mlsanityData to 14 Oct 2026

An All-Atom Generative Model for Designing Protein Complexes

arXiv 2504.13075 ↗First posted 17 Apr 2025cs.LGcited by 10

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Abstract (arXiv)

Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 86 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

28 of 86 references.

References · 28

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2022Flow Matching for Generative Modelingcited by 5,702
  3. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  4. 2023Diffusion Model Alignment Using Direct Preference Optimizationcited by 790
  5. 2023Improved Techniques for Training Consistency Modelscited by 439
  6. 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 305
  7. 2023Flow Matching on General Geometriescited by 265
  8. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 236
  9. 2022ProGen2: Exploring the Boundaries of Protein Language Modelscited by 159
  10. 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 150
  11. 2020ProGen: Language Modeling for Protein Generationcited by 128
  12. 2024Diffusion Language Models Are Versatile Protein Learnerscited by 98
16 more
  1. 2022Conditional Antibody Design as 3D Equivariant Graph Translationcited by 75
  2. 2023Structure-informed Language Models Are Protein Designerscited by 68
  3. 2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 64
  4. 2023End-to-End Full-Atom Antibody Designcited by 49
  5. 2024Improved motif-scaffolding with SE(3) flow matchingcited by 43
  6. 2024Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generationcited by 36
  7. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodiescited by 35
  8. 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
  9. 2022Protein Sequence and Structure Co-Design with Equivariant Translationcited by 31
  10. 2024Antigen-Specific Antibody Design via Direct Energy-based Preference Optimizationcited by 30
  11. 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
  12. 2024ProteinBench: A Holistic Evaluation of Protein Foundation Modelscited by 25
  13. 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packingcited by 24
  14. 2023A Hierarchical Training Paradigm for Antibody Structure-sequence Co-designcited by 13
  15. 2025Elucidating the Design Space of Multimodal Protein Language Modelscited by 11
  16. 2024Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substratescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.