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List of papers about Proteins Design using Deep Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 1955 |
| 2026-08-01 | 1956 |
| 2026-08-06 | 1956 |
Today
— stars today
This week
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growth rate 0.00%/day
# List of papers about Protein 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/)
<!-- >
>1. Mini protein, binders, metalloprotein, antibody, peptide & molecule designs are included
>2. More de novo protein design paper list at [Wangchentong](https://github.com/Wangchentong)'s GitHub repo: [paper_for_denovo_protein_design](https://github.com/Wangchentong/paper_for_denovo_protein_design)
>3. Our notes of these papers are shared in a **[Zhihu Column](https://www.zhihu.com/column/c_1475864742820929537)** (simplified Chinese/English), more suggested notes at [RosettAI](https://www.zhihu.com/column/rosettastudy) -->
*Papers last week, updated on 2026.07.25:*
+ De Novo Design of Protein Nanopores: From Minimal Peptides to AI-Driven Design
+ [[Chem. Rev. (2026)](https://pubs.acs.org/chreay/article-abstract/doi/10.1021/acs.chemrev.5c00990/5221890/De-Novo-Design-of-Protein-Nanopores-From-Minimal)]
+ Deep Learning for Proteins Notebook Series Teaches AI for Biomolecular Structure Prediction and Design
+ [[The Biophysicist. 2026](https://thebiophysicist.kglmeridian.com/view/journals/biop/7/1/article-p16.xml)]
+ De Novo Design of Protein Switches with Diffusion-Based Ensemble Sampling
+ [[bioRxiv 2026.07.20.739027](https://www.biorxiv.org/content/10.64898/2026.07.20.739027v1)]
+ Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
+ [[arXiv:2607.20057](https://arxiv.org/abs/2607.20057)] • [[code](https://github.com/XL-S224/AAMFM)]
+ ABOPD: Antibody CDR Design via On-Policy Distillation
+ [[arXiv:2607.18835](https://arxiv.org/abs/2607.18835)]
+ Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion
+ [[bioRxiv 2026.07.22.740177](https://www.biorxiv.org/content/10.64898/2026.07.22.740177v1)] • [[code](https://github.com/yilunqi/Shape-conditioned-protein-design)]
+ Programmable design of synthetic plant immune receptors for pathogen protein recognition
+ [[Science](https://www.science.org/doi/10.1126/science.aee1792)]
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<p align="center">
<br>
<!-- <img src="dl_pd.png" alt="deep learning for protein design" width="500"> -->
<img src="cover.jpg" alt="deep learning for protein design">
</p>
<!-- ## Menu -->
<!-- > Heading [[2]](#2-model-based-design) follows a **"generator-predictor-optimizer" paradigm**, Heading [[3]](#3-function-to-scaffold), [[4]](#4scaffold-to-sequence)&[[6]](#6-function-to-structure) follow ["Inside-out" paradigm](https://www.nature.com/articles/nature19946)(*function-scaffold-sequence*) from [RosettaCommons](https://www.rosettacommons.org/), Heading [[5]](#5function-to-sequence)&[[7]](#7-other-tasks) follow other ML/DL strategies -->
<p align='center'>
<strong><a href='#0-benchmarks-and-datasets'>0) Benchmarks and datasets </a></strong>
<br>
<a href="#01-sequence-datasets-benchmarks">Sequence dataset/benchmarks</a> •
<a href="#02-structure-datasets-benchmarks">Structure datasets/benchmarks</a> •
<a href="#03-databases">Public database</a> •
<a href="#04-similar-list">Similar list</a> •
<a href="#05-guides">Guides</a>
<br>
<strong><a href="#1-reviews">1) Reviews and surveys</a></strong>
<br>
<a href="#11-de-novo-proteiExcerpt of 440,613 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:de0f945e5686b408, topic:deep-learning
matched fp:de0f945e5686b408, name:protein
