# An All-Atom Generative Model for Designing Protein Complexes

arXiv: https://arxiv.org/abs/2504.13075 · HTML: https://mlsanity.com/paper/2504.13075/

## 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 (most cited first, 28 of 28)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Diffusion Model Alignment Using Direct Preference Optimization](https://mlsanity.com/paper/2311.12908/) (2023)
- [Improved Techniques for Training Consistency Models](https://mlsanity.com/paper/2310.14189/) (2023)
- [Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design](https://mlsanity.com/paper/2402.04997/) (2024)
- [Flow Matching on General Geometries](https://mlsanity.com/paper/2302.03660/) (2023)
- [Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem](https://mlsanity.com/paper/2206.04119/) (2022)
- [ProGen2: Exploring the Boundaries of Protein Language Models](https://mlsanity.com/paper/2206.13517/) (2022)
- [SE(3)-Stochastic Flow Matching for Protein Backbone Generation](https://mlsanity.com/paper/2310.02391/) (2023)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [Diffusion Language Models Are Versatile Protein Learners](https://mlsanity.com/paper/2402.18567/) (2024)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [Structure-informed Language Models Are Protein Designers](https://mlsanity.com/paper/2302.01649/) (2023)
- [DPLM-2: A Multimodal Diffusion Protein Language Model](https://mlsanity.com/paper/2410.13782/) (2024)
- [End-to-End Full-Atom Antibody Design](https://mlsanity.com/paper/2302.00203/) (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)
- [AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies](https://mlsanity.com/paper/2308.05027/) (2023)
- [Full-Atom Peptide Design based on Multi-modal Flow Matching](https://mlsanity.com/paper/2406.00735/) (2024)
- [Protein Sequence and Structure Co-Design with Equivariant Translation](https://mlsanity.com/paper/2210.08761/) (2022)
- [Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization](https://mlsanity.com/paper/2403.16576/) (2024)
- [PPFlow: Target-aware Peptide Design with Torsional Flow Matching](https://mlsanity.com/paper/2405.06642/) (2024)
- [ProteinBench: A Holistic Evaluation of Protein Foundation Models](https://mlsanity.com/paper/2409.06744/) (2024)
- [DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing](https://mlsanity.com/paper/2306.01794/) (2023)
- [A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design](https://mlsanity.com/paper/2311.16126/) (2023)
- [Elucidating the Design Space of Multimodal Protein Language Models](https://mlsanity.com/paper/2504.11454/) (2025)
- [Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates](https://mlsanity.com/paper/2405.08205/) (2024)

## Built on by (most cited first, 10 of 10)

- [La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching](https://mlsanity.com/paper/2507.09466/) (2025)
- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)
- [Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling](https://mlsanity.com/paper/2505.21452/) (2025)
- [From Words to Amino Acids: Does the Curse of Depth Persist?](https://mlsanity.com/paper/2602.21750/) (2026)
- [A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion](https://mlsanity.com/paper/2605.03360/) (2026)
- [SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems](https://mlsanity.com/paper/2607.12380/) (2026)
- [SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign](https://mlsanity.com/paper/2609.03377/) (2026)
- [From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions](https://mlsanity.com/paper/2603.18505/) (2026)
- [Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling](https://mlsanity.com/paper/2607.23518/) (2026)
- [Joint Discrete and SE(3) Diffusion for Protein Interface Design](https://mlsanity.com/paper/2610.14699/) (2026)

## 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.