**
David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil, Danny A. Bitton
[mAbs. Vol. 14. No. 1. Taylor & Francis, 2022](https://www.tandfonline.com/doi/full/10.1080/19420862.2021.2020203)
**Guided Generative Protein Design using Regularized Transformers**
Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin B. Givechian, Dhananjay Bhaskar, Smita Krishnaswamy
[arXiv preprint arXiv:2201.09948 (2022)](https://arxiv.org/abs/2201.09948)
**Towards Controllable Protein design with Conditional Transformers**
Noelia Ferruz, Birte Höcker
[arXiv preprint arXiv:2201.07338 (2022)](https://arxiv.org/abs/2201.07338)/[Nature Machine Intelligence (2022)](https://www.nature.com/articles/s42256-022-00499-z) • review of [Heading 5.4](#54-transformer-based)
**ProtGPT2 is a deep unsupervised language model for protein design**
Noelia Ferruz, View ProfileSteffen Schmidt, View ProfileBirte Höcker
[bioRxiv](https://www.biorxiv.org/content/10.1101/2022.03.09.483666v1.full)/[Nature Communications](https://www.nature.com/articles/s41467-022-32007-7#citeas) • [model::huggingface](https://huggingface.co/nferruz/ProtGPT2) [datasets::hugingface](https://huggingface.co/datasets/nferruz/UR50_2021_04) • [lecture](https://www.youtube.com/watch?v=BA5C0kLcErM) • [research highlights](https://www.nature.com/articles/s41587-022-01518-5) • [news](https://cen.acs.org/physical-chemistry/protein-folding/Generative-AI-dreaming-new-proteins/101/i12#)
**Few Shot Protein Generation**
Ram, Soumya, and Tristan Bepler.
[arXiv preprint arXiv:2204.01168 (2022)](https://arxiv.org/abs/2204.01168)
**RITA: a Study on Scaling Up Generative Protein Sequence Models**
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, Debora Marks
[arXiv preprint arXiv:2205.05789 (2022)](https://arxiv.org/abs/2205.05789) • [code](https://huggingface.co/lightonai/RITA_xl)
**ProGen2: Exploring the Boundaries of Protein Language Models**
Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani
[arXiv:2206.13517](https://arxiv.org/abs/2206.13517) • [code](https://github.com/salesforce/progen) • [guide](https://github.com/ZeeSid/BioLM_Tutes/tree/main)
**AbLang: an antibody language model for completing antibody sequences**
Tobias H Olsen, Iain H Moal, Charlotte M Deane
[Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac046](https://academic.oup.com/bioinformaticsadvances/article/2/1/vbac046/6609807)
**Reprogramming Pretrained Language Models for Antibody Sequence Infilling**
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
[arXiv:2210.07144](https://arxiv.org/abs/2210.07144) • [code](https://github.com/IBM/ReprogBERT)
**AbBERT: Learning Antibody Humanness via Masked Language Modeling**
Denis Vashchenko, Sam Nguyen, Andre Goncalves, Felipe Leno da Silva, Brenden Petersen, Thomas Desautels, Daniel Faissol
[bioRxiv 2022.08.02.502236](https://doi.org/10.1101/2022.08.02.502236)
**Accelerating Antibody Design with Active Learning**
Seung-woo Seo, Min Woo Kwak, Eunji Kang, Chaeun Kim, Eunyoung Park, Tae Hyun Kang, Jinhan Kim
[bioRxiv 2022.09.12.507690](https://www.biorxiv.org/content/10.1101/2022.09.12.507690v1)
**Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling**
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
[ICLR 2023](https://openreview.net/forum?id=axFCgjTKP45)/[arXiv:2210.07144](https://arxiv.org/abs/2210.07144)
**Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries**
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Rajmonda Sulo Caceres, Tristan Bepler, Matthew E. Walsh
[bioRxiv 2022.10.07.502662](https://www.biorxiv.org/content/10.1101/2022.10.07.502662v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/10/07/2022.10.07.502662/DC1/embed/media-1.pdf) • code will be available
**ZymCTRL: a conditional language model for the contollable generation of artificial enzymes**
Noelia Ferruz
[NeurIPS 2022](https://www.mlsb.io/papers_2022/ZymCTRL_a_conditional_language_model_for_the_controllable_generation_of_artificial_enzymes.pdf)/[bioRxiv 2024.05.03.592223](https://www.biorxiv.org/content/10.1101/2024.05.03.592223v1) • [hugging face](https://huggingface.co/nferruz/ZymCTRL) • [poster](https://nips.cc/media/PosterPDFs/NeurIPS%202022/59047.png?t=1669864213.082831)
**Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model**
Chu, Simon, and Kathy Wei.
[NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. 2023](https://openreview.net/forum?id=QrH4bhWhwY)/[arXiv:2301.02748](https://arxiv.org/abs/2301.02748)
**Unlocking de novo antibody design with generative artificial intelligence**
Amir Shanehsazzadeh, Matt McPartlon, George Kasun, Andrea K. Steiger, John M. Sutton, Edriss Yassine, Cailen McCloskey, Robel Haile, Richard Shuai, Julian Alverio, Goran Rakocevic, Simon Levine, Jovan Cejovic, Jahir M. Gutierrez, Alex Morehead, Oleksii Dubrovskyi, Chelsea Chung, Breanna K. Luton, Nicolas Diaz, Christa Kohnert, Rebecca Consbruck, Hayley Carter, Chase LaCombe, Itti Bist, Phetsamay Vilaychack, Zahra Anderson, Lichen Xiu, Paul Bringas, Kimberly Alarcon, Bailey Knight, Macey Radach, Katherine Bateman, Gaelin Kopec-Belliveau, Dalton Chapman, Joshua Bennett, Abigail B. Ventura, Gustavo M. Canales, Muttappa Gowda, Kerianne A. Jackson, Rodante Caguiat, Amber Brown, Douglas Ganini da Silva, Zheyuan Guo, Shaheed Abdulhaqq, Lillian R. Klug, Miles Gander, Engin Yapici, Joshua Meier, Sharrol Bachas
[bioRxiv (2023): 2023-01](https://www.biorxiv.org/content/10.1101/2023.01.08.523187v4) • [data](https://github.com/AbSciBio/unlocking-de-novo-antibody-design) • [news](https://www.genengnews.com/topics/drug-discovery/antibodies/absci-eyes-ind-for-platforms-first-de-novo-antibody-within-two-years/) • [blog](https://www.science.org/content/blog-post/computing-our-way-antibodies) • commercial
**A universal deep-learning model for zinc finger design enables transcription factor reprogramming**
David M. Ichikawa, Osama Abdin, Nader Alerasool, Manjunatha Kogenaru, April L. Mueller, Han Wen, David O. Giganti, Gregory W. Goldberg, Samantha Adams, Jeffrey M. Spencer, Rozita Razavi, Satra Nim, Hong Zheng, Courtney Gionco, Finnegan T. Clark, Alexey Strokach, Timothy R. Hughes, Timothee Lionnet, Mikko Taipale, Philip M. Kim & Marcus B. Noyes
[Nat Biotechnol (2023)](https://www.nature.com/articles/s41587-022-01624-4)
**XuperNovo®/ProteinGPT**
XtalPi
[news](https://mp.weixin.qq.com/s?__biz=MzI4MzUwNjI5OQ==&mid=2247499137&sn=d8c9e006cdb131dcf5639db6824bb0e3&chksm=eb8b1e95dcfc97835268d9e66636e63a4c6eb2f6fde780a4d45180872ea8d79bbd1d29363aff) • [news2](https://mp.weixin.qq.com/s/h_mpZXnQQ_o8vSWzXl3wcQ) • [website](https://www.xtalpi.com/en/macromolecular-drug-discovery) • commercial
**Evaluating Prompt Tuning for Conditional Protein Sequence Generation**
Andrea Nathansen, Kevin Klein, Bernhard Y. Renard, Melania Nowicka, Jakub M. Bartoszewicz
[bioRxiv 2023.02.28.530492](https://www.biorxiv.org/content/10.1101/2023.02.28.530492v1) • [code](https://gitlab.com/dacs-hpi/protein-prompt-tuning)
**AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning**
Xiaopeng Xu, Tiantian Xu, Juexiao Zhou, Xingyu Liao, Ruochi Zhang, Yu Wang, Lu Zhang, Xin Gao
[bioRxiv 2023.03.17.533102](https://www.biorxiv.org/content/10.1101/2023.03.17.533102v1) • [code](https://github.com/charlesxu90/ab-gen) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/03/21/2023.03.17.533102/DC1/embed/media-1.docx) • [data](https://zenodo.org/record/7657016)
**Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs**
Buehler, Markus J.
[Patterns 4.3 (2023)](https://www.cell.com/patterns/fulltext/S2666-3899(23)00023-5) • [code](https://github.com/lamm-mit/AttentionCrossTranslation)
**ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models**
Lee, Youhan, and Hasun Yu.
[arXiv preprint arXiv:2303.16452 (2023)](https://arxiv.org/pdf/2303.16452.pdf)/[ICLR 2023](https://openreview.net/forum?id=9XAZBUfnefS)
**REXzyme: A Translation Machine for the Generation of New-to-Nature Enzymes**
Sebastian Lindner, Michael Heinzinger, Noelia Ferruz
paper coming soon • [hugging face](https://huggingface.co/AI4PD/REXzyme)
**xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein**
Bo Chen, Xingyi Cheng, Li-ao Gengyang, Shen Li, Xin Zeng, Boyan Wang, Gong Jing, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song
[bioRxiv 2023.07.05.547496](https://www.biorxiv.org/content/10.1101/2023.07.05.547496v1) • [news](https://mp.weixin.qq.com/s/XQn8je49z23UYby8pR7fkA) • [website](https://www.biomap.com/aigp-light-beta/form) • commercial
**TULIP - a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes**
Barthelemy Meynard-Piganeau, Christoph Feinauer, Martin Weigt, Aleksandra M Walczak, Thierry Mora
[bioRxiv 2023.07.19.549669](https://www.biorxiv.org/content/10.1101/2023.07.19.549669v1) • [code](https://github.com/barthelemymp/TULIP-TCR/)
**Efficient and accurate sequence generation with small-scale protein language models**
Yaiza Serrano, Sergi Roda, Victor Guallar, Alexis Molina
[bioRxiv 2023.08.04.551626](https://www.biorxiv.org/content/10.1101/2023.08.04.551626v1)
**IMPROVING ANTIBODY AFFINITY USING LABORATORY DATA WITH LANGUAGE MODEL GUIDED DESIGN**
Ben Krause, Subu Subramanian, Tom Yuan, Marisa Yang, Aaron Sato, Nikhil Naik
[bioRxiv 2023.09.13.557505](https://www.biorxiv.org/content/10.1101/2023.09.13.557505v1)
**PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling**
Tianlai Chen, Sarah Pertsemlidis, Rio Watson, Venkata Srikar Kavirayuni, Ashley Hsu, Pranay Vure, Rishab Pulugurta, Sophia Vincoff, Lauren Hong, Tian Wang, Vivian Yudistyra, Elena Haarer, Lin Zhao, Pranam Chatterjee
[arXiv:2310.03842](https://arxiv.org/abs/2310.03842) • [code](https://github.com/programmablebio/pepmlm)
**De novo generation of antibody CDRH3 with a pre-trained generative large language model**
HaoHuai He, Bing He, Lei Guan, Yu Zhao, Guanxing Chen, Qingge Zhu, Calvin Yu-Chian Chen, Ting Li, Jianhua Yao
[bioRxiv 2023.10.17.562827](https://www.biorxiv.org/content/10.1101/2023.10.17.562827v1) • [code](https://github.com/TencentAILabHealthcare/PALM) • [data](https://doi.org/10.5281/zenodo.7794583)
**NL2ProGPT: Taming Large Language Model for Conversational Protein Design**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=sFJr7okOBi)
**SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders**
Garyk Brixi, Tianzheng Ye, Lauren Hong, Tian Wang, Connor Monticello, Natalia Lopez-Barbosa, Sophia Vincoff, Vivian Yudistyra, Lin Zhao, Elena Haarer, Tianlai Chen, Sarah Pertsemlidis, Kalyan Palepu, Suhaas Bhat, Jayani Christopher, Xinning Li, Tong Liu, Sue Zhang, Lillian Petersen, Matthew P. DeLisa & Pranam Chatterjee
[Commun Biol 6, 1081 (2023)](https://www.nature.com/articles/s42003-023-05464-z) • [code](https://github.com/programmablebio/saltnpeppr)
**Binary Discriminator Facilitates GPT-based Protein Design**
Zishuo Zeng, Rufang Xu, Jin Guo, Xiaozhou Luo
[bioRxiv 2023.11.20.567789](https://www.biorxiv.org/content/10.1101/2023.11.20.567789v2) • [code](https://github.com/zishuozeng/GPT_protein_design) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/12/21/2023.11.20.567789/DC1/embed/media-1.xlsx)
**ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers**
Pascal Notin, Ruben Weitzman, Debora S Marks, Yarin Gal
[bioRxiv 2023.12.06.570473](https://www.biorxiv.org/content/10.1101/2023.12.06.570473v1) • [code](https://github.com/OATML-Markslab/ProteinNPT)
**The promises of large language models for protein design and modeling**
Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, and Peter N. Robinson
[Frontiers in Bioinformatics 3 (2023)](https://www.frontiersin.org/articles/10.3389/fbinf.2023.1304099/full)
**Conversational Drug Editing Using Retrieval and Domain Feedback**
Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao
[ICLR (2024)](https://openreview.net/forum?id=yRrPfKyJQ2) • [code](https://github.com/chao1224/ChatDrug) • [website](https://chao1224.github.io/ChatDrug)
**ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning**
A. Ghafarollahi, M.J. Buehler
[arXiv:2402.04268](https://arxiv.org/abs/2402.04268) • [code](https://github.com/lamm-mit/ProtAgents)
**Designing proteins with language models**
Ruffolo, J.A., Madani, A.
[Nat Biotechnol 42, 200–202 (2024)](https://www.nature.com/articles/s41587-024-02123-4) • review
**ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing**
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian
[arXiv:2402.16445](https://arxiv.org/abs/2402.16445) • [code](https://arxiv.org/pdf/2402.16445.pdf)
**Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins**
Moritz Ertelt, Vikram Khipple Mulligan, Jack B. Maguire, Sergey Lyskov, Rocco Moretti, Torben Schiffner, Jens Meiler, Clara T. Schoeder
[PLOS Computational Biology 20(3): e1011939](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011939) • [code](https://github.com/meilerlab/PTMPrediction)
**Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint**
Moritz Ertelt, Jens Meiler, and Clara T. Schoeder
[ACS Synth. Biol. 2024](https://pubs.acs.org/doi/10.1021/acssynbio.3c00753) • [code](https://github.com/meilerlab/PLM_restraint)
**Design of Antigen-Specific Antibody CDRH3 Sequences Using AI and Germline-Based Templates**
Toma M. Marinov, Alexandra A. Abu-Shmais, Alexis K. Janke, Ivelin S. Georgiev
[bioRxiv 2024.03.22.586241](https://www.biorxiv.org/content/10.1101/2024.03.22.586241v1.full)
**Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences**
Jeffrey A. Ruffolo, Stephen Nayfach, Joseph Gallagher, Aadyot Bhatnagar, Joel Beazer, Riffat Hussain, Jordan Russ, Jennifer Yip, Emily Hill, Martin Pacesa, Alexander J. Meeske, Peter Cameron, Ali Madani
[bioRxiv 2024.04.22.590591](https://www.biorxiv.org/content/10.1101/2024.04.22.590591v1) • [code](https://github.com/Profluent-AI/OpenCRISPR)
**Functional Protein Design with Local Domain Alignment**
Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang, Long-Kai Huang, Wenbing Huang, Wei Liu, Jianhua Yao, Yu Rong
[arXiv:2404.16866](https://arxiv.org/abs/2404.16866)
**The Continuous Language of Protein Structure**
Lukas Billera, Anton Oresten, Aron Stålmarck, Kenta Sato, Mateusz Kaduk, Ben Murrell
[bioRxiv 2024.05.11.593685](https://www.biorxiv.org/content/10.1101/2024.05.11.593685v1) • [code](https://github.com/MurrellGroup/InvariantPointAttention.jl)
**Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates**
Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang, Lei Li
[arXiv:2405.08205](https://arxiv.org/abs/2405.08205)
**A generative foundation model for antibody sequence understanding**
Justin Barton, Aretas Gaspariunas, David A Yadin, Jorge Dias, Francesca L Nice, Danielle H Minns, Olivia Snudden, Chelsea Povall, Sara Valle Tomas, Harry Dobson, James HR Farmery, Jinwoo Leem, Jacob D Galson
[bioRxiv 2024.05.22.594943](https://www.biorxiv.org/content/10.1101/2024.05.22.594943v1) • [huggingface](https://huggingface.co/alchemab)
**Decoupled Sequence and Structure Generation for Realistic Antibody Design**
Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park
[arXiv:2402.05982](https://arxiv.org/abs/2402.05982) • [code](https://github.com/lkny123/ASSD_public)
**MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor**
Li Wang, Xiangzheng Fu, Jiahao Yang, Xinyi Zhang, Xiucai Ye, Yiping Liu, Tetsuya Sakurai, Xiangxiang Zeng
[arXiv:2406.00735](https://arxiv.org/abs/2406.02610)
### 5.5 Bayesian-based
**Discovering de novo peptide substrates for enzymes using machine learning**
Lorillee Tallorin, JiaLei Wang, Woojoo E. Kim, Swagat Sahu, Nicolas M. Kosa, Pu Yang, Matthew Thompson, Michael K. Gilson, Peter I. Frazier, Michael D. Burkart & Nathan C. Gianneschi
[Nature communications 9.1 (2018)](https://www.nature.com/articles/s41467-018-07717-6) • [code](https://github.com/peter-i-frazier/pool)
**Biological Sequences Design using Batched Bayesian Optimization**
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle, Lucy Colwell
[Machine Learning and the Physical Sciences Workshop (NeurIPS 2019)](https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_141.pdf)
**Lattice protein design using Bayesian learning**
Takahashi, Tomoei, George Chikenji, and Kei Tokita.
[arXiv:2003.06601](https://arxiv.org/abs/2003.06601)/[Physical Review E 104.1 (2021): 014404](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.104.014404)
**Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences**
Ziyue Yang, Katarina A Milas, Andrew D White
[bioRxiv 2022.08.05.502972](https://www.biorxiv.org/content/10.1101/2022.08.05.502972v2) • [code](https://github.com/ur-whitelab/wazy) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/08/06/2022.08.05.502972/DC1/embed/media-1.pdf) • [Colab](https://colab.research.google.com/github/ur-whitelab/wazy/blob/master/colab/WazyAlphaFold2.ipynb)
**AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation**
Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, Derrick-Goh-Xin Deik, Philippe A. Robert, Victor Greiff, Eva Smorodina, Puneet Rawat, Kamil Dreczkowski, Rahmad Akbar, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Amos Storkey, Haitham Bou-Ammar
[arXiv preprint (2022)](https://arxiv.org/abs/2201.12570)/[Cell Reports Methods (2023): 100374](https://www.sciencedirect.com/science/article/pii/S2667237522002764)
**Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders**
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
[ICML 2022](https://arxiv.org/abs/2203.12742) • [code](https://github.com/samuelstanton/lambo)
**Statistical Mechanics of Protein Design**
Takahashi, Tomoei, George Chikenji, and Kei Tokita.
[arXiv preprint arXiv:2205.03696 (2022)](https://arxiv.org/abs/2205.03696)
**PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design**
Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho
[arXiv:2210.04096](https://arxiv.org/abs/2210.04096)
**A probabilistic view of protein stability, conformational specificity, and design**
Jacob A. Stern, Tyler J. Free, Kimberlee L. Stern, Spencer Gardiner, Nicholas A. Dalley, Bradley C. Bundy, Joshua L. Price, David Wingate, Dennis Della Corte
[bioRxiv 2022.12.28.521825](https://www.biorxiv.org/content/10.1101/2022.12.28.521825v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/12/30/2022.12.28.521825/DC1/embed/media-1.pdf)
**Design of antimicrobial peptides containing non-proteinogenic amino acids using multi-objective Bayesian optimisation**
Murakami Y, Ishida S, Demizu Y, Terayama K.
[ChemRxiv. Cambridge: Cambridge Open Engage; 2023](https://chemrxiv.org/engage/chemrxiv/article-details/645f192ef2112b41e97720f3) • [code](https://github.com/ycu-iil/MODAN)
**Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2**
Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse, Ajitha Rajan, Diego A. Oyarzún, Javier Antonio Alfaro
[arXiv:2305.11194](https://arxiv.org/abs/2305.11194) • [code](https://github.com/aryopg/vaxformer)
**Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization**
Yanzheng Wang, Boyue Wang, Tianyu Shi, Jie Fu, Yi Zhou, Zhizhuo Zhang
[bioRxiv 2023.11.06.565922](https://www.biorxiv.org/content/10.1101/2023.11.06.565922v1)
**Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design**
Negin Manshour, Fei He, Duolin Wang, Dong Xu
[NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. 2023](https://openreview.net/forum?id=CsjGuWD7hk)
### 5.6 RL-based
**Model-based reinforcement learning for biological sequence design**
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell
[International conference on learning representations. 2019](https://openreview.net/forum?id=HklxbgBKvr&fileGuid=3xgr169o12oUrbxS)
**Structured Q-learning For Antibody Design**
Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Liu Furui, Jun Wang, Jan Peters, Haitham Bou Ammar
[arXiv preprint arXiv:2209.04698 (2022)](https://arxiv.org/abs/2209.04698)
**Protein Sequence Design in a Latent Space via Model-based Reinforcement Learning**
Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyunjoo Ro, Ho Min Kim, Meeyoung Cha
[ICLR 2023](https://openreview.net/forum?id=OhjGzRE5N6o)/[NeurIPS 2022](https://www.mlsb.io/papers_2022/Protein_Sequence_Design_in_a_Latent_Space_via_Model_based_Reinforcement_Learning.pdf) • [Supplementary](https://openreview.net/attachment?id=OhjGzRE5N6o&name=supplementary_material)
**Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization**
Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon
[arXiv:2209.06259](https://arxiv.org/abs/2209.06259)/[NeurIPS 2022](https://www.mlsb.io/papers_2022/Designing_Biological_Sequences_via_Meta_Reinforcement_Learning_and_Bayesian_Optimization.pdf) • [poster](https://nips.cc/media/PosterPDFs/NeurIPS%202022/58993.png?t=1669588933.2017226)
**Self-play reinforcement learning guides protein engineering**
Yi Wang, Hui Tang, Lichao Huang, Lulu Pan, Lixiang Yang, Huanming Yang, Feng Mu & Meng Yang
[Nature Machine Intelligence (2023)](https://www.nature.com/articles/s42256-023-00691-9) • [code](https://github.com/melobio/EvoPlay)
**Curiosity Driven Protein Sequence Generation via Reinforcement Learning**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=tPjVRmHqCg)
**Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design**
Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Roland Mertelsmann, Gabriel Kalweit, Joschka Boedecker
[arXiv:2401.05341](https://arxiv.org/abs/2401.05341)
**Peptide Vaccine Design by Evolutionary Multi-Objective Optimization**
Dan-Xuan Liu, Yi-Heng Xu, Chao Qian
[arXiv:2406.05743](https://arxiv.org/abs/2406.05743)
### 5.7 Flow-based
**Biological Sequence Design with GFlowNets**
Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
[arXiv preprint arXiv:2203.04115 (2022)](https://arxiv.org/abs/2203.04115) • [lecture](https://www.youtube.com/watch?v=YRbFDThaAmo)
### 5.8 RNN-based
**Deep learning to design nuclear-targeting abiotic miniproteins**
Carly K. Schissel, Somesh Mohapatra, Justin M. Wolfe, Colin M. Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna A. Wood, Annika B. Malmberg, Andrei Loas, Rafael Gómez-Bombarelli & Bradley L. Pentelute
[Nature Chemistry 13.10 (2021)](https://www.nature.com/articles/s41557-021-00766-3) • [code](https://github.com/learningmatter-mit/peptimizer)
**Recurrent neural network model for constructive peptide design**
Müller, Alex T., Jan A. Hiss, and Gisbert Schneider.
[Journal of chemical information and modeling 58.2 (2018)](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00414)
**Machine learning designs non-hemolytic antimicrobial peptides**
Alice Capecchi, Xingguang Cai, Hippolyte Personne, Thilo Köhler, Christian van Delden, and Jean-Louis Reymond
[Chemical Science 12.26 (2021)](https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc01713f)
**Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides**
Duy Phuoc Tran, Seiichi Tada, Akiko Yumoto, Akio Kitao, Yoshihiro Ito, Takanori Uzawa & Koji Tsuda
[Scientific reports 11.1 (2021)](https://www.nature.com/articles/s41598-021-90245-z)
**De novo antioxidant peptide design via machine learning and DFT studies**
Parsa Hesamzadeh, Abdolvahab Seif, Kazem Mahmoudzadeh, Mokhtar Ganjali Koli, Amrollah Mostafazadeh, Kosar Nayeri, Zohreh Mirjafary & Hamid Saeidian
[Scientific Reports 14.1 (2024)](https://www.nature.com/articles/s41598-024-57247-z) • [code](https://github.com/mephisto121/DeepGenAntiOxidantPeptide)
### 5.9 LSTM-based
**Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria**
Deepesh Nagarajan, Tushar Nagarajan, Natasha Roy, Omkar Kulkarni, Sathyabaarathi Ravichandran, Madhulika Mishra
Dipshikha Chakravortty, Nagasuma Chandra
[Journal of Biological Chemistry 293.10 (2018)](https://www.jbc.org/article/S0021-9258(20)40390-4/fulltext)
**Deep learning enables the design of functional de novo antimicrobial proteins**
Javier Caceres-Delpiano, Roberto Ibañez, Patricio Alegre, Cynthia Sanhueza, Romualdo Paz-Fiblas, Simon Correa, Pedro Retamal, Juan Cristóbal Jiménez, Leonardo Álvarez
[bioRxiv (2020)](https://www.biorxiv.org/content/10.1101/2020.08.26.266940v1.full)
**ECNet is an evolutionary context-integrated deep learning framework for protein engineering**
Yunan Luo, Guangde Jiang, Tianhao Yu, Yang Liu, Lam Vo, Hantian Ding, Yufeng Su, Wesley Wei Qian, Huimin Zhao & Jian Peng
[Nature communications 12.1 (2021)](https://www.nature.com/articles/s41467-021-25976-8)
**Deep learning for novel antimicrobial peptide design**
Wang, Christina, Sam Garlick, and Mire Zloh.
[Biomolecules 11.3 (2021)](https://www.mdpi.com/2218-273X/11/3/471)
**Antibody design using LSTM based deep generative model from phage display library for affinity maturation**
Koichiro Saka, Taro Kakuzaki, Shoichi Metsugi, Daiki Kashiwagi, Kenji Yoshida, Manabu Wada, Hiroyuki Tsunoda & Reiji Teramoto
[Scientific reports 11.1 (2021)](https://www.nature.com/articles/s41598-021-85274-7)
**In silico proof of principle of machine learning-based antibody design at unconstrained scale**
Akbar, Rahmad, et al.
[Mabs. Vol. 14. No. 1. Taylor & Francis, 2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986205/pdf/KMAB_14_2031482.pdf) • [code](https://github.com/csi-greifflab/manuscript_insilico_antibody_generation)
**Large-scale design and refinement of stable proteins using sequence-only models**
Jedediah M. Singer , Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Alexander Zaitzeff, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, Eric Klavins
[PloS one 17.3 (2022)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265020) • [code](https://zenodo.org/record/4906529)
**Deep-learning based bioactive therapeutic peptides generation and screening**
Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, Yang Jiao, Yang Yang, Yi Pan, Xuli Wu, John Z.H. Zhang
[bioRxiv 2022.11.14.516530](https://www.biorxiv.org/content/10.1101/2022.11.14.516530v1) • [code](https://github.com/haiping1010/New_peptide_iteration) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/11/16/2022.11.14.516530/DC1/embed/media-1.pdf)
**Deep-learning based bioactive peptides generation and screening against Xanthine oxidase**
Haiping Zhang, Konda Mani Saravanan, John Z.H. Zhang, Xuli Wu
[bioRxiv 2023.01.11.523536](https://www.biorxiv.org/content/10.1101/2023.01.11.523536v1)
**Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening**
Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, Yang Jiao, Yang Yang, Yi Pan, Xuli Wu, and John Z. H. Zhang
[Journal of Chemical Information and Modeling 63.3 (2023)](https://pubs.acs.org/doi/10.1021/acs.jcim.2c01485) • [code](https://github.com/haiping1010/New_peptide_iteration/tree/master/iteration_main_protease_Antiviral_pep)
### 5.10 Autoregressive-models
**Efficient generative modeling of protein sequences using simple autoregressive models**
Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi & Martin Weigt
[Nature communications 12.1 (2021): 1-11](https://www.nature.com/articles/s41467-021-25756-4) • [code](https://github.com/pagnani/ArDCA.jl)
**Conformal prediction for the design problem**
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan
[arXiv:2202.03613v4](https://arxiv.org/abs/2202.03613) • [code](https://github.com/clarafy/conformal-for-design)
### 5.11 Boltzmann-machine-based
**How pairwise coevolutionary models capture the collective residue variability in proteins?**
Figliuzzi, Matteo, Pierre Barrat-Charlaix, and Martin Weigt.
[Molecular biology and evolution 35.4 (2018): 1018-1027](https://academic.oup.com/mbe/article/35/4/1018/4815777) • [code](https://github.com/matteofigliuzzi/bmDCA)
**A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences**
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijević
[arXiv:2210.10838](https://arxiv.org/abs/2210.10838) • [slides](https://drive.google.com/file/d/1spTU-iZ4EEq8ZICRHBw8CstpYQXCxMy8/view)
**Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment**
Cyril Malbranke, William Rostain, Florence Depardieu, Simona Cocco, Remi Monasson, David Bikard
[bioRxiv 2023.03.20.533501](https://www.biorxiv.org/content/10.1101/2023.03.20.533501v1) • [code](https://github.com/CyrilMa/DesignCas9WithCLD) • [Supplementary](https://www.biorxiv.org/content/10.1101/2023.03.20.533501v1.supplementary-material)
**Protein Discovery with Discrete Walk-Jump Sampling**
Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi
[arXiv:2306.12360](https://arxiv.org/abs/2306.12360)/[ICLR 2024 under review](https://openreview.net/forum?id=zMPHKOmQNb) • [code](https://github.com/Genentech/walk-jump) • [lecture](https://www.youtube.com/watch?v=r28m5Vk77Wk)
### 5.12 Diffusion-based
**denoising-diffusion-protein-sequence**
Zhangzhi Peng
Paper unavailable • [github](https://github.com/pengzhangzhi/protein-sequence-diffusion-model)
**Protein Design with Guided Discrete Diffusion**
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
[arXiv:2305.20009](https://arxiv.org/abs/2305.20009) • [code](https://github.com/ngruver/NOS) • [lecture](https://www.youtube.com/watch?v=Hm8Z0SIyLqw)
**PRO-LDM: Protein Sequence Generation with Conditional Latent Diffusion Models**
Zixuan Jiang, Sitao Zhang, Rundong Huang, Shaoxun Mo, Letao Zhu, Peiheng Li, Ziyi Zhang, Xi Chen, Yunfei Long, Renjing Xu, Rui Qing
[bioRxiv 2023.08.22.554145](https://www.biorxiv.org/content/10.1101/2023.08.22.554145v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/08/23/2023.08.22.554145/DC1/embed/media-1.pdf)
**Protein generation with evolutionary diffusion: sequence is all you need**
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, Kevin K Yang
[bioRxiv 2023.09.11.556673](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) • [code](https://github.com/microsoft/evodiff) • [data](https://zenodo.org/record/8045076) • [lecture](https://www.youtube.com/watch?v=e1e-_SkyNjw)
**AntiBARTy Diffusion for Property Guided Antibody Design**
Jordan Venderley
[arXiv:2309.13129](https://arxiv.org/abs/2309.13129)
**ProT-Diff: A Modularized and Efficient Approach to De Novo Generation of Antimicrobial Peptide Sequences through Integration of Protein Language Model and Diffusion Model**
Xue-Fei Wang, Jing-Ya Tang, Han Liang, Jing Sun, Sonam Dorje, Bo Peng, Xu-Wo Ji, Zhe Li, Xian-En Zhang, Dian-Bing Wang
[bioRxiv 2024.02.22.581480](https://www.biorxiv.org/content/10.1101/2024.02.22.581480v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/02/23/2024.02.22.581480/DC1/embed/media-1.docx)
**TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation**
Lin Zongying, Li Hao, Lv Liuzhenghao, Lin Bin, Zhang Junwu, Chen Calvin Yu-Chian, Yuan Li, Tian Yonghong
[arXiv:2402.17156](https://arxiv.org/abs/2402.17156) • [code](https://github.com/Linzy19/TaxDiff)
**Diffusion on language model embeddings for protein sequence generation**
Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga Kardymon, Dmitry Vetrov
[arXiv:2403.03726](https://arxiv.org/abs/2403.03726)
**AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation**
Tianlai Chen, Pranay Vure, Rishab Pulugurta, Pranam Chatterjee
[bioRxiv 2024.03.03.583201](https://www.biorxiv.org/content/10.1101/2024.03.03.583201v1)
**Atomically accurate de novo design of single-domain antibodies**
Nathaniel R. Bennett, Joseph L. Watson, Robert J. Ragotte, Andrew J. Borst, DeJenae L. See, Connor Weidle, Riti Biswas, Ellen L. Shrock, Philip J. Y. Leung, Buwei Huang, Inna Goreshnik, Russell Ault, Kenneth D. Carr, Benedikt Singer, Cameron Criswell, Dionne Vafeados, Mariana Garcia Sanchez, Ho Min Kim, Susana Vazquez Torres, Sidney Chan, David Baker
[bioRxiv 2024.03.14.585103](https://www.biorxiv.org/content/10.1101/2024.03.14.585103v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/03/18/2024.03.14.585103/DC1/embed/media-1.pdf)
**Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models**
Viet Thanh Duy Nguyen, Nhan Nguyen, Truong Son Hy
[bioRxiv 2024.04.17.589997](https://www.biorxiv.org/content/10.1101/2024.04.17.589997v1) • [code](https://github.com/HySonLab/Protein_Redesign)
### 5.13 GNN-based
**Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins**
Markus J. Buehler
[arXiv:2305.04934](https://arxiv.org/abs/2305.04934) • [code](https://github.com/lamm-mit/MateriomicTransformer)
### 5.14 Score-based
**Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering**
Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel, Paul J. Zurek, Tanja Knaus, Elie Patel, Daniel Boros, Friedrich-Maximilian Weberling, Matthew R. A. Kenneth, Oskar J. Klein, Elliot J. Medcalf, Jacob Moss, Michael Herger, Tomasz S. Kaminski, Francesco G. Mutti, Pietro Lio, Florian Hollfelder
[bioRxiv (2024.04.08)](https://www.biorxiv.org/content/10.1101/2024.04.08.588565v1.full.pdf)
**Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences**
Minsu Kim, Federico Berto, Sungsoo Ahn, Jinkyoo Park
[arXiv:2306.03111](https://arxiv.org/abs/2306.03111) • [code](https://github.com/kaist-silab/bootgen)
## 6. Function to Structure
> These models generate protein structures(including side chains) from expected function or recover a part of protein structures(aka. **inpainting**)
### 6.1 LSTM-based
**One-sided design of protein-protein interaction motifs using deep learning**
Syrlybaeva, Raulia, and Eva-Maria Strauch.
[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.03.30.486144v2) • [code](https://github.com/strauchlab/iNNterfaceDesign) • [our notes](https://zhuanlan.zhihu.com/p/521613546) • [lecture](https://www.youtube.com/watch?v=bSWkXy56rt8)
### 6.2 Diffusion-based
**Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models**
Namrata Anand, Tudor Achim
[GitHub (2022)](https://nanand2.github.io/proteins/)/[arXiv (2022)](https://arxiv.org/abs/2205.15019) • [our notes](https://zhuanlan.zhihu.com/p/520488133) • [lecture](https://www.youtube.com/watch?v=i8fGzddGbU8)
**Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures**
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma
[bioRxiv 2022.07.10.499510](https://www.biorxiv.org/content/10.1101/2022.07.10.499510v5)/[ICML (2023)](https://icml-compbio.github.io/2023/papers/WCBICML2023_paper143.pdf) • [code](https://github.com/luost26/diffab) • [hugging face](https://huggingface.co/spaces/luost26/DiffAb)
**Illuminating protein space with a programmable generative model**
John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, Gevorg Grigoryan
[Generate Biomedicines Preprint](https://cdn.generatebiomedicines.com/assets/ingraham2022.pdf)/[bioRxiv 2022.12.01.518682](https://www.biorxiv.org/content/10.1101/2022.12.01.518682v1)/[Nature (2023)](https://www.nature.com/articles/s41586-023-06728-8) • [website](https://generatebiomedicines.com/chroma) • [news](https://www.nature.com/articles/s41587-023-01705-y) • [code](https://github.com/generatebio/chroma) • [colab](https://colab.research.google.com/github/generatebio/chroma/blob/main/notebooks/ChromaTutorial.ipynb) • commercial
**Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction**
Zuobai Zhang, Minghao Xu, Aurélie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
[arXiv:2301.12068](https://arxiv.org/abs/2301.12068) • [code](https://github.com/DeepGraphLearning/SiamDiff)
**TRDiffusion**
[TIANRANG XLab](https://xlab.tianrang.com/)
[news](https://mp.weixin.qq.com/s/9rJ6IoJbf6cvz3UqE-rpIg) • [website](https://xlab.tianrang.com/xCREATOR) • commercial
**An all-atom protein generative model**
Alexander E Chu, Lucy Cheng, Gina El Nesr, Minkai Xu, Po-Ssu Huang
[bioRxiv 2023.05.24.542194](https://www.biorxiv.org/content/10.1101/2023.05.24.542194v1) • [code](https://github.com/alexechu/protpardelle)
**DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing**
Yangtian Zhan, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang
[arxiv 2023.06.01](https://arxiv.org/abs/2306.01794) • [code](https://github.com/DeepGraphLearning/DiffPack)
**AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies**
Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Lian, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
[arXiv:2308.05027](https://arxiv.org/abs/2308.05027) • [lecture](https://www.youtube.com/watch?v=95w0Ht3m0JY)
**Generative Diffusion Models for Antibody Design, Docking, and Optimization**
Zhangzhi Peng, Chenchen Han, Xiaohan Wang, Dapeng Li, Fajiie Yuan
[bioRxiv 2023.09.25.559190](https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1) • [code](https://github.com/pengzhangzhi/ab_opt) • [website](https://pengzhangzhi.github.io/ab_opt_homepage/)
**Bridging Sequence and Structure: Latent Diffusion for Conditional Protein Generation**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=DP4NkPZOpD)
**Guiding diffusion models for antibody sequence and structure co-design with developability properties**
Amelia Villegas-Morcillo, Jana M. Weber, Marcel J.T. Reinders
[bioRxiv 2023.11.22.568230](https://www.biorxiv.org/content/10.1101/2023.11.22.568230v1)/[NeurIPS 2023 Generative AI and Biology Workshop](https://openreview.net/forum?id=bPcgbKDCUQ) • [code](https://github.com/amelvim/antibody-diffusion-properties)
**A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation**
Yongkang Wang, Xuan Liu, Feng Huang, Zhankun Xiong, Wen Zhang
[arXiv:2312.15665](https://arxiv.org/abs/2312.15665) • [code](https://github.com/wyky481l/MMCD)
**Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion**
Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani
[arXiv:2401.06151](https://arxiv.org/abs/2401.06151) • [code](https://github.com/Profluent-Internships/MMDiff)
**Proteus: exploring protein structure generation for enhanced designability and efficiency**
Chentong Wang, Yannan Qu, Zhangzhi Peng, Yukai Wang, Hongli Zhu, Dachuan Chen, Longxing Cao
[bioRxiv 2024.02.10.579791](https://www.biorxiv.org/content/10.1101/2024.02.10.579791v2)
**Full-Atom Peptide Design with Geometric Latent Diffusion**
Xiangzhe Kong, Wenbing Huang, Yang Liu
[arXiv:2402.13555](https://arxiv.org/abs/2402.13555)
**A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation**
R Vishva Saravanan, Soham Choudhuri, Bhaswar Ghosh
[bioRxiv 2024.03.14.584934](https://www.biorxiv.org/content/10.1101/2024.03.14.584934v1) • [code](https://github.com/BhaswarGhoshLab/HYDRA) • [dataset](http://huanglab.phys.hust.edu.cn/pepbdb/)
**Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization**
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu
[arXiv:2403.16576](https://arxiv.org/abs/2403.16576)
**HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures**
Xuezhi Xie, Pedro A Valiente, Jisun Kim, and Philip M Kim
[ACS Central Science (2024)](https://pubs.acs.org/doi/full/10.1021/acscentsci.3c01488) • [code](https://github.com/xxiexuezhi/HelixDiff)
**Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner**
Yue Hu, Feng Tao, Jun Wen Lan, Jing Zhang
[bioRxiv 2024.04.25.587828](https://www.biorxiv.org/content/10.1101/2024.04.25.587828v1) • [code](https://github.com/YueHuLab/AlphaPanda)
**Target-Specific De Novo Peptide Binder Design with DiffPepBuilder**
Fanhao Wang, Yuzhe Wang, Laiyi Feng, Changsheng Zhang, Luhua Lai
[arXiv:2405.00128](https://arxiv.org/abs/2405.00128) • [code](https://github.com/YuzheWangPKU/DiffPepBuilder)
**Improving Antibody Design with Force-Guided Sampling in Diffusion Models**
Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò
[arXiv:2406.05832](https://arxiv.org/abs/2406.05832)
### 6.3 RoseTTAFold-based
**Deep learning methods for designing proteins scaffolding functional sites** / **Scaffolding protein functional sites using deep learning**
Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Ivan Anishchenko, Minkyung Baek, Joseph L. Watson, Jung Ho Chun, Lukas F. Milles, Justas Dauparas, Marc Expòsit, Wei Yang, Amijai Saragovi, Sergey Ovchinnikov, David Baker
[bioRxiv(2021)](https://europepmc.org/article/ppr/ppr419387)/[Science(2022)](https://www.science.org/doi/10.1126/science.abn2100) • [RFDesign](https://github.com/RosettaCommons/RFDesign) • [our notes](https://zhuanlan.zhihu.com/p/477854488) • [lecture](https://www.youtube.com/watch?v=-EJ8SXTBin0) • [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold) • [Supplementary](https://www.science.org/doi/suppl/10.1126/science.abn2100/suppl_file/science.abn2100_sm.pdf), [Other Supplementary](https://www.science.org/doi/suppl/10.1126/science.abn2100/suppl_file/science.abn2100_data_s1_and_s2.zip)
**Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models** / **De novo design of protein structure and function with RFdiffusion**
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, David Baker
[Bakerlab Preprint](https://www.bakerlab.org/wp-content/uploads/2022/11/Diffusion_preprint_12012022.pdf)/[bioRxiv 2022.12.09.519842](https://www.biorxiv.org/content/10.1101/2022.12.09.519842v2)/[Nature (2023)](https://www.nature.com/articles/s41586-023-06415-8) • [news](https://www.bakerlab.org/2022/11/30/diffusion-model-for-protein-design/), [news2](https://www.ipd.uw.edu/2023/03/rf-diffusion-now-free-and-open-source/), [news3](https://www.nature.com/articles/d41586-023-02227-y) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/12/10/2022.12.09.519842/DC1/embed/media-1.pdf) • [lecture](https://www.youtube.com/watch?v=wIHwHDt2NoI), [lecture2](https://www.youtube.com/watch?v=828WPIIOwaA) • [RFdiffusion:code](https://github.com/RosettaCommons/RFdiffusion), [Colab](https://colab.research.google.com/github/sokrypton/ColabDesign/blob/v1.1.1/rf/examples/diffusion.ipynb) • [blog](https://www.science.org/content/blog-post/protein-design-ai-way)
**De novo design of high-affinity protein binders to bioactive helical peptides**
Susana Vázquez Torres, Philip J. Y. Leung, Isaac D. Lutz, Preetham Venkatesh, Joseph L Watson, Fabian Hink, Huu-Hien Huynh, Andy Hsien-Wei Yeh, David Juergens, Nathaniel R. Bennett, Andrew N. Hoofnagle, Eric Huang, Michael J. MacCoss, Marc Expòsit, Gyu Rie Lee, Elif Nihal Korkmaz, Jeff Nivala, Lance Stewart, Joseph M. Rodgers, David Baker
[bioRxiv 2022.12.10.519862](https://www.biorxiv.org/content/10.1101/2022.12.10.519862v1)/[Nature (2023)](https://www.nature.com/articles/s41586-023-06953-1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/12/10/2022.12.10.519862/DC1/embed/media-1.pdf)
**Joint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space Diffusion**
Sidney Lyayuga Lisanza, Jacob Merle Gershon, Sam Wayne Kenmore Tipps, Lucas Arnoldt, Samuel Hendel, Jeremiah Nelson Sims, Xinting Li, David Baker
[bioRxiv 2023.05.08.539766](https://www.biorxiv.org/content/10.1101/2023.05.08.539766v1) • [code](https://github.com/RosettaCommons/protein_generator#proteingenerator-generate-sequence-structure-pairs-with-rosettafold) • [hugging face](https://huggingface.co/spaces/merle/PROTEIN_GENERATOR) • [lecture](https://www.youtube.com/watch?v=bS71K2U0amA)
**The structural landscape of the immunoglobulin fold by large-scale de novo design**
Jorge Roel-Touris, Lourdes Carcelen, Enrique Marcos
[bioRxiv 2023.10.03.560637](https://www.biorxiv.org/content/10.1101/2023.10.03.560637v1)/[Protein Science (2024)](https://onlinelibrary.wiley.com/doi/10.1002/pro.4936) • [Supplementary](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.4936&file=pro4936-sup-0001-supinfo.docx) • [code](https://github.com/JorgeRoel/betasandwich) • [data](https://zenodo.org/record/8380285)
**Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom**
Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A Sutherland, Andrew Hitchcock, C Neil Hunter, Minkyung Baek, Frank DiMaio, David Baker
[bioRxiv 2023.10.09.561603](https://www.biorxiv.org/content/10.1101/2023.10.09.561603v1)/[Science](https://www.science.org/doi/10.1126/science.adl2528) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/10/09/2023.10.09.561603/DC1/embed/media-1.pdf) • [code](https://github.com/baker-laboratory/RoseTTAFold-All-Atom)
**Amalga: Designable Protein Backbone Generation with Folding and Inverse Folding Guidance**
Shugao Chen, Ziyao Li, Xiangxiang Zeng, Guolin Ke
[bioRxiv 2023.11.07.565939](https://www.biorxiv.org/content/10.1101/2023.11.07.565939v1)
**Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning**
Karla M Castro, Joseph L Watson, Jue Wang, Joshua Southern, Reyhaneh Ayardulabi, Sandrine Georgeon, Stephane Rosset, David Baker, Bruno E Correia
[bioRxiv 2024.05.07.592871](https://www.biorxiv.org/content/10.1101/2024.05.07.592871v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/05/10/2024.05.07.592871/DC1/embed/media-1.pdf)
**Diversifying de novo TIM barrels by hallucination**
Beck, Julian, Sooruban Shanmugaratnam, and Birte Höcker.
[Protein Science 33.6 (2024)](https://onlinelibrary.wiley.com/doi/10.1002/pro.5001)
**De novo designed proteins neutralize lethal snake venom toxins**
Susana Vázquez Torres, Melisa Benard Valle, Stephen P. Mackessy, Stefanie K. Menzies, Nicholas R. Casewell, Shirin Ahmadi, Nick J. Burlet, Edin Muratspahić, Isaac Sappington, Max D.Overath, Esperanza Rivera-de-Torre, Jann Ledergerber, Andreas H. Laustsen, Kim Boddum, Asim K.Bera, Alex Kang,Evans Brackenbrough, Iara A. Cardoso, Edouard P. Crittenden, Rebecca J.Edge, Justin Decarreau, Robert J. Ragotte, Arvind S. Pillai, Mohamad Abedi, Hannah L. Han,Stacey R. Gerben, Analisa Murray, Rebecca Skotheim, Lynda Stuart, Lance Stewart, Thomas J.A. Fryer, Timothy P. Jenkins, David Baker
[PREPRINT (Version 1) available at Research Square](https://www.researchsquare.com/article/rs-4402792/v1)
### 6.4 CNN-based
**De Novo Design of Site-specific Protein Binders Using Surface Fingerprints**
Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E. Correia
[Protein Science 30.CONF (2021)](https://infoscience.epfl.ch/record/290120)/[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.06.16.496402v2)/[Nature (2023)](https://www.nature.com/articles/s41586-023-05993-x) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2022/06/17/2022.06.16.496402/DC1/embed/media-1.pdf) • [masif_seed](https://github.com/LPDI-EPFL/masif_seed) • [masif](https://github.com/LPDI-EPFL/masif) • [lecture](https://www.youtube.com/watch?v=4S4J7gbhAa0)
**Targeting protein-ligand neosurfaces using a generalizable deep learning approach**
Anthony Marchand, Stephen Buckley, Arne Schneuing, Martin Pacesa, Pablo Gainza, Evgenia Elizarova, Rebecca Manuela Neeser, Pao-Wan Lee, Luc Reymond, Maddalena Elia, Leo Scheller, Sandrine Georgeon, Joseph Schmidt, Philippe Schwaller, Sebastian Josef Maerkl, Michael Bronstein, Bruno Emmanuel Correia
[bioRxiv 2024.03.25.585721](https://www.biorxiv.org/content/10.1101/2024.03.25.585721v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/03/28/2024.03.25.585721/DC1/embed/media-1.pdf) • [code](https://github.com/LPDI-EPFL/masif-neosurf)
### 6.5 GNN-based
**Iterative refinement graph neural network for antibody sequence-structure co-design**
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola
[arXiv preprint arXiv:2110.04624 (2021)](https://arxiv.org/abs/2110.04624) • [RefineGNN](https://github.com/wengong-jin/RefineGNN) • [lecture1](https://www.youtube.com/watch?v=uDTccbg_Ai4), [lecture2](https://www.youtube.com/watch?v=px5iC79jtfc)
**Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model**
Fu, Tianfan, and Jimeng Sun.
[Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022](https://dl.acm.org/doi/abs/10.1145/3534678.3539285) • [code](https://github.com/futianfan/energy_model4antibody_design)
**Conditional Antibody Design as 3D Equivariant Graph Translation**
Xiangzhe Kong, Wenbing Huang, Yang Liu
[ICLR 2023](https://openreview.net/forum?id=LFHFQbjxIiP)/[arXiv:2208.06073](https://arxiv.org/abs/2208.06073)
**End-to-End Full-Atom Antibody Design**
Xiangzhe Kong, Wenbing Huang, Yang Liu
[arXiv:2302.00203](https://arxiv.org/abs/2302.00203) • [code](https://github.com/THUNLP-MT/dyMEAN)
**AbODE: Ab Initio Antibody Design using Conjoined ODEs**
Yogesh Verma, Markus Heinonen, Vikas Garg
[arXiv:2306.01005](https://arxiv.org/abs/2306.01005)
**Joint Design of Protein Sequence and Structure based on Motifs**
Zhenqiao Song, Yunlong Zhao, Yufei Song, Wenxian Shi, Yang Yang, Lei Li
[arXiv:2310.02546](https://arxiv.org/abs/2310.02546)
**De novo protein design using geometric vector field networks**
Weian Mao, Muzhi Zhu, Zheng Sun, Shuaike Shen, Lin Yuanbo Wu, Hao Chen, Chunhua Shen
[arXiv:2310.11802](https://arxiv.org/abs/2310.11802)/[ICLR 2024 under review](https://openreview.net/forum?id=9UIGyJJpay)
**A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications**
Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Yang Liu, Yu Rong, Wenbing Huang
[arXiv:2403.00485](https://arxiv.org/abs/2403.00485) • review
**GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation**
Haitao LIN, Lirong Wu, Huang Yufei, Yunfan Liu, Odin Zhang, Yuanqing Zhou, Rui Sun, Stan Z Li
[bioRxiv 2024.05.15.594274](https://www.biorxiv.org/content/10.1101/2024.05.15.594274v1) • [code](https://github.com/Edapinenut/GeoAB)
**Topological Neural Networks go Persistent, Equivariant, and Continuous**
Yogesh Verma, Amauri H Souza, Vikas Garg
[arXiv:2406.03164](https://arxiv.org/abs/2406.03164) • [code](https://github.com/Aalto-QuML/TopNets)
### 6.6 Transformer-based
**Protein Sequence and Structure Co-Design with Equivariant Translation**
Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang
[arXiv:2210.08761](https://arxiv.org/abs/2210.08761)/[ICLR 2023](https://openreview.net/forum?id=pRCMXcfdihq) • [Supplementary](https://openreview.net/attachment?id=pRCMXcfdihq&name=supplementary_material) • [code](https://github.com/shichence/ProtSeed)
**Deep Learning for Flexible and Site-Specific Protein Docking and Design**
Matt McPartlon, Jinbo Xu
[bioRxiv 2023.04.01.535079](https://www.biorxiv.org/content/10.1101/2023.04.01.535079v1) • [code](https://github.com/drorlab/DIPS)
**Full-Atom Protein Pocket Design via Iterative Refinement**
Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik, Qi Liu
[arXiv:2310.02553](https://arxiv.org/abs/2310.02553) • [code](https://github.com/zaixizhang/FAIR)
**Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design**
Anonymous
[ICLR 2024 under review](https://openreview.net/forum?id=Dr4qD9bzZd)
**Fast and accurate modeling and design of antibody-antigen complex using tFold**
Fandi Wu, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin, Fan Yang, Ningqiao Huang, Yang Xiao, Rubo Wang, Huaxian Jia, Yu Rong, Yuyi Liu, Houtim Lai, Tingyang Xu, Wei Liu, Peilin Zhao, Jianhua Yao
[bioRxiv 2024.02.05.578892](https://www.biorxiv.org/content/10.1101/2024.02.05.578892v1) • [website](https://drug.ai.tencent.com/cn)
**PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets**
Zhang Zaixi, Wanxiang Shen, Qi Liu, Marinka Zitnik
[bioRxiv 2024.02.25.581968](https://www.biorxiv.org/content/10.1101/2024.02.25.581968v1) • [code](https://github.com/zaixizhang/PocketGen) • [website](https://zitniklab.hms.harvard.edu/projects/PocketGen/)
### 6.7 MLP-based
**Protein Complex Invariant Embedding with Cross-Gate MLP is A One-Shot Antibody Designer**
Cheng Tan, Zhangyang Gao, Stan Z. Li
[arXiv:2305.09480](https://arxiv.org/abs/2305.09480)
### 6.8 Flow-based
**Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design**
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola
[arXiv:2402.04997](https://arxiv.org/abs/2402.04997) • [code](https://github.com/andrew-cr/discrete_flow_models) • [lecture](https://www.youtube.com/watch?v=yzc29vhM2Aw)
**PPFlow: Target-Aware Peptide Design with Torsional Flow Matching**
Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li
[bioRxiv 2024.03.07.583831](https://www.biorxiv.org/content/10.1101/2024.03.07.583831v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2024/03/08/2024.03.07.583831/DC1/embed/media-1.zip)
**Full-Atom Peptide Design based on Multi-modal Flow Matching**
Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, Jianzhu Ma
[arXiv:2406.00735](https://arxiv.org/abs/2406.00735) • [code](https://github.com/Ced3-han/PepFlowww)
## 7. Other tasks
### 7.1 Effects of mutation & Fitness Landscape
**Deep generative models of genetic variation capture the effects of mutations**
Adam J. Riesselman, John B. Ingraham & Debora S. Marks
[Nature Methods](https://www.nature.com/articles/s41592-018-0138-4) • [code::DeepSequence](https://github.com/debbiemarkslab/DeepSequence) • Oct 2018
**Deciphering protein evolution and fitness landscapes with latent space models**
Xinqiang Ding, Zhengting Zou & Charles L. Brooks III
[Nature Communications](https://www.nature.com/articles/s41467-019-13633-0) • [code::PEVAE](https://github.com/xqding/PEVAE_Paper) • Dec 2019
**Is transfer learning necessary for protein landscape prediction?**
Shanehsazzadeh, Amir, David Belanger, and David Dohan.
[arXiv preprint arXiv:2011.03443 (2020)](https://arxiv.org/abs/2011.03443)
**Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions**
Amirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, David H. Brookes, Yijie Huang, O. Ozan Koyluoglu, Jennifer Listgarten & Kannan Ramchandran
[Nature Communications](https://www.nature.com/articles/s41467-021-25371-3) • [code](https://github.com/amirmohan/epistatic-net) • Sep 2021
**The generative capacity of probabilistic protein sequence models**
Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M. Levy, Vincenzo Carnevale & Allan Haldane
[Nature Communications](https://www.nature.com/articles/s41467-021-26529-9) • [code::generation_capacity_metrics](https://github.com/alagauche/generative_capacity_metrics) • [code::sVAE](https://github.com/ahaldane/MSA_VAE) • Nov 2021
**Learning the local landscape of protein structures with convolutional neural networks**
Anastasiya V. Kulikova, Daniel J. Diaz, James M. Loy, Andrew D. Ellington & Claus O. Wilke
[Journal of Biological Physics 47.4 (2021)](https://link.springer.com/article/10.1007/s10867-021-09593-6)
**Learning Protein Fitness Models from Evolutionary and Assay-labeled Data**
Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, Jennifer Listgarten
[Nature Biotechnology (2022)](https://www.nature.com/articles/s41587-021-01146-5) • [Supplementary Information](https://static-content.springer.com/esm/art%3A10.1038%2Fs41587-021-01146-5/MediaObjects/41587_2021_1146_MOESM1_ESM.pdf) • [code](https://github.com/chloechsu/combining-evolutionary-and-assay-labelled-data)
**Proximal Exploration for Model-guided Protein Sequence Design**
Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng
[BioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.04.12.487986v1) • [code](https://github.com/HeliXonProtein/proximal-exploration) • commercial
**Efficient evolution of human antibodies from general protein language models and sequence information alone**
Brian L. Hie, Duo Xu, Varun R. Shanker, Theodora U.J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Peter S. Kim
[bioRxiv (2022)](https://www.biorxiv.org/content/10.1101/2022.04.10.487811v1) • [code](https://github.com/brianhie/efficient-evolution)
**Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval**
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y.
[ICML (2022)](https://arxiv.org/abs/2205.13760)/[arXiv:2205.13760](https://arxiv.org/abs/2205.13760) • [code](https://github.com/OATML-Markslab/Tranception) • [hugging face](https://huggingface.co/ICML2022/Tranception)
**Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments**
Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si
[bioRxiv 2022.08.11.503535](https://www.biorxiv.org/content/10.1101/2022.08.11.503535v1)
**Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness**
Sharrol Bachas, Goran Rakocevic, David Spencer, Anand V. Sastry, Robel Haile, John M. Sutton, George Kasun, Andrew Stachyra, Jahir M. Gutierrez, Edriss Yassine, Borka Medjo, Vincent Blay, Christa Kohnert, Jennifer T. Stanton, Alexander Brown, Nebojsa Tijanic, Cailen McCloskey, Rebecca Viazzo, Rebecca Consbruck, Hayley Carter, Simon Levine, Shaheed Abdulhaqq, Jacob Shaul, Abigail B. Ventura, Randal S. Olson, Engin Yapici, Joshua Meier, Sean McClain, Matthew Weinstock, Gregory Hannum, Ariel Schwartz, Miles Gander, Roberto Spreafico
[bioRxiv 2022.08.16.504181](https://www.biorxiv.org/content/10.1101/2022.08.16.504181v1) • [poster](https://nips.cc/media/PosterPDFs/NeurIPS%202022/58999.png?t=1668022673.3853557)
**Construction of a Deep Neural Network Energy Function for Protein Physics**
Yang, Huan, Zhaoping Xiong, and Francesco Zonta
[Journal of Chemical Theory and Computation (2022)](https://pubs.acs.org/doi/10.1021/acs.jctc.2c00069)
**Inferring protein fitness landscapes from laboratory evolution experiments**
Sameer D’Costa, Emily C. Hinds, Chase R. Freschlin, Hyebin Song, Philip A. Romero
[bioRxiv 2022.09.01.506224](https://www.biorxiv.org/content/10.1101/2022.09.01.506224v1) • [Supplementary](https://www.biorxiv.org/content/10.1101/2022.09.01.506224v1.supplementary-material)
**BayeStab: Predicting Effects of Mutations on Protein Stability with Uncertainty Quantification**
Shuyu Wang, Hongzhou Tang, Yuliang Zhao, Lei Zuo
[Protein Science (2022)](https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4467) • [code](https://github.com/HongzhouTang/BayeStab) • [website](http://www.bayestab.com)
**Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design**
Neil Thomas, Atish Agarwala, David Belanger, Yun S. Song, Lucy Colwell
[bioRxiv 2022.10.28.514293](https://www.biorxiv.org/content/10.1101/2022.10.28.514293v1) • [code](https://github.com/google-research/slip)
**Protein design using structure-based residue preferences**
David Ding, Ada Y Shaw, Sam Sinai, Nathan J Rollins, Noam Prywes, David Savage, Michael T Laub, Debora S Marks
[bioRxiv 2022.10.31.514613](https://www.biorxiv.org/content/10.1101/2022.10.31.514613v2) • [code](https://github.com/ddingding/CoVES)
**Accurate Mutation Effect Prediction using RoseTTAFold**
Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L Watson, David Baker
[bioRxiv 2022.11.04.515218](https://www.biorxiv.org/content/10.1101/2022.11.04.515218v1)
**Learning the shape of protein micro-environments with a holographic convolutional neural network**
Michael N. Pun, Andrew Ivanov, Quinn Bellamy, Zachary Montague, Colin LaMont, Philip Bradley, Jakub Otwinowski, Armita Nourmohammad
[bioRxiv (2022)](https://arxiv.org/abs/2211.02936) • [code](https://github.com/StatPhysBio/protein_holography)
**Infer global, predict local: quantity-quality trade-off in protein fitness predictions from sequence data**
Lorenzo Posani, Francesca Rizzato, Rémi Monasson, Simona Cocco
[bioRxiv 2022.12.12.520004](https://www.biorxiv.org/content/10.1101/2022.12.12.520004v1)
**Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting**
Alvaro Martin, Carolin Berner, Sergey Ovchinnikov, Anastassia Andreevna Vorobieva
[bioRxiv 2023.06.06.543955](https://www.biorxiv.org/content/10.1101/2023.06.06.543955v1) • [colab](https://colab.research.google.com/github/vorobieva/ColabFold/blob/main/beta/ESMFold_melting.ipynb)
**PoET: A generative model of protein families as sequences-of-sequences**
Timothy F. Truong Jr, Tristan Bepler
[arXiv:2306.06156](https://arxiv.org/abs/2306.06156) • [code](https://github.com/OpenProteinAI/PoET)
**Rapid protein stability prediction using deep learning representations**
Lasse M BlaabjergMaher M KassemLydia L GoodNicolas JonssonMatteo CagiadaKristoffer E JohanssonWouter BoomsmaAmelie SteinKresten Lindorff-Larsen
[eLife 12:e82593](https://elifesciences.org/articles/82593) • [code](https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/)
**A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins**
Pan Tan, Mingchen Li, Yuanxi Yu, Fan Jiang, Lirong Zheng, Banghao Wu, Xinyu Sun, Liqi Kang, Jie Song, Liang Zhang, Yi Xiong, Wanli Ouyang, Zhiqiang Hu, Guisheng Fan, Yufeng Pei, Liang Hong
[arXiv:2307.12682](https://arxiv.org/abs/2307.12682)
**Transfer learning to leverage larger datasets for improved prediction of protein stability changes**
Henry Dieckhaus, Michael Brocidiacono, Nicholas Randolph, Brian Kuhlman
[bioRxiv 2023.07.27.550881](https://www.biorxiv.org/content/10.1101/2023.07.27.550881v1) • [code](https://github.com/Kuhlman-Lab/ThermoMPNN) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/07/30/2023.07.27.550881/DC1/embed/media-1.docx)
**Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale**
Jinyuan Sun, Tong Zhu, Yinglu Cui, Bian Wu
[bioRxiv 2023.08.09.552725](https://www.biorxiv.org/content/10.1101/2023.08.09.552725v1) • [code](https://github.com/Wublab/Pythia)
**Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO**
Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter
[arXiv:2309.00408](https://arxiv.org/abs/2309.00408)
**Zero-shot Mutation Effect Prediction on Protein Stability and Function using RoseTTAFold**
Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L. Watson, David Baker
[Protein Science](https://onlinelibrary.wiley.com/doi/10.1002/pro.4780) • [dissertation](https://www.proquest.com/openview/dba5569e5efd0dc60fc7bedccb6afee3/)
**Accurate proteome-wide missense variant effect prediction with AlphaMissense**
Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvile Žemgulyte, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G. Schneider, Andrew W. Senior, John Jumper, Demis Hassabis, Pushmeet Kohli, Žiga Avsec
[Science0,eadg7492DOI:10.1126/science.adg7492](https://www.science.org/doi/10.1126/science.adg7492) • [code](https://github.com/deepmind/alphamissense) • [data](https://console.cloud.google.com/storage/browser/dm_alphamissense)
**What makes the effect of protein mutations difficult to predict?**
Floris Julian van der Flier, Dave Estell, Sina Pricelius, Lydia Dankmeyer, Sander van Stigt Thans, Harm Mulder, Rei Otsuka, Frits Goedegebuur, Laurens Lammerts, Diego Staphorst, Aalt D.J. van Dijk, Dick de Ridder, Henning Redestig
[bioRxiv 2023.09.25.559319](https://www.biorxiv.org/content/10.1101/2023.09.25.559319v1) • [code](https://github.com/florisvdf/mutation-predictability)
**Fast, accurate ranking of engineered proteins by target binding propensity using structure modeling**
Xiaozhe Ding, Xinhong Chen, Erin E. Sullivan, Timothy F. Shay, Viviana Gradinaru
[bioRxiv 2023.01.11.523680](https://www.biorxiv.org/content/10.1101/2023.01.11.523680v2)/[Molecular Therapy (2024)](https://www.cell.com/molecular-therapy-family/molecular-therapy/fulltext/S1525-0016(24)00219-3) • [code](https://github.com/GradinaruLab/APPRAISE) • [colab](https://colab.research.google.com/github/GradinaruLab/APPRAISE/blob/main/Colab_APPRAISE.ipynb)
**Neural network extrapolation to distant regions of the protein fitness landscape**
Sarah A Fahlberg, Chase R Freschlin, Pete Heinzelman, Philip A Romero
[bioRxiv 2023.11.08.566287](https://www.biorxiv.org/content/10.1101/2023.11.08.566287v1) • [Supplymentary](https://www.biorxiv.org/content/biorxiv/early/2023/11/09/2023.11.08.566287/DC1/embed/media-1.pdf)
**Accelerating protein engineering with fitness landscape modeling and reinforcement learning**
Haoran Sun, Liang He, Pan Deng, Guoqing Liu, Haiguang Liu, Chuan Cao, Fusong Ju, Lijun Wu, Tao Qin, Tie-Yan Liu
[bioRxiv 2023.11.16.565910](https://www.biorxiv.org/content/10.1101/2023.11.16.565910v1)
**Protein Design by Directed Evolution Guided by Large Language Models**
Trong Thanh Tran, Truong Son Hy
[bioRxiv 2023.11.29.568945](https://www.biorxiv.org/content/10.1101/2023.11.28.568945v1) • [Supplementary](https://www.biorxiv.org/content/biorxiv/early/2023/11/29/2023.11.28.568945/DC1/embed/media-1.pdf?download=true) • [code](https://github.com/HySonLab/Directed_Evolution)
**High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2**
Christof Angermueller, Zelda Marie, Benjamin Jester, Emily Engelhart, Ryan Emerson, Babak Alipanahi, Zachary Ryan McCaw, Jim Roberts, Randolph M Lopez, David Younger, Lucy Colwell
[bioRxiv 2023.12.01.569227](https://www.biorxiv.org/content/10.1101/2023.12.01.569227v1)
**Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models**
Yijie Zhang, Zhangyang Gao, Cheng Tan, Stan Z.Li
[arXiv preprint arXiv:2312.04019 (2023)](https://arxiv.org/abs/2312.04019)
**DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design**
Wengong Jin, Xun Chen, Amrita Vetticaden, Siranush Sarzikova, Raktima Raychowdhury, Caroline Uhler, Nir Hacohen
[bioRxiv 2023.12.10.570461](https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1) • [Supplementary1](https://www.biorxiv.org/content/biorxiv/early/2023/12/10/2023.12.10.570461/DC1/embed/media-1.xlsx) • [Supplementary2](https://www.biorxiv.org/content/biorxiv/early/2023/12/10/2023.12.10.570461/DC2/embed/media-2.pdf)
**Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution**
Varun R. Shanker, Theodora U.J. Bruun, Brian L. Hie, Peter S. Kim
[bioRxiv 2023.12.19.572475](https://www.biorxiv.org/content/10.1101/2023.12.19.572475v2)
**EvolMPNN: Predicting Mutational Effect on Homologous Proteins by Evolution Encoding**
Zhiqiang Zhong, Davide Mottin
[arXiv:2402.13418](https://arxiv.org/abs/2402.13418)
**Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning**
Tian Lan, Shuquan Su, Pengyao Ping, Gyorgy Hutvagner, Tao Liu, Yi Pan & Jinyan Li
[Nat Mach Intell 6, 315–325 (2024)](https://www.nature.com/articles/s42256-024-00803-z) • [code](https://github.com/tianlt/Deepdirect)
**Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design**
Nhat Khang Ngo, Thanh V. T. Tran, Viet Thanh Duy Nguyen, Truong Son Hy
[bioRxiv 2024.04.13.589381](https://www.biorxiv.org/content/10.1101/2024.04.13.589381v1) • [code](https://github.com/HySonLab/LatentDE)
**AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation**
Lijun Liu, Jiali Yang, Jianfei Song, Xinglin Yang, Lele Niu, Zeqi Cai, Hui Shi, Tingjun Hou, Chang-yu Hsieh, Weiran Shen, Yafeng Deng
[arXiv:2404.10573](https://arxiv.org/abs/2404.10573)
**Protein engineering with lightweight graph denoising neural networks**
Bingxin Zhou, Lirong Zheng, Banghao Wu, Yang Tan, Outongyi Lv, Kai Yi, Guisheng Fan, and Liang Hong
[Journal of Chemical Information and Modeling (2024)](https://pubs.acs.org/doi/10.1021/acs.jcim.4c00036) • [code](https://github.com/bzho3923/ProtLGN)
**VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction**
Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine
[bioRxiv 2024.04.24.590982](https://www.biorxiv.org/content/10.1101/2024.04.24.590982v1) • [code](https://github.com/JSchlensok/VespaG)
**Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design**
Amar Jeet Yadav, Shivank Kumar, Shweata Maurya, Khushboo Bhagat, and Aditya K. Padhi
[Physical Chemistry Chemical Physics (2024)](https://pubs.rsc.org/en/content/articlelanding/2024/cp/d4cp01014k)
**Aligning protein generative models with experimental fitness via Direct Preference Optimization**
Talal Widatalla, Rafael Rafailov, Brian Hie
[bioRxiv 2024.05.20.595026](https://www.biorxiv.org/content/10.1101/2024.05.20.595026v1) • [code](https://github.com/evo-design/protein-dpo)
### 7.2 Protein Language Models (pLM) and representation learning
> More detailed protein representation learning list:
>[Lirong Wu](https://github.com/LirongWu)'s [awesome-protein-representation-learning](https://github.com/LirongWu/awesome-protein-representation-learning)
**Unified rational protein engineering with sequence-based deep representation learning**
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi & George M. Church
[Nature methods 16.12 (2019)](https://www.nature.com/articles/s41592-019-0598-1)
**Protein Structure Representation Learning by Geometric Pretraining**
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang
[arXiv](https://arxiv.org/abs/2203.06125) • Jan 2022
**Evolutionary velocity with protein language models**
Brian L. Hie, Kevin K. Yang, and Peter S. Kim
[bioRxiv](https://www.biorxiv.org/content/10.1101/2021.06.07.447389v1.full.pdf)
**Advancing protein language models with linguistics: a roadmap for improved interpretability**
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug
[arXiv:2207.00982](https://arxiv.org/abs/2207.00982)
**Deciphering the language of antibodies using self-supervised learning**
Jinwoo Leem, Laura S. Mitchell, James H.R. Farmery, Justin Barton, Jacob D. Galson
[Patterns (2022): 100513](https://www.sciencedirect.com/science/article/pii/S2666389922001052) • [code](https://github.com/alchemab/antiberta)
**On Pre-training Language Model for Antibody**
Anonymous(Paper under double-blind review)
[ICLR 2023](https://openreview.net/forum?id=zaq4LV55xHl) • [Supplementary](https://openreview.net/attachment?id=zaq4LV55xHl&name=supplementary_material)
**Antibody Representation Learning for Drug Discovery**
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Tristan Bepler, Rajmonda Sulo Caceres
[arXiv:2210.02881](https://arxiv.org/abs/2210.02881)
**Learning Complete Protein Representation by Deep Coupling of Sequence and Structure**
Bozhen Hu, Cheng Tan, Jun Xia, Jiangbin Zheng, Yufei Huang, Lirong Wu, Yue Liu, Yongjie Xu, Stan Z. Li
[bioRxiv 2023.07.05.547769](https://www.biorxiv.org/content/10.1101/2023.07.05.547769v1)
**Leveraging Ancestral Sequence Reconstruction for Protein Representation Learning**
D. S. Matthews, M. A. Spence, A. C. Mater, J. Nichols, S. B. Pulsford, M. Sandhu, J. A. Kaczmarski, C. M. Miton, N. Tokuriki, C. J. Jackson
[bioRxiv 2023.12.20.572683](https://www.biorxiv.org/content/10.1101/2023.12.20.572683v1) • [code](https://github.com/RSCJacksonLab/local-ancestral-sequence-embeddings)
**Protein language models are biased by unequal sequence sampling across the tree of life**
Frances Ding, Jacob Steinhardt
[bioRxiv 2024.03.07.584001](https://www.biorxiv.org/content/10.1101/2024.03.07.584001v1)
**InstructPLM: Aligning Protein Language Models to Follow Protein Structure Instructions**
Jiezhong Qiu, Junde Xu, Jie Hu, Hanqun Cao, Liya Hou, Zijun Gao, Xinyi Zhou, Anni Li, Xiujuan Li, Bin Cui, Fei Yang, Shuang Peng, Ning Sun, Fangyu Wang, Aimin Pan, Jie Tang, Jieping Ye, Junyang Lin, Jin Tang, Xingxu Huang, Pheng Ann Heng, Guangyong Chen
[bioRxiv 2024.04.17.589642](https://www.biorxiv.org/content/10.1101/2024.04.17.589642v1)
### 7.3 Molecular Design Models
> Unlike **function-scaffold-sequence** paradigm in protein design, major molecular design models based on paradigm form DL from 3 kinds of level: **atom-based**, **fragment-based**, **reaction-based**, and they can be categorized as [Gradient optimization](#731-gradient-optimization) or [Optimized sampling](#732-optimized-sampling)(gradient-free). [Click here for detail review](https://www.sciencedirect.com/science/article/pii/S1359644621002531)
> In consideration of learning more various of generative models for design, these recommended latest models from **Molecular Design** might be helpful and even be able to be transplanted to protein design.
> More paper list at :
> 1. [CondaPereira](https://github.com/CondaPereira)'s GitHub repo: [Essay_For_Molecular_Generation](https://github.com/CondaPereira/Essay_For_Molecular_Generation).
> 2. [AspirinCode](https://github.com/AspirinCode)'s :[papers-for-molecular-design-using-DL](https://github.com/AspirinCode/papers-for-molecular-design-using-DL)
> 3. [Alex Morehead](https://github.com/amorehead)'s :[awesome-molecular-generation](https://github.com/amorehead/awesome-molecular-generation)
#### 7.3.1 Gradient optimization
**Differentiable scaffolding tree for molecular optimization**
Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., & Sun, J.
[arXiv preprint arXiv:2109.10469](https://arxiv.org/abs/2109.10469) • [code](https://github.com/futianfan/DST) • Sept 21
**Equivariant Energy-Guided SDE for Inverse Molecular Design**
Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
[arXiv:2209.15408](https://arxiv.org/abs/2209.15408)
**Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design**
Keir Adams, Connor W. Coley
[arXiv:2210.04893](https://arxiv.org/abs/2210.04893) • [code](https://github.com/keiradams/SQUID)
**Structure-based Drug Design with Equivariant Diffusion Models**
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia
[NeurIPS 2022](https://www.mlsb.io/papers_2022/Structure_based_Drug_Design_with_Equivariant_Diffusion_Models.pdf)/[arXiv:2210.13695](https://arxiv.org/abs/2210.13695) • [code](https://github.com/arneschneuing/DiffSBDD)
#### 7.3.2 Optimized sampling
**Generating 3D Molecules for Target Protein Binding**
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji
[International Conference on Machine Learning 39 (2022)](https://proceedings.mlr.press/v162/liu22m.html) • [GraphBP](https://github.com/divelab/graphbp)
**Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets**
Peng, Xingang, et al.
[International Conference on Machine Learning 39 (2022)](https://proceedings.mlr.press/v162/peng22b.html) • [code](https://github.com/pengxingang/Pocket2Mol)
**Reinforced Genetic Algorithm for Structure-based Drug Design**
Fu, Tianfan, et al.
[arXiv preprint arXiv:2211.16508 (2022)](https://arxiv.org/abs/2211.16508)/[ICML22](https://openreview.net/forum?id=_Sfd-icezCa) • [code](https://github.com/futianfan/reinforced-genetic-algorithm) • [website](https://deepai.org/publication/reinforced-genetic-algorithm-for-structure-based-drug-design)
**Molecule Generation For Target Protein Binding with Structural Motifs**
Zhang, Zaixi, et al.
[International Conference on Learning Representations 11 (2023)](https://openreview.net/forum?id=Rq13idF0F73) • [code](https://github.com/zaixizhang/FLAG)
**3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction**
Guan, Jiaqi, et al.
[International Conference on Learning Representations 11 (2023)](https://openreview.net/forum?id=kJqXEPXMsE0) • [code](https://github.com/guanjq/targetdiff)