A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911646983651328 |
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| author | Cheng, Chaoran Guan, Jiaqi Ren, Milong Gong, Chengyue Liu, Cong Chen, Xinshi Liu, Ge Xiao, Wenzhi |
| author_facet | Cheng, Chaoran Guan, Jiaqi Ren, Milong Gong, Chengyue Liu, Cong Chen, Xinshi Liu, Ge Xiao, Wenzhi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_03360 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion Cheng, Chaoran Guan, Jiaqi Ren, Milong Gong, Chengyue Liu, Cong Chen, Xinshi Liu, Ge Xiao, Wenzhi Quantitative Methods Machine Learning 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. |
| title | A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2605.03360 |