SimpleFold: Folding Proteins is Simpler than You Think
Authors: Yuyang Wang
Jiarui Lu
Navdeep Jaitly
Joshua M. Susskind
Miguel Ángel Bautista
Venue: ICLR 2026 Poster (2026)
Keywords: Generative models
protein structure prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We tackle protein folding as it was a text-to-3D generative model

Abstract: Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessary condition to build performant models. In this paper, we introduce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks}. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair representations or multiple training objectives curated for this specific domain. Instead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term. We scale SimpleFold to 3B parameters and train it on approximately 9M distilled protein structures together with experimental PDB data. On standard folding benchmarks, SimpleFold-3B achieves competitive performance compared to state-of-the-art baselines, in addition SimpleFold demonstrates strong performance in ensemble prediction which is typically difficult for models trained via deterministic reconstruction objectives. SimpleFold challenges the reliance on complex domain-specific architectures designs in protein folding, opening up an alternative design space for future progress.

Published as a conference paper at ICLR 2026
SIMPLEFOLD: FOLDING PROTEINS IS SIMPLER THAN YOU THINK
Yuyang Wang, Jiarui Lu", Navdeep Jaitly", Josh Susskind, Miguel Angel Bautista Apple
ABSTRACT
Protein folding models have achieved groundbreaking results typically via a com- bination of integrating domain knowledge into the architectural blocks and train- ing pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessary condition to build performant models. In this paper, we intro- duce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair rep- resentations or multiple training objectives curated for this specific domain. In- stead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term. We scale SimpleFold 3B parameters and train on approximately 9M distilled protein structures together with experimental PDB data. On standard folding benchmarks, SimpleFold-3B achieves competitive performance compared to state-of-the-art baselines, in addition SimpleFold demonstrates strong perfor- mance in ensemble prediction which is typically difficult for models trained via deterministic reconstruction objectives. SimpleFold challenges the reliance on complex domain-specific architectures designs in protein folding, opening up an alternative design space for future progress.
INTRODUCTION
Protein folding, the task of predicting a protein's three-dimensional atomic structure from its amino acid (AA) sequence, is longstanding challenge in computational biology with far-reaching im- plications in drug discovery (Jumper et al., 202I Back et al., 2021). this paper, we approach the protein folding problem purely from generative modeling perspective without making strong assumptions about the natural generation process protein structures. We draw parallels between text-to-3D generation (Poole et al. 2022 Lin et al. (2023a Hong et al. (024a|bb b)), where the input AA sequence plays the role of "text prompt" to generative model which outputs the all-atom 3D coordinates. Inspired by the recent success of generative models in the vision domain we build seneral-purposs yet powerful architecture based solely on standard transformer blocks with adaptive layers (Vaswaní al. 2017
protein folding and vision generative models (i.e., text-to-image
Peebles & Xie 2023) which we trained at larger scale than previous protein folding models, both in terms of model size and training data.
Established protein folding models like AlphaFold2 (Jumper et al., (021) and RoseTTAFold (Back jet al. 2021) have achieved groundbreaking accuracy by relying on carefully engineered architec- tures that integrate computationally heavy domain-specifi, designs for protein folding tasks such as multiple sequence alignments (MSAs), pair representations, and triangle updates (Jumper et al.) 2021 Back et al. 2021). These design choices attempt to hard-code underlying structure generation process into these models, instead of opting to let models to learn this directly from data, which could be beneficial for variety of reasons. For example, (Lin al.) 2023b) showed that for orphan proteins (those with few or no close homologs) approaches based on protein language models (PLM) tend outperform approaches that rely on MSA like AlphaFold2. Although folding models initially treated protein structure prediction as a deterministic problem via
current understanding of the
Work was completed while J.I., and N.J. were Apple.

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(a)
(d)
GDT-TS (CASP14)
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Simp efold-100M
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Figure Example predictions of SimpleFold on targets (a) chain of 7QSW (RubisCO large subunit) and (b) chain A of 8DAY (Dimethylallytrryptooaa) synthase 1), with ground truth shown in light aqua and prediction in deep teal. (c) Generated ensembles of target chain B of 6NDW (Flagellar hook protein FlgE) with SimpleFold finetuned on MD ensemble data. (d) Performance of SimpleFold on CASP14 with increasing model sizes from 100M to 3B. (e) Inference time of different sizes of SimpleFold on consumer level hardware, i.e., M2 Max 64GB Macbook Pro.
reconstruction objectives (Jumper et al., 2021; Back et al. 2021 Lin al. 2023b), recent works have explored building generative modeling for folding (Jing et al. 2024a). Generative approaches provide way model how native protein structures appear in nature, i.e., as a non-deterministic minimizer of the the Gibbs free energy of the atomic system. Generative models naturally capture generate ensembles of viable conformations instead of a single deterministic output (Jing et al. 20233 Abramson et al. 2024 Wohlwend et al. 2024
this uncertainty and make it straightforward
Bose et al. 2023 Watson et al. [202bb] Yim al. 2023b aj Geffner al. 2025 Lin et al. 2024).
However, these approaches still employ the expensive architectural components from AlphaFold2
like pair representations and triangle updates.
In this work, we propose SimpleFold, a fow-matching based folding model that directly maps protein sequence to its full 3D atomic structure without relying on MSA. pair representations, trian- gular updates or any equivariant modules. Our architecture is inspired by recent transformer-basee flow matching generative models (Peebles Xie 2023 Ma et al. 2024), with a strong emphasis on departing from current architecture designs using general-purpose transformer backbone trained end-to-end with a flow-matching training objective. Crucially, we demonstrate that strong folding performance (see Fig. can be achieved without explicit pairwise representations, triangle updates, or MSA, which significantly reduces architectural complexity and challenges preconceived notions around the necessity of these designs (Lin et al. 2023b). SimpleFold represents strong departure from previous of protein folding models, and we summarize our contributions as follows:
We revisit protein folding as a conditional generative task and introduce SimpleFold, a flow-based transformer folding model that eliminates MSA, pairwise representations, and triangle modules.
We scale SimpleFold to 3B parameters and train it on approximately 9M distilled structures to- gether with PDB experimental data.
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Our most powerful SimplcFold-3B shows strong results in folding compared to baselines with hard-coded heuristic designs and also achieves competitive performance on ensemble generation.
We train a family of models ranging from 100M to largest 3B (see Fig. Id). Simplefold- 100M recovers ~90% performance of our best model on major folding benchmarks while being very efficient in inference even on consumer-level devices.
SIMPLEFOLD
2.1
FOLDING WITH FLOW-MATCHING
SimpleFold casts protein folding as flow-matching generative model which generates protein struc- tures from noise, conditioned on a given amino acid sequence. This "amino acid sequence-to-prokd structure" generative model conceptually very similar "text-to-image" generative models. Flow- matching models (Lipman et al. [023] Albergo & Sanden-Eijnded (2023) approach generation as a time-dependent process that moves noise to data through integrating an ordinary differential equa- tion (ODE) over time. For time / [0, 1J. flow matching defines path of probability distributions (xx))) that continuously transforms a tractable distribution Po (e.g., Gaussian) into an arbitrarily complex data distribution pD. In practice, the transformation is parameterized by learnable time- dependent velocity field (*(X,, t), and the generative process is defined by integrating the ODE, dx,- vo(x,, t) dt, from noise to data.
2023) (also referred to rectified flow (Liu et al. 2023 Esser et al.) 2024h) between samples from the empirical data distribution x ~ pp and noise samples E N ((0, f). such that x, = /x + (1 I)e, where the target velocity defined as v, time and data via &g regression objective E[] [o]]vvo( (x,,))) v v,
SimpleFold implements a linear interpolant path (Albergo & Vanden-
In flow matching, we train network Va match the target across
In particular, given a protein with N₂ heavy atoms, we build linear interpolant between noise and all-atom positions where e, x E N x3 conditioned on the amino acid sequence E R2a where Nr number of residues amino acids the protein. Unlike earlier work that modeled only the C₂ backbone with flow-mate www-macching models (Lin & AlQuraishi] 2023 Lin et al. 2024] Geffner et al., we generate full-atom conformations including both backbones and side chains.
2025).
Training objective. The network Vo takes the amino acid sequence conditioning input Vo (x,x,s, s, t) to model the target velocity field. In particular, the flow-matching objective is defined as follows:
FFM Ex, [lvo(x,, (xx=)ll²
We also include additional local distance difference test (LDDT) loss similar to (Abramson et al. 2024). This loss measures the atomic pairwise distances error between the generated structure (x,) at timestep and ground truth structures During training, x(x,) is estimated through one step Euler, i.c., x(x:) = Xx + (1 (x The LDDT loss is formulated as follows:
Eie, I(6y < <Co(1b - 80 -8;
LDDD Ex,s,e.
(2)
Σ1 < <C)
where 6,g = |xx; - Xyy1 and 81 |[x(x;); *(x:); /I denote the distances between atom i. in
ground truth and predicted structures, respectively. The term a(-) is a nonlinear function on pair distance errors and C is cutoff distance which controls neighboring atoms be included in the loss. The model is trained with a weighted combination of now-matching and LDDT terms:
f= 4 + (
(3)
where (t) a weighting term related to timestep in flow process and is also dependent different training phases (see Sect. 4.1).
Timestep resampling. To improve training efficiency and force generating structures with fine details (Esser al. 2024 Geffner al. 2025), the timestep is sampled from the distribution:
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p(t) 0. (0.8, 1.7) 0.022/(0. 1), where LN is logistic-normal distribution (Atchison & Shen 1980) and 24 uniform distribution. Unlike popular timestep resampling in image generation Esser et al. 2024), where timesteps more densely sampled in the middle of the flow process (i.e., around 0.5), we shift the sample weight towards timesteps that are closer to clean data (i.e., 1), similar findings in (Geffner al. 2025) in the context of unconditional generation. This improves quality of generated samples especially in modeling refined structures of side chain atoms. We attribute this to the fact that protein structures contain a strong coarse-to-fine hierarchy "secondary structure baccbbo side chain", thus oversampling close to the data manifold drives the model better learn the refined atomic positions. Additional details regarding the LDDT loss and timestep resampling can be found in Appendix C.I
2.2
ARCHITECTURE
Fig. 2 shows an architecture diagram of Sim- plerold, which contains three major mod- ules: light-weighted atom encoder and decoder which are symmetric (i.e., same number of blocks and hidden size) and heavy residue trunk. All modules are implemented with stan- dard transformer blocks with adaptive layers conditioned on the timestep (see bottom left of Fig. 2
Amino acid
...ALAREK..... %
Time
Atom Encoder
Pretrained Protein LM
Grouping
Taken
Time
Residue Trunk
Skip conn.
The atom encoder takes in "noisy" atomic co- ordinates x, together with their corresponding atomic features (e.g.. atomic type and charge, Appendix A for details) and outputs atom tokens a E IR N2 xda In the atom encoder we use local attention mask that constraints atom latents to only attend to a local neighborhood around their residue (i.e., atom tokens only at- tend to atom tokens of nearby residues in the sequence). The grouping operation takes the output of the atom encoder and conducts av- erage pooling to atom tokens within the same residue to obtain residue tokens E xd.
Scale, Shift
MHA
Scale
Ungrouping
Scale, Shift
SwiGLU
3 Atom Decoder
Scale
Token
Shared building block in all 1,2,3
(see an illustration of grouping and ungrouping operations in Fig. 5)
Figure 2: Overview of SimpleFold's architec- ture built on general-purpote standard Trans- former block with adaptive layers. Atom encoder, residue trunk, and atom decoder all share the same general- purposed building block. Our model cir- cumvents the need for pair representations or tri- angular updates.
Similar to text-to-image and text-to-3D gener- ative models, we
frozen pretrained pro- tein language model (PLM) to embed the AA sequence into informative latent representa- tion. We leverage ESM2-3B (Lin et al., (023b) in all our models encode the AA sequence into per-residue conditioning embeddings e E Nxd. Sequence embeddings are then concate- nated with the residue tokens along the channel dimension and fed into the residue trunk. The residue trunk contains most of the parameters of the model and is where most of the compute is spent on. The ungrouping operation projects residue tokens to corresponding atom tokens. Specif- ically, we broadcast the same residue token to the number of atoms particular residue contains, which is defined by AA types. A skip connection from the output of atom encoder is also added to distinguish between different atoms within the same residue.
Finally, the atom decoder updates the atom tokens and outputs the predicted velocity field v. Local attention masks are also applied in the atom decoder as the encoder. The overall architecture of SimpleFold incorporates the hierarchical structure proteins implementing a "fine coarse fine" scheme to balance the performance and efficiency.
SimpleFold strongly departs from the design choices in previous work (Chakravarty & Porter 2022 Lin al. 2023b Abramson al. 2024). Unlike AlphaFold2 (Chakravarty & Porter 2022)
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ESMFold (Lin al. 2023b) which explicitly keep pair representation initialized by embeddings from expensive MSA search or attention score from the pretrained PLM, SimpleFold only keeps a single sequence representation which does not require triangle update and is thus far more efficient. In contraposition to previous works Lin et al. (2024); Chakravarty & Porter ; Lin et al. (2023b) which rely on equivariant architectures to generate physically meaningful results, SimpleFold is built on standard non-equivariant Transformer blocks.
2.3 SAMPLING
To fold protein with given amino acid sequence in inference, we initialize atomic coordinates as Gaussian noise Xo ~ NO, I) and integrate the learned vector field from = 0 to = 1, which generates a full-atom structure corresponding the input sequence. We perform stochastic generation using Langevin-style SDE formulation of the flow process, leveraging the equivalence between the learned velocity field Va and score function Sy, namely sø s,t) (lv 8,
x,)/(1- - -) (Albergo et al. 2023 Song et al. 2021). In particular, we apply the Buler-Maruyama integrator (Ma et 2024)
dx; vx(x,, s, dt + (t)ss((x, t, dt + V dW,
(4)
2
where w() >> is time-dependent diffusion coefficient, W, is reverse-time Wiener process, and 2(1-) which defines stochasticity scheduler T controls the scale of stochasticity. We find w(t) = ビ+ッ following SNR of flow process and yj is small constant for numerical stability, gives the best sampling quality. We stick this setting in all our experiments unless mentioned otherwise. Similar to previous flow-matching based protein generative models (Geffner et al. (025). we find that balances the generation of accurate refined structures and modeling the ensemble of conformations.
2.4 TRAINING ON DISTILLED DATA
We train SimpleFold with a data mix of 3 different sources. First, we include around 160K structures from PDB with cutoff of May 2020 following ESMFold (Lin et al., 20236). Additionally, we use the SwissProt set from AFDB. Within SwissProt distilled structures, we select samples with average pLDDT greater than 85 and standard deviation of pLDDT smaller than 15, which yields approximately 270K distilled samples. Moreover, we use representative protein structures for each cluster in AFESM Yeo al. 2025 We filter these structures with pLDDT larger than 0.8 resulting in more than 1.9M distilled strictures. All SimpleFold models except the largest 3B model are trained on the combination of three datasets listed above, adding up to approximately 2M structures.
To train our biggest model SimpleFold-3B, we explore an extended version AFESM (which we call AFESM-E) by also including structures beyond the cluster representatives. In particular, for each cluster, we randomly pick maximum of 10 proteins structures with average PLDDT larger than 80, which resulting benefit from larger training sets, we train our largest SimpleFold-3B on the distilled AFESM-E data together with PDB and SwissProt.
total of 8.6M distilled structures. Since larger models with larger capacity
3 RELATED WORK
Protein Folding Since the development of AlphaFold2 (Chakravarty & Porter 2022) and Rose TTAFold (Back et al.) (02T)) which achieved groundbreaking performance in protein folding with learning- baaed methods, many works have continued investigate this problem (Ahdritz al., 2024 Baek et al. 2023 Li et al. 2022). AlphaFold2 introduced domain specific modules like tri- angle attention, explicit modeling pair representations, and MSA to extract evolutionary informa- tion of proteins. OmegaFold (Wu et al. 2022) and ESMFold (Lin al. 2023b) replaced MSA with learned embeddings from pretrained PLM, which are efficient in inference and especially beneficial for orphan proteins. Some works also aimed at accelerating the models through efficient implemen- tations of AlphaFold2 modules, like FastFold (Cheng et al. 2022) and MiniFold (Wohlwend et al.). These folding models are built regression objectives and lack diversity for ensemble generation.
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Flow-Matching for Proteins Generative models, especially diffusion and now-matching based methods, have been introduced to protein modeling. Alphaallo//EMMlov (Jing et al.) 2024a) pro- posed to tune AlphaFoldd/ESMMFll with flow-matching objectives and demonstrated advantages in ensemble generation. AlphaFold3 (Abramson et al. 2024) and its architectural reproductions (e.g.. Boltz-1 (Wohlwend et al. 2024), Protenix (Team 20.5) Chai-1 (Boitreaud et al., 2024 also
used diffusion to build generative models for protein complexes of biomolecular interactions. In addition, several works have investigated diffusion or How-matching models for de novo protein structure generation with heuristic architectural designs from AlphaFold, like RFDiffusion (Watson al., 2023a), Genie-2 (Lin al., 2024), P(all-atom) (Qu et al. (024).. (Jing et al. (023) also de- veloped crafted equivariant diffusion process. Proteina (Geffner et al. 2025) attempts to build simplified architecture but still explicitly applies pair representation, and it only models C. gen- eration. In strong departure from previous protein folding models, SimpleFold aims at tackling the folding problem with a general purpose transformer backbone and learning symmetries in the underlying data generation process directly from training data (Wang et al. 2023).
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
We train a family of SimpleFold models different sizes (i.e., 100M, 360M, 700M, 1.IB, 1.6B, and 3B) to investigate the scaling ability of proposed framework in folding (see detailed configurations in Tab. 5). The overall training of SimpleFold consistent of two training stages pre-training and finetuning. During the pre- pre-training stage of SimpleFold we use a large dataset containing as much available data as possible. Finetuning, on the other hand, is performed on high-quality data to increase the fidelity of generated structures (see details in Appendix C.I).
4.2 PROTEIN FOLDING
We evaluate SimpleFold on two widely adopted folding benchmarks: CAMEO22 and CASP14, which are rigorous tests for generalization, robustness, and atomic-level accuracy in folding models. We set r = 0.01 for SimpleFold in inference which empirically shows best general performance in folding. We report standard structure prediction metrics: TM-score and GDT-TS assess global structural similarity; LDDT and LDDT-C, measure local atomic accuracy across all atoms and C, atoms, respectively; RMSD measures the averaged distance of atomic positions between two superimposed structures. For each metric, report both the mean and the median score over all the test samples (separated by slashes). We report all the metrics for all-atom models and only report TM-score and GDT-TS for backbone-only models (see details in Appendix Dj.
Tableegsummarizes results on CASP14 and CAMEO22. We group approaches based on strategies to encode protein sequence, namely MSA protein language model (PLM). For example, AlphaFold2 is MSA-based while ESMFold leverages PLM in place of MSA search. We also color baselines based whether they are trained with generative objectives, i.e., diffusion How-matching or au- toregression instead of direct regression to ground truth structures, e.g., AlphaFlow and ESMFlow are How-matching models finetuned from AlphaFold2 and ESMFold, respectively.
Despite its simplicity, SimpleFold achieves competitive performance compared with these baselines. In both benchmarks, SimpleFold shows consistently better performance than ESMFlow which is also a flow- mow-mhihhing model built with ESM embeddings. On CAMEO22, SimpleFold demonstrates comparable results the best folding models (e.g., ESMFold, RosFol,,,,d,, and AlphaFold2). In on most met-
particular, SimpleFold achieves over 95% performance of AossTTTTFol/////paaaaaaa ries without applying expensive and heuristic triangle attention and MSA. A. On the more challenging CASP14 benchmark, SimpleFold achieves even better performance than ESMFold. In particular, SimpleFold-333 obtains a TM-score of 0.720 / 0.792 and GDT-TS of 0.639 / 0.703 in comparison to 0.701 0.792 and 0.622/ / 0.711 1 of ESMFold. SimpleFold also shows competitive or even better performance to baselines that applies MSA like Rose TTAFold and AlphaFlow.
Moreover, scaling up the model sizes of SimpleFold models results in better performance across the board, which indicates the benefit of designing a general purpose approach that benefits from scale. It is notable that scaling up model sizes improves performance substantially more in CASP14, i.e

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Table I: Performance of protein folding the CAMEO22 (top) and CASP14 (bottom) benchmarks. For each metric, we report the average / median over all samples. Here, orange denotes basclines trained with regression objectives, green denotes baselines trained with generative objectives (i.e..
wwwpusiionnoow-mtticinin or autoregression), and blue denotes our SimpleFold, which is trained with generative objective but without MSA.
Type
Model
TM-score
GDT-TT
LDDTT
L.DT-CC
RMSD T
CAMEO22
Roseld (Back et al.
2021
0,780/ 0,860
0,860
0,7775
0.575 / 0.605
0.798 0.8227
5.721 7.8664
MSA- based
AlphaFlow Jing et die 20240
0.840/ 0.9227
0.808/ 0.853
0.741 /0.798
0,855 0.899
3.846/ 2.122
AlphaFool2 Jumper
2021
0.863/0.944
0.844/ 0.900
0.816 0.856
0.893 /0.923
3.578 / 1.857
Rose Back et at. 2025
0.864 0 0.947
0.845 / 0.904
0.727 / 0.766
0.893 0.926
3,571 / 1.707
ESM3 (Hayes et al. 2025
0.746 0,840
0.694/ / 0.758
ESMDil eLu 20/4
0.754 0.847
0.701 0.766
PLM-
EigenFold Uing al.
2023
0,750 0.884
0.710 0.790
based
OmegaFold Wu et a
0022
0,805 0.899
0.767 / 0,844
0.746 / 008815
0.829 / 0.8892
3.294/2..0
ESMFlow (ing et al.
20/24a
0.818 0.893
0.774/
0.696 0.745 0.745
0.827 / 00.66
4.528
2.693
ESMFold [in
20256
0..93
0.875
0.792/0.834
0.871
/.009
SimpleFold-100M
0,803 0.877
0.746 0,787
0.721 / 0.752
0.822 /0.552
4.897 2.855
SimpleFold-360M
0,826 0.905
0.782 0.841
0.773 / 0,880
0,844 /0.878
2.681
Ours
SimpleFold-70000
0.829 0.915
0.788 / 0.845
0.775 / 0.800
0.850/0.888
4.557 2.4423
SimpleFold-I.IB
0.833 0.924
0.793 0.855
0.776 0.800
0.850/08883
/
SimpleFold-I.66
0.835 0.916
0.799 / 0,864
0.782 0,811
0.853 0.889
4,397 2,17
SimpleFold-3B
0.837 0.916
0.802 / 0.867
0.773 / 0.802
0,852 /0884
4.225 /2.175
CASP14
Roseod (Back et
2021
0.654 0.678
0.562 0.572
0.464/ / 0.456
0.705 0.722
9.676 6.420
MSA-
Alphaflow Ding et am Kuzem
0,740/0.811
0,661 0.661/0.711 0.7711
0.632 / 0.662
0.767 00799
7.091 3.949
based
Rose' Rese''TAAooa Back el al.
2023
0.802 0.881
0.740 / 0.824
0.638 / 0.669
0.824 / 00.669
6.744 3.299 292
AlphaFold2 Jumper et
0,845/ 0.900
0.783 0.855
0.778 / 0.817
0.856/0.897
5.027/3.00
ESMDiff (Lu al.
2024a
0,521 0..499
0.447 / 0.430
ESM3 (Hayes et a1
20255
0,534 1 0.567
0.459/ / 0,488
PI.M-
HigenFold Ling cl al.
2023
0.590 0.637 0.637
0.539 0.575
based
ESMFlow Ding of at.
20,44
0.627 0.679
0.539 0.544
0.525 0.539
0.669 0.730
10.503 6,974
OmegaFold Wi et
2022
0.693 0.773
0.625 / 0.723
0.627 / 0,726
0.715 0.824
9,845 / 4,042
ESMPold [Lm ct at.
20235
0,701 0.792
0.622
0.637 / 0.705
0.725 / 00802
8.679 / 4.016
SimpleFold-100M
0.611 0.611/0.622 70.628
0.513 0.554
0.537 / 0.544
0.659 00.685
11.157 / 8.976
SimpleFold-360M
0.674 0.758
1.655
0.657
0.703 0.766
9.382
Ours
SimpleFold-700M
0,680/ / 0.767
0.591 / 0.668
0.630/0.674
0.773
9.289 4.431
SimpleFold-I.IB
0.697 0,796
0.607 / 0.668
0.640/0.670
0.723 0.7758
9.249 4.462
SimpleFold-14.
0.712 0.801
0.630 0.709
0.660 0.669
0.741 0.799
8.424 4,722
SimpleFold-3B
0.720/ / 0.792
0.709
0.666 0.709
0.747 0.829
3.992
the more challenging benchmark, than in CAMEO22. This is a clear empirical evidence that models with larger capacity are more capable of solving complex folding tasks.
4.3
ENSEMBLE GENERATION
4.3.1 MOLECULAR DYNAMIC ENSEMBLE
SimpleFold trivially models the distribution of protein structures, due its generative training ob- jective. Namely, SimpleFold does not only generate one deterministic structure for an input AA sequence but is also capable of generating the ensemble of different conformations. To demonstrate this ability of SimpleFold, we benchmark the performance on the ATLAS dataset (Vander Meer- sche et al. 2024), which assess generation of molecular dynamic (MD) ensemble structures. We set T 0.6 (F4.4) 4 in inference add more stochasticity than folding tasks.
Compared to baselines without additional tuning on MD simulation data in ATLAS (e.g., MSA- subsampling), SimpleFold achieves superior performance on generating ensembles that match the distribution from MD simulations. We also report the results of SimpleFold-MD, a finetuned model on the training data split of ATLAS, comparing to baselines that are also additionally tuned (i.e., ESMDiff (Lu et al. 2024a), ESMFlow-Mi (Jing et al. 2024a), and AlphaFlow- UJng et al.) 2024a)). As shown in Tab. 2 SimpleFold consistently achieves better performance than ESMFlow- MD where both rely on the ESM embedding without MSA. SimpleFold also shows better perfor-
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Table 2: Evaluation MD ensembles. Results of baseline models taken from (Jing al. 2024a
Lu et al. 20244), to which the evaluation pipeline for our SimpleFold (SF) and SimpleFold-Mi (SF- MD) adheres.
Tuning
Timed AlphaFlow-MD
AF2
MSA-sub.
SimpleFold
ESMDiff
ESMFlow-MD
SimpleFold-MD
Pairwise RMSD r
0.10
0.22
0.44
0.18
0.19
0.48
0.45
Global RMSF
0.21
0.29
0.45
0.49
0.31
0.60)
0.48
Per target RMSF r
0.52
0.51
0.60
0.68
0.76
0.85
0.67
RMWD
3.58
4.28
7,48
3.60
2.61
4.17
RMWD trans contri |
2.86
3.33
3.74
5.18
3.13
2.28
3.40
RMWD contri
2.24
1.74
3.37
1.74
1.30
MD PCA W2
1.99
2.23
1.62
2.29
1.51
1.32
1.34
Joint PCA W2
3,57
6.32
3.19
2.18
2.85
% PC sim > 0.5
23
21
23
26
38
Weak contacts
0.37
0.52
0.55
0.62
0.56
Transient contacts J
0.28
0.27
0.27
0.26
0.34
0.41
0.34
Exposed residue
0.32
0.37
0.49
0.50
0.60
Exposed MI matrix
0.02
0,10
0.14
0.20
0.25
0.32
Table 3: Two-state conformation results. For the last two metrics, both mean and median are reported over the targets. Results are taken from the ESMDiff paper (Lu et 2024a). to which the evaluation pipeline for the rest models adhere.
Res, flex. (global)
Res. flex. (per-target)
Res, flex. (global)
Res, flex. (per-target)
Type
Model
TM-ens
TM-ens
Apo/holo
Foid-switch
FoldFlow2 (Huguct et al
2024
0.027
0.057 0.005
0.216 0.200
0.051
0,009/ 0.005
0.199/0 0.11
Camppell
2024
0.211
0.269
Soq-
Str2Str (L al 2174h
0.174
0.3307
0.731 00.228
0.161
1.246/0.233
0.615/0.644
Rigenfold Diny
0.614/0.651
based
ESMDai Lo
A7248
0.420
0.489 0.515
0,838 0.877
0.402
0,341 0.288
0.626 0.685
ESMFlow
Dnn
3124
MSA-
MSA-Subs,
Jumper al
2021
0.398
0.404 0.371
0.856 0.894
0.350
0.320 0.303
0.714 0.765
AlphaFlow
mgg ot A0244
0.527 /0.527
0.864 0.893
0.385
0.376
0.788
SimpleFold 100M
0.492
0.500 00.332
0,852 8.887
0.391
0.291 0.241
0.656 0.677
SimpleFold 360M
0.537
0 0.528
0,864 0.898
0.359
0,310/ 0.314
0.689/ 0.746
Ours
SimpleFold.700m
0.552
0.524 00.538
0,870 0.899
0.307
0.328 0.310
0.693 0.713
SimpleFold 1.1BB
0.557
0.526 0..37
0,870 900
0.337
0,346 / 0.344
0.69% 0.755
SimpleFoodd11668
0.501
0.522 0.508
0.877 0.912
0,240
0.339 0.318
0.721 0.770
SimpleFold 3B
0.639
0.550 0.552
0.893 0.916
0.292
0,288 0.263
0.734/ 0.766
mance than AlphaFlow-Mi on metrics related ensemble observables (e.g., exposed residue and MI matrix), which are key feature in the identification of cryptic pockets in drug discovery.
4.3.2 MULTI-STATE STRUCTURE PREDICTION
We also evaluate the capacity of SimpleFold in generating protein structures with multiple natu- ral conformation. We adopt the benchmarking set of Apo/holo (Saldaño et al.) 2022) and Fold- switch (Chakravarty Porter 2022) following Jlng et al. (2023). The model is assessed to produce diverse yet accurate set of samples "covering" both conformational states and reflecting correct local flexibility. As shown Tab. 3 SimpleFold obtains state-of-the-art performance on Apo/holo, where SimpleFold outperforms strong MSA-based approaches like AlphaFlow significantly. On Fold-switch, SimpleFold shows comparable or even better performance than ESMFlow which is also applies flow-matching objective and is built ESM embeddings. The results validate the pability of our SimpleFold in predicting the structures of high quality (i.e., ensemble TM-score) as well as correctly modeling the flexibility in structures (i.e., residue flexibility). Also, the overall per- formance of SimpleFold increases with the model size growing, which further showcase potential of our proposed framework in generating protein ensembles.
4.4 EFFECTS OF SCALING IN PROTEIN FOLDING
SimpleFold benefits from increasing model sizes as proven by recent success of generative models in vision and language generation. We note that the effects of scaling both training data and model
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Published as a conference paper at ICLR 2026
0.80
0.80
StrckiPaad 1006
a 1034
0.70
05
0.65
0 6.
Sirplarad la
0.60
0.65
(3800)
0 9.0
0:55
0.55
0.50
Simpaioo-3
0.60
KOB-SPMMER SMI
100 10015"1APSSP (3,29)
30a
101a
1011
10
10
20
30
10K
25K
50C
100K
200K
Training Gf
Training
Training Steps
(A)
(e)
(e)
TM Score
Score
TM-Arore
0.82
80
0.80
0.80
6.75
0.75
Simptaro6-3300
5:352
0.70
Smasfa 5:302
rOB (L994)
simpkiradd
smskess 18
FBD-5P (4318)
0.65
TEB-SS-AREINE (9.20)
3012
1011
10
10
15K
236
SUC
108K
200X
400K
Training Gf ops
Training Steps (x10f)
Training Steps
(b)
(d)
(9)
Figure 3: Scaling behavior of SimpleFold. Training Gflops vs. folding performance GDT-TS and (b) TM-score. Training steps vs. folding performance on (c) GDT-TS and (d) TM-score. How data scale affects the performance (e) GDT-TS and (f) TM-score. All models are benchmarked on CAMEO22.
sizes have not yet been rigorously investigated in protein folding. The section empirically shows the scaling behavior of SimpleFold from both model and data perspectives, highlighting important considerations for building powerful biological generative models. We train models with different sizes from the smallest with 100M parameters to the largest with 3B parameters on full pre- pre-training data containing PDB, SwissProt from AFDB, and AFESM (AFESM-E for 3B model). Fig. Bia)- (d) illustrate how model sizes affect folding performance (also see Fig. (d). Larger models trained with larger training budget (i.e., training Gflops and training iterations) achieve better performance. We believe these results highlight the positive scaling behavior of SimpleFold and an direction of progress to obtain more powerful generative models in biology.
We also show the benefits of scaling up training data SimpleFold. We train SimpleFold-700M with different sources of training data. As shown in Fig. Be and (f), SimpleFold when increasing the total number of unique structures in the data mix, the final performance of SimpleFold tends to improve after sufficient training iterations. These experimental results support core contribution to build a simplified and scalable folding model that benefits from the growing total of protein data available either experimentally distilled from different models.
5 CONCLUSIONS AND FUTURE WORK
We introduced SimpleFold. a flow-matching generative model for protein folding that represents strong departure from the architectural designs in previous approaches. SimpleFold is solely built with penerol-purpose transformer blocks with adaptive layers, dispensing away with heuristic de- signs like expensive pair representations and triangular updates introduced by AlphaFold2. This simplified framework allows us to train SimpleFold at scale both in terms of model size and training data. Our largest (and most powerful) model, SimpleFold-33 demonstrates competitive perfor- mance standard folding tasks and it also show very strong even state-of-the ofthe-ot-the-rtt results ensemble generation tasks. To the best of our knowledge, SimpleFold is the first work that rigor- ously demonstrates good scaling behavior in protein folding. We believe SimpleFold represents

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Published as a conference paper at ICLR 2026
disruptive approach for protein folding that relies scaling up general purpose architecture blocks to learn the symmetries of the underlying data generation process directly from training data.
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A DATA PIPELINE
We largely adopt the data pipeline implemented in Boltz- 17 Wohlwend et al. 2024), which is an open-source replication of AlphaFold33 (Abramson et al. 2024). Tab. 4 lists the input features for SimpleFold. is noted that since SimpleFold does not apply MSA template search, input features are also simplified compared to AlphaFold.
In cropping larger proteins, follow cropping algorithm that combines both spatial and contigu- ous cropping strategies introduced in previous work (Chakravarty & Porter 2022. Abramson et al.) 2024 Wohlwend et al. 2024). Following this setting, set the neighborhood size in cropping uniformly between zero and 40 tokens to balance spatial and contiguous cropping.
Table 4: Input features to SimpleFold.
Feature
Shape
Description
sisidue_inder
N₂
Residue number in the token's original input chain.
token_index
N₂
Token number. Increases monotonically.
restype
N.
One-hot encoding of the sequence: 20 amino acids unknown.
emmee
N7, 37,37,2501 2560]
Protein sequence embedding from all layers in ESM2-3B.
noised_ppo
[Na.3]
Noised atom positions, in Á (random rolation applied).
pos
N).,
Atom positions
reference conformer Å (no rotation upplied).
mask
N..
Mask indicating atoms used in reference conformer.
element
N., 128]
One-hot encoding of the element number each atom.
ref charge
Na)
Charge for each atom in reference conformer.
ref atom name_ chars
Nay 4,64]
One-hot encoding of atom names in reference conformer.
ref_space_ut uid
Na)
Encoding of residue index associated with reference conformer.
time
Timestep in flow process. Number of residues, N,.
length
During training, atomic positions of protein tion. After centering, we scale the position by global factor of 1/16 to make the atomic positions live in the [-1, 1] interval. Similarly, we also scale ref_pos by 1/5 standardize the positions in reference conformers.
centered and augmented with random rota-
B MODEL ARCHITECTURE
B. ARCHITECTURE COMPARISON TO ALPHAFOLD2
Fig. depicts the comparison of major compute blocks AlphaFold2 and SimpleFold (Fig. 4( borrowed from original AlphaFold2 paper (Chakravarty & Porter, (2022). As shown in the fig- ure, SimpleFold does not rely on either explicit pair representations or MSA. Instead, we only keep a sequence-level representation and leverage embeddings extracted from pretrained PLM (i.e., ESM2 (Lin et al. 2023b)). Compared AlphaFold's Evoformer block which includes expensive tri- angle attention to interact between pair and sequence representations, SimpleFold follows simple DiT architecture (Peebles Xie, 2023) which is more computationally efficient.
B.2 COMPARISON TO ALPHAFLOW AND ESMFLOW
Though SimpleFold, AlphaFlow and ESMFlow (Jing et al. 2024a) all use flow fow-matching training objectives, the architectural design and the training paradigm are drastically different: the architec- tural design and the training paradigm are drastically different.
AlphaFlow and ESMFlow are built upon the AlphaFold (Chakravarty & Porter 2022) and ESM- Fold (Lin al. 2023b) network architectures, respectively. This means they inherit Nomain-specinf heuristic architectural designs like pair representation and triangle attention. On the other hand, SimpleFold based purely standard transformer blocks without any domain-specific network
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(a)
bookk shandel evights
aoiion
Outer producc
Trungh LEIng
Troroghh Leking
Friipgle
Toam
starting
anding
(b)
attettion
spreeentation
ixproaetation
Figure 4:
Major neural network blocks of (a) Evoformer in AlphaFold2, and (b) Transformer with adaptive layer in SimpleFold.
blocks. In addition, AlphaFlow and ESMFlow use generative training objective merely as a fine- tuning strategy top of already fully trained checkpoints from AlphaFold2 and ESMFold which use deterministic regression objective. On the contrary, SimpleFold built from the ground up to be a pure generative model trained from scratch with flow-matee flw-matching objective.
Building SimpleFold from the ground up as a generative model that is trained from scratch with flow-matching objective results in improvements in multi-state benchmarks over models that only fine-tune pre-trained deterministic models like AlphaFold and ESMFold.
B.3 IMPLEMENTATION DETAILS
We adopt a modern implementation stack for all the transformer blocks including QK- normalization (Esser et al., 2024) and SwiGLU (Shazeer 2020) place of standard FFN for better performance and training stability. To encode the positional information of atoms and residues, we employ rotary position embedding (RoPE) (Su al., 2024). Particularly
each attention block within the residue trunk, they query and key vectors of the n-th residue in amino acid sequence are rotated by e0n In both the atom encoder and decoder, we extend the positional embedding to 4D axial RoPE. The first three axes are 3D atomic coordinates from reference conformers (see Appendix A)). which are local structures predicted at the amino acid level by a rule-based chemin- formatic method. The last axis is the ID indexing to the corresponding residue token. Each axis in 4D axial RoPE controls rotation of a quarter of the hidden dimension in both query and key.
B.4 MODEL CONFIGURATIONS
Table 5 lists the configurations of different SimpleFold models from the smallest 94M to largest 2.86B. In implementation, we apply the same architecture for the atom encoder and atom decoder. Though AlphaFold2 is similar to our smallest SimpleFold- (both are around 95M), its forward Gflops are much higher than our largest SimpleFold-3B (~ 30Tflops 1.4Tflops). This is because AlphaFold2 relies on expensive triangle update well as explicit modeling pair representations from MSA. SimpleFold, on the other hand, is built on general-purposed transformer blocks which much computationally efficient.
in terms of number of parameters
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Table 5: Configurations of different variants of SimpleFold with comparison AlphaFold2 and ESMFold in number of parameters and forward Gflops.
Atom Enc. Dec.
Residue Trunk
Model
# Paramms
Gflops
Dim.
Heals
Blocks
Dim.
Heads
# Blocks
AlphaFold2
95M
30935.0
ESMFold
710M
3399.7
SimpleFFoldIO00e
94M
66.5
256
4
768
12
SimpleFold-
360M
189.9
1024
SumplcFold-700M
687M
310.4
256
4
1152
28
SimpleFold-I.IB
1.11B
496.0
384
6
1280
20
36
SimpleFold-L.d .6B
1,58B
750.0
512
8
1536
24
36
SimplcFold-3B
2.86B
1382.4
2048
B.5 GROUPING AND UNGROUPING
Fig. illustrates how grouping and ungrouping operations are conducted in SimpleFold. In group- ing. conduct average pooling over atoms tokens from one residue obtain a residue token. While in ungrouping, we replicate the same updated residue tokens to all atoms within the residue.
Alanine
Proline
Lysine
HyC
HgN
OH
OH
OH
NHz
NH
NH₂
(a)
Atom tokens
N
CA
C
O
CB
N
CA
C
0
CB
CG
CD
N
CA
C
O
CB
CG
CD
CG
CD
Average Pool
Average Pool
Average Pool
Grouping
Residue tokens
A
K
(b)
Residue tokens
A
K
Ungrouping
Replicate
Replicate
Replicate
Atom tokens
CA
C
O
CB
N
CA
C
CB
CG
CA
C
o
CB
CG
CD
CG
Figure 5: Illustration (a) grouping and (b) ungrouping operations SimpleFold.
C TRAINING AND INFERENCE
C.1 ADDITIONAL TRAINING DETAILS
Pre-training. In pre-training, SimpleFold is trained on structures from all three data sources, namely PDB, SwissProt from AFDB, and AFESM (AFESM-E for 3B model). We set the maxi- mal amino acid sequence length to 256, where we keep shorter sequence without padding while crop longer sequences to 256 residues. We set a<t) effective batch size 512 except for 1.6B and 3B models which are trained with batch size 1024 and 3072, respectively. We use AdamW optimizer (Losschilov Hutter 2019) with learning rate 0.0001 and linear warmup for the first 5000 steps. We also apply SO(3) data augmentation during training, which randomly rotates structure targets, and rely on the capacity of the model to directly leam such symmetries during training.
Eq. 3 All models are trained with
Finetuning. In finetuning, SimpleFold trained on PDB and SwissProt subsets only which contain higher quality data. We set maximal sequence length to 512 which allows access to
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larger protein structures in this training phase and accordingly half effective batch size. We set a(t) = 8ReLU(t 0.5) in Eq. which gradually increases weight of LDDT loss to maximum value of 5 when approaching clean data learning rate 0.0001 finetuning. In both pre-training and finetuning, we apply an exponential moving average (EMA) of all model weights with decay of 0.999 following common practice in flow-matching generative models.
1). We keep AdamW
optimizer with the same
Timestep Resampling. In training, resample timestep with p(() 0.02 1) 0.98LN(0.8, 1.7), and logit-normal distribution LN is given:
(logi -m)
LN m,
exp
(5)
t(1 /
2x²
We set m = 0.8, s = 1.7 to sample timestep more densely around = so the model better learns capture the refined details as shown Fig
2.5
Resampled
Uniform
2.0
1.5
Density
1.0
0.5
0.0
0.0
0.2
0.6
0.8
1.0
Timestep
Figure 6: Distribution of resampled timestep compared to uniform distribution.
Rigid alignment. Following Abbrmmso et al. (2024): Wohlwend et al. (2024), we apply rigid alignment between one-step denoising atomic coordinates and true coordinates before computing the flow-matching MSE loss (Eq. [ in training to reduce the loss variance. In particular, x(x,) is estimated through one step Euler, 1.c., x(xx) X - (1-t) ve(x,,
s. s, and the true coordinates is aligned with the denoised coordinates through Kabsch algorithm (Wohlwend et al. (2024) to obtain x'. The velocity target is re-calculated by interpolating the aligned and noise Though such rigid alignment strategy helps in faster convergence, it does not make significant difference in final performance as also mentioned Wohlwend al. (2024).
LDDT Loss.
Following AlphaFold3 (Abramson et al. 2024), the nonlinear function or in Eq. 2 is
given as:
(x) ] (sigmoid(0.5 - æ² + sigmoid(1 - x) + sigmoid(2 x) sigmoid(4 - x)); (6)
which mimics the how LDDT is computed for evaluation. set the cutoff distance 15Aín
Eq. 2 which is the typical setting for the LDDT metric.
Batching. During training we copy one protein B. times per GPU with different flow timestep t sampled and accumulate gradients from B, different proteins on different GPUs, following Al- phaFold2 (Chakravarty Porter 2022; Abramson et al. 2024). Therefore, the effective batch size B. x By We empirically find that this strategy leads to more stable gradient and better perfor- mance than naively building a batch with randomly selected proteins. Tab, lists the detailed setting of training batch for different model sizes.
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Table 6: Settings of pre-training and finetuning batches for different SimpleFold models.
Pre-training
Finetuning Prot. B,
Model
# Copies Bg
# Prot. By
Eff. Bsz.
Copies B.
EfT. Bsz.
SimpleFold- 100M
16
32
512
8
32
256
SimpleFold- 360M
16
512
8
32
SimpleFold-700M
$12
256
SimpleFold-L.1B
16
512
256
SimpleFool-LL6
128
1024
128
512
SimpleHold-3B
24
128
3072
12
128
1536
Amino acid
BALANEK.....
Pretrained Protein LM
Encoder
Grouping
Residue Blocks
PLDDT Module
PLDDT
Figure 7: Illustration of PLDDT module training.
Confidence Module. Providing confidence estimation for generated protein structures greatly help understand the quality of generation (Chakravarty & Porter 2022 Lin et al. 2023b). To this end, develop additional predicted LDDT (pLDDIT) module which predicts per-residue LDDT value (ranging from 0 to 100) as a confidence score. After the folding model is fully trained, train the PLDDT module in separate training stage while freezing all the parameters in the folding model (see Fig. 7) During training the PLDDT module, we sample protein structures & on the fly, and feed & into the folding model with timestep final residue tokens The PLDDT module is composed of layers of standard transformer blocks without adaptive layers, which takes in r and outputs PLDDT. Following (Chakravarty Porter 2022), the target LDDT is discretized into 50 bins and the PLDDT module is trained through cross-entropy objective.
for adaptive layers acquire the
After SimpleFold is pretrained and finetuned, we train the PLDDT module with all other components frozen. The PLDDT module is trained on combination of PDB and SwissProt data, which contains experimental and high-quality distilled data. During PLDDT training,
set att) 1, and Simple- Fold generates structure samples on the fly with 200 steps and т = 0.3. As finetuning, We set maximal sequence length to 512 and apply AdamW optimizer with the learning rate 0.0001. Fig. shows the training pipeline for PLDDT module. In particular, we use fully trained SimpleFold-1.68 extract residue tokens.
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C.2 ADDITIONAL INFERENCE DETAILS
During inference, we use the Mareraaa integrator shown in Eq. starting from i, 0.0001 and the number of time steps is set be 500 without additional statement. In practice, we
7 = 0.01 in w(t) (t)) = 0 for t 2 0.99 and discretize the time interval logarithmically from t = f. to 1. After each sampler step, we rescale the center of all atomic positions to origin to align with the training
2(1-t) for numerical stability, And following (Geffner et al. 2025 we set
[+1
setting. At the end of the flow trajectory, we rescale the coordinates by multiplying 16 to map the protein structure back to A scale.
C.3 INFERENCE TIME
Tab. lists inference time of SimpleFold in comparison to baseline models, AlphaFold2, ESMFold, AlphaFlow, and ESMFlow. SimpleFold shows advantage in inference efficiency especially when sequence is longer (e.g., 1024). Also, ESM2 adds little overhead inference.
Table 7: Inference time (in seconds) of different models. All models are benchmarked single H100 with batch size 1.
Sequence length
Steps/Recycles
64
128
256
512
1024
AlphaFold2
3.0
3.7
25.5
111.5
ESMFold
3
1.100
1.1
1.745
43.6
AlphaFlow
10
10.0
12.3
26.6
85.2
371.7
ESMFlow
10
3.7
3.7
5.8
24.6
145.5
FSM2
<0.1
<0.1
0.1
0.2
SimpleFold- 100M
200
3.6
3.6
3.8
4.2
5.6
SimpleFold-3666
200
7.2
7.4
7.6
11.6
SimpleFold-700m
200
Simplefold-L.IB
200
12.6
12.8
12.8
22.2
SimpleFold-I.6B
200
13.0
13.0
13.8
18.2
29.4
SimpleFold-3B
200
14.0
14.0
15.6
27.8
44.6
SimpleFold- 100M
500
9.0
9.0
9.5
SimpleFold-
500
18.0
18.5
SimpleFold-700m
500
24.5
25.5
26
28.5
41.5
Simplefold-1.ID
500
31.5
32.0
32
37.5
55.5
SimpleFold-L.6B
500
32.5
32.5
34.5
45.5
73.5
SimpleFold-3B
500
35.4
35.4
37.2
72.2
111.4
D EVALUATION
D.1 FOLDING BASELINES
AlphaFold2. The AlphaFold2 (AF2) baseline was established using the official implementation wrapped using ColabFold (Mirdita al. 2022). We utilized the standard released weights with three model recycles. We adopt the MMSeqs2 engine (Steinegger & Söding 2017) to search for multiple sequence alignments (MSAs) as model input. No template Amber relax is applied the predictions.
Fo. We utilized the release models of RoseTTAFold (Back al. 2021 via Colab- Fold (Mirdita et al. (022), employing its publicly available pre-trained model weights. We keep the default configurations of both models for inference and use MMssess 2017) for MSA search. In specific, we use the proposed pipeline in the ColabFold Notebook in- cluding the end-to-end 3-track model forward, TRFold refinement and side-chain packing using SCRWL4 (Krivov et al. 2009).
& Söding
Rose To.. Experiments of RoseTTAFold (Back et al. 2023) are similarly conducted via ColabFold (Midita et 2022) with the pre-trained model weights. We follow the default inference configuration as described in the RoseTTAFold2 (Back et al.) 2023) official repository by setting -n_recycles=3, -nseqs=256 and -ubcrop==11.
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Our experiments employed the ESMFold implementation from ColabFold (Mirdita et al. 2 and model checkpoints from (Lin et al. 2023b). We used the pretrained esmfold v1 model for inference recommended by the authors, the performance of which is better than the comfold_ve vo model which was used for experiments ESM2 paper (Lin et al. 2023b). We set the number of recycles to be 3, aligned with the AF2 setting.
ESMFold.
OmegaFold. The implementation of OmegaFold used was based on the original repository (Wu jtt al. 2022). We relied on the default pre-trained model shipped with the release. The inference pipeline strictly adhering to the default setting.
EigenFold. The EigenFold implementation as provided (Jing et al. 2023) was leveraged for our baseline runs. We utilized the standard pre-trained weights for decoder and make node/edge em- beddings from OmegaFold as instructed by the authors. Defaults settings applied during inference
included -alpha 1 bbeta -elbo_step 0.2
AlphaFlow/ESMFlov We utilized the codebase for AlphaFlow and ESMFlow released by the authors of (Jing et al. 2024a), employing its pre-trained model checkpoints on PDB data (with suffix pdb_base 2004020242.000 pt). The setup largely mirrored the default configurations for both models described in the repository, specifically by setting tmax to be 1.0 and the flow steps to be 10.
ESMEESMDiHT. The implementation of ESMDiff and ESM3 used was based on the ESMDiff original repository (Lu al. 20024).. No additional training performed; the provided pre-trained model was used directly (both pretrained ESM3 and finetuned ESMDiff) to predict the structures for each target. We used standard hyperparameters listed by the authors, including steps=25, T=1.4, top_p=0.9.
D.2 PDB CUTOFF DATE
Tab. lists the PDB cutoff date most baselines in training. SimpleFold uses May 1, 2020 as the cutoff date following most baselines.
Table 8: Cutoff date of PDB for training.
Model
PDB cutoff
AlphaFold2 Chakravarty & Porter
2022
May 1, 2018
RoseTTAAFld2 (Back el
2023
April 30, 2020
FSMFold [Lin et al. 2024h
May I, 2020
EigenFold (Jing el al. 2123
April 30, 2020
AlphaFlow (ing ci al.
2024a
May 1, 2018
ESMFlow (Jing et al. 2024a
May 1, 2020
ESM3 (Hayes et al. 2025
May 1, 2020
ESMDii (Lai al. 2024a
May I, 2020
SimpleFold
May 1, 2020
D.3 ESTIMATION OF GFLOPS
We leverage SpepSpeed Rasley et al. 2020 library flops for SimpleFold as well baseline models. get__moel_profil_ function to get the compute profile for the models. estimating the flops, we set the number residues to be 256 and number of atoms to be 2304, namely, 9 atoms per residue.
to estimate forward use
In particular,
deepspeed. www.eedddpffiiiinggg
D.4 TARGETS IN FOLDING TASKS
List of 183 targets in CAMEO22 Haas et al. 2018
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7dz2-C, 7eoz-A, A 7fgb-A, Afgp-A, 7fh0-B, 71t7-A, 71x4-A, 7mlz-B,
7mj3-A, 7n3y-A, 7 7n99-A, 7oj1-A, 7oj2-A, 7 7pcl-A, Apce-A,
A 7pk5-A, 7pkw-A, 7p14-A, 7p17-A, 7pqi-A, 7pqw-A, 7pup-A, 7pwe-A,
7 7q6g-A, 7q83-D, 7q9e-C, 7qau-A, 7qpe-A, 7qsw-B, 7qsw-C, 7qsx-A,
7qys-L, 7r08-E, 7r0o-B, 7r3w-D, 7r49-B, 7rlk-D, 7rmy-A, 7roa-A, Arpn-A,
7rt7-D, 7rup-A, 7ruq-A, 7s03-A, 7s8k-B, 7sao-A, 7sbd-H, 7sfn-B, 7skh-B,
A 7snc-A, 7snj-A, 7s00-A, 7spn-A, 7sz2-B, 7t12-B, 7t1j-B, 7t5w-B,
7te2-A, 7tgi-B, 7th2-C, 7tif-A, 7tol-A, 7tvw-A, 7u04-H, 7u0e-H, 7uav-A,
A 7upm-A, A 7uqv-D, 7uwg-C, 7ux0-A, 7uxt-A, 7v2s-B, 7v5f-A,
7 7vbo-A, 7vd7-B, 7vf3-B, 7vfc-A, C, 7vi8-B, 7vil-A, 7vma-A,
7 7 7vp3-C, 7vp6-D, A 7 7 7vrf-A, A
7 7 7w06-A, 7w16-A, 7w42-B, 7w52-B,
7w6x-A,
7w7h-E,
7u89-A,
A, 7wa9-A, Awbn-A, 7wf6-A, 7wf8-B, 7wf9-A, Awfx-A,
7whf-G,
7wj0-A,
7wjt-B, 7wq5-A, 7wua-A, 7x0g-A, 7x0q-A, 7x0r-B, 7x15-A,
7xlk-A,
7x7w-A,
7x8c-B, 7xce-A, 7xjt-B, 7xtm-B, 7y0i-A, 7y39-B, 7y3k-A,
7y3w-A, 7y4n-A,
7y78-B, 7y79-B, 7y8u-E, 7y9b-A, 7ycv-A, 7ymo-A, 7yrt-C,
7yta-B, 7yvt-B,
A, 7 7z06-A,
7zc8-A, 7zgi-B, 72gm-A, 7zk1-A,
7zty-A, 7zva-A,
A 8a28-A, 8a4a-A, Bag9-A, 8ajp-A, 8b26-A, 8b55-A, 8b5t-A, 8b5v-A,
8d08-D, 8d7f-A, 8day-A, 8dgg-A, 8di0-C, 8dil-A, 8dkr-B, 8doa-A, 8ds5-A,
Ab73-A, 8cwp-A, 8cx1-A, 8d03-A,
8dt0-A, 8dt6-C, 8dte-A,
8dys-A, 8e8t-B,
8e8u-C, 8gxf-B, 8qcw-A
List of 70 targets in CASP14 Pereira al. 2021)
T1024, T1025, T1026, T1027, T1028, T1029, T1030, T1031, T1032, T1033, T1034, T1035, T1036s1, T1037, T1038, T1039, T1040, T1041, I1042, T1043, T1045s1, T1045s2, T1046s1, T1046s2, T1047s1, T1047s2, T1048, T1049, T1050, T1052, T1053, T1054, T1055, T1056, T1057, T1058, T1060s2, T1060s3, T1061, T1062, T1064, T1065s1, T1065s2, T1067, T1068, T1070, T1072s1, T1073, T1074, T1076, T1078, T1079, T1080, T1082, T1083, T1084, T1087, T1088, T1089, T1090, T1091, T1092, T1093, T1094, T1095, T1096, T1098, T1099, T1100,
T1101
D.5 EVALUATION PIPELINE
Folding. In evaluation for folding tasks (Tab, 99. all metrics for all-atom models are computed using OpenStructure (Biasini et al. 2013) unless mentioned otherwise. In particular, we deploy the official docker image OpenStructure 2.9. and use the following command to evaluate structures.
ost -m [MODEL_FILE) \
-r CREFERENCE_FILER \
-0 [OUTPUT_FILL)
-- f-ullllt tallt-tolee totolrrttl --min-pep-lenth 4 -dld --bb-lddt --rigid-scores --tm-score
Notably, for protein folding/ / generation models that cannot output all-atom structures, we instead adopt the TM-score (Zhang & Skolnick] 2004) for evaluation because the OpenStructure pipeline fails in those cases. We compile the TMscore cpp c++ source code and compare two structures as follows:
TMscore -se (NODEL_FILE) (REFERENCE_FILE)
MD ensemble generation. For ATLAS MD ensemble generation (Tab. we base our evaluation pipeline on the dataset split and benchmarking metrics used in previous studies (Jing al., 2024a b) Lu et a 2025). which cover from predicting flexibility to ensemble observables. To obtain the
https://giit
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predicted ensemble, N = 250 JJing et al. 2024a) conformations are sampled from baselines and SimpleFold for each of the 82 test targets, where the median across all targets is reported for each metric. In specific, RMSF; the root mean of 2- 2-Wasserstein distance (W2 distance) and its translation and variance con- tribution, W2 distance between predicted and true ensembles regarding the first two principal com- ponents from PCA by either MD or joint (MD and predicted), and the percentage of samples with cosine similarity > 0.5 between the top principal components of predicted and true ensemble; for the observables, we evaluate the Jaccard similarity (J) of the weak contacts, transient contacts, and exposed residue as well as the Spearman correlation p of the exposed mutual information (MI) ma- trix. We refer the readers to Jing et al. (2024a) for more detailed definition of these metries. For ESMDiff (Lu et al. 2024a), the Jaccard similarity of exposed residue and the Spearman correlation of exposed MI are left empty because it only generates backbone conformation.
report the Pearson's 's correlation for pairwise RMSD, global and per-target
Two-state prediction. In order to evaluate the two-state conformation prediction tasks (Tab. 3). follow the evaluation pipeline in EigenFold (Jing al. 2023): the global and per-target residue flexbility (in terms of RMSD Pearson's correlation r) is calculated after sequence alignment and structural superposition. The TM-ensemble score (at ensemble size 5 following Ding et al.) (2023) is calculated by computing the maximum TM-score (Zhang & Skolnick (2004) between the ensemble and either ground truth conformation, and averaged across both. We use the same command as above to compute the T'Mccore.
E
ADDITIONAL EXPERIMENTS
E.
DE Novo AND ORPHAN PROTEINS
We further compare our model with AlphaFold2 and ESMFold on two additional datasets: de novo (designed) proteins and orphan proteins as established in Chowdhury et al. (2022). These evaluation sets are really important because they represent new protein-coding innovations that cannot be traced to ancestral genes (for example proteins that designed from scratch those who might evolve so rapidly that they lose detectable homology). The orphan proteins dataset contain 77 targets that have no known sequence homologs (i.e., maximal MSA depth is 1). De novo proteins contain synthetic proteins that were originally de novo designed with computational tools like Rosetta and Amber. We filter it to 62 targets by cutoff data of May-01-2020 such that targets are not used in training all three models. As shown in the following two tables, SimpleFold shows better performance than AlphaFold2 and ESMFold on de novo benchmark. On the orphan protein dataset, SimpleFold shows significant better LDDT than AlplaFold2 while being comparable in other metrics. This evidence supports SimpleFold being a strong generalizable single-sequence folding model that doesn't 'it rely on MSA.
Table 9: Performance of protein folding on the de novo and orphan protein targets.
Model
TM-score 1
GDT-TS1
LDDT
LDDT-C,
RMSD 1
De Novo
AlphaFold2 (Jmpper et al. 2021 ESMFold (Ln ct al. 2025b
0.831 0.866
0.850 0.899
0.781 0.880
0.876 0.894
2.950/ 2.307 3.024 / 1.924
0.839 0.871
0.852 0.885
0.781 0.810
0.878
0.904
SimpleFold-92 (ours)
0,852 / 0.880
0.877 /0.288
0.807 / 0.823
0.906 0.922
2.729 / 1.535
De Novo
AlphaFold2 Jumper et al. 2021 ESMFold (Lin et at. 2025b
0.430 / 00.79
0.747 0.752
0.618 / 0.611 0.485 / 0.471
0.778 0.816
3.251 / 2.935
0.391 / 0.320
0.700 / 0.706
0.731 0.761
3.775 3.329
SimpleFold 3B (ours)
0.433 0.390
0.728 0.750
0.651 / 0.687
0.764 0.799
2.646/3.11
E.2 TRAINING WITH SELF-DISTILLED DATA
A relatively common conception for SimpleFold is that such general purpose architecture and training recipe is only useful as student that distills knowledge from teachers using strong domain- specifie inductive biases (i.e., AlphaFold2 and ESMFold). In practical terms, the concern is that the
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general purpose recipe of SimpleFold will completely fail when is not trained data distilled from other models with strong domain-spectioo architectures and training objectives (i.e., AlphaFold2 and ESMFold predictions in AFDB and AFESM). In order to clearly understand if this is actually valid concern we trained SimpleFold models via self-distillation, without using any data from AFDB or AFESM.
We start by training a SimpleFold-700M model on PDB data only (SimpleFold-700M- Tab. [O). We then use SimplaFoldd7700-PPDE to generate self-distillation data (on the same pro- tein sequences contained in the filtered SwissProt subset of AFDB and AFESM datasets for a fair comparison) and train a new model, SimpleFold-700M perform a second step of self-distillation where we take SimpleFold-700M. R and use it to generate self-distillation data one more time and train a final model which we denote as SimpleFold-700M: R2. All these models follow the training paradigm described in Sect. 4.1 of the main paper. Struc- tures with pLDDT larger than 80 are included in the pre-training phase and those with pLDDT larger than 85 are included in the finetuning phase. It is noted that train a separate pLDDT modules for both self-distileed version of SimpleFold-700M on PDB data only, and we use these pLDDT modules to filter the self-distilled data.
in
on this self-distilled data. Finally, we
Our results on both CASPI4 and CAMEO22 on Tab. show that SimpleFold does not necessarily require training data distilled from other models to obtain reasonable performance. While training on AFDB/AFESM data provides an edge (potentially due to the use of MSA in AlphaFold2), it does not represent fundamental requirement for SimpleFold.
Table 10: Performance of SimpleFold trained with self-distilled data.
Model
TM-score
GDT-TS
LDDT
I.DDT-C& f
RMSD
CAMEO22
SimpleFold-700M
0.785 0.864
0.726 0.767
0.703 0.719
0.799 00.826
5.565 / 3.240
SimpleFold-700M-R
0.798 0.876
0.741 / 0.802
0.725 0.756
0.813 0.8847
5,435 3,158
SimpleFold-700M-RR
0,805 0.878
0.749 0.796
0.727
0.754
0.819 0.855
5.160 3,106
SimpleFodd7000--FFDD/AAA
0.829/ 0.915
0.788 0.845
0.775 0.809
0.850/
4.557 2,423
CASP14
SimpleFood-770MMPPP
/
0.501 / 0.507
0.555 0.586
0.644 0.671
12.796 / 9.504
SimpleFold-700M.R
0.644 0.695
0.556 0.577
0.604/ 0.636
0.694 0.766
11.482 7.581
SimpleFold-700M-B
0.649/ 0.698
0.565 0.595
0.601 / 00.21
0.696 0.765
11.327 6.605
Simpldfod--0OM--AFDBAAEEEE
0.680 0.767
0.591 0.668
0.630/ 0.674
0.764
9.289 /,431
E.3 CONFIDENCE MEASURE WITH PLDDT
Fig. ([a) shows example of a predicted structure with PLDDT where red and orange denotes low PLDDT and blue denotes high PLDDT. As illustrated, SimpleFold is confident about most predictions of secondary structures while being uncertain about flexible loops. Fig. (b)) and (c) depict comparison of pLDDT and actual LDDT-C,. We include targets from CAMEO22 and 1000 random selected protein chains from PDB after Jan 2023. PLDDT achieves the Pearson's 's corelation of 0.77 w.r.t LDDT-CC., which indicates that PLDDT module of SimpleFold correctly models the overall quality of predicted structures. the generative flow process to output PLDDT. Therefore, it can be applied to measure the quality of predictions from other models seamlessly, which we leave for future investigation.
also noted that our PLDDT module does not adhere to
E.4 MD ENSEMBLE GENERATION
Tab. lists the results of SimpleFold and SimpleFold-MD on MD ensemble generation of AT- LAS. In particular, no tuning is applied to SimpleFold whereas SimpleFold-MD is tuned on ATLAS training data. It is shown that on MD ensemble generation, SimpleFold also benefits from scaling, namely, larger SimpleFold and SimDleFold-MD achieve better performance.
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Published as a conference paper at ICLR 2026
50
EX01 #LUD1
80
07.
05.0
EXITC
50
Aecraged p40.00
(a)
(b)
(c)
Figure 8: (a) An example prediction of SimpleFold with pLDDT (color red to dark blue denote PLDDT low to high following visualization from Chakravarty & Porter (022)). (b) (c) Compar- ison of pLDDTT and LDDT-C..
Table 11: Evaluation of SimpleFold (SF) of different sizes on MD ensembles.
Nn Toniig
Totel
SF. BR-100M
SF 30056
SP-200M
SP-1.18
SF-1.68
SF-38
SF-MD JIUM
SF RD-M 300M
SF-MD. TIOM
SF-MD-1. 1.10
SF-MD 1.48
SF.D 3B
Clobal R38SP
0.34
0.31
Per tanget RMSSF'
0.59
0.63
0.65
0.64
0.63
0.50
0.62
0.57
0..7
0.68
0.68
0.5
RMWD
5.41
4.30
4.35
4.25
4.30
4.22
5.8%
4.71
4.56
4.12
400
4.17
contri
MD PCA W2
1.79
1.54
1.43
153
1.52
1.36
151
1.30
Juint PCA W2
4.49
2.00
2.82
2.91
2.5
2.59
4.78
3.36
3,37
288
2.20
2.18
PC
29
34
Trassent Jontacts.1
0.38
Expased reditee 1
0.47
0.48
0.86
0.53
0.81
0.39
1155
0.50
0.00
0.6
6.1
0.00
Expseed mabrix
0.34
0.3
0.24
0.24
0.16
0.14
0.31
0.33
0.35
0.33
0.32
E.5 LDDT LOss
LDDT loss plays an important role in SimpleFold training. In practice, we find LDDT loss is required to generate structures with refined local atomic positions, which largely affects the LDDT metric in folding tasks. We also find that in the second training phase when finetuning the pretrained model on high-quality data, PDB and SwissProt (filtered at PLDDT 85). Adding a loss weight &ReLU(t 0.5) (Eq. 3) helps getting better results than keeping a 1 as pretraining. Tab. shows the effect different LDDT loss weighting strategies finetuning. Applying loss weight schedule ReLU(t 0.5) achieves best overall performance.
Table 12: Ablation of LDDT loss weighting on CAMEO22.
Model
o(t)
T'Mscore f
GDT-TS
LDDT
LDDT-C,
RMSD J
SimpleF'old7700
0.0
0.831 / 0.907
0.785 /0.845
0.711 /0.746
0.847 / 0.882
4.445/2.423
SimpleFold
1.0
0.913
0.785 /0.844
0.767 0.799
01846
4.586/2.742
SimpleFold-7000
+ 8 ReLU(t 0.5)
0.826 0.904
0.784/08844
00.72
0.848 0.884
4.476 2.588
E.6 INFERENCE SETTINGS
Tab. 13
Tab. 14 and Tab. 15 show the ablation of inference settings of SimpleFold-700M on
CAMEO22, CASP14, and Apo/cold-swichh, respectively. By default, set number of steps to 500, r = 0.01, and w(t) = Iet for folding tasks while set 7 = 0.8 for multi-state tasks to encour- age stochasticity in inference. +
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Table 13: Ablation of inference settings CAMEO22.
Steps
r
(())
log time
TM-score
GDT-TS
LDDT f
LDDT-C. f
RMSD
0.0
1-t
0.831 /09118
0.790 0.844
0.777 0.811
0.851 0.887
4,497/2.410
0.01
1
0,829 0.915
0.788 00.845
0.8009
0.850 0.886
1.557/2.425
500
0.02
T
0.831 /09919
0.788 70.845
0.775
0.808
0,850 0.886
4.501 /2.519
500
0.05
T
0.831 /0.913
0.787 0.848
0.775 0.808
0.849 0.885
4,461 224429
1-
500
0.1
T
0.830/ 0.913
0,785 0.839
0.773
0.807
0.848 0.884
L
500
0.2
T
0.826 0.909
0.781 0.832
0.768/ 00805
0.845 0.882
4.597 2.558
0.01
fan
K0-O
0.917
0.788 845
0.803
0.884
4.504 2.403
0.904
0.768
0.001
0.857
4.665 2.443
500
0.01
T
0,829 0.916
0.788 0.841
0.775 0.808
0.849 0.885
4.571 2.421
500
0.01
1-1
F
0.832/ 00919
0.790 0.848
0.776 0.811
0.851 0.886
4
250
0.01
T
0.828 0,916
0.785 0.850
0.776 0.808
0.849 0.884
200
0.01
T
0.831 00915
0.788 0.884
0.777 0.808
0.850 0.888
2.499
0.01
T
0.826 0.912
0.8845
0.774
0.850 0.885
4.581 2.478
100
0.01
T
0.821 0.902
0.779 0.883
0.769 0.802
0.847 0.884
4,922 2.450
50
0.01
T
0.654 /0.618
0.605 0.599
0.615 0.644
0.717 0.746
10,971 11.024
Table 14: Ablation of inference settings on CASP14.
# Steps
(=(4)
log time
TM-score
GDT-TS
LDDT
LDDT-C, f
RMSD
500
0.0
T
0.684 0.762
0.591 /0.678
0,628 0.662
0.713 0.766
9.184 /4,226
500
0.01
T
0,680 0.767
0.591 /0.668
0,630 0.674
0.714 0.763
9.289 /4.431
1
500
0.02
T
0,677 / 0.770
0,589/ 0.667
0.629) 0.676
0.712 0.758
9.319 4,66
500
0.05
1-t
T
0.675 0.778
0.587 0.665
0,624 (1.611
0,711 / 0.767
9.521 4.867
1
500
0.1
T
0.675 0.779
0.585 00668
0.621 0.662
0.706 0.766
9.391 5.09
500
0.01
1
T
0.673 0.780
0,585 0.647
0.617 0.652
0,708 0.764
9.167 5.018
500
0.01
tan X(1-0)
T
0.683 / 0.768
0.591 0.660
0.629 0.638
0.755
8.787 / 4,294
500
0.01
T
0,671 0.737
0,572 0.642
0,005 0.002
0.691 0.742
9,414 / 4,876
500
0.01
0.680 0.767
0.590 0.669
0.626 0.668
0.712 0.756
9.313 4,388
500
0.01
F
0.677 0.76
0.586 0.671
0.656
0.709 0.758
9,317 4,281
250
0.01
T
0.679 0.777
0.593 0.683
0.627 0.677
0.715 0.764
9.374 4,765
200
0.01
T
0.677 0.767
0.588 0.699
0.660
0.713 0.758
9.363 4.544
150
0.01
T
0.657 0.742
0.572 0.624
0.625
0.641
0.709 0.750
11.305 / 7.234
100
0.01
T
0.633 / 0.680
0.558 00584
0.615 0.655
0,701 0.759
12.370/ 6.615
50
0.01
T
0.481 0.358
0,402 0.305
0.452 /0374
0.554 0)465
17.792 17.868
Table 15: Ablation of inference settings on Apo and Fold-switch.
Res. flex. (global) f
Res. flex. (per-target)
Res. flex. (global) 1
Res. flex. (per-targel) 1
TM-ens
TM-ens f
Apo/holo
Fold-switch
0.2
0.466
0.484 / 00.78
0.868 / 0.901
0.297
0.281 0.245
0.699 / 0.750
0.4
0.538
0.501 / 0.512
0.869 0.901
0.314
0.305 / 0.228
0.697 0 0.748
0.6
0.531
0.513 / 0.510
0.870/ / 0.901
0.302
0,313/0,255
0.695 0095/00777 0.734
0.552
0.524 / 0.538
0.870 0.899
0.307
0.328/ 0.310
0.693 0.713
0.562
0.525 0.520
0.867 0.896
0.319
0.337 0.329
0.687 0.711
F ADDITIONAL VISUALIZATION
F.1
FOLDING
F.2 ENSEMBLE GENERATION
F.3 FAILURE CASES
Fig. 11 shows some examples of failure cases from CAMEO22 and CASP14. In particular, we show predictions with TM-score smaller than 0.6 and also include predictions from ESMFold (Lin al.) 20236). In these shown cases, SimpleFold mostly predicts the secondary structures correctly. How- ever, the relative positions between the different secondary structure domains well modeled.
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7SKJ-A
7VMH-C
7X0Q-A
8B5V-A
BAJP-A
T1065s1
T1101
T1058
T1079
T1074
Figure 9: Examples of folding results from SimpleFold with ground truth shown in light aqua and prediction deep tea (first row from CAMEO22 targets and second row from CASP14 targets).
1A6D-A
1CFC-A
4ZRB-C
4QHF-A
3UYI-A
Figure 10: Examples of ensemble generation results from SimpleFold. We align 5 generated con- formations the same protein for visulization.
Interestingly, this failure mode can also be observed in ESMFold, e.g., 7SZ2-B and 7WF9-A. We attribute this to the ESM2 embedding shared by SimpleFold and ESMFold. This indicates a future direction to build more powerful protein language models for representation learning that further benefits protein folding models.
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Published as a conference paper at ICLR 2026
PDB
SimpleFold
mAm
ESMFold
7YWQ-A
8D03-A
7SZ2-B
7VD7-B
7WFB-4
PDB
SimpleFold
ESM/Fold
T1092
T1042
T1070
T1038
Figure 11: Examples of failure cases (TM-score < 0.6) of SimpleFold predictions with ground truth shown in light aqua and prediction in deep tea (first row from CAMEO22 targets and second row from CASP14 targets). We also include predictions from ESMFold for comparison.
28


Reviews:
[{"review_id": "eImzOZmgZI", "reviewer": "Reviewer_MgVM", "summary": "The paper presents SimpleFold, a flow-matching generative model for protein folding. It uses a standard transformer architecture to generate full-atom structures, conditioned on embeddings from a frozen ESM2-3B protein language model (PLM). This approach notably avoids the explicit pair representations and triangular updates common in models like AlphaFold. The largest 3B parameter model, trained on approximately 9 million distilled and experimental structures, achieves performance that is competitive with, but not superior to, existing state-of-the-art baselines on standard folding and ensemble generation benchmarks.", "questions": "See **Weaknesses**.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The primary strength is **architectural simplification**. The work demonstrates that a general-purpose transformer can achieve comparable performance to more complex, bespoke architectures, provided it is leveraged at a large scale and conditioned on a powerful pretrained PLM. The clear empirical validation of scaling laws for both model and data size is a useful, albeit expected, finding.", "weaknesses": "**Limited Novelty**: The approach is highly derivative. It combines a standard transformer with a known flow-matching objective and, most importantly, relies heavily on the powerful ESM2-3B PLM. These attempts have already appeared in previous work and are not surprising; they cannot be called new folding methods.\n\n**Insignificant Performance**: In the context of the rapidly advancing protein design field, the results are not a significant breakthrough. The model achieves \"competitive\" or \"comparable\" performance, failing to clearly outperform established baselines like AlphaFold2 or ESMFold. This incremental result does not demonstrate a clear advantage.", "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": "IXbOZCAd8F", "reviewer": "Reviewer_iwqT", "summary": "This paper proposes SimpleFold, a flow-matching based protein folding model that relies solely on general-purpose transformer blocks for efficient structure prediction. In contrast to prior methods that employ triangular updates, explicit pair representations, or multi-objective training, SimpleFold utilizes standard transformer blocks enhanced with adaptive layers and is trained with a generative flow-matching objective augmented by a structural loss term. Evaluated on standard folding benchmarks, the SimpleFold-3B model—trained on both distilled structures and PDB data—achieves performance competitive with state-of-the-art baselines. Additionally, the model demonstrates strong capability in ensemble prediction tasks.", "questions": "•\tSimpleFold appears to adopt training structures and strategies similar to prior flow-based folding methods such as AlphaFlow or ESMFlow. Could the authors provide further insight or ablation studies to explain why SimpleFold is more effective at recovering multi-state protein conformations? Is this capability attributable to the architectural design, the training objective, or the data used?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "•\tSimpleFold effectively removes complex components commonly used in previous protein folding models—such as MSA processing, pairwise representations, and triangle modules—leading to significant computational speedup while maintaining competitive prediction accuracy.\n\n•\tThe model shows promising results in ensemble generation, highlighting its potential for capturing conformational diversity.\n\n•\tThe paper is clearly structured and easy to follow.", "weaknesses": "•\tThe scope of SimpleFold remains limited to single-chain protein structure prediction. Given the emergence of efficient and generalizable predictors such as ESMFold and Protenix-Mini [1]—which are also capable of predicting biomolecular complexes—SimpleFold does not demonstrate a clear advantage in either efficiency or accuracy, raising questions about its practical added value.\n\n•\tThe overall framework constitutes a relatively straightforward integration of existing techniques from computer vision and protein modeling (e.g., flow matching and transformer adaptations), and the core technical innovation appears incremental.\n\n[1] Protenix-Mini: Efficient Structure Predictor via Compact Architecture, Few-Step Diffusion and Switchable pLM", "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": "VnYwPDirfB", "reviewer": "Reviewer_WgMN", "summary": "This paper introduces SimpleFold, a novel protein folding model based on flow-matching and a standard Transformer architecture. The central thesis challenges the necessity of complex, domain-specific architectural components (like MSAs, explicit pair representations, triangle updates, equivariant modules) prevalent in state-of-the-art models such as AlphaFold2. SimpleFold adopts a generative approach, treating folding as analogous to text-to-image generation, mapping amino acid sequences (encoded via a frozen PLM, ESM2-3B ) to all-atom 3D coordinates using only standard Transformer blocks with adaptive layers conditioned on time. The authors scale SimpleFold up to 3 billion parameters and train it on a large dataset combining PDB experimental structures and approximately 9 million distilled structures . On standard benchmarks (CAMEO22, CASP14), SimpleFold-3B achieves performance competitive with SOTA baselines. Notably, it demonstrates strong performance in ensemble prediction tasks, which is often challenging for models trained with deterministic objectives. The work suggests that scale and general-purpose architectures might suffice for learning complex folding patterns, opening alternative design avenues.", "questions": "- How frequently do SimpleFold's predictions contain steric clashes or distorted covalent geometries compared to models like AlphaFold2 (before relaxation) or other generative models? Is a relaxation step necessary/used for evaluation?\n- Given the performance gap on CASP14, do the authors believe further scaling (model size, data) can close this gap, or are there fundamental limitations to the purely data-driven approach without explicit biases for certain protein families or topologies?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The core argument—that domain-specific inductive biases might be replaceable by scale and general architectures—is highly stimulating and challenges conventional wisdom in protein folding. If validated further, this could significantly simplify model design in structural biology and beyond.\n- SimpleFold's reliance solely on standard Diffusion Transformer blocks makes the architecture remarkably simple compared to those with attention bias in AF3. This generality facilitates leveraging advances from the broader Transformer ecosystem and potentially simplifies implementation and optimization (like flash-attn and deepspeed).\n- The paper provides a clear demonstration of performance scaling with model size (up to 3B parameters) and training compute on folding benchmarks. This empirical evidence supports the claim that scaling is a viable path for improving performance with this simpler architecture. The scaling with data size is also shown.", "weaknesses": "- While competitive, SimpleFold-3B does not consistently surpass the very best models like AlphaFold2, especially on the challenging CASP14 benchmark. This suggests that current domain-specific designs and/or MSA information still provide an edge, particularly for difficult targets.\n- SimpleFold 1.6B and 3B models achieve accuracy comparable to (or on par with) ESMFold. However, their inference speed is noted to be slower. This presents a critical trade-off, and the justification for using these models over ESMFold is unclear given this substantial speed limitation.", "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 (Poster)