# Diffusion Model with Representation Alignment for Protein Inverse Folding

arXiv: https://arxiv.org/abs/2412.09380 · HTML: https://mlsanity.com/paper/2412.09380/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think](https://mlsanity.com/paper/2410.06940/) (2024)
- [Diffusion Language Models Are Versatile Protein Learners](https://mlsanity.com/paper/2402.18567/) (2024)
- [Deconstructing Denoising Diffusion Models for Self-Supervised Learning](https://mlsanity.com/paper/2401.14404/) (2024)

## Built on by (most cited first, 10 of 10)

- [ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies](https://mlsanity.com/paper/2506.12830/) (2025)
- [End-to-End Chart Summarization via Visual Chain-of-Thought in Vision-Language Models](https://mlsanity.com/paper/2502.17589/) (2025)
- [High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers](https://mlsanity.com/paper/2505.01693/) (2025)
- [Evidence-Driven Marker Extraction for Social Media Suicide Risk Detection](https://mlsanity.com/paper/2502.18823/) (2025)
- [EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization](https://mlsanity.com/paper/2506.09496/) (2025)
- [EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation](https://mlsanity.com/paper/2510.25132/) (2025)
- [Vision-Driven Prompt Optimization for Large Language Models in Multimodal Generative Tasks](https://mlsanity.com/paper/2501.02527/) (2025)
- [Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models](https://mlsanity.com/paper/2501.15571/) (2025)
- [Weak Supervision Dynamic KL-Weighted Diffusion Models Guided by Large Language Models](https://mlsanity.com/paper/2502.00826/) (2025)
- [Elevating Visual Question Answering through Implicitly Learned Reasoning Pathways in LVLMs](https://mlsanity.com/paper/2503.14674/) (2025)

## Abstract (arXiv)

Protein inverse folding is a fundamental problem in bioinformatics, aiming to recover the amino acid sequences from a given protein backbone structure. Despite the success of existing methods, they struggle to fully capture the intricate inter-residue relationships critical for accurate sequence prediction. We propose a novel method that leverages diffusion models with representation alignment (DMRA), which enhances diffusion-based inverse folding by (1) proposing a shared center that aggregates contextual information from the entire protein structure and selectively distributes it to each residue; and (2) aligning noisy hidden representations with clean semantic representations during the denoising process. This is achieved by predefined semantic representations for amino acid types and a representation alignment method that utilizes type embeddings as semantic feedback to normalize each residue. In experiments, we conduct extensive evaluations on the CATH4.2 dataset to demonstrate that DMRA outperforms leading methods, achieving state-of-the-art performance and exhibiting strong generalization capabilities on the TS50 and TS500 datasets.