DCFold: Efficient Protein Structure Generation with Single Forward Pass
Authors: Zhe Zhang
Yuanning Feng
Yuxuan Song
Keyue Qiu
Hao Zhou
Wei-Ying Ma
Venue: ICLR 2026 Oral (2026)
Keywords: consistency model
protein structure generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has established AlphaFold3 as a foundation model for diverse generation and design tasks. However, its iterative design substantially increases inference time, limiting practical deployment in downstream settings such as virtual screening and protein design. We propose DCFold, a single-step generative model that attains AlphaFold3-level accuracy. Our Dual Consistency training framework, which incorporates a novel Temporal Geodesic Matching (TGM) scheduler, enables DCFold to achieve a 15× acceleration in inference while maintaining predictive fidelity. We validate its effectiveness across both structure prediction and binder design benchmarks.

Published as a conference paper at ICLR 2026
DCFOLD: EFFICIENT PROTEIN STRUCTURE GENERA- TION WITH SINGLE FORWARD PASS
Zhe Zhang..2 Yuanning Feng Yuxuan Song!.4 Keyue Qiu'
Hao Zhou
Wei- Ma
Institute for Al Industry Research (AIR), Tsinghua University
2 Department of Computer Science and Technology. Tsinghua University
3 School of Computer Science and Technology, Huazhong University of Science and Technology ByteDance Seed
ABSTRACT
AlphaFold3 introduces diffusion-based architecture that elevates protein struc- ture prediction all-atom resolution with improved accuracy. This state-of-the art performance has established AlphaFold3 as foundation model for diverse generation and design tasks. However, its iterative design substantially increases inference time, limiting practical deployment in downstream settings such as vir- tual screening and protein design. We propose DCFold, a single-step genera- tive model that attains AlphaFold3-level accuracy. Our Dual Consistency train- ing framework, which incorporates a novel Temporal Geodesic Matching (TGM) scheduler, enables DCFold achieve 15x acceleration in inference while main- taining predictive fidelity. We validate its effectiveness across both structure pre- diction and binder design benchmarks.
INTRODUCTION
Proteins realize their biological functions through intricate three-dimensional con- formations, and predicting such struc- tures has long been a central challenge in computational biology. AlphaFold2 marked breakthrough by combining mul- tiple sequence alignments with geomet- ric constraints an end-to-end frame- work, achieving experimmetll accu- racy (Jumper et al. 2021). Building on this foundation, AlphaFold3 reformulates the architecture into an all-atom framework and introduces a diffusion-based structure module, thereby enabling the generative modeling of not only proteins but also a wide spectrum of biomolecular complexes (Abramson al. 2024). Consequently, this series of models are widely adopted as foundation models for downstream appli- cations such virtual screening and pro- tein design (Alhumaid & Tawfik, 2024
Folding time sequence length
Surcess
100
AF3
200
DCFold
82.9
78.6
80
Folding Time (6)
150
Success Rate (%)
60
Dimuston 59%
40
50-
20
Peirformer 30%
0.
255
256-383
512-639
640- 767
895
896
AF3
DCFold
384-5
76
Figure The acceleration ratio and generative quality
of DCFold on Posebusters V2.
Baselious et al. 2024 Jendrusch et al.) 2025 Frank et al. 2024 Bennett et al.) 2023). However,
AlphaFold3's architecture, which relies on iterative Pairformer recycling and multi-step diffusion
Ho al. 2020), requires substantially greater computational overhead than AlphaFold2, restricting its accessibility in downstream workflows.
More specifically, we observe that on long sequences, the execution time of AlphaFold3 is measured in minutes, which severely limits its usability in downstream tasks that demand high throughput. For instance, small-scale laboratory screening often requires predictions for thousands of candidates (Li al. 2023), and when extended large public databases, this number grows infeasible
"Correspondence Hao Zhou (zhouhao wwwpaa/ww@aiiisnnggaac.....o

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scale; protein design tasks typically involve comparable computational demand. Previous work such as BindCraft has attempted to mitigate this by manually reducing the number of recycling iterations on simpler structures, thus trading accuracy for efficiency (Pacesa et al., 2024). However, such compromises inevitably degrade predictive performance. Moreover, in hallucination-basee approaches, the multistep iterative refinement process hinders feasible gradient backpropagation, ultimately preventing the broader community from adopting AlphaFold3 as foundation model for diverse applications.
To accelerate the diffusion process, recent advances in generative modeling have explored the use of high-order solvers and consistency models. While high-order solvers improve efficiency, they rarely reduce the number of sampling steps below 10 (Lu et al. 2022 Zhao al.
2023 Consistency mod-
els. on the other hand, have achieved remarkable success in image generation and benefited from refined training schedules (Song et al. 2023 [Sogg Dhariwal 2023 Lu & Song 2024). However, directly applying them to AlphaFold3 faces two major challenges: (i) standard schedules assume fixed- dimendsammnii data and pair steps by constant Euclidean distance, which fails accommodate variable protein sequence lengths and leads to unstable training dynamics (details in Section (.4);
and (ii) AlphaFold3's architecture also relies on iterative Pairformer recycles, introducing an addi- tional bottleneck that conventional diffusion consistency methods cannot address.
To address these challenges, we propose DCFold, single-step folding model trained under Dual Consistency framework that attains AlphaFold3-Ievel accuracy. We mitigate the inference bottle- neck by jointly enforcing Pairformer Consistency and Diffusion Consistency, thereby eliminating both sources iterative overhead. Crucially, we address the fundamental challenge of diffusion acceleration through rigorous theoretical derivations, and subsequently introduce novel Temporal Geodesic Matching (TGM) scheduler, which adaptively pairs timesteps in the intrinsic geometric space of proteins. Together, these innovations preserve the predictive accuracy of AlphaFold3 while drastically reducing inference costs, enabling one-step predictions that are both efficient and reliable. We extensively validate the effectiveness of DCFold on structure prediction benchmarks, which provide rigorous and fair evaluation protocol. Beyond this standard setting, we further assess DC- Fold in the more practical binder design tasks, where both inference speed and structural accuracy critical to this setting.
In short, we summarize our contributions as follows:
We propose DCFold, inference-efficien structure prediction model that achieves perfor- mance and flexibility comparable to state-of-ther the-art applications. By leveraging the Dual Consistency framework, DCFold eliminates the iterative overhead inherent in AlphaFold3's architecture.
We identify the key limitations of conventional consistency model (CM) methods when applied to variable-length protein sequences, and introduce Temporal Geodesic Matching (TGM) for a novel consistency schedule that both stabilizes training and yields improved performance.
We evaluate the performance of DCFold across a diverse set of benchmarks and set- tings. On both Posebusters V2 and Recent PDB, it reaches AlphaFold3-level accuracy while achieving a notable 15x speedup. Implemented in the binder design pipeline, DC- Fold demonstrates strong foundational capabilities while employing a lightweight archi- tecture that ensures feasible gradient propagation. This design significantly improves the success rate of in silico screening by enabling faster and more reliable candidate evaluation.
2 PRELIMINARY
Diffusion models have emerged as a powerful class of generative models, achieving state-of-the- art performance across image, audio, and molecular generation tasks (Ho et al., 20200 Rombach et al. [2222, Trippe et al. 2022). A key limitation of standard diffusion samplers is their reliance on dozens to hundreds of function evaluations, which renders inference prohibitively expensive in high-dimensional settings such as protein folding. To address this bottleneck, recent work has focused on diffusion acceleration, aiming to distill or redesign the sampling process into far fewer steps. Among these approaches, Consistency Models (CMs) (Song et al. (2023) provide a principled framework built upon the probability flow ODE (PF-ODE), which establishes a bijective mapping between the clean data distribution and the noise distribution. CMs introduce consistency function fo(2,,t) t) that directly maps a noisy sample z, at time t back to the clean signal Zg. subject to the boundary condition fo 0) Fi- Training then proceeds by discretizing the PF-ODE into
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Binder
MSA
Eubeola
Pairhmeer
Painformur
Prizforner
D Musio
?
Hallucination
Complex
Assembly
l'eature Extmetion
Irpul Feature
Binder Hallucination
MSA
Eubeolla
Parrhmeer
Complex Assemubly
Festure Extraction
Impui Fomure
Pairformer Consistency
Diffusion Consistnnyy
Figure 2: Overview of Dual Consistency framework (top: AlphaFold3; bottom: DCFold).
curriculum of time intervals t, and minimizing a loss that enforces functional consistency across adjacent timesteps,
Lcm = [wo(lidd (Ja(Eta) 45+1), (o 4:)]]
(1)
where 12 + denotes positive weighting function, -) is metric function, 0- is an EMA copy of the network, and žt, is obtained by one-step PF-ODE integration. This objective ensures that the model predictions are invariant the choice of sampling timestep. thereby col- lapsing multi-step trajectory into a single-step or few-step generator. Building on this foundation, subsequent refinements such iCT (Song Dhariwal 2023), sCM (Lu & Song 2024), and ECM GGeng et al. 2024), have optimized the weighting functions, discretization schedules. and training methodologies, resulting in substantial improvements in both efficiency and sample quality.
3
METHOD
3.1 OVERVIEW
We introduce DCFold, high- high-aceuracy single-step predictor. In Section 3.2, ponents of the Dual Consistency framework, which enforces consistency across the two major bot- tlenecks of AlphaFold3. In Section 3.3 zoom in on the diffusion acceleration challenge and identify the key issue with prior consistency-basee methods when training on variable-length se- quences within diffusions. To tackle this challenge for complex structure prediction, propose Temporal Geodesic Matching (TGM), which stabilizes training on the protein sequence modality.
describe the com-
3.2 DUAL CONSISTENCY
We identify the major factors impeding AlphaFold3's inference efficiency as the iterative diffusion process and Pairformer recycling, as illustrated in Figure I To address the first challenge, we investigate the behavior of AlphaFold3 under few- step sampling and find that its failure pri- marily arises from the sampling procedure itself. The default strategy of injecting extra stochastic noise and enlarging the ODE step size turns out to be detrimental in this regime: the enlarged step size significantly amplifies the bias in ODE predictions. To stabilize performance, we modify the sampler by disabling noise injection (setting the noise factor % = 0), fixing the rescaling factor À = and normalizing the step size with 77 1, thereby enabling stable one-step sampling.
Stage (i)
Module
Leonfieence 10
Laitiusion
Cpairformer
Diffusion
Pairformer
10
Table 1: Training stages and the weights of each term.
The first challenge concerns computational efficiency. After enabling one-step sampling, the Pair- former becomes the critical bottleneck. To tackle this, introduce Dual Consistency, which applies consistency learning to both the diffusion module and the Pairformer.
Diffusion Consistency Although we already have a functional one-step sampler, we aim to max- imize its utility. Specifically, apply consistency distillation to the diffusion module, aligning its

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Algorithm Temporal Geodesic Matching (TGM)
Require: Dataset D, pretrained diffusion model 0, noise distribution p(t), weighting function w(t), [0,
training progress stx_s
steps
1: while 0 not converged do
2: Sample zo D, N), pt))
3:
r' t max (r(t, 0)
4:
It To + tc; Ze t 1o trlc
5:
[(w(() [o(t,, (Er)
I- using the same random seed
6:
0 Vo
7: end while
single-step performance with that of the multi-step counterpart, which also provides natural warm- up for the subsequent Pairformer consistency stage. The training objective minimizes the MSE between the outputs of the diffusion module timestep t and reference timestep Formally, the diffusion consistency loss
Lammsiin Er,t.. (t()MMS (fo( - (x,,)
where fe denotes diffusion module parameterized by and sg(0) denotes "stop-gradient" operator. We find w(t) to have negligible effect in experiments and therefore set (t) =
Pairformer Consistency For the most critical bottleneck in AlphaFold3, Pairformer, we observe that the architecture updates internal protein representations iteratively across multiple cycles. While increasing the number of cycles generally improves prediction accuracy, it also scales inference time linearly. Importantly, because each Pairformer cycle depends on the output of the previous one, single forward pass through the network inherently provides representations corresponding to differ- ent cycle depths. This allows us to assess the model's progressive refinement of structural accuracy without the need for explicit time sampling as required in diffusion-baset denoising processes.
To exploit this property, we introduce cycle consistency loss. Suppose pairformer runs for N cy- cles (with N our experiments). After the n-th cycle, the model produces a pair representation on and single representation 8n. We directly adopt the total transmission error the loss function:
C pairformer
(MSB (Zi, 21-1) + MSE (8;, 8141)).
(3)
Notably, we adopt the weighting strategy from AlphaFold's supervised MSE loss. In particular, positions corresponding to nucleic acids and small molecules assigned higher weights than amino acids. This ensures that structurally sensitive residues contribute proportionally to the loss. Let the column vector denote the per-token weighting coefficient used AlphaFold3. For the single representations in both Diffusion Consistency and Pairformer Consistency, we directly apply a as the weight. In contrast, for the pair representations in Pairformer, we adopt multiplicative composition, using vava as the weighting matrix, where the square root is applied element- wise.
We further find that incorporating the confidence loss Leonfidence from AlphaFold3's confidence head improves training stability, where Leonfidecce is defined as:
Leonfidence Lptah
Lpde + Cresatved + O'pae Lquxs
where Opac 1, and the definitions of all loss terms follow AlphaFold3. Consequently, training procedure can be summarized two stages: (i) train a one-step sampler, where only the diffusion module is updated, with the training objective given by Leonfidence and Lailtusion (ii) apply pair- former consistency, where only a 16-block Pairformer updated, with the training objective given by Leontidence and Lpantoemmr- We summarize the weights of them in Table I
3.3 TEMPORAL GEODESIC MATCHING
While consistency-Be based methods have shown promise, directly applying them to complex architec- tures like AlphaFold often results in weight collapse, high training cost, or reliance task task-specific mappings. The core issue lies in scheduling for variable-size outputs such as protein structures. Conventional schedulers pair timesteps (1,r) at fixed Fuclidean intervals, producing ill-posed
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curriculum: on long sequences, even small At triggers drastic distribution shifts that demand unre- alistic predictive leaps, whereas on short sequences the same interval provides only weak signals. This mismatch overlooks the non-uniform accumulation of information with data dimensionality, leading to instability and collapse.
To address these limitations, we introduce Temporal Geodesic Matching (TGM), a general and scal- able distillation framework. TGM explicitly selects training pairs (t, such that their geodesic dis- tance on the temporal information manifold is preserved, thereby offering a principled mechanism to stabilize training and extend consistency learning large-scale protein modeling tasks. Unlike mnclldean-based heuristics, TGM aligns the distillation dynamics with the intrinsic statistical geom- etry of the diffusion trajectory. By doing so, it ensures stability and fidelity even in high-dimensional structured output spaces such as protein backbones.
We begin by formalizing the diffusion trajectory as a geometric object. Let pa pe(2) denote the family of intermediate distributions induced by the forward diffusion process. We interpret it as coordinate charting a one-dimensional temporal information manifold Mr, where each point corresponds to a distribution (z)).
Definition We define the temporal metric via the Fisher information with respect to the diffusion time t, which we refer to the temporal Fisher information, and use it the Riemannian metric tensor of Me:
g(t) ((t:=I(()=BB))) =
(4)
Definition 2 On the manifold where the temporal Fisher information serves as the Riemannian metric tensor, the geodesic distance between two time points and r is defined as the corresponding geodesic length:
(t,r,r) = S: VI(T)dr.
(5)
Our central thesis is that a stable and efficient distillation process must be grounded in the Kullback- Leibler (KL) divergence, as this is the canonical metric underlying the variational objective of diffu- sion models. We motivate the introduction of the Fisher information through the following theorem:
Proposition 1 (Local Metric-KL Equivalence) For a small step Al = - 2 0, the geodesic distance between neighboring distributions is given by:
dg dt(t,r) V2DKL. (x))lpp)(()) + -(
(6)
The proof of Proposition is provided the Appendix A. The metric d provides principled
measure of distributional discrepancy along the temporal axis. Building this, TGM stabilizes
steps E [0, 1], training by enforcing a consistent alignment rule: for given training progress u max steps
each timestep is paired with a reference point at a fixed temporal distance, i.e., d r) = C(u), where ((u) a monotonically decreasing function. In our experiments, we specify C(O) = Co as a hyperparameter, C(1) = 0, C(u) = Co(1 - u) > O, and approximate r(4, t Co (1 w) via one-step Buler method. While it is also feasible to employ higher-order Vz(t)
numerical solvers, we did not observe significant performance gains from doing so. Furthermore, provide the analytical form of I(t):
Proposition 2 For any diffusion model that satisfies the classical setting of po(x)zo) = N(x;p= a())zo,0² (I)T):
(t))
á(t) (tt) 100l
I(t) Exo--paata
2D
(tt)
where denotes the dimensionality of the vector:
This analytical form underscores the universality of TGM. In most generative tasks, data natu- rally be represented as fixed-length vectors. Furthermore, when normalized (as in image generation) or invariant to random rotations (as in protein folding), the ||æro|| term admits simplification to Var(z)) under the assumption E[zo] = O. In our experiments, due to AlphaFold's adoption of the
EDM framework et 2022 we present here the specific form of I(t) that is used:
1/p 8max
1/P) 8
2D p
I(t)
(8)
1/p Smax
1/P L/p min
+ (1 - 1)
8
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Table 2: Posebusters V2 RMSD benchmark results. RMSD below different thresholds.
We report the percentage of predictions with
Best (%)
Worst (%)
Method
<
1
<
2
< 3
5
1
< 2
< 3
AlphaFold3
67.14
82.86
87.14
93.81
45.71
70.00
79.05
87.62
AF3 ODE
51.43
74.77
83.81
92.38
37.62
66.19
75.71
87.62
DCFold (Ours)
58.10
78.57
86.67
94.29
46.67
71.43
80.00
90.48
Table 3:
TM-score and Success Rate (SR) on different protein categories in the Homology Recent
PDB dataset. Values in parentheses denote the absolute improvement relative to AF3 ODE.
PL-complex
Monomer
PP-complex
Method
TM-score
SR (%)
TM-score
SR (%)
TM-score
SR (%)
AF3 ODE
0,815
92.3
0.830
92.9
0.763
87.0
AlphaFold3
0.810 (-0.6)
93.9 (+1.6pp)
0.839 (+1.0)
94.5 (+1-6pp)
0.788 (+3.2)
91.1 (44.0pp)
DCFold (Ours)
0.824 (+1.2)
94.9 (+2.6pp)
0.850 (+2.3)
95.7 (+2.9pp)
0.800 (+4.8)
92.2 (+5.2pp)
where the definition of Smin and Smax follow EDM, which used in AlphaFold3's diffusion pro- cess to control the noise strength. Here we incorporate the data dimensionality D into the training schedule to balance the differences in learning difficulty across amino acid sequences of varying lengths. Importantly, as the dimensionality increases, the KI. divergence between distributions ac- cumulates linearly, causing classical consistency training to exaggerate information disparities for long sequences. And we provide in Algorithm [ the procedure for applying TGM to the diffusion module.
3.4 DOWNSTREAM TASK
After ensuring the consistency of AlphaFold3, we find that our method now holds substantial po- tential for downstream applications. As a representative example, we validate the effectiveness of DCFold in the task of binder design. This task typically requires models to perform large-scale sam- pling, followed by stringent multi-stage filtering to eliminate implausible sequences, leaving only small subset of viable candidates. Moreover, in binder hallucination based design frameworks, network must be fully differentiable and amenable to gradient- based optimization (Pacesa al.) 2024). These properties make DCFold particularly well-suited for this setting, allowing it to fully demonstrate its performance advantages. The experimental details are presented in Section 4.3
4 EXPERIMENT
We design experiments evaluate both the accuracy and practical utility of DCFold. In Sec- tion 4. we evaluate the structural prediction capability of DCFold, showing that DCFold matches or surpasses AlphaFold3 while reducing cost. Section 4.3 assess binder hallucination, demon-
strating that the reshaped output distribution improves downstream design success. Section 4.4 iso- lates the effect of TGM and shows its advantage over prior consistency schedules. Together, these results highlight the efficiency, stability, and applicability of DCFold across protein modeling tasks.
4.1 STRUCTURE PREDICTION
In this section, we demonstrate that DCFold retains strong capability for one-step prediction.
Baselines We compare these AlphaFold3 variants: (i) AlphaFold3 (Abramson et al. 2024]
The original configuration of AlphaFold3 employs the full set of recycling cycles and diffusion steps, serving as a strong baseline as well as the reference target that DCFold aims to approximate. (ii) AF3 ODE- AlphaFold3 configured with single sampling step and single recycling cycle, serving as a reference baseline without retraining. (iii) AF3 TGM - a partially distilled AlphaFold3 variant, which builds upon AF3 ODE by applying only TGM diffusion consistency distillation without pairformer distillation. This isolates the contribution of TGM to performance under one- step sampling. (iv) DCFold fully distilled model after applying dual consistency training.

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which uses only 1 recycle and 1 diffusion denoising step. Both the baseline and the initialization of DCFold are derived from Protenix, an open-source reimplementation of AlphaFold3. (v) Protenix- Mini We also include lightweight variant of Protenix, which reduces the parameter count from 368M to 135M and uses 2-step ODE sampling to lower computational cost.
Data For training, we use PDB entries released after September 30, 2021, organized following the Protenix scheme with identical filtering protocols. Evaluation is performed on two benchmarks: (i) PoseBusters V2 (Buttenschoen et al. 2024), a curated benchmark of recent high-quality pro- tein-ligand crystal complexes with drug-liks like molecules, restricted to post- 20st-2021 releases; and (ii) the Low Homology Recent PDB dataset (Jumper et al. 2021 Team et al. 2025), containing numerous protein and nucleic acid interfaces. Introduced in AlphaFold3, we employ the Protenix open- implementation. All entries predating the training cutoff are excluded from evaluation.
Metrics On Posebusters V2, we evaluate predictions using the RMSD) between predicted and ex- perimental ligand coordinates. For each complex, we report the proportions of generated poses whose best and worst RMSDs (with respect to the ground-truth structure) fall below the thresholds of I, 2, 3, and Å. Ground truth is not used for any filtering, so this does not introduce data leak- age. These metries quantify how Dual Consistency reshapes AlphaFold3's ' output distribution. On RecentPDB, we measure backbone accuracy using the TM-score (Biasini et al.. 2013), where val- ues above 0.5 indicate correct folds; the success rate is defined as the proportion of structures with RMSD < A; and local accuracy is assessed using IDDT (Mariani et al. 2013). which ranges from 0-100 and reflects residue-level geometric precision.
Overall, DCFold achieves accuracy comparable AlphaFold3 while using only single recycle and diffusion step, demonstrating both efficiency and robustness. The results in Table [ Table and Figure highlight these key observations:
AlphaFold3 admits single-step generation. With proper choice of ODE parameters, the AF3 ODE solver is capable of generating approximately correct protein structures.
DCFold enhances generative performance. Training with Dual Consistency substantially im- proves the performance of the AF3 ODE model: across several RMSD thresholds, DCFold ap- proaches or even matches AlphaFold3, demonstrating that the distilled model effectively recovers accuracy despite relying on only single recycle and diffusion step.
DCFold reshapes the distribution of generated structures. Dual Consistency reshapes the output distribution of AlphaFold3 by effectively tightening it. This effect is reflected in the improved worst- case RMSD, indicating more stable and reliable predictions, while the best-case RMSD remains largely unchanged. Such a redistribution reduces extreme errors and enhances the consistency of single-step predictions, which is particularly valuable for accelerating downstream scientific work- flows where both efficiency and reliability are critical.
The improvement is especially evident in Success Rate, where DCFold achieves substantially larger gains than in average TM-score. This observation further supports our claim that DCFold reshapes the distribution of generated structures. In particular, DCFold demonstrates a stronger ability than AlphaFold3 to avoid generating implausible biological complexes.
Both components of Dual Consistency are beneficial. In the IDDT experiments shown in Figure DCFold delivers accuracy on par with AlphaFold3. We further conduct ablation studies disentan- gling the effects of Diffusion Consistency and Pairformer Consistency, and find that both compo- nents contribute complementary gains. Together, these results highlight that Dual Consistency is the key driver behind the observed improvements.
4.2 DIVERSITY AND CONFIDENCE
To more comprehensively characterize the performance of DCFold we conducted an extended analysis of its structural diversity and predictive confidence on the Posebusters V2 benchmark.
Metrics. For each test sequence, we sampled five structures and computed all pairwise TM-scores among these predictions. We report the dataset-level average of these pairwise values as the Diver- sity metric (lower is better). We further compute the mean PLDDT across all sampled structures as the Confidence metric (higher is better).
DCFold maintains strong sample diversity and confidence. As shown in Table 4 after Dual Consistency training. DCFold exhibits no substantial deviation from AlphaFold3 in either metric. Diversity shows slight decrease, whereas confidence displays slight increase. These trends sug-
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200
1.0
1.0
0.799
0.8150.813
0.821
0.8
0.8
150
LDOT Scorte
1000 Score
0.623 0.6230.637 0.637
0,622 0.650
0.6
0.489 0.507 5070,4900.501
0.6
100
LDOT
0.4
50
0.2
0.0
Painformer Cycles
Complex LDOT
AF3 ODE
AF3 TGM
DCFald (Cuuss)
Protenix-mini
AF3
Figure 3: IDDT performance on the Recent PDB dataset.
Table 4: Diversity and confidence metrics on the Posebusters V2 benchmark.
Method
Diversity (L)
Confidence (f)
AF3 (5 samples)
0.9646 0.0410
93.97 +: 2.92
AF3 (15 samples)
0.9642 + 0.0415
93.95 1 2.93
AF3 (5 seeds x sample)
0.9697 = 0.0421
93.90 + 3.01
DCFold (5 samples)
0.9701 + 0.0565
94.14 + 2.97
DCFold (15 samples)
0.9708 0.0567
94.13 + 2.96
DCFold (5 seeds x sample)
0.9712 + 0.0570
94.15 + 2.97
gest that enforcing Dual Consistency mildly concentrates the structural distribution while preserving high prediction quality.
To assess the robustness of these observations under increased sampling, we additionally evaluated: (i) 15 samples drawn under a fixed seed, and (ii) 5 random seeds with sample each. Under both settings, neither AlphaFold3 nor DCFold exhibited meaningful improvements in diversity. This behavior aligns with the well-known strong conditionality of AlphaFool-serres models, which tends to limit diversity gains from additional sampling alone.
Importantly. DCFold remains compatible with a broad set of diiversty-enhaneein strategies proposed in prior work, including sampling MSAs, clustering masking MSA columns, and tuning dropout Steele rates (Wayment- Sttele et al. 2024, Wallner [023)] Kalakoti & Wallner (0255). Our acceleration approach is orthogonal to these methods, and all such techniques can be directly applied to DCFold
with expected diversity improvements comparable to those previously reported for AlphaFold3.
4.3 BINDER HALLUCINATION
After maintaining the consistency between Pairformer and Diffusion, DCFold achieves efficient inference and stable gradient backpropagation with modest computational cost. We focus on the binder hallucination task, which serves as a representative benchmark due to its stringent require- ments: it demands fully differentiable folding model, while the filtering stage eliminates large fraction of implausible candidates. As result, success in this setting critically depends on achieving efficient inference. Following the same hallucination strategy and filtering pipeline BindCraft (de- tails provided in Appendix [B.2p (Pacesa et al.) 2024)), we leverage confidence scores and additional loss terms from DCFold as feedback signals for sequence evaluation. To ensure fair comparison, folding constraints are consistently computed using the outputs of AlphaFold2, thereby avoiding potential numerical discrepancies confidence calibration between DCFold and AlphaFold2.
Data We adopt the six representative entries from Cao et al. (002) as the design targets, namely IL2-Ra, TrkA, H3, VirB8, ALK, and LTK. They span multiple functional categories, including receptors, enzymes, transcription factors, and bacterial proteins. They have been widely adopted in prior studies common benchmarks for design and docking tasks. For each case, we restrict binder length to 55-65 residues and perform a continuous -8-hour hallucination run.
We compute the Success Rate using the same two filters as BindCraft. The model-based constraint is derived from AlphaFold2's confidence score, whereas the physics- based constraint re- lies physical metrics obtained from Rosetta. Additional details are provided in Appendix BB2
Metrics
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Table 5: In silico success rates across six targets for binder design (values shown as physics-based constraints/ model-based constraints).
IL-2Ra
TrkA
H3
VirB8
ALK
LTK
Average
BindCraft .38/884
.2//.88
.16.52
.15.772
.14/448
.43/.70
.26/.69
DCFold (Ours)
.371.79
.31/.84
.231.71
.21/855
.12/.54
.471.33
.29/.78
DCFold achieves higher in silico success rates than the AF2-based BindCraft baseline across the majority of targets. With the incorporation of DCFold, AlphaFold3 can readily support binder hal- lucination strategies that were previously only feasible within the AlphaFold2 framework. Notably, DCFold achieves much higher success rates on several targets (c.g., H3, VirB8, and LTK), indi- cating that our reshaping of AlphaFold3's output distribution translates into tangible improvements in downstream design tasks. These findings highlight that DCFold bridges the methodological gap pipelines, and unlocks additional performance gains.
between AlphaFold2- and AlphaFold3- We have added more details about the experimental results in Appendix C.2 Figure 7 visualizes representative binder-target complexes, illustrating the interactions between the generated binders and their targets.
4.4 EMPIRICAL VALIDATION OF TGM
We conduct experiments on feasible generic consistency-mode baselines, including CD (Song et al.] 2023), sCM (Lu & Songl 2024), ECM (Geng et al. 2024), and TGM. Results on Posebusters V2 are summarized Table 6 We observe that among all runnable baselines, a naive implementation of CD leads to training collapse and severely degrades performance. Only ECM and TGM are able to enhance the performance of the diffusion module, with TGM yielding the largest performance gains. Therefore, in the following experiments, we take BCM as the representative of prior general consis- tency models and investigate how TGM exhibits distinct behavior on protein folding tasks. Detailed hyperparameter settings for each method are provided in Appendix B.3 We conduct an in-depth analysis of the sources of im- provement introduced by TGM and present the gradi- ent norm and loss curve throughout training in Figure 5 We observe that the training dynamics of ECM exhibits poor smoothness, characterized by distinct staircase-like patterns, and is accompanied by large gradient vari- ance. This corroborates our hypothesis in Section 3.3 that classical consistency algorithms degrade under variable- length sequences. In contrast, TGM consistently main- tains balanced gradients, indicating that the learning dif- ficulty of the network remains at fixed distance from its current capacity, effectively counteracting the adverse effects introduced by variable-length sequences.
Table 6: Success Rates Different Consistency
Models Posebusters V2.
Method Time (s/step)
Success rate (%)
CD
18.5
25.6.
SCM
38.1
ECM
11.6
75.71
TGM
11.6
77.5
p.10
0.08
0.08
0.06
Selavve
0.02
0.0
0.2
0.0
0.2
0.6
0.6
0.8
1.0.0
In addition, we further assess whether the Euler method employed in TGM introduces excessive numerical error in Figure g We observe that the сттог is relatively large during the early stages of training but decreases as train- ing progresses, leading to more accurate estimates in later stages. Moreover, the error remains consistently low throughout the entire training process, indicat- ing that our approximation is sufficiently reliable. This also explains why employing higher-order algorithms does not yield substantially greater benefits.
Figure 4: The relative error the Euler solver for r(t,
Due the substantial computational overhead of sCM, processing long sequences often results in out-of- memory (OOM) errors, preventing it from participating in fair comparison.

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3.0
2.5-
TGM (Ours)
ECM
1.4
TGM (Ours)
ECM
2.0
1.3
Gradient
1.2
1.0
1.1
0.5
1.0
14 (y
0.0
0.9
Step (x<10")
Stop (x:10")
Figure 5: Gradient norm and loss curve during training for ECM and TGM.
5 RELATED WORK
Protein Structure Prediction Protein structure prediction has rapidly advanced with deep learn ing. Classical methods such as Rosetta (Rohl et al. (204) and co-evolutionary analysis (Marks et al.) 2011 Ovchinnikov al. 2017) provided key insights but were limited accuracy and scalability.
The advent of deep neural networks enabled models like RaptorX (Xu 2019) and trRosetta (Yang
ct al., 2020) to exploit large multiple sequence alignments (MSAs), setting the stage for decisive breakthrough. AlphaFold2 (Jumper et al., 2021) combined evolutionary information with novel
attention architecture, achieving near-experimental resolution.
Efforts reduce reliance on MSAs led models such as ESMFold (Lin et al., 2022). OmegaFold (W et al. 2022). and Selixfold-Single (Fang et al.) 2022), which leverage protein language models prediction, albeit lower accuracy. Extensions like AlphaFold-Muliin Multimee (Evans et al. 2 generalized AF2 to protein-protein interactions, establishing it as foundation model. Building on this, AlphaFold3 (Abramson et al. 2024) introduced diffusion-based struc- ture module and unified biomolecular representation, enabling prediction of protein Mootein-ligand, nucleic acid, and heterogeneous complexes. Despite setting new standards in accuracy and scope, AF3's s computational overhead remains key barrier, driving research into acceleration, distillation, and approximation (Cheng et al. 2022).
for fast
Diffusion Acceleration Recent advances in diffusion acceleration fall into three categories: training-free solvers, training-based distillation, and flow-based reformulations. Training- solvers leverage higher-order integration, predictor-corrector schemes, and adaptive noise sched- ules to achieve high-quality generation in few dozen steps, though performance often degrades in the extreme few-step regime (Song et al., 2020 Lu et al. 2022; Zhao al. 2023). Training- based distillation compresses long diffusion chains into compact generators: progressive distillation iter- atively reduces step counts, adversarial variants integrate GAN-style objectives, and Consistency Models (CMs) enforce self-consistency across time to enable single- or few-step generation with strong fidelity (Salimans & Ho, 2022 (Sauer et al. 2024) Song et al. 2023). In parallel, flow-based methods reformulate diffusion as velocity fields with straightened trajectories, allowing efficient
integration with simple solvers (Liu et al. 2022 Lipman et al. 2022).
CONCLUSION
We present DCFold, dual-consistency distillation framework that compresses AlphaFold3 into a high-fidelity single-step sampler. By jointly enforcing diffusion and Pairformer consistency and introducing the Temporal Geodesic Matching schedule, DCFold achieves stable training on variable- length protein sequences while reducing inference cost by up to 15x. Experiments on structure prediction and binder design show that DCFold matches surpasses AlphaFold3 in accuracy and substantially improves downstream usability, bridging A AlphaFold2's efficiency with AlphaFold3's accuracy to enable scalable, differentiable protein design.
ETHICS STATEMENT
This work focuses on methodological contributions to protein structure prediction and design. All experiments conducted publicly available datasets such as the Protein Data Bank (PDB)
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and established benchmarks, without involving human subjects, sensitive personal data, animal studies. The proposed methods are intended solely for advancing computational biology research. Therefore, we do not identify any specific ethical concerns associated with this work.
REPRODUCIBILITY STATEMENT
We disclose all training details in Section 3.2 and Section 3.3 enabling full reproducibility of our experimental results. Moreover, we will release both the pretrained weights and the source code to ensure transparency and facilitate future research.
ACKNOWLEDGMENTS
The authors would thank the anonymous reviewers for reviewing the draft. This work is supported by the Natural Science Foundation of China (Grant No. 62376133), Beijing Nova Program (Grant No. 20240484682) and Wuxi Research Institute of Applied Technologies, Tsinghua University under Grant 20242001120.
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PDB ID: 7r6r mycobacteriophage immunity repressor-DNA complex)
PDB ID: 7wux (AziU3/U2 complexed with 155.65)-07-sulfo DADH)
PDB ID: 7pzb
(Clr-cAMP-DNA complex)
Experimental result
AlphaFold 3
DCFold
Figure 6: A structure prediction case study of DCFold, compared against AlphaFold3 and the ex- perimental result.
A
B
C
D
C2200
Figure 7: Examples from binder-design experiments, with targets: (A) ALK, (B) H3, (C) 1L2Ro.
and (D) VirB8.
A DERIVATION OF TGM
A. PROOF OF LOCAL METRIC-KL EQUIVALENCE
We investigate the KL divergence between two distributions defined on the manifold M,:
DEL (prllp)) = f (ar) log (r(x) =] f p-((r) p. (x) log (x)) dr dr
-
(9)
(r())
We perform a Taylor expansion of log Pt- (z) and substitute the result into the KL divergence.
log logp((x) + (At) 02
log log = log - At Ot
log ((At)))
(10)
0+2
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Substituting it into the KL divergence yields:
DRI(PrllP.)) = / pr(x))
-
log pc(x)) 2 (21)) 0
logp((z) + 0 (())))) de
01
01
=
p((x)) -m((a)
A
8
Pr
+
1
(a1)
8
(z) ((1))))
0t
2
1
82
A O1
logp:(t) (r)
+
At)
log Pa (1) ( de
(11)
2
012
The first-order term vanishes:
/ (x(2)) log pe (x) dx = 0,
(12)
H
while the second-order term takes the following form:
(At)²
f pr(x)) lo) dx + (At)² / P((x) σe log log
02
(13)
2
012
The term on the right-hand side is given by
(At)² / pr(a))
log, log, (x)
dr = (tt)
(14)
Ot
The simplification of the left-hand side relies on the property that the integral of the score function vanishes:
0 0
0t ʃ (((x))
θ ot
log logp((z)) dx = = /
ô at p((x)
a log (x) dx + / pi(x)
0²
0
0
log da
Ot
ōt
0t
(15)
Thus, the term on the left-hand side can also be expressed in terms of I(t):
log dr / / (r((z) 0t
82
2 2 Te Pr log dr I(t)
(16)
ot
Thus, the second-order term implicitly encodes the temporal Fisher information (A)) [Ar) Z(t), that is DKL. (p- (p))))((((() metric-Ki KL equivalence becomes evident.
(A0) I(1) +
(Ar) I(1) ++ With this, local
A.2 TEMPORAL FISHER INFORMATION IN EDM
We assume the forward process of diffuusion is defined as (r( = N(E = ( logg:())))
a(t)zo. o²(())), = (x)) [ log pe())) 27 = Eng (x|
We employ a multivariate Gaussian distribution with dimensionality D: p(x) =
(20)"//11)77 exp (-) ( Ex 14). = (E) = (o²(t)] = 0 2n (t),
(o²(t)1) - [t),, which yields the following simplification:
[| -
(x( (2T) exp
(17)
202(1)
D
||0
a(t)zoll
log (x = log (2T) - D log (t)
(18)
2
20²
0
log logp)()/20)) = (t)
(t))
20² (-2((t) (x - a(t)zo) 20)
ot)) (t) || a(t)zoll
Ot
ā(t)
o(l) + a(t)
a(t)
|a aa)xol1
(x - -a((tat))) T x0
(1)
(²(
o(t) 6())
a(t)
-0
(a(t)2) Do
ott (2(t)
22())
D b(t) ((1)
00 a(t) Too
o(t) ((t)
ott)
a(1)
(1=12- D) 6(1)
(19)
a(t)
o(t)
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Thus, I(t) can be decomposed into three components:
a()
a(()
2
I(t) Beo (x)
(1=11 D)
x 20
((t)
o(t)
(00)
[t) ((())
((tt) (1))
(t)a(t)
= - Es
(|=112 D)²
(z xz) + o²(1) (|211 D)(==au))) ao)
(20)
Since the first term follows a chi-squared distribution ||=||² Σ, x ~ x²( this part, we introduce the data dimension D) ||| D, E [[]]=|]| D) Var[[=|[]]
the second term is E [(> >=) = E (2: = (z)).) [2 ()()) = (t)) = |
The third term, namely the cross-term, vanishes: E [[[|=||] (= [0)] = [||=|| (2 zo)]
D zol = E Σ 2, [5 (5 = E,, [|2, =0
o(t) a(t)
a(t) ott
I(t)= = Ezo
2D +
|
(21)
In most prior works, due the effect of data normalization, we can assume that E( 0, and therefore | can be expressed in terms of Var[zu]-
In the EDM framework, all) = 1() data
1/p (smax + (1 - 1) 1/v
smink) 1/1)) = )
This yields
more concise expression for (():
I(t) = a(t)
2D p. .ssmux 8
3/p)
2D = 1/p + (1 - {)
min
(22)
(t)
1/p 1/p
B IMPLEMENTATION DETAILS
B.1 TRAINING CONFIGURATION
To ensure clarity and reproducibility, we provide a detailed description of the training setup. Our full training pipeline was executed on a cluster equipped with 64 NVIDIA H800 GPUs, corresponding to an effective batch size of 64. Stage 1 focuses on learning diffusion consistency. DCFold was trained for approximately 40 hours, spanning a total of 9,000 optimization steps. This stage establishes the foundational generative capabilities leveraged in subsequent training. Stage 2 aims to refine the structural reasoning components through Pairformer consistency training. This phase required around 7 hours of computation and was conducted for 1,500 steps. The shorter duration reflects both the stability provided by Stage and the efficiency of fine-tuning the Pairformer module.
B.2 BINDER HALLUCINATION
After initial binder design with DCFold, sequences are refined to improve stability and solubility using ProteinMPNN with soluble weights, while preserving residues within 4 Á of the target inter- face. For each binder, 20 variants are generated temperature 0.1 with no backbone noise. These sequences are re-predicted using the AF2 monomer model (3 recycles, 2 template- single-sequence mode to validate structural robustness. Resulting complexes are energy-minimized with Rosetta FastRelax (200 iterations) and evaluated using Analzer with sidechain and backbone movement. Final designs are filtered using predefined thresholds (pLDDT >> 0.8, ipTM 0.5, i.AAE < 0.35, shape complementarity > 0.55, 3 unsaturated H-bonds, binder surface hydrophobicity < 35%. RMSD < 3.5 A), yielding a high-confidence set of candidates.
models) in
We evaluate binder quality using two constraint sets. Model-based Constraints are derived from AlphaFoodd confidence outputs, requiring pLDDT > 0.8, interface pTM > 0.5, global pTM > 0.45, and interface PλE < 0.4. Physics-based Constraints are based on physical interface metrics from Rosetta, including shape complementarity 0.5, dSASA > 1, 6 interface residues, 2 interface hydrogen bonds, surface hydrophobicity < 0.37, and < 6 unsaturated hydrogen bonds. All metrics aligned with the filters used in BindCraft.
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Table 7: Average inference time of AlphaFold3 and DCFold across token bins.
#Tokens
AlphaFold3 Avg Time (s)
DCFold Avg Time (s)
< 255
92.63
3.76
256-383
103.31
5.77
384-511
112.35
7.17
512-639
126.41
10.87
640-767
142.78
14.65
768 895
169.20
20.02
896
212.12
27.40
B.3 HYPERPARAMETER SETTINGS FOR CONSISTENCY MODEL BASELINES
For completeness, provide the implementation details of all baselines considered in experi- ments:
CD: Mean squared error (MSE) the metric function with weight decay rate of 0.995.
sCM: = 2000 warm-up iterations.
ECM: = 2.0, b b 0.1, 3000, and k = 4.0.
TGM: Hyperparameter search yields Co = 32 and 3 = 2. In addition, we inherit the exponential decay scheduling parameters from AlphaFold3's EDM configuration, with p 7, Smax = 160, and 8min = 4 x 10-4
For all methods, set the weighting function to 1.
C EXPERIMENT DETAILS
C.I RUNTIME CHARACTERISTICS ACROSS SEQUENCE LENGTHS
To comprehensively assess the efficiency of DCFold, we report detailed bin-wise runtime statistics on the Posebusters V2 benchmark. Since AlphaFold3 supports folding protein-ligant complexes, we use total number of input tokens for each test entry as the length metric and partition sequences into bins of size 128. The average inference time for each bin is summarized in Table
Both AlphaFold3 and DCFold exhibit increasing runtime as sequence length grows. However, the relative acceleration provided by DCFold is most pronounced for short sequences, where it achieves up to 24x speedup. For moderately long sequences, DCFold still provides more than 7.7x x accel- eration, demonstrating consistent efficiency gains across all token ranges.
We hypothesize that this trend stems from the differing computational bottlenecks of the meth- ods. The reduction in Diffusion NFE afforded by DCFold yields significantly larger improvement factor compared to the reduction in Pairformer cycles. As sequence length increases, the Pairformer component becomes the dominant cost, diminishing the relative impact of the diffusion speedup. Conversely, in shorter sequences, the Pairformer bottleneck less pronounced, enabling the diffu- sion efficiency gains to translate directly into substantial end-to-end acceleration.
C.2 BINDER HALLUCINATION
We conducted experiments single H800 GPU. On the targets used in Table the average GPU time for one full hallucination with BindCraft 138s, while DCFold requires 105s. Since we follow the same pipeline as BindCraft, the total serial runtime also includes the time for ProteinMPNN and the re- rrepriiittion step in addition to the design model's GPU time. We also provide the total number of designs generated in experiments in Table Overall, DCFold attains slightly better efficiency while producing a comparable number of samples, ensuring a fair comparison.
Our binder design benchmark features six protein targets. Table shsows the details of the targets.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
We use large language models (LLMs) solely for auxiliary editing purposes, including spelling cor- rection and minor grammatical adjustments. Importantly, L.MMs not involved in the conception
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Table 8: The total number of generated samples in the binder hallucination experiments.
IL-2Ra
TrkA
H3
VirB8
ALK
LTK
BindCraft DCFold (Ours)
312
243
269
347
188
348
375
256
295
439
177
402
Table 9: Detailed information of binder targets in the binder hallucination experiments.
Target
PDB ID
Family
Description
ALK
7NWZ
Immune receptor
Neural receptor tyrosine kinase involved in development
H3
3ZTJ
Receptor tyrosine kinase
Core nucleosomal histone in eukaryotic chromatin
11.2Ro
1792
Histone protein
Component of the interleukin-2 receptor complex in the immune system Homolog of ALK expressed in various tis- sues Neurotrophic signaling receptor activated by NGF
LTK
7NX0
Bacterial secretion sys-
tem protein
TrkA
21FG
Receptor tyrosine kinase
VirB8
403V
Receptor tyrosine kinase
Structural protein of the type IV secretion system in Gram-negative bacteria
of research ideas or the development of code. parency our work.
We disclose this usage explicitly to ensure trans-
18


Reviews:
[{"review_id": "aYq1MbHB7j", "reviewer": "Reviewer_9xiz", "summary": "This paper introduces DCFold, a single-step protein structure generation model designed to match AlphaFold3-level accuracy while significantly improving inference efficiency. The motivation stems from AlphaFold3’s diffusion-based iterative design, which, despite its accuracy, incurs high computational cost and long inference times, limiting its use in practical protein design and virtual screening workflows.  \n\nDCFold proposes a Dual Consistency (DC) training framework that incorporates a novel Temporal Geodesic Matching (TGM) scheduler to maintain structural fidelity without iterative refinement. As a result, DCFold achieves up to 15× faster inference compared to AlphaFold3 while preserving predictive accuracy. The model’s effectiveness is validated on both structure prediction and binder design benchmarks, demonstrating strong performance with substantially reduced computation.", "questions": "Q1 How does distilling to a single-step model affect aspects such as the confidence model calibration and sampling diversity?  \nQ2 I found the purpose of Proposition 1 somewhat unclear—could the authors clarify its specific role in the later theoretical derivations or proofs?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Proposes a single-step protein generation framework that achieves AlphaFold3-level accuracy with a 15× speedup, addressing a key efficiency bottleneck in diffusion-based methods.  \n- Introduces a novel Dual Consistency training scheme with Temporal Geodesic Matching, providing a principled way to maintain structural fidelity without iterative refinement.", "weaknesses": "- Lacks comparison with other training-based distillation methods such as Protenix-mini, which also reduce inference cost by compressing model size. It remains unclear how “fewer steps” (as in DCFold) compares to “smaller models” in terms of trade-offs between efficiency and performance.  \n- The **PoseBusters V2 benchmark** results report “best” and “worst” performance without clarifying how these were derived; additional explanation would improve interpretability.  \n- The paper provides limited details on the **binder design task** setup. It is unclear how much faster DCFold is compared to *BindCraft*, and how many samples were used to compute the *in silico* success rates.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hYO2g2nwMo", "reviewer": "Reviewer_XzaU", "summary": "The paper presents DCFold, a model that greatly increases inference speed for AlphaFold3, which is very important for downstream tasks, such as virtual screening. It achieves this performance using an implementation of consistency models on both the model trunk, and the diffusion head. The authors also add a \"Temporal Geodesic Matching\" scheduler to stabilize consistency training. The model achieves the promised inference speed-up without sacrificing performance", "questions": "- For Alphafold, inference time is greatly dependent on sequence length. Is this 15x time improvement consistent across lengths? \n- How did the training time compare with the training time of the original AlphaFold code? \n- The benchmarks in table 2 only compare performance against AlphaFold3. If I understand correctly, this is referring to the protenix implementation of AlphaFold3 (which never achieved the same performance). This should be clarified. If these numbers are indeed from protenix, it would also be interesting to see a comparison against other implementations (the original AlphaFold3, Boltz2, OpenFold3, etc)\n- It would also be interesting to see a comparison with protenix-mini, another attempt to reduce inference time (with a different approach) as well as a discussion about the difference between the two codes. \n- The meaning of ECM is never introduced\n- How is \"in silico success rate\" defined in Table 4? \n- In the same table, results from other binder design algorithms (e.g. RFDiffusion, FoldFlow) should also be added.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "**Originality** There have been other attempts to speed up implementations of AlphaFold3. However, the idea of using consistency training for both the trunk and the diffusion is very innovative, as well as the addition of Temporal Geodesic Matching. \n\n**Quality**: The experiments are thorough and convincing, and the results show a good model performance\n\n**Clarity**: The manuscript is very well written, and clearly explained. The figures are generally good, particularly figure 2, which is a very clear illustration of the approach. \n\n**Significance**: As the authors explain, speeding up inference for AlphaFold3 is very important for multiple downstream applications like virtual screening", "weaknesses": "There are no major weaknesses, just some small things: \n\n- The meaning of ECM is never introduced", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5lkCw3O0tE", "reviewer": "Reviewer_KePm", "summary": "This paper introduces **DCFold**, a single-step generative model for protein structure prediction, designed to overcome the slow inference speed of the iterative, diffusion-based AlphaFold3.\n\nThe core contribution is a **Dual Consistency** training framework, featuring a novel **Temporal Geodesic Matching (TGM) scheduler**. This allows DCFold to achieve AlphaFold3-level accuracy in a single generative step.\n\nThe authors report a **15x acceleration in inference time** while maintaining high predictive fidelity, with performance validated on both structure prediction and binder design tasks.", "questions": "1.  **Regarding the Gradient-Based Hallucination Method:** Could the authors provide more detail on the implementation of the gradient-based hallucination? The iterative nature of diffusion models makes obtaining a direct gradient with respect to the final structure non-trivial. Methods like BoltzDesign circumvent this by using gradients from the Pairformer's internal representations. Does DCFold's single-step nature allow for a direct backpropagation through the structure generation process, similar to approaches like BindCraft, or is another mechanism used?\n\n2.  **Stochasticity and Sampling Diversity in a Single-Step Model:** The SDE-based sampling in diffusion models like AlphaFold3 provides a beneficial stochasticity, which can capture protein dynamics and yield improved results when multiple samples are drawn. Does the single-step, distilled architecture of DCFold retain this inherent randomness? Furthermore, can DCFold's performance be improved by generating multiple samples for a single target, and if so, could the authors show the trend of performance improvement as the number of samples increases?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  Good acceleration was achieved by simultaneously distilling the Pairformer and the diffusion model, with no obvious degradation in performance.\n2.  The paper proposes a novel time step sampling strategy that effectively stabilizes training and improves the model's performance.\n3.  The mathematical derivations in the appendix are easy to follow and have no obvious errors.", "weaknesses": "1.  **Lack of Granularity in Inference Time Analysis:** The time complexity of different computational stages in protein structure prediction scales differently with protein length (L). As such, the inference time for a model like AlphaFold3 can vary significantly across proteins of different lengths. The manuscript only reports the mean inference time on the evaluation set, which obscures these details. A more thorough analysis showing a direct comparison of inference times for DCFold and AlphaFold3 binned by protein length would be necessary to fully assess the claimed acceleration and understand its behavior in different regimes.\n\n2.  **Ambiguity in Pose Selection for PoseBusters Benchmark:** The methodology for selecting the \"best\" and \"worst\" poses in the PoseBusters benchmark results is unclear. If the ground truth structure was used to rank the generated poses and select these examples, this would constitute a form of data leakage, as the ground truth is not available in a real-world prediction scenario. Since ranking generated poses without a ground truth is a significant challenge in itself, the authors should clarify this selection process to ensure the validity of the reported results.\n\n3.  **Unclear Metric Definition and Counter-intuitive Results in Table 3:** The definition of \"Success Rate\" used in *Table 3* is not clearly described in the manuscript. Furthermore, the results presented are somewhat surprising: the distilled, single-step model reportedly outperforms the original AlphaFold3 teacher model across the board on the Homology Recent PDB dataset. While not impossible, it is counter-intuitive for a student model to uniformly exceed the performance of its teacher. This warrants a more detailed explanation of the metric and a discussion of these results.\n\n4.  **Insufficient Detail in Binder Hallucination Throughput:** The evaluation of the binder hallucination task lacks sufficient detail to assess the claimed speed benefits. The paper states that a \"continuous 48-hour hallucination run\" was performed and reports the *proportion* of designs that passed filters. However, it omits the crucial information of the *total number* of designs generated by DCFold and the baseline (BindCraft) within that time frame. Without this denominator, it is impossible to quantify the throughput advantage gained from the distillation process.\n\n5.  **Unconventional Experimental Setup and Incomplete Results for Binder Design:** The choice of a 55-65 residue length for the binder design task is an unconventional setting that requires justification, as this is a relatively short range. More importantly, the evaluation is limited to quantitative metrics. The manuscript does not show any examples of the successfully designed binder structures. This absence of qualitative, visual evidence makes it difficult to independently assess the structural plausibility and quality of the generated binders.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9raWHiquLA", "reviewer": "Reviewer_M62B", "summary": "The paper introduced DCFOLD, a distilled version of AlphaFold3 (AF3) that generates protein structures in a single forward pass, achieving up to 15× faster inference while maintaining similar accuracy. The proposed method introduces a training framework that: 1) aligns one step predictions with the full multi-step diffusion process and 2) enforces representation alignment on AF recycling iterations. The paper introduces a scheduler which aims to stabilize the training on variable-length proteins. Experiments on PoseBusters V2, Recent PDB, and binder hallucination tasks claim near AF3-level accuracy with reduced inference cost.", "questions": "Please see in the Weakness section some of my questions. More details questions follow:\n\n1. how is $u$ scheduled or computed in practice?\n2. how is $r$ chosen in Algoritm 1?\n3. how are categorical features handled?\n\nMinor:\n1. Text clarity can be improved\n2. Some of the references need to be fixed e.g. AlphaFold3 line 306 page 6 is referenced to AlphaFold2 paper.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. The proposed framework addresses a bottleneck in AF3 as it is computationally at inference time. Speeding up the inference time will help to address tasks such as protein design and virtual screening.\n\n2. The DC framework is relatively novel.\n\n3. The results are promising reporting a 15x speedup while retaining similar performances on selected tasks.", "weaknesses": "1. Several details are missing or inconsistently used. Some examples:\n- In Eq. 1, $w(t)$ is not defined, Later, it is set to 1, but its role is never justified.\n- The reference timestep $r$ is ambiguous in Algorithm 1. Is it a function or scalar?\n- The variable $u \\in [0, 1]$, in Algorithm 1 is introduced as \"training progress\" but never clearly explained.\n- The stop gradient operator is defined in Eq. 2, but never used anywhere\n\n2. Missing important details make the training procedure non-reproducible. \n- $\\mathcal{L}$ in Algorithm 1 seems to be a combination of $\\mathcal{L}_{diffusion}$, $\\mathcal{L}_{pairwise}$, and $\\mathcal{L}_{confidence}$ (this latter never defined). The combination of those three losses is unclear. Are they combined linearly or trained sequentially in stages? If sequential, how are modules frozen or fine-tuned? What are the relative weighting coefficients?\n- $\\alpha$ defined after Eq. 3 is unclear. Sometimes is a scalar, sometimes a vector, sometimes a pairwise matrix?\n\n3. The evaluation setup is significantly weaker than the Boltz2[1] benchmark used for AF3 and related models. Here, some of them:\n- FEP accuracy on public benchmarks\n- Local protein dynamics\n\n4. The theoretical part about TGM lacks clarity. E.g. what are $C_0$, $s_{max}$, and $s_{min}$?\n\n[1] Passaro, Saro, et al. \"Boltz-2: Towards accurate and efficient binding affinity prediction.\" BioRxiv (2025): 2025-06.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)