# Generative De Novo Protein Design with Global Context

arXiv: https://arxiv.org/abs/2204.10673 · HTML: https://mlsanity.com/paper/2204.10673/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2001.11003/) (2020)
- [Deep Learning in Protein Structural Modeling and Design](https://mlsanity.com/paper/2007.08383/) (2020)
- [Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design](https://mlsanity.com/paper/2106.13058/) (2021)

## Built on by (most cited first, 16 of 16)

- [PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction](https://mlsanity.com/paper/2402.08198/) (2024)
- [Bridge-IF: Learning Inverse Protein Folding with Markov Bridges](https://mlsanity.com/paper/2411.02120/) (2024)
- [RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design](https://mlsanity.com/paper/2301.10774/) (2023)
- [Improving Protein Sequence Design through Designability Preference Optimization](https://mlsanity.com/paper/2506.00297/) (2025)
- [ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search](https://mlsanity.com/paper/2506.00925/) (2025)
- [Mask prior-guided denoising diffusion improves inverse protein folding](https://mlsanity.com/paper/2412.07815/) (2024)
- [Protein Inverse Folding From Structure Feedback](https://mlsanity.com/paper/2506.03028/) (2025)
- [Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?](https://mlsanity.com/paper/2306.17702/) (2023)
- [Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models](https://mlsanity.com/paper/2312.04019/) (2023)
- [Deep Manifold Transformation for Protein Representation Learning](https://mlsanity.com/paper/2402.09416/) (2024)
- [EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization](https://mlsanity.com/paper/2506.09496/) (2025)
- [Decoupled Sequence and Structure Generation for Realistic Antibody Design](https://mlsanity.com/paper/2402.05982/) (2024)
- [AlphaFold Database Debiasing for Robust Inverse Folding](https://mlsanity.com/paper/2506.08365/) (2025)
- [RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding](https://mlsanity.com/paper/2512.00126/) (2025)
- [SurfDesign: Effective Protein Design on Molecular Surfaces](https://mlsanity.com/paper/2606.07567/) (2026)
- [SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding](https://mlsanity.com/paper/2609.01353/) (2026)

## Abstract (arXiv)

The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released.