A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion
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Abstract (arXiv)
We present A-CODE, a fully atomic unified one-stage protein co-design model that simultaneously refines discrete atom types and continuous atom coordinates. Unlike predominant two-stage methods that cascade structure design with amino acid-level sequence design, our approach is fully atomic within a unified multimodal diffusion framework, in which residue identities are inferred solely from atom-level predictions. Built upon the powerful all-atom architecture, A-CODE achieves superior designability for unconditional protein generation, outperforming all existing one-stage and two-stage design models. For binder design, A-CODE rivals and even outperforms existing state-of-the-art two-stage design models and, compared with the existing one-stage co-design model, achieves a drastic tenfold improvement in success rate on hard tasks. The inherent flexibility of our atomic formulation enables, for the first time, seamless adaptation to non-canonical amino acid (ncAA) modeling. Our fully atomic framework establishes a new, versatile foundation for all-atom generative modeling that can be naturally extended to complex biomolecular systems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 5 May 2026.
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References · 16
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2022Flow Matching for Generative Modelingcited by 5,426
- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
- 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
- 2024Simple and Effective Masked Diffusion Language Modelscited by 800
- 2024Discrete Flow Matchingcited by 301
- 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 294
- 2023Flow Matching on General Geometriescited by 260
- 2023A Reparameterized Discrete Diffusion Model for Text Generationcited by 152
- 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 147
- 2024Diffusion Language Models Are Versatile Protein Learnerscited by 93
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