# La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

arXiv: https://arxiv.org/abs/2507.09466 · HTML: https://mlsanity.com/paper/2507.09466/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 78 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Building Normalizing Flows with Stochastic Interpolants](https://mlsanity.com/paper/2209.15571/) (2022)
- [Stochastic Interpolants: A Unifying Framework for Flows and Diffusions](https://mlsanity.com/paper/2303.08797/) (2023)
- [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://mlsanity.com/paper/2401.08740/) (2024)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design](https://mlsanity.com/paper/2402.04997/) (2024)
- [SE(3) diffusion model with application to protein backbone generation](https://mlsanity.com/paper/2302.02277/) (2023)
- [SE(3)-Stochastic Flow Matching for Protein Backbone Generation](https://mlsanity.com/paper/2310.02391/) (2023)
- [Guided Flows for Generative Modeling and Decision Making](https://mlsanity.com/paper/2311.13443/) (2023)
- [Diffusion Language Models Are Versatile Protein Learners](https://mlsanity.com/paper/2402.18567/) (2024)
- [Restart Sampling for Improving Generative Processes](https://mlsanity.com/paper/2306.14878/) (2023)
- [Proteina: Scaling Flow-based Protein Structure Generative Models](https://mlsanity.com/paper/2503.00710/) (2025)
- [DPLM-2: A Multimodal Diffusion Protein Language Model](https://mlsanity.com/paper/2410.13782/) (2024)
- [Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds](https://mlsanity.com/paper/2301.12485/) (2023)
- [Improved motif-scaffolding with SE(3) flow matching](https://mlsanity.com/paper/2401.04082/) (2024)
- [Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation](https://mlsanity.com/paper/2405.20313/) (2024)
- [Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2](https://mlsanity.com/paper/2405.15489/) (2024)
- [A Latent Diffusion Model for Protein Structure Generation](https://mlsanity.com/paper/2305.04120/) (2023)
- [An All-Atom Generative Model for Designing Protein Complexes](https://mlsanity.com/paper/2504.13075/) (2025)
- [ProxelGen: Generating Proteins as 3D Densities](https://mlsanity.com/paper/2506.19820/) (2025)

## Built on by (most cited first, 30 of 30)

- [Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner](https://mlsanity.com/paper/2510.03206/) (2025)
- [Riemannian MeanFlow](https://mlsanity.com/paper/2602.07744/) (2026)
- [Sublinear iterations can suffice even for DDPMs](https://mlsanity.com/paper/2511.04844/) (2025)
- [Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions](https://mlsanity.com/paper/2511.09465/) (2025)
- [HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens](https://mlsanity.com/paper/2512.15133/) (2025)
- [General Multimodal Protein Design Enables DNA-Encoding of Chemistry](https://mlsanity.com/paper/2604.05181/) (2026)
- [Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching](https://mlsanity.com/paper/2606.03199/) (2026)
- [Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models](https://mlsanity.com/paper/2511.02986/) (2025)
- [FragmentFlow: Scalable Transition State Generation for Large Molecules](https://mlsanity.com/paper/2602.02310/) (2026)
- [From Words to Amino Acids: Does the Curse of Depth Persist?](https://mlsanity.com/paper/2602.21750/) (2026)
- [Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards](https://mlsanity.com/paper/2603.26378/) (2026)
- [A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion](https://mlsanity.com/paper/2605.03360/) (2026)
- [Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation](https://mlsanity.com/paper/2605.09981/) (2026)
- [Few-step Cofolding with All-Atom Flow Maps](https://mlsanity.com/paper/2606.08375/) (2026)
- [Emyx: Fast and efficient all-atom protein generation](https://mlsanity.com/paper/2606.19377/) (2026)
- [SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign](https://mlsanity.com/paper/2609.03377/) (2026)
- [Relaxed Sequence Sampling for Diverse Protein Design](https://mlsanity.com/paper/2510.23786/) (2025)
- [All-Atom GPCR-Ligand Dynamics Simulation via a Residual Latent Flow Mode](https://mlsanity.com/paper/2602.03902/) (2026)
- [Seasoning Generative Models for a Generalization Aftertaste](https://mlsanity.com/paper/2603.18817/) (2026)
- [Steerable Neural ODEs on Homogeneous Spaces](https://mlsanity.com/paper/2605.11133/) (2026)
- [Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design](https://mlsanity.com/paper/2605.27413/) (2026)
- [Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling](https://mlsanity.com/paper/2606.01628/) (2026)
- [Design-CP: Context Parallelism for Design of Protein Nanoparticles](https://mlsanity.com/paper/2607.05439/) (2026)
- [Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling](https://mlsanity.com/paper/2608.16094/) (2026)
- [Unlocking Multimodal Protein Language Models at Inference Time](https://mlsanity.com/paper/2608.25855/) (2026)
- [SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems](https://mlsanity.com/paper/2610.01381/) (2026)
- [CODesign: Consistency from Data to Trajectory in All-Atom Protein Binder Co-Design](https://mlsanity.com/paper/2610.01773/) (2026)
- [Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features](https://mlsanity.com/paper/2610.02189/) (2026)
- [Learning Latent Protein Languages for Autoregressive Generation](https://mlsanity.com/paper/2610.03978/) (2026)
- [What Does Fr'echet Distance Measure? A Directional Decomposition](https://mlsanity.com/paper/2610.05518/) (2026)

## Abstract (arXiv)

Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating fully atomistic structures jointly with the underlying amino acid sequence. This is challenging, for instance, because the model must reason over side chains that change in length during generation. We introduce La-Proteina for atomistic protein design based on a novel partially latent protein representation: coarse backbone structure is modeled explicitly, while sequence and atomistic details are captured via per-residue latent variables of fixed dimensionality, thereby effectively side-stepping challenges of explicit side-chain representations. Flow matching in this partially latent space then models the joint distribution over sequences and full-atom structures. La-Proteina achieves state-of-the-art performance on multiple generation benchmarks, including all-atom co-designability, diversity, and structural validity, as confirmed through detailed structural analyses and evaluations. Notably, La-Proteina also surpasses previous models in atomistic motif scaffolding performance, unlocking critical atomistic structure-conditioned protein design tasks. Moreover, La-Proteina is able to generate co-designable proteins of up to 800 residues, a regime where most baselines collapse and fail to produce valid samples, demonstrating La-Proteina's scalability and robustness.