English

A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion

Quantitative Methods 2026-05-06 v1 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2605.03360,
  title  = {A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion},
  author = {Chaoran Cheng and Jiaqi Guan and Milong Ren and Chengyue Gong and Cong Liu and Xinshi Chen and Ge Liu and Wenzhi Xiao},
  journal= {arXiv preprint arXiv:2605.03360},
  year   = {2026}
}
R2 v1 2026-07-01T12:49:50.408Z