# Progressive Multi-Modality Learning for Inverse Protein Folding

arXiv: https://arxiv.org/abs/2312.06297 · HTML: https://mlsanity.com/paper/2312.06297/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [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)
- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration](https://mlsanity.com/paper/2311.16845/) (2023)
- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment](https://mlsanity.com/paper/2303.05725/) (2023)
- [AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB](https://mlsanity.com/paper/2202.01079/) (2022)
- [Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings](https://mlsanity.com/paper/2210.16848/) (2022)

## Built on by (most cited first, 5 of 5)

- [DPLM-2: A Multimodal Diffusion Protein Language Model](https://mlsanity.com/paper/2410.13782/) (2024)
- [FoldToken: Learning Protein Language via Vector Quantization and Beyond](https://mlsanity.com/paper/2403.09673/) (2024)
- [Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction](https://mlsanity.com/paper/2503.17666/) (2025)
- [Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design](https://mlsanity.com/paper/2411.17795/) (2024)
- [DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction](https://mlsanity.com/paper/2411.17798/) (2024)

## Abstract (arXiv)

While deep generative models show promise for learning inverse protein folding directly from data, the lack of publicly available structure-sequence pairings limits their generalization. Previous improvements and data augmentation efforts to overcome this bottleneck have been insufficient. To further address this challenge, we propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. To our knowledge, MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks. To further assess the biological plausibility, we present systematic quantitative analysis techniques that provide interpretability and reveal more about the laws of protein design.