An All-Atom Generative Model for Designing Protein Complexes

Dongyu Xue; Quanquan Gu; Ruizhe Chen; Xiangxiang Zeng; Xiangxin Zhou; Zaixiang Zheng

REVIEW 4 major objections 5 minor 88 references

An All-Atom Generative Model for Designing Protein Complexes

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read APM generates protein complexes from scratch, with all-atom sidechain modeling producing roughly twice the computed binding energy of a Chroma-based baseline.

desk verdict Genuinely new multi-chain all-atom generative model with honest limitations, but the 'binding capability' claim rests on Rosetta energies and one SOTA sentence is undercut by its own peptide table. read the letter →

arxiv 2504.13075 v3 pith:4XLIQK7F submitted 2025-04-17 cs.LG

Ruizhe Chen , Dongyu Xue , Xiangxin Zhou , Zaixiang Zheng , Xiangxiang Zeng , Quanquan Gu This is my paper · ORCID

classification cs.LG

keywords multi-chainproteingenerationall-atomdesignflowmatchingsidechaintorsionanglescomplexesantibodypeptidebindingenergy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

APM is a generative model built to do what most protein foundation models do not: design multi-chain complexes, not just single chains, and output all-atom structures rather than backbones that need separate sidechain packing. The authors' central claim is that adding sidechain torsion angles to a residue-level flow-matching model is what makes inter-chain interactions learnable, and that this lets APM generate two-chain complexes from scratch with markedly more favorable computed binding energies than the Chroma baseline. In the headline comparison, 100-residue-plus-100-residue complexes generated by APM have a mean all-atom relaxed binding energy of -130.31 Rosetta energy units, versus -62.15 for Chroma redesigned with ProteinMPNN. The model also performs multi-chain folding and inverse folding and, after supervised fine-tuning or even zero-shot, improves antibody CDR-H3 and peptide design metrics. The authors are explicit that the actual binding effectiveness still requires wet-lab validation, so the claim is about computed binding strength until experimental confirmation.

What carries the argument

The load-bearing object is the all-atom residue representation — amino acid type, backbone frame in $\mathrm{SE}(3)$, and sidechain torsion angles $\chi \in [0,2\pi)^4$ — plus the three-module architecture that uses it. Module one (Seq&BB) is a flow-matching generator over discrete sequence tokens and $\mathrm{SE}(3)$ backbone frames, trained with decoupled noising of the two modalities plus a consistency loss; module two (Sidechain) is a one-step packer that predicts $\chi$ from the generated sequence and backbone; module three (Refine) takes the predicted all-atom structure and corrects sequence and backbone before the next denoising step. The integrated design matters because sidechain prediction needs clean sequences and structures, so the sidechain cannot simply be another flow-matching head without amino-acid-type leakage; instead it is activated only late in sampling ($t \ge 0.8$), and the Refine module lets all-atom information feed back into the backbone and sequence. A protein language model supplies sequence understanding to all three modules.

What would settle it

Synthesize genes for a set of APM-generated two-chain complexes spanning the reported ΔG range, express and purify the chains, and measure dissociation constants by surface plasmon resonance or isothermal titration calorimetry; if the measured affinities do not correlate with the reported Rosetta ΔG values, the claim that APM designs binding-capable complexes is unsupported.

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Extended reading notes

Core claim

On its own terms, APM's discovery is that a protein complex can be generated natively as a joint object — sequence, backbone, and sidechain conformations together — rather than as separate chains stitched together or as a backbone that is later packed. The model represents each residue by amino-acid type, an $\mathrm{SE}(3)$ backbone frame, and up to four sidechain torsion angles $\chi$, and it generates the sequence and backbone with flow matching while a dedicated sidechain module predicts $\chi$ and a refinement module re-optimizes the whole all-atom structure during the last part of sampling. The authors argue that the all-atom loop is doing real work: ablating it (APMBB, residue-level only) lowers computed binding strength and raises interface RMSD, and the sidechain torsion angles carry amino-acid-type information that would leak if the sidechain were noised jointly. In multi-chain inverse folding, APM's amino acid recovery is 61.26% versus 46.17% for ProteinMPNN, and in the downstream tasks APM's fine-tuned antibody designs have lower total and binding energies than the compared methods while its peptides are the only ones producing a meaningful share of high-quality DockQ designs.

Load-bearing premise

The load-bearing premise is that Rosetta's computed binding energy after relaxation is a trustworthy stand-in for real binding: if those energies do not predict what happens in a wet-lab binding assay, the paper's central claim about 'binding capabilities' reduces to a claim about favorable simulation scores.

Editorial extensions

If this is right

Designing a complex no longer has to be staged as separate backbone generation, sequence design, and sidechain packing: APM outputs all three together, and its ablation suggests the sidechain loop is essential to interface quality.
Complex generation with two chains of length 100-100 yields computed binding energies roughly double those of the Chroma-plus-ProteinMPNN pipeline, indicating the all-atom co-generation path is competitive at directly producing tightly bound interfaces.
Multi-chain inverse folding works: APM's 61.26% amino acid recovery on the multi-chain test set exceeds the single-chain-oriented ProteinMPNN baseline, so sequences of existing complexes can be redesigned while preserving structure.
Supervised fine-tuning turns the general model into a specialist: antibody CDR-H3 co-design and peptide design both improve, while zero-shot sampling still produces low computed binding energies, making the same checkpoint usable in both modes.
Longer binder design against targets such as IL-7RA, PD-1, and TNF-α is reachable zero-shot, with computed binding energies comparable to the RFdiffusion baseline across six of seven targets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the Rosetta proxy survives wet-lab testing, the natural next step is to measure the correlation between ΔG rankings and experimental affinities; a positive correlation would make the model's energies usable as a screening prior for binder leads.
The three-module split suggests a general recipe for discrete-continuous co-generation: keep the sidechain as a one-step conditional predictor rather than a noised channel, and let refinement feed all-atom information back into the backbone. The same pattern could transfer to other joint design problems, though the paper does not claim this.
The chain-by-chain sampling mode produces weaker interfaces and visible clashes, which suggests that simultaneous generation is important for interface complementarity; the paper reports this behavior but does not elevate it to a design principle.
Zero-shot antibody designs with low binding energy but unnatural CDR-H3 shapes imply that the model's binding prior is generic rather than antibody-specific, and that targeted fine-tuning changes the binding mode rather than merely improving affinity.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a real contribution—a three-module all-atom generative model that natively handles multi-chain complexes, with code out and a lot of careful engineering detail. The reader's conditional verdict and the stress-test note are about right, and I'd send it to review.

What's actually new: APM is, as far as I can tell, one of the first systems to generate multi-chain complexes end-to-end with explicit sidechain torsions, using flow matching for sequence+backbone, a separate sidechain module, and an all-atom refine module. The decoupled noising and two-phase training are sensible original choices, and the ablation APM vs APMBB in Table 4 does show the all-atom modules matter for interface quality. The multi-chain inverse-folding results (AAR 61% vs ProteinMPNN 46%) are strong, and the antibody CDR-H3 numbers look competitive. The paper is honest about its own limitations—folding is weak, RefineModule is limited, multi-chain generation degrades beyond two chains—and the appendix is thorough.

Soft spots, in proportion:

1. The central claim in the abstract, 'designing protein complexes with binding capabilities from scratch,' rests entirely on Rosetta ΔG values after relaxation. The authors say in Section F that wet-lab validation is still needed. That is exactly right, and it means the headline claim should be read as 'favorable Rosetta binding energies,' not demonstrated binding. The reader's stress-test note on this is fair. It's a standard proxy in the field, but it is load-bearing and unverified.

2. The text overstates one table. In peptide design (Table 5), the paper says APM 'significantly outperforms other methods' on functionality, but RFDiffusion has better mean ΔG (-23.27 vs -19.90) and a higher fraction below zero (78.6% vs 69.3%). That sentence should be corrected or qualified; it's a small thing but it undercuts trust.

3. The multi-chain generation comparison is thin: only Chroma (+ProteinMPNN) as baseline, no error bars or significance tests, and fixed length combinations that may not reflect native complex difficulty. This is addressable with more baselines (MultiFlow, RFDiffusion) and per-complex statistics.

None of these are fatal. The architecture is described precisely enough to reproduce, code is released, and the honest tone gives me confidence the authors are not hiding the load-bearing assumption.

Who this is for: computational protein designers and anyone tracking generative models for complexes. It deserves a serious referee; I'd accept it and ask for claim softening, error bars, and one more baseline. I'd probably cite it as a point of comparison in my own work.

Referee Report

4 major / 5 minor

Summary. The paper presents APM, an all-atom generative model for multi-chain protein complexes. The model is composed of three modules: a sequence/backbone flow-matching module, a sidechain prediction module, and a refinement module, trained in two phases with a mixture of single-chain and multi-chain protein data and ESM2 embeddings. The authors report benchmarks on single-chain folding and inverse-folding, multi-chain folding and inverse-folding, unconditional multi-chain complex generation, antibody CDR-H3 co-design on the RAbD benchmark, peptide design on PepBench/LNR, and zero-shot binder design against several targets. The headline claim is that APM designs protein complexes with binding capabilities from scratch, supported mainly by pyRosetta binding-energy calculations and by Boltz-1 confidence and DockQ metrics.

Significance. APM addresses a timely gap in multi-chain all-atom protein generation. If the reported results are reliable, the model is a useful contribution: it natively supports multi-chain generation without pseudo-linkers, integrates sidechain torsion angles for inter-chain modeling, and achieves competitive or superior scores on standard antibody and peptide benchmarks. The authors release code, and the two-phase training scheme with a consistency loss and a refine module is clearly described. However, the paper's strongest claim, that APM designs complexes 'with binding capabilities from scratch,' is only supported by computational proxies (Rosetta ΔG, pLDDT/ipTM), which the authors themselves note require wet-lab validation; the headline should be scaled back accordingly.

major comments (4)

[§4.3.2, Table 4; §F] The central claim of the abstract ('designing protein complexes with binding capabilities from scratch') is supported only by pyRosetta relaxation and ΔG calculations in Table 4 and by pLDDT/ipTM confidence scores in Section F. The authors explicitly concede in Section F that 'the actual effectiveness still requires validation through wet lab experiments.' No ITC, SPR, co-purification, or other affinity measurement is reported. As written, the headline claim is load-bearing and stronger than the evidence: the results establish that APM generates complexes with favorable Rosetta energies under the chosen relaxation protocol, not that it designs binders. I recommend either adding experimental validation or qualifying the abstract and Section 4.3.2 claims as computational binding-affinity predictions.
[§4.4.2, Table 5] The claim that APM's peptide designs show 'significantly outperforming other methods' on functionality is not supported by Table 5. In that table, RFDiffusion achieves a better mean ΔG (-23.27 vs -19.90 for APMSFT) and a higher fraction of favorable complexes (%<0 = 78.58 vs 69.34). RFDiffusion also has higher pLDDT (69.65 vs 60.36), ipTM (0.73 vs 0.66), and Success (46.28% vs 29.22%). APM's advantage is limited to DockQ and the high-quality DockQ fraction. The text should be revised to report these performance gaps accurately and to describe APM as competitive on functionality and foldability rather than superior.
[§4.3.2, Table 4] The comparison for binding energies uses only three fixed length combinations (50-100, 100-100, 100-200), and the reported averages and medians are presented without variance, confidence intervals, or statistical tests. Because the difficulty of achieving favorable Rosetta energies depends strongly on chain lengths and composition, a near-constant length regime cannot establish a general claim of superior inter-chain modeling. I ask for seed-level variability, analogous to Table 7 in Appendix D.2, and, if feasible, a benchmark on a broader set of native complex geometries with matched chain-length distributions.
[§4.3.2, APMBB ablation] The sentence 'this proves the importance of the all-atom information in the inter-chain interactions modeling' is too strong for the reported ablation. APMBB differs from full APM not only in the absence of all-atom information in the backbone generation path, but also in the absence of the SidechainModule and RefineModule entirely, including their sequence-level corrections. The observed energy gap could be due to the refinement module or to differences in the sampling schedule rather than to the sidechain torsions per se. A cleaner ablation would feed the same Seq&BBModule predictions through the Sidechain/Refine modules with and without the torsion features, or otherwise isolate the information channel.

minor comments (5)

[Table 4] The APM entry for 100-100 ΔGRAA reads '-130.31-134.57' and should be '-130.31/-134.57'; this formatting issue appears in at least one other table row.
[Table 3, §4.3.1] The text should state more explicitly that the multi-chain folding comparison favors APM only against Boltz-1 without MSA; the gap to Boltz-1 with MSA (RMSD 12.6 vs 5.40, TM 0.64 vs 0.87) is large and deserves a clearer caveat in the main discussion.
[§A.3] The word 'resdiue' should be 'residue'.
[§3.3.1, Eq. (19)] The consistency loss in Eq. (19) uses tS and tT before the notation for the decoupled noising times is formally defined; a brief definition would improve readability.
[§3.4, Eq. (24)] The sequence sampling temperature schedule in Eq. (24) uses hyperparameters Tmax and λ whose numerical values appear only in Appendix D.1; stating them in the main text would aid reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: APM's headline results are measured with external instruments and benchmarks, and the paper's training objectives do not contain the evaluation metrics.

full rationale

APM's derivation chain is self-contained. The training objectives are flow-matching reconstruction of sequence and backbone (Lflow-matching), sidechain packing and reconstruction (LPacking), and a refinement correction loss (Lcorr) against PDB ground-truth structures; none of these losses includes pyRosetta ΔG, Boltz-1 confidence, DockQ, or the RAbD/PepBench metrics. The headline binding-capability results in Table 4 are computed by pyRosetta after relaxation for both APM and the Chroma baselines under the same length combinations, and no equation in the paper makes the reported ΔG a fitted parameter of APM or a term in its training loss. The RAbD, PepBench/LNR, and binder-design evaluations likewise rely on external benchmarks and predictors (SAbDab/RAbD, Boltz-1, DockQ, pyRosetta), and the SFT protocols are not shown to optimize the evaluation metrics. Several citations are to the authors' own prior work (ProteinBench, AbDPO, DPLM), but these supply evaluation protocols, baselines, and related-work context rather than the validity of APM's generative mechanism; they are not load-bearing in the sense of making a prediction equal to its input. The only notable caveat is Section F's explicit concession that 'the actual effectiveness still requires validation through wet lab experiments,' which is an external-validity limitation on the binding-capability claim, not a circular derivation. No step was found where a result is defined in terms of itself or where a fitted value is renamed as a prediction.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

APM is a deep learning system; its performance rests on modeling choices (torsion representation, flow matching schedules, PLM integration) and on evaluation assumptions (Rosetta ΔG as binding proxy, representative test sets). No new physical entities are introduced. The hand-chosen hyperparameters listed above influence results and are not derived from first principles.

free parameters (7)

Sidechain/Refine activation threshold T = 0.8
Hand-chosen cutoff; the SidechainModule and RefineModule are only activated for the last 20% of denoising steps, which directly determines output quality.
Consistency loss weight = 0.3
Hand-picked scale for L_consistency in L_Seq&BB; affects the smoothness of the generative trajectory.
Refine auxiliary loss weights = 0.25 each
Weights for L_BB-FAPE and L_dist in L_Refine; hand-set balance between correction and auxiliary objectives.
Inference rotation schedule constant c = 10
Inference uses an exponential rotation schedule exp(-c*t) while training uses a linear schedule; this train/test mismatch is chosen by hand and is not ablated (Section A.1).
Sequence sampling temperature parameters = T_max=30, lambda=30
Exponential decay temperature for amino-acid sampling; a hand-designed decoding heuristic that strongly affects sequence quality and diversity (Section D.1).
Data filtering thresholds = pLDDT >85 (Swiss-Prot), pLDDT >95 (AFDB)
Hand-selected thresholds that determine which single-chain samples enter training (Section 4.1).
Folding/inverse-folding task probability = 50%
Chosen fraction of training steps that present a conditional task to enforce sequence-structure dependency (Section 3.1).

assumptions (5)

domain assumption Sidechain conformation can be fully represented by amino acid type plus up to four torsion angles; this suffices for modeling inter-chain interactions.
Section 3.1 defines all-atom structure via torsion angles; if interactions require finer detail, the all-atom claim weakens.
domain assumption The uniform SO(3) prior and geodesic interpolation define a valid flow-matching path; the exponential inference schedule with c=10 is compatible despite a linear training schedule.
Section A.1; the train/test schedule mismatch is untested and could bias structure quality.
domain assumption Single-chain ESM2-650M representations, aggregated per chain, generalize to multi-chain complex modeling.
Section 3.2.3 uses ESM2, trained on single chains, to encode each chain in a complex separately.
domain assumption The multi-chain test set of 273 proteins (samples missing cluster IDs) is representative for multi-chain folding and inverse-folding evaluation.
Section 4.3.1; these convenience samples were dropped from training due to missing cluster IDs and may not reflect the full distribution.
domain assumption Rosetta relax ΔG is a meaningful proxy for binding affinity in evaluating generated complexes.
Section 4.3.2 uses pyRosetta energies; Section F admits wet-lab validation is still required, making this a load-bearing evaluation assumption.

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Cite this review

Pith. "Pith review of An All-Atom Generative Model for Designing Protein Complexes." pith.science (2026). https://pith.science/paper/4XLIQK7F

@misc{pith2026250413075,
  author       = {Pith},
  title        = {Pith review of: An All-Atom Generative Model for Designing Protein Complexes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4XLIQK7F}},
  note         = {Machine review of arXiv:2504.13075}
}

read the original abstract

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.

Figures

Figures reproduced from arXiv: 2504.13075 by the authors.

**Figure 1.** Interactions cause minor atom-level protein structure changes in the binding surface (middle colored part). Blue indicates the isolated structure, pink indicates the binding structure. design models for specific functions. The former includes methods such as protein folding models (Jumper et al., 2021; Lin et al., 2023; Baek et al., 2021), inverse-folding models (Dauparas et al., 2022; Hsu et al., 2022; Zheng et a… view at source ↗

**Figure 2.** Overview of APM. APM consists of three modules: (1) A flow-matching based Seq&BB Module for generating backbone structure and sequence simultaneously; (2) a Sidechain Module for generating the all-atom structure based on the previous module’s generation; (3) A Refine Module adjusts the sequence and structure with all-atom information. The iterative denoising process enables the generation of multi-chain proteins wit… view at source ↗

**Figure 3.** The two-phase of the training process of APM. In training phase I, the Seq&BB Module and Sidechain Module are trained separately. In training phase II, the three modules form the integral APM, and are trained in an iterative paradigm. In any phase, the training data is a mixture of PDB (Berman et al., 2000) single/multi-chain proteins, Swiss-Prot proteins, and AFDB (Varadi et al., 2022) proteins. ing sidechain confo… view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Showcases of the three length combinations. For each case, the gray structure represents APM’s generated structure and the colored structure represents the backbone relaxed structure. Different chain is highlighted with different colors. We also report the two ∆G and t…

**Figure 5.** Figure 5: The distribution of four sidechain torsion angles in all amino acid types. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

**Figure 6.** Figure 6: APM generated proteins with chain lengths of 50-100, 100-100, 100-150, 100-200, 100-300, and 200-200. entire training process is completed, the Seq&BB Module can be used independently, allowing for protein generation at the residue level. To verify the importance of al…

**Figure 7.** Figure 7: APM generated proteins with more than 2 chains. The top row, generated protein complexes composed of 4 chains; the bottom row, generated protein complexes composed of 3 chains. The length of each chain is highlighted with a unique color. 90% of single secondary structu…

**Figure 8.** Figure 8: Samples generated by APM in the “chain-by-chain” manner. The generation order is from short chain to long chain, the length of each chain is highlighted with a unique color. all chains simultaneously. As shown in [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

**Figure 9.** Figure 9: The CDR-H3s generated in a zero-shot manner display distinct patterns compared to natural ones. The antigen is represented in pink, the antibody heavy chain in purple, and the antibody light chain in blue, with the CDR-H3 highlighted in red. The differences between CDR…

**Figure 10.** Figure 10: 5 ground truth samples with ∆G exceeding 0. The receptor is shown in white, with the peptide highlighted in red. Slight clashes are marked by red boxes. Additionally, ∆G for each sample are listed below them. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_10.png]

**Figure 11.** Figure 11: Visualization of peptides generated by different methods. The blue regions represent the given receptors, while the pink regions show the generated peptides, with all-atom structures displayed at the interface regions. From left to right: Ground truth structures, APM,…

**Figure 12.** Figure 12: Folded structures of sequences generated by different methods using Boltz-1. The structures are colored according to the AlphaFold-style pLDDT confidence scheme. From left to right: Ground truth structures, APM, APM zero-shot, PepGLAD, and PPFlow. The PDB IDs for the …

**Figure 13.** Figure 13: Visualization of binder design results across six protein targets. From left to right: (1) GroundTruth: native complex structures, (2) APM: structures generated by APM, (3) RFDiffusion: structures generated by RFDiffusion, (4) APM(folded): highest pLDDT APM sequences …

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Reviewed August 16, 2026 · model on record in the stance chip above.

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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

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