Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design
Authors: Nianzu Yang
Songlin Jiang
Jian Ma
Huaijin Wu
Shuangjia Zheng
Wengong Jin
Junchi Yan
Venue: NeurIPS 2025 poster ()
Keywords: antibody design
alphafold3
Primary area: machine_learning_for_sciences
TL;DR: In this work, we repurpose AlphaFold3-like protein folding models for antibody sequence and structure co-design.

Abstract: Diffusion models hold great potential for accelerating antibody design, but their performance is so far limited by the number of antibody-antigen complexes used for model training. Meanwhile, AlphaFold3-like protein folding models, pre-trained on a large corpus of crystal structures, have acquired a broad understanding of biomolecular interaction. Based on this insight, we develop a new antigen-conditioned antibody design model by adapting the diffusion module of AlphaFold3-like models for sequence-structure co-diffusion. Specifically, we extend their structure diffusion module with a sequence diffusion head and fine-tune the entire protein folding model for antibody sequence-structure co-design. Our benchmark results show that sequence-structure co-diffusion models not only surpass state-of-the-art antibody design methods in performance but also maintain structure prediction accuracy comparable to the original folding model. Notably, in the antibody co-design task, our method achieves a CDR-H3 recovery rate of 65% for typical antibodies, outperforming the baselines by 87%, and attains a remarkable 63% recovery rate for nanobodies.

Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design
Nianzu Yang", Songlin Jiang",, Jian Ma Huaijin Wu', Shuangjia Zheng",
Wengong Jin Junchi Yan
School of CS & School of AI, Shanghai Jiao Tong University
Khoury College of Computer Sciences, Northeastern University
School of AI & GIFT, Shanghai Jiao Tong University
(yangnianzu, clorf6, maj mani,nn,, shuangj ia. zheng, zheng.yanjuuchhi yanjunchi) tu.sstttueeeie edu. cn
w. w.jiinhnrtheasterr.. edu
Abstract
Diffusson models hold great potential for accelcrating antibody design, but their performance is so far limited by the number of antibody-antigen complexes used for model training. Meanwhile, AlphaFold3-like protein folding models, pre-trained on large corpus of crystal structures, have acquired broad un- derstanding of biomolecular interaction. Based on this insight, we develop a new antigen-condition conditiondd antibody design model by adapting the diffusion mod- models for sequence--truucture co-diffusion. Specifically,
ule of AlphaFold3- extend their structure diffusion module with sequence diffusion head and fine-tune the entire protein folding model for antibody sequence-structure co- design. Our benchmark results show that sequence-structur co-diffusion models not only surpass tate-of-the-ar antibody design methods in performance but also maintain structure prediction accuracy comparable the original folding model. Notably, in the antibody co-design task, our method achieves CDR-H3 recov- ery rate of 65% for typical antibodies, outperforming the baselines by 87%, and attains a remarkable 63% recovery rate for nanobodies. Our code is available at attps://ge giggtu....... cwm/ commyangnianzu05115 mmzu0551//MDess/..
1 Introduction
Monoclonal antibodies Y-shaped proteins that specifically recognize and neutralize the pathogens (commonly known as antigens) during immune responses [Janeway et al., 2001]. The binding specificity determined by their aamplemmnttrrtyydetemmiii and structures exhibit significant variability. Recent work on generative models have shown great potential in designing CDRs that bind to an antigen [Jin et al., 2022a, Luo et al., 2022, Kong et al., 2023a, Martinkus et al., 2024, Zhu et al., 2024]. However, their performance is limited by the scarcity of seribodd-antigee complex data, as highlighted by Zhu al..
regions (CDRs), whose sequences
To address this issue, we seek to leverage foundation models, such as protein folding models, that have learned general knowledge of biomolecular interaction from a large corpus of general protein interaction data. Our hypothesis is that the general knowledge of protein-protein interaction learned by protein folding models, such as AlphaFold3 (AF3) [Abramson al., 2024], Chai-1 [Discovery et al.. 2024], Boltz-1 [Wohlwend et al., 2024], and Protenix [Chen et al., 2025], are transferrable to the task of modeling of antibody- complexes, a specialized type of protein interaction.
Correspondence author. denotes equal contribution. This work was partly supported by NSFC (92370201, 62222607) and Shanghai Municipal Science and Technology Major Project under Grant 2021HH2DZX000..
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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heavy chain
Aufizen
VII
CDRAI1
2R
ED642
FR
CER-I
Right chain
Antibody
Figure I: Illustration of antibody-antigen complex. Typical antibodies consist of heavy and light chains, each with variable domain (VH and VL, respectively) composed of three CDRs and framework regions. CDRs are highly variable and mainly determine the antibody specificity for antigens. In contrast, nanobodies consist of only a heavy chain.
To validate this hypothesis. we need to effectively endow current protein folding models with the additional capability of generating both sequence and structure of an antibody protein. Our key observation is that AFš-like folding models already utilize diffusion modules for structure prediction. Indeed, diffusion models [Ho et al., 2020, Song et al., 2021, Karras al., 2022] have shown various success in the co-design of antibody sequences and structures [Luo et al., 2022, Martinkus et al., 2024, Zhu al., 2024]. Therefore, we propose to integrate sequence diffusion into the diffusion modules of AF3-like folding models for sequence-structume co-design. We demonstrate the feasibility of this idea using Boltz-1 [Wohlwend et al., 2024], an open-source implementation of AF3 provided with training scripts. Specifically, we augment the diffusion module of Boltz-1 1 with a sequence diffusion head. We implement two approaches for sequence diffusion: discrete diffusion approach trained in [Austin et al., 2021a] fashion, and a continuous diffusion approach trained using EDM [Karras et al., 2022] method. After properly aligning sequence and structure diffusion steps, we fine-tune the entire Boltz-1 1 network using a training set of antibody-antigen complexes. Extensive benchmark results show that the modified AF3-like model outperforms existing baseline methods in antibody sequence and structure co-design tasks, while retaining Boltz-1's I's structure prediction capability. In summary, our key contributions are as follows:
the D3PM-absorbing
To the best of our knowledge, are the first leverage the general protein interaction knowledge from AF3-like folding foundation models to effectively address the issue of limited antibody-antige complex data in the context of antibody sequence and structure co-design task.
Expanding on the structural diffusion framework in AF3-like models, we introduce sequence diffusion alongside tailored training strategy to enable effective co-design.
The results show that modified AF3-like model outperforms existing baselines in antibody sequence and structure co-design tasks, while retaining its original structural prediction capability.
2 Related Works
Diffusion Model.
Diffusion-based generative methods [Song al., 2021, Ho et al., 2020, Li et al., 2025] have demonstrated remarkable effectiveness across various domains [Li et al., 2023, 2024], such image and video generation [Epstein et al., 2023, Wu et al., 2024a]. These models can be classified by the type of data they target: continuous or discrete, with the theoretical foundations of continuous diffusion being more developed and mature. Recently, diffusion models have also shed light on new possibilities in the scientific domain. In particular, continuous diffusion methods have excelled in tasks like molecular generation [Guan al., 2023, Qiang et al., 2023, Yang et al., 2024] and protein structure design [TTrippe et al., 2023, Watson et al., 2023], due to their suitability for continuous spatial coordinate data. Meanwhile, discrete diffusion methods have shown promise in handling sequential data, proving effective in designing protein sequences [Gruver et al., 2023a, Ma al., 2025] and DNA [Avdeyev et al., 2023, Wang al., 2024], which consist of discrete elements like amino acids and nucleotides.
Protein Folding and Design Model. AI models have revolutionized molecule science, including molecular representations [Yang et al., 2022, Yan et al., 2023], molecule generation [Hoogeboom al., 2022, Wu et al., 2024b], protein docking problem [Wu et al., 2024c] and the protein folding problem. Groundbreaking models like A AlphaFold2 [Jumper et al., 2021], trained on the Protein Data Bank (PDB) [Berman et al., 2000], achieved unprecedented accuracy in structure prediction. This
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Diflision Modale
Final Resalt
Ippul-
Conditoning
Trink
Reprosstabien
Suquites Deniting
Figure 2: Overview of our modified AF3-like model. We build upon AF3-like folding models by integrating sequence diffusion with the existing structure diffusion, enabling the co-diffusion of structure and sequence. The input for sequence denoising network directly reuses the residue token-level representations originally used for structure denoising in AF3-like models. Initially, CDR tokens are marked as <x>, which will be specified as one of the 20 amino acid tokens the final output. Through replacement sampling, our method allows the CDR design to be conditioned on fixed co-crystal structures.
progress has continued with models like AlphaFold3 [Abramson et al., 2024], which uses a diffusion- based approach to handle a wider range of molecular interactions, and the powerful RoseTTAFold series [Baek et al., 2021, 2023, 2024]. Building on this success, the field has advanced to the inverse problem: de novo protein design. Generative diffusion models are central to this effort. These models operate in different ways, such as performing diffusion in 3D coordinate space to design protein backbones for tasks like motif scaffolding [Ingraham et al., 2023], or in sequence space to co-design sequence strrucce pairs for creating functional, multistate proteins [Lisanza et al., 2024].
Antibody Design. Most antibody design efforts focus the design of CDRs. Traditional meth- ods [Li et al., 2014, Lapidoth et al., 2015, Adolf-Bryfogle et al., 2018] for antibody design primarily rely on energy-based optimization, which can be quite time-consuming. In contrast, recent deep learning-based methods have been proposed to enhance antibody design, broadly divided into dis- criminative and generative models. Discriminative models [Jin et al., 2022a,b, Kong al., 2023b,a] typically utilize graph neural networks to extract features from the context, including antigens and antibody framework regions, predict the most probable CDRs structure and sequence. Generative models [Luo et al., 2022, Martinkus et al., 2024, Zhu et al., 2024], which are mainly diffusion based, excel at modeling the complex distributions of both structure and sequence. By accurately capturing these distributions, they can sample more diverse set of antibody candidates from them.
3 Methodology
In Sec. 3.1, first present detailed description of the task we investigate in this paper. In Sec. 3.2, we introduce key notations by reviewing the workflow of AF3. Finally, in Sec. 3.3, we detail how straightforward modifications AF3-like models enable support for antibody co-design.
3.1 Problem Description
We present a diagram of an antibody-antigen complex in Fig. In this work, we follow the widely adopted setting for antibody design: re-designing CDRs in experimentally resolved co- crystal structures, where all non- non-CDR regions (antibody framework sqqeecce/struutuuree antigen sequences/structurrs, and antibody-antigen binding pose) given. The objective is to co-design the sequence and structure of CDRs within this predefined context.
Particularly, rather than designing a single CDR at time, we aim to simultaneously design all CDRs once, which is more challenging task [Martinkus et al., 2024]. Within this study, we use the notation system from AF3 characterize protein complexes, employing {k:} and {xi} to specify the amino acid (a.k.a. residue) composition and structure of the complex, respectively. k; a one-hot encoding that represents the type of each amino acid token (20 standard amino acid types + <X>). The <x> token is originally used in AF3-like folding models to indicate positions where the residue type is unknown, for which only the backbone atom coordinates will be predicted. Each element in {xi) represents the three- firee-dimensioall coordinates of an atom.
We use the <X> token to represent residues within the CDRs that need to be designed. In the final output, each <X> token is replaced with a specific residue from the 20 standard types, accompanied by their corresponding atomic coordinates, thus achieving the goal of the antibody co-design.
3

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3.2 Preliminary: AlphaFold3-like Folding Models
AF3-like methods are structured as conditional diffusion models, consisting mainly of two compo- nents: the conditioning trunk and the diffusion module. These models also feature confidence module, but it is trained independently and is not the primary focus here, so we do not discuss it. Below, notations with subscript i/tj are token-level representations, while I/lm are atom-level. The modules introduced in the original AF3 paper are highlighted in the following discussion, and corresponding details can be referenced in the supplementary materials of AF3?.
Conditioning Trunk. This trunk processes raw inputs denoted as { such as the protein sequences to be predicted, MSA results, and structural template information. Initially, these inputs pass through an InputEmbedderr which performs basic encoding to obtain input features (s. }. These initial input features are used derive the preliminary single and pair representations. The single and pair representations are then updated iteratively through a stack of three modules: TemplateModule, MSAModule, and PairFormerStaaee Finally, the input features {s.")"") } obtained at the beginning, along with the iteratively updated single and pair representations, denoted as {s/munk 3. and {z/ymk) respectively, are fed to the following diffusion module to condition the structural diffusion process.
Structure Diffusion. AF3 adopts EDM [Karras et al., 2022] for structure diffusion training. Notably, EDM differs from typical diffusion models [Ho al.. 2020, Song al., 2021] that sample a timestep to determine the noise level based on a pre-defined noise schedule during training. Instead, EDM directly samples the noise level by drawing the logarithm of the noise intensity from a defined Gaussian distribution.
Specifically, during AF3's training, the noise level is sampled from the distribution data exp(eepppp 1.2 -112 1.5 1.5- 1)) and is fed, along with the three outputs from the previous trunk, into the DiffusionCon ditioning module. In this module, relative position encoding between residue tokens is incorporated into ( } to facilitate both token-level and atom-level self-attention [Vaswani. 2017] 7] used later, resulting in the output {z,}}. Besides, within this module, the sampled noise level is embedded using random Fourier features [Tancik et al., 2020] and then integrated with {s (s!"puss and {s!!unk ) to
construct {s:).
Next, following EDM, AF3 scales the noisy atom coordinates {x] } to unit variance, which gives {I_olsy] }. (r: }, {s|nnn] } and {z,z} are further processed in the AtomAttentionEavoderr Initially, this module yields three atom-level representations: {q:}, {cı), (pt... These representations are all based on atomic features from the raw inputs and incorporate additional information from (rm"iy, }. (s/munk) } and {Z:z), J. respectively. Then, sequence-loca atom attention is applied to update {qı} } using {ci) and (pim). The updated {qı} are then aggregated to form the token-level representations {ar]-
Subsequently, {s:} is used to update (a.) The updated {a,]: J. along with outputs {a.} and (z.,) } from the previous
module, is fed into the form full self-attention on the token level. This module further updates {a.} and layer normalization applied to (a.).
module per-
The residue-level representations {a,). together with the atom-level representations {qr}, {ct} and } are then inputted into the AeanAttenionDecoder module, which yields {r-ipdarc), J. {x is rescaled back and combined with the input noisy structure (1-poey), in line with EDM, to obtain the final predicted denoised structure {xpenoised)
The structure diffusion training process outlined above is encapsulated within the DoffueionMMduue of AF3, as formalized in Algorithm 20 of AF3's supplementary materials. In summary, this process uses the outputs from the trunk, in conjunction with the raw inputs, to further construct atom-level and token-level representations. These representations are then fed to the structure denoising network for recovery.
For inference, AF3 adopts the EDM stochastic sampler and from a sequence of 200 decreasing levels defined by:
)²
data
1/P +
200 -
σ 1/p)
minx
= 0, 1,
199
2 *C' Click here to access the supplementary materials of AF3.

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Algorithm 1 Sequence and Structure Co-diffusion Module: CoDiffusionModule
Input: naisy structore [x] ). noiey sequenee (R4 }. noise level for stracture o, me inputs (f").
munk's ouiputs ( { {s!"h) J {
} hyper-purameeer { (aaus ):
1: {e beuubrry e- subotiute the specified dimensions within (="""")
} with (k."")
Preconditiuinng for digucion
2: {s:]. [2;;] onttusineCndtttinnn (a. (f*) (eliilac)) ). g. (a/gth) (ssa)
Keter to Algorithm 21 in AF3
e Scale coonlinates with approximately ani variance
3: saaee x } fo2 data
# Seguracestuad mom attendioon and oggregationn to cINORNA graninnd toleny
[ag]. (9t))(r)) (ct). (Pin) = AtomsAftentioEaccderr (f (x( (o) (mu)))
Kefer to Algorithm 5 in AF3
Full self attention token ferel
5: Ag LinenNoBiss( ay)))om(==:)
6: (mr)
Transfomrr (fa, (Ri (x:1))
Refer to Algorilhm 23 in AF3
7: a. LayerNorm (a:)
Structure donosing
8: [1] [pdase
AtommAttendimnDecdder ([a.) (9i) (er) (pin))
Refer Algorshi 6 AF3
9: affexiied o (olan + o?) x + o/v ya + o² splaee
Sioguence donolting
10: (belancscd) -enoinDenoieer ((a,)
Output: desoised structure [x] (efmene) denoisedl saquence (lefettioa))
where daaats mmxx min and p are hyper-parameter
3.3
Our Method
The input includes residue tokens from antibodies and antigens. For the antibody CDRs that need to be designed, these positions are filled with <X> tokens. Considering the discrete nature of residue tokens as categorical variables, along with the resemblance of the input format to masked language models (MLMs) like BERT [Devlin et al., 2019] where <X> is analogous to the <MASK> tokens used in MLMs - the D3PM-absorbing [Austin et al., 2021 a) method, inspired by MLMs, intuitively aligns well with this task. Therefore, we choose to use D3PM-absorbing for sequence diffusion.
During the training of diffusion- co-design methods, it is required corrupt the sequence and structure to matching degree, which can be achieved by sampling them at the same timestep. However, as noted in Sec. 3.2, AF3-like models perform structure diffusion training by directly sampling noise levels, unlike D3PM D3PM-absorbing, which uses timestep sampling. Furthermore, to the best of our knowledge, there no established theory for training discrete diffusion without sampling timesteps during so far. Aligning sequence and structure diffusion is therefore challenging.
Continuous diffusion an alternative for sequence. It is worth noting that Hoogeboom al. propose a method for applying continuous diffusion to discrete data by adding Gaussian noise to their one-hot encodings in training. This allows sequence to be trained also using the EDM method. In this way, sequence diffusion can readily align with structure diffusion training in AF3. For the generation of discrete data, this method tends be less effective than discrete diffusion [Austin et al., 202 la, Gruver et al., 2023a], as supported by results in Sec. 4.1, which we have also implemented. However, our results also reveal its unique advantage for structure prediction. Since this paper primarily focuses on the additional implementation of sequence design compared to AF3, which involves discrete data, we have concentrated on discussing the method using D3PM-absorbing for sequence diffusion in the following main paper. The implementation of the continuous version, being relatively more straightforward and simpler, is provided in Appendix B.4.
Structure & sequence diffusion alignment. Although the training method for structure diffusion directly samples noise levels, we notice that AF3 uses predefined sequence of 200 decreasing noise levels during sampling. This approach closely resembles the concept of discrete timesteps. Thus, we consider modifying structure diffusion training to also sample noise levels only from these 200 noise levels. This allows us to create a predefined noise scheduler for training, where we sample timestep and retrieve the corresponding noise level according to this scheduler. Accordingly, we set the number of diffusion steps for sequence to 200, thus enabling easy alignment. Since these same 200 levels are used during both training and inference of the structure diffusion, the approach shifts towards style resembling the classic DDPM's 'ss discrete time training [Ho et al., 2020]. Empirical results suggest that fine-tuning with this new structure diffusion approach preserves the model's ability to accurately predict complex structures.
5

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Instantiation of discrete sequence diffusion. Diffusion is applied specifically to CDRs. <X> is naturally suited as the absorbing state for residues in CDRs. The forward process is characterized by discrete transition matrix that determines the probability of token mutating into <X>, The corresponding prior a point mass on the sequence of which the CDRs are made up entirely of <X> On top of AF3, we introduce an additional module named TokenDenoiser. Since we need to make predictions at token-leved token-level representations are required for TokenDenoiser. Recalling from See 3.2, the AF3's structure diffusion process already drives token-level representations {a.) J. which are obtained via full self-attention on token level and serve the purpose of structure denoising. Here we choose to directly reuse {a,} as the input to TokenDenoiser. The TokenDenoiser is implemented simply as a basic MLP in this paper, consisting of LINEAR layers connected by GELU activation function.
Next, we detail how to input the corresponding noisy sequence, denoted as (k into the diffusion module at each sampled timestep during training. As mentioned in Sec. 3.2, {s)rpulh) from the trunk serves as a basic encoding of the raw input, where part of its continuous dimensions is actually the one-hot encoding to the tokens. Thus, we can easily achieve the input of the noisy sequence at sampled timestep by directly replacing these dimensions with (km resulting in (s(placed) j. Then, {s, (spplacd
}
fed into our structure and sequence co-diffusion module and its subsequent usage remains
consistent with that of {s] insputss. }. We denote the restored sequence as [k[enoised]. Note that except for (sinpts)
{s: ). all these notations are associated with a specific timestep t. However, for simplicity, we omit f in the notations presented here.
We fully fine-tune the AF3-like models by combining the original objective of AF3 for structure prediction with an additional objective specifically for sequence diffusion. This additional objective is to maximize the likelihood of the denoising process on the ground truth CDRs sequences, thereby learning the optimal parameters for the TokenDenoiser. Our sequence sampling follows the D3PM- absorbing standard method, as used by Gruver et al.. To allow our sampling to be conditioned on the given co-crystal structures and redesign CDRs, we employ the replacement sampling technique. Orginally from image inpainting domain [Lugmayr et al., 2022], this technique has been applied in molecule and protein generation tasks to condition the sampling on certain motifs [Schneuing et al., 2024, Trippe et al., 2023]. Refer to Appendix B for more details our training and sampling.
Our method is outlined in Fig. 2. We extend the AF3's MoflusionMMdule with sequence diffusion to enable structure and sequence co-design, which we call CoDiffusionModule, formalized in Alg. 1. follows a feature extraction process akin to the DiffusionModule, but reuses the final obtained token-level representations for sequence design.
Training strategy. Similar AF3 models that use token cropping for training, we also implement this technique. Our training process is divided into four stages as follows:
1st-stage: We only input antibody sequences, using a maximum token size of 256 to accommodate the VH and VI. sequences. This allows for a larger batch size, enabling the model to quickly learn design CDRs and predict antibody structures based only on antibody sequences.
2nd-stage & 3rd-stage: We continue to input full VH and VI. sequences while randomly selecting tokens from the antigen epitope and the nearby regions to introduce the antigen context. Both stages follow the same cropping method detailed in Alg. S2, differing only in maximum token size: 384 in the second stage and 512 in the third.
4th-stage: We perform full- parameter fine-tuning, which may affect the model's ability to predict structure learned during pre-training. Besides, considering the first three stages focus on predicting structures around the antibody and nearby antigen regions, the model might underperform when predicting structures of antigen regions far from the antibody. To address this, we implement a sampling strategy where there is a 50% probability of selecting tokens from regions around the antibody and a 50% probability of selecting tokens from any area within the complex. This approach helps the model retains its newly acquired antibody co-design capability while also preserving its pre-trained ability to predict complex structures. The maximum token size for this stage is kept at 512.
Avoiding data leakage. Even when the CDRs in the antibody query sequences for MSA are filled with the unknown token <X>, the MSA results contain a significant number of sequences that, while not exactly identical to the antibody sequences, have sequences in positions corresponding to the

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Table 1: Comparison of simultaneously designed CDRs for
Table 2: Results on typical an-
typical antibodies, with the best and the second best highlighted.
tibodies with simultaneously de- signed CDRs, highlighting the
CDR
Method
AAR
RASD
CDR
Method
AAR
RMSD.
DIITAb
60,92%
1.52A
DHITAD
$6,72%
144AA
best and the second-best results.
III
dyMEAN
70.31%
1.65A
LI
dyMEAN
73.06/
1.58A
MFDesiga-C
74,89%
1.88
MFDesiga-C
82.98%
141À
Mathel
LospAAR
Lap-RMSS
IMP
MFDesign-D
74.95%
1.61A
MFDesign-D
82.98%
1.65A
DiffAb
33,53%
1,44
DiffAb
55.08%
1,21À
DNIAA
15,946
503A
53.35%
H2
dyMEAN
66.38%
1.47A
1.2
dyMEAN
76.01%
1.23A
MrDDesigeCC
65.59%
1.26A
MFDesign-C
88.8%%
1,00A
dsMEAN
20,90%
7864
5.60%
MFDesiga-D
67.54%
1.44A
MFDesign-D
878818
1.15A
DiIAb
22.26%
4,29A
DiTAb
43.03%
1,80A
MFDoage-C
61.71%
4.134
68.37%
HI3
dyMKAN
6.15A
L3
dyMKAN
$2,69%
1.59A
MFDesiga-C
63.136
3,54A
MFDesiga-C
78 13.93%
1.55A
MIDDedgo-
63.385
4.2
MEDesign-D
3.71A
MFDesign-D
80.15%
1.69A
antibody CDRs highly consistent with the CDRs ground-truth sequences. This can cause the model learn to predict CDR sequences directly from the MSA results, leading data leakage. To address this, in both our training and inference, we exclude sequences where the regions corresponding to the CDRs the query antibody sequence have a similarity to the CDRs that exceeds a threshold, as detailed in Appendix A.4. This processing is to ensure fair benchmarking, In practical antibody design, filtering not required.
AF3-like folding models inherently support the input of <x> tokens. For these unknown residues, they only sample and denoise the backbone atom coordinates. In line with this design, we exclusively generate the backbone atom coordinates for the CDRs. Subsequently, following Luo et al. and Zhu al., we perform side-chain packing and structure relaxation using PyRosetta [Chaudhury et al., 2010] to obtain final all-atom structure.
4 Experiments
Due to the unavailability of AF3's training scripts, we opt to make modifications based on Boltz-1 in this work, which is a faithful reimplementation by Wohlwend et al. that follows the same variable and function definitions as AF3. Our method is termed MFDesign in subsequent sections, which stands for the Modified Folding model for the co-Design of antibody sequence and structure.
4.1 Antibody Sequence and Structure Co-design
Dataset. The data for evaluation comes from the Structural Antibody Database (SAbDab) [Dunbar et al., 2014]. Boltz-1 itself is pre-trained on PDB structures released before the same cut-off date as AlphaFold3, i.e., September 30, 2021. In previous studies [Luo et al., 2022, Kong et al., 2023a, Martinkus et al., 2024, Zhu et al., 2024], their experimental setups do not consider the release date when splitting data. To prevent data leakage, meaning the test data should not have been seen during Boltz- I's pre-training phase, we cannot directly adopt the splits used by previous works. Instead, we need consider the release date when partitioning the data. We first follow the approach of DiffAb [Luo et al., 2022] and AbDiffuser [Martinkus et al., 2024] by using MMSeq2 [Steinegger and Söding, 2017] to cluster antibodies according to CDR-H3 sequences at 50% sequence identity. These clusters are then divided into training, validation, and test sets in an 9:0.5:0.5 ratio, ensuring that all samples released before the cut-off date are included in the training set. This results in 5,843 training samples, 187 validation samples, and 204 test samples, with the test set comprising 161 regular antibodies and 43 nanobodies. Our test set is much larger than those previous studies, offering more robust demonstration of the models' ability to generalize. Due to GPU memory limitations, we ultimately removed samples with more than 2000 amino acids. Morcover, it's worth noting that some prior works [Jin et al., 2022b, Kong et al., 2023a, Zhu et al., 2024] employ the RAbD Benchmark [Adolf-Bryfogle et al., 2018], which includes 60 diverse complexes, to test antibody generation performance. However, since these data are released in the PDB before the cut-off date and Boltz-1 1 has already seen them, we do not adopt the RAbD Benchmark for evaluation to ensure fair comparisons. We provide our detailed data processing procedure in Appendix A to establish standardized pipeline, which aims to assist future studies in replicating or building upon our work.
Baselines. We focus on the more challenging setting of designing all CDRs at once [Martinkus et al., 2024], rather than designing a single CDR at a time. Consequently, we do not compare our approach with those methods that are limited to designing single CDR at a time. Although both
7

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Table 3: Comparison of generated nanobodies with simultaneously designed CDRs, assessed based on AAR and RMSD for CDR-H1, H2, and H3, as well as Loop-AAR, Loop-RMSD, IMP metrics, with the best and the second- best results highlighted. Note that we do not compare against dyMEAN here, as its source code does not support processing nanobodies.
Method
AAR/S
CDR-HI
RMND/A
AAR/S
CDR
412
RMSD/A /
AAR/%
CDR-HI
RMND/ A
Loop-AAR/ %
Leep-RMSD Á
IMP/5
DITAb
45.17
31.11
1.54
15,39
10.53
MFDesign-C C
05.71
2.45
35.00
1.2
01.00
3,71
61.01
4.36
43.14
MF Design-D
67.34
2A2
58,33
1.78
63.51
3.81
60.00
4.41
35.26
AbDiffuser [Martinkus et al., 2024] and AbX [Zhu et al., 2024] are capable of designing all CDRs simultaneously, we exclude these methods from our comparison due to the unavailability of available source code for AbDiffuser and the absence of training scripts for AbX [Zhu et al., 2024]. Ultimately, we choose DiffAb [Luo al., 2022] and dyMEAN [Kong et al., 2023a] as our baselines because they serve as representative methods for the generative and discriminative categories of antibody co-design methods, respectively. For our method, we provide results for sequence diffusion implemented with both discrete and continuous diffusion. We denote the discrete version of the model as MF DDDeeiin and the continuous version as MFDesign-C.
Metrics. We adopt the following metrics to evaluate all methods: (1) AAR (%): measures the accuracy of the generated sequences of CDRs by comparing amino acid identity with native sequences; deviation of C. atom coordinates between generated
(2) RMSD (Å): calculates the woot-mean-squas and native CDRs. Following Zhu et al., we introduce additional metrics for the middle loop residues within CDR-H3, which primarily contribute antigen binding: (3) Loop-AAR (%): applies AAR specifically to the middle loop residues of CDR-H3, assessing sequence recovery in this key region; (4) LooD-RMSD (Å): uses RMSD to evaluate the structural accuracy of the middle loop residues within CDR-H3. Furthermore, we use: (5) IMP (%) is the percentage of designed antibodies with enhanced binding energy (AG) compared to the original, assessed with Rosetta's 's InterfaceAnalyzer tool [Alford et al., 2017]. We also evaluate the binding energy AG of designed antibodies. with full results detailed in Appendix E.3 due to space constraints. For generative-based methods, DiffAb and our method, we generate 20 candidates per sample and report the averaged results.
For a fair comparison, we follow the same experimental setting as previous works [Luo et al., 2022, Kong et al., 2023a, Martinkus et al., 2024, Zhu et al., 2024], which involves re-designing CDRs within given co-crystal structures. In this setup, the sequences and structures of all non- non-CDR regions, including the framework and antigen, along with the untibody-antigen provided. By using the replacement sampling technique mentioned in Sec. 3.3, our method enables CDRs design based on the given co-crystal structures. The results are presented below.
binding conformation,
Results on Typical Antibodics. Table and Table present the results of simultaneously designed CDRs for typical antibodies, fully demonstrating the superiority of our method. From Table 1, we see that MFDesign-C achieves the best results RMSD across all CDRs. Its performance in AAR also superior to the baselines, except for H2. MFDesign-D, while not surpassing our own continuous version on CDR-L2, outperforms the baselines in AAR across the remaining CDRs by a notable margin. This reflects the suitability of each model for different data types: coordinates continuous, so the continuous version generally performs better in R.SDD; sequences are discrete, so the discrete version generally performs better in AAR. Notably, for the more challenging CDR-H3, our method improves AAR by 87.49% and reduces RMSD by 17.48% compared to the best baseline. Table shows the results when further ignoring the relatively conserved regions at both ends of CDR-H3 and focusing only on the highly variable middle loop, as these residues are the primary contributors to antigen binding [Kong al., 2023a, Zhu et al., 2024]. In this case, the best performance in Loop-AAR among the baselines, achieved by dyMEAN, is only 20.80% while MFDesign-D achieves an accuracy of 63.38% representing a 204.71% % improvement. In terms of Loop-RMSD, MFDesign-D attains a 17.89% reduction compared to the best baseline DiffAb. Besides, our method reaches 65.37% in the IMP metric. We attribute the superior performance of our method the broad knowledge of protein interactions originally acquired by AFk-like model during pre- pre-training. Moreover, the AF3-like model themselves already have good structure prediction capabilities, which may also explain the effectiveness of MFDesign in RMSD-related metrics. To further validate the robustness and generalization of our approach, we additionally evaluated our model on the widely used RAbD benchmark, which serves as standard dataset in previous antibody design studies. As shown in Table 88 and Table $9, our method consistently outperforms all baseline methods across multiple metrics, confirming its superior performance and general applicability.
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Table 4: Performance comparison on the structure prediction task, evaluated by RMSD, Loop-RMSD and Tcore
POR IC:
Gronnd tock
Mcld POR Brt
Our Plnokaad Makii
QLA H.S. KEWLLEAMDY
Predkce QUW LUMDDYY
Given CDR?
Fine-tuned?
RMSD |
Loop-RMSD
I
TM-seore
x
K
221A
91. 11.84%
x
1733
4,90A
93. 89%
x
1.39
3.33
95.45%
1.65
4.55A
94.25%
Figure 3: Examples of predicted structures.
Results on Nanobodies. Nanobodies, a.k.a. single-domain antibodies, consist of only a single heavy chain and are gaining increasing attention due to thier favorable properties, such high solubility and stability [Bannas et al., 2017]. Their structure is simpler, but because they lack a light chain to assist in antigen binding, their CDR-H3 region tends to be longer compared to typical antibodies and nanobody-antigen complex data is scarcer than typical antibodies. This longer CDR-H3 aids in antigen binding but also makes it more challenging to design. In this study, we validate the effectiveness of our method for designing nanobodies, where the labeled data is even more less, and summarize the results in Table 3. We observe that for all methods, performance decreases compared to typical antibodies, which may be due to the limited data availability. The baseline DiffAb shows a significant drop, especially in nanobody CDR-H3, where its AAR is only 15.39% 30.86% decrease. In contrast, both the continuous and discrete versions of our method maintain an AAR above 60% for CDR-H3, with Loop-AARs also above 60%. In addition, ensure the credibility of our methods, we report the standard error of the mean (SEM) for all antibody and nanobody results (Tables $10-S12). We also include the average inference time per sample for different methods, with detailed analysis provided in Appendix E.10.
4.2 Structure Prediction
In this section, we evaluate our method in the structure prediction task, a.k.a. protein folding problem. Unlike in Sec. 4.1, we do not use impainting for sampling for this task because revealing any ground-truth structure information is not allowed here. We observe that the origin Boltz-1 struggles with accurately predicting antibody-antigen binding sites, a common issue among existing structure prediction models. This limitation becomes evident when Boltz- is tasked with predicting complex structures for antibody-antigen pairs from the test set in Sec. 4.1. For each sample, the model generates five predicted structures, and the average TM-score across these predictions is only 0.62. As our model fine-tuned from Boltz-1, inherits this limitation.
Thus, we choose to predict antibody structures alone, a relatively simpler task, yet still challenging. particularly in accurately predicting the CDR-H3 region. We experiment with two input types: one where the CDRs are represented as <x>, and another where the CDRs are provided. We compare our fine-tuned Boltz-l model with the original, non-fine-tuned model. For the first type, we apply filtering to the MSA results. For the second type, we do not filter the MSA results. We adopt the test set used in Sec. 4.1. We use the model implemented in discrete sequence diffusion here for evaluation.
Metrics. We utilize the following set of metrics for detailed evaluation: (1) RMSD (Å): measures the root-mean-sapee square deviation of all C. atom coordinates after aligning the entire protein structure, providing an overall measure of structural accuracy; (2) Loop-RMSD (A): calculates the RMSD for the C. atoms specifically in the middle loop of the CDR-H3 region after whole-structure alignment, which is particularly challenging to predict; (3) TM-score: ranges from 0 to I, with higher scores indicating better similarity between the predicted and reference structures. We generate 20 predictions for each sample and report the averaged results.
Structure Prediction of Antibody As shown in Table 4, in scenarios where the input antibody's CDRs are unknown, our fine-tuned model consistently outperforms the original across all three metrics. In Fig. 3A, we demonstrate a case where CDRs are not present in the input. We see that the original Boltz-1 and our fine-tuned model can both accurately predict the framework regions of this sample. However, for the CDRs, the original model's prediction is less precise. Our model, despite starting with unknown residues, can predict CDRs sequences during the sampling process. As the sequence becomes clearer, this allows for more accurate CDRs structure predictions. This suggests that our method could provide valuable insights into what the functional structure of an antibody might look like, when only the framework regions sequences are given.

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Table 5: Comparison of simultaneously designed CDRs for typical antibodies using different discrete diffusion variants, with the Best and second- best results are highlighted.
Metiaal
122
12
Laq-ARR:
Loep RAEED-
IMP//
Mi Deigg- (abuark)
140
198
68:84
88
68:80
2016
198
63.00
Table 6: Nanobody design results CDRs, loop regions, and overall binding improvement (IMP).
Mechod
AAR75I
CDR-H
RMSD7 A
XAR7S #1
CDR
RSNDTAL
AAR/%
CDRJHS
RMSDDAT
Losp-AAR
Loop SoppRMSD À
IMP
MFDesiunC
6571
2
55.00
142
61:40
371
61.01
DFDrggee Caboort)
AN
33
178
ARI
641
15.05
MIFlleig (ontorm
RY38
With the CDRs provided, we observe that the results are improved for both compared to when CDRs are absent. Our fine-tuned model performs worse than the original model on three metrics. However, the gap is slight, with the TM-score decreasing by only 1 1.26%. This indicates that our fine-tuning retains a structure prediction capability comparable to that the original model. This slight decline in performance might be due to changes in the training method for structural diffusion. Additionally, because we apply full parameter fine-tuning, it might change the feature space learned during pre-training that more beneficial for structure prediction. The added sequence prediction task may introduce gradients that conflict with structure prediction, leading to decreased performance in the latter. In Fig. 3B, present the prediction of the fine-tuned model for a nanobody, whose CDR-H3 is longer and thus more challenging to predict accurately than that typical antibody. However, our fine-tuned model successfully predicts the structure of the CDR-H3 for this sample, further exemplifying our method's good structure prediction ability.
Structure Prediction of General Protein Complex beyond antibody-
To evaluate the generalizability of MF-Design applications. we assessed structure prediction performance on 128 pure protein complexes from the Boltz-1 test set and compared it against the original Boltz-1 model. Among our variants, MFDesign-C was selected due its superior structure modeling capabilities. As shown in Table S13, MFDesign-C achieves comparable accuracy to Boltz-1 across key structural metrics, including TM-score, RMSD, and DockQ. Given that MF-Design was fine- tuned specifically for antibody modeling, moderate decrease in performance on general protein complexes is anticipated, reflecting the trade-off introduced by domain -spaif- adaptation.
4.3
Ablation of the Discrete Methods
We selected D3PM-absorb as the diffusion kernel for sequence generation due to its natural for discrete amino acid sequences. Its absorbing noise process mirrors masked language modeling, proven technique for sequence-based tasks. This choice is supported by prior work demonstrating its superiority text generation [Austin et al., 2021b] and its successful application in recent antibody design models [Gruver et al., 2023b].
To validate this decision, conducted ablation study comparing D3PM-absorb with D3PM- uniform. As shown in the results Table and 6, D3PM-absorb achieved significantly better sequence reconstruction (Average Accuracy of Recovery), while D3PM-unifome offered clear structural benefits. This confirms that D3PM-absorb the more effective and suitable choice for our model.
5 Conclusion
This paper proposes an antigen-come conditione antibody design model by extending AlphaFold3-Iin models with sequence-structure co-diffusion. By utilizing their acquired understanding of biomolecu- lar interactions during pre-training, our method effectively overcomes the challenge posed by the limited availability of labeled antibody-antigen complex data. Our modified AF3-like model achieves superior performance over existing baselines in the co-design of antibody sequence and structure, while maintaining structure prediction accuracy comparable to the original AF3 model.
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References
Charles Janeway, Paul Travers, Mark Walport, Mark Shlomchik, et al. Immunobioloyy: the immune system in health and disease, volume 2. Garland Pub. New York, 2001. (Cited on page 1)
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi S. Jaakkola. Iterative refinmmnn graph neural network for antibody erruuncc-strrccuree co-design. In International Conference on Learning Representations, 2022a. (Cited on pages and 3)
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma. Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. (Cited on pages 1, 2, 7, 25, 31, 32, 33 and 34)
Xiangzhe Kong, Wenbing Huang, and Yang Liu. End-to-end full-atom antibody design. In Pro- ceedings of the 40th International Conference on Machine Learning, pages 17409-17429, 2023a. (Cited on pages 1, 3, 7, 8, 32 and 34)
Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang. Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, et Abdiffuser: full-atom generation of in-vitro functioning antibodies. Advances in Neural Information Processing Systems, 36, 2024. (Cited on pages 1, 2, 3, 7, 8 and 34)
Tian Zhu, Milong Ren, and Haicang Zhang. Antibody design using a score-based diffusion model guided by evolutionary, physical and geometric constraints. In Forty-first International Conference on Machine Learning, 2024. (Cited on pages 1, 2, 3, 7, 8, 31 and 32)
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew Ballard, Joshua Bambrick, et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, pages 1-3, 2024. (Cited on pages 1, 3, 27 and 29)
Chai Discovery, Jacques Boitreaud, Jack Dent, Matthew McPartlon, Joshua Meier, Vinicius Reis, Alex Rogozhnikov, and Kevin Wu. Chai-1; Decoding the molecular interactions of life. bioRxiv, pages 2024- 10, 2024. (Cited on page 1)
Jeremy Wohlwend, Gabriele Corso, Saro Passaro, Mateo Reveiz, Ken Leidal, Wojtek Swiderski. Tally Portnoi, Itamar Chinn, Jacob Silterra, Tommi Jaakkola, et al. Boltz- 1: Democratizing biomolecular interaction modeling. bioRxiv, pages 11, 2024. (Cited pages 1, 2, 7, 27, 28 and 33)
Xinshi Chen, Yuxuan Zhang, Chan Lu, Wenzhi Ma, Jiaqi Guan, Chengyue Gong, Jineai Yang, Hanyu Zhang, Ke Zhang, Shenghao Wu, Kuangqi Zhou, Yanping Yang, Zhenyu Liu, Lan Wang, Bo Shi, Shaochen Shi, and Wenzhi Xiao. Protenix advancing structure prediction through comprehensive alphafold3 reproduction. bioRxiv, pages 2025-01, 2025. (Cited on page 1)
Jonathan Ho. Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. (Cited on pages 2, 4 and 5)
Yang Song. Jascha Sohl- Dickkstiin Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference Learning Representations, 2021. (Cited pages 2 and 4)
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion- based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022. (Cited on pages 2, 4, 29 and 30)
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34:1 7981-17993, 2021a. (Cited on pages 2, 5, 28, 29 and 30)
Yang Li, Jiale Ma, Yebin Yang, Qitian Wu, Hongyuan Zha, and Junchi Yan. Generative modeling reinvents supervised learning: Label repurposing with predictive consistency learning. In Forty- second International Conference on Machine Learning, 2025. (Cited on page 2)
11

---

Yang Li, Jinpei Guo, Runzhong Wang, and Junchi Yan. T2t: From distribution learning in training to gradient search in testing for combinatorial optimization. Advances in Neural Information Processing Systems, 36:50020-50040, 2023. (Cited on page 2)
Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, and Junchi Yan. Fast 12t: Optimization consistency speeds up diffusion- training-to-tes to-testing solving for combinatorial optimization. Advances in Neural Information Processing Systems, 10:00199-30000 2024. (Cited on page 2)
Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self- guidance for controllable image generation. Advances in Neural Information Processing Systems, 1111622-1162333 2023. (Cited on page 2)
Jianzong Wu, Xiangtai Li, Yanhong Zeng, Jiangning Zhang, Qianyu Zhou, Yining Li, Yunhai Tong. and Kai Chen. Motionbooth: Motion-aware customized text-to-video generation. In The Thirty- eighth Annual Conference on Neural Information Processing Systems, 2024a. (Cited on page 2)
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. In The Eleventh International Conference on Learning Representations, 2023. (Cited on page 2)
Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, and Yanyan Lan. Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d. In International Conference on Machine Learning, pages 28277-28299. PMLR, 2023. (Cited
page 2)
Nianzu Yang, Huaijin Wu, Kaipeng Zeng, Yang Li, Siyuan Bao, and Junchi Yan. Molecule generation for drug design: a graph learning perspective. Fundamental Research, 2024. (Cited page 2)
Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi S. Jaakkola. Diffusion probabilistic modeling of protein backbones in 3d for the motif- scaffolding problem. In The Eleventh International Conference Learning Representations, 2023. (Cited on pages 2, and 29)
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew Borst, Robert J Ragotte, Lukas F Milles, et al. De novo design of protein structure and function with rfdiffusion. Nature, 62097976;:1089-1100 2023. (Cited on pages and 34)
Nate Gruver, Samuel Don Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance- Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson. Protein design with guided discrete diffusion. In Thirty-sevnnth Conference Neural Information Processing Systems, 2023a.
(Cited on pages 2, 5 and 6)
Jian Ma, Fandi Wu, Tingyang Xu, Shaoyong Xu, Wei Liu, Liang Yan, Minghao Qu, Xiaoke Yang, Qifeng Bai, Junyu Xiao, et al. An adaptive autoregressive diffusion approach to design active humanized antibodies and nanobodies. Nature Machine Intelligence, pages 1-15, 2025. (Cited on page 2)
Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, and Jian Zhou. Dirichlet diffusion score model for biological sequence generation. In International Conference on Machine Learning. pages 1276-1276 1301. PMLR, 2023. (Cited on page 2)
Zhen Wang. Ziqi Liu, Wei Zhang, Yanjun Li, Yizhen Feng, Shaokang Lv, Han Diao, Zhaofeng Luo, Pengju Yan, Min He, et al. Aptadiff: de novo design and optimization of aptamers based on diffusion models. Briefings in Bioinformatics, 25 2024. (Cited on page 2)
Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, and Junchi Yan. Learning substructure invariance for out-of-distribution molecular representations. Advances in Neural Information Processing Systems, 35: 12944- 2022. (Cited page 2)
Ge Yan, Huaijin Wu, and Junchi Yan. Quantum 3d graph learning with applications to molecule embedding. In International Conference on Machine Learning, pages 39126-39137. PMIR, 2023. (Cited on page 2)
12

---

Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling. Equivariant diffusion for molecule generation in 3d. In International conference on machine learning, pages 8867-8887.
PMLR, 2022. (Cited on pages 2, 5 and 30)
Huanjin Wu, Xinyu Ye, and Junchi Yan. Qvae-mole: The quantum with spherical latent variable learning for 3-d molecule generation. Advances in Neural Information Processing Systems, 37: 22745-223 22771, 2024b. (Cited on page 2)
Huaijin Wu, Wei Liu, Yatao Bian, Jiaxiang Wu, Nianzu Yang, and Junchi Yan. Ebmdock: Neural probabilistic protein-protein docking via differentiable energy model. In The Twelfth International Conference on Learning Representations, 2024c. (Cited on page
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zidek, Anna Potapenko, al. Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583 588889 2021. (Cited on page 2)
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Πyy Shindyalov, and Philip Bourne. The protein data bank. Nucleic Acids Research, 28(1):
235-242, 2000. (Cited on page 2)
Minkyung Back, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al. Accurate prediction of protein structures and interactions using three-track neural network. Science, 373(6557):871-876 2021. (Cited on page 3)
Minkyung Baek, Ivan Anishchenko, Ian R Humphreys, Qian Cong. David Baker, and Frank DiMaio. Efficient and accurate prediction of protein structure using rosettafold2. bioRxiv, pages 2023-05,
2023. (Cited on page 3)
Minkyung Back, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, and Frank DiMaio. Accurate prediction of protein-nus nucleic acid complexes using rosettafoldna. Nature Methods, 21(1): 117-121, 2024. (Cited on page 3)
John Ingraham, Or Bar-On, Shvo Meker, Sephora Springs, Daria repecka, Richard Johnson, Roy Bar-Ziv, Pascal Notin, Tristan Bricken, Maire Mcpartlon, et al. Illuminating protein space with programmable generative model. Nature, 623(7989); 1070-1078, 2023. (Cited on page
Sidney Lyayuga Lisanza, Jue Yang, Kyunghyun An, Jue Park, Matthew Bick, Christoffer Norn, Anindya Roy, Qian Cong, Hahnbeom Lee, Andrew Kang, et al. Multistate and functional protein design using rosettafold sequence space diffusion. Nature Biotechnology, pages 2024. 2024. (Cited
on page 3)
Tong Li, Robert J Pantazes, and Costas D Maranas. Optmaven-a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes. PLOS ONE, 9()): e105954, 2014. (Cited on page 3)
Gideon D Lapidoth, Dror Baran, Gabriele M Pszolla, Christoffer Norn, Assaf Alon, Michael D Tyka, and Sarel Fleishman. Abdesign: A algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins: Structure, Function, and Bioinformatics, (8): 1406, 2015. (Cited on page 3)
Jared Adolf-Bryfoge Oleks Kalyuzhniy, Michael Kubitz, Brian D Weitzner, Xiaozhen Hu, Yumiko Adachi, William R Schief, and Roland L Dunbrack Jr. Rosettantibooyydssin (rabd): A general framework for computational antibody design. PLoS Computational Biology, 1404)ee100611,, 2018. (Cited on pages 3 and 7)
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Antibody-antige docking and design via hierarchical structure refinement. In International Conference on Machine Learning, pages 10217- 10227. PMLR, 2022b. (Cited on pages 3 and 7)
Xiangzhe Kong, Wenbing Huang, and Yang Liu, Conditional antibody design as 3d equivariant graph translation. In The Eleventh International Conference on Learning Representations, 2023b. (Cited page 3)
13

---

A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. (Cited on page
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537-7547, 2020. (Cited on page 4)
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Technologies, Volume I (Long and Short Papers), pages 4171-4188 2019. (Cited on page 5)
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11461-11471, 2022. (Cited on pages 6, 29 and 30)
Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov. Weitao Du, Carla Gomes, Tom Blundell. Pietro Lio, et al. Structure-baser drug design with equivariant diffusion models. Nature Computational Science, 4(12):899 9099 2024. (Cited on pages 6, 29 and 30)
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray. Pyrosetta: script-based interface for implementing molecular modeling algorithms using rosetta. Bioinformatics, 26(5):689-69) 691, 2010. (Cited on pages 7 and 32)
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M Deane. Sabdab: the structural antibody database. Nucleic Acids Research, 42 (DI):DI 140-D1146 146, 2014. (Cited on pages 7 and 25)
Martin Steinegger and Johannes Söding. Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biolechnology, 15211)11066-108, 2017. (Cited on pages and 26)
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew O'Meara, Frank DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram Mulligan, Kalli Kappel, et al. The rosetta all-atom energy function for macromolecular modeling and design. Journal of Chemical Theory and Computation, 13(6):33113044 2017. (Cited on page 8)
Peter Bannas, Julia Hambach, and Friedrich Koch-Nolte. Nanobodies and nanobody- basedd human heavy chain antibodies as antitumor therapeutics. Frontiers in Immunology, 8:1603, 2017. (Cited on page 9)
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in neural information processing systems, 11117918-19999 2021b. (Cited on page 10)
Nate Gruver, Samuel Stanton, Nathan Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance- Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew G Wilson. Protein design with guided discrete diffusion. Advances in neural information processing systems, 36:12489-122 12517, 2023b. (Cited on page 10)
Cyrus Chothia and Arthur M Lesk. Canonical structures for the hypervariable regions of immunoglob- ulins. Journal of Molecular Biology, 196(4):901-917, 1987. (Cited on pages 25 and 32)
James Dunbar and Charlotte M Deane. Anarci: antigen receptor numbering and receptor classification. Bioinformatics, 92(2))298-300, 2016. (Cited on page 25)
VI Levenshtein. Binary codes capable correcting deletions, insertions, and reversals. Proceedings of the Soviet Physics Doklady, 1966. (Cited on page 26)
14

---

Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger. Colabfold: making protein folding accessible all. Nature Methods, 19(6):679-60) 682. 2022. (Cited on page 27)
Marie-Paule Lefranc, Christelle Pommié, Manuel Ruiz, Véronique Giudicelli, Elodie Foulquier, Lisa Truong, Valérie Thouvenin- Chouvenin-Contet, and Gérard Lefranc. Imgt unique numbering for immunoglob- ulin and cell receptor variable domains and ig superfamily v-like domains. Developmental & Comparative Immunology, 27(1):55-77, 2003. (Cited on page 32)
Nathaniel R Bennett, Joseph L Watson, Robert J Ragotte, Andrew J Borst, Déjenaé L See, Connor Weidle, Riti Biswas, Ellen L Shrock, Philip JY Leung, Buwei Huang, et al. Atomically accurate de novo design of single-domain antibodies. biorxiv, 2024. (Cited on page 34)
Rubo Wang. Fandi Wu, Xingyu Gao, Jiaxiang Wu, Peilin Zhao, and Jianhua Yao. IgGM: A generative model for functional antibody and nanobody design. In The Thirteenth International Conference on Learning Representations, 2025. (Cited on page 34)
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob de Haas, Neville Bethel, et al. Robust deep learning-basee protein sequence design using proteinmpnn. Science, 378(66155)499-55) 2022. (Cited on page 34)
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Appendix
Contents
Data Preprocess
25
A.1 Preliminary Data Filtering
25
A.2 Deduplication
25
A.3 Train Validation & Test Sets Curation
26
A.4 MSA Construction
27
Supplementary Details about the Our Method
28
B. Token Cropping
28
B.2 Training Objective
28
B.3
Sampling
29
B.3.1
Sampling w/ Replacement Technique
29
B.3.2
Sampling w/o Replacement Technique
30
B.4
Sequence Diffusion in the Continuous Space
30
B.5
Limitations and Future Work
31
B.6
Broader Impacts
C Details about the Adopted Metries
31
C.1 Metries Adopted in Sec. 4.1
C.2 Metrics Adopted in Sec. 4.2
32
D More Details about the Implementations
32
D.1 Details about the Implementations of Baselines
"aee............................
32
D.2 Details about the Implementations of Our Method
More Experimental Results
33
E.1 Results of Generating One CDR at Time
33
E,2
Comparison against More Baselines: RFantibody and IgGM
34
E.3 Metric beyond the AAR and RMSD: Binding Energy AG
................................
34
E,4
Diversity Analysis..
35
E,5
Per-CDR Structural Accuracy Analysis
37
E.6
Ablation of the 4th Training Stage
37
................................
E.7
Results RAbD Dataset
................................
38
E.8
Statistical Results
38
E,9
General Protein-Protein Structure Prediction
38
E.10
Inference Speed
39
E, Visualization of More Examples
39
F
Correspondence of Modules Referenced in This Paper to AlphaFold 3 Algorithms
41
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G Notations
41
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A Data Preprocess
Here, we present a detailed description of our data processing pipeline for the Structural Antibody Database (SAbDab) [Dunbar et al., 2014], with the goal of establishing a standardized approach that facilitates reproducibility and enables future research endeavors this field. As a comprehensive repository of antibody structures that undergoes weekly updates, SAbDab provides invaluable resource for structural antibody research. The data used in this work are collected from SAbDab up to November 27, 2024. The full data processing scripts and processed data will be released upon acceptance.
A.1 Preliminary Data Filtering
Based on the summary file in TSV format provided by SAbDab, only include samples whose annotated ant aatigen_typp field falls into one of the following five categories:
protein
protein
protein
protein
protein
protein
protein
protein
protein
protein
protein
protein
protein
protein protein
Furthermore, we only consider antibodies that either contain both heavy and light chains or consist of a single heavy chain (i.e., nanobody), with a resolution no worse than 4.5 Á. Following DiffAb [Luo al., 2022], we also utilize the Chothia scheme [Chothia and Lesk, 1987] for numbering the CDRs. After obtaining antibody sequences through parsing SAbDab's 's Chothia-scheme numbered antibodies, further validate the CDR positions using the AbNumber' toolkit, which is based on ANARCI [Dunbar and Deane, 2016]. Moreover, we filter out antibody sequences that can not be correctly identified by AbNumber. These problematic sequences are found either have incorrect lengths, missing regions among FRI/CDRI/PE /TT//////CD//////DRRRR
or notably long CDR3 regions (e.g. chain in PDB ID: 4k3e, which is an antibody heavy chain). Note that DiffAb also filters out sequences with abnormally long CDR3 regions in their preprocessing.
A.2 Deduplication
In the TSV format summary file provided by SAbDab, there may be multiple antibody-antigen complex samples that correspond to the same PDB structure. We closely examine the variable domain sequences of the antibody heavy chains from these samples derived from the same PDB structure. After parsing the PDB structures and using the AbNumber tool to calibrate the CDRs regions, employ AbNumber's aligmment tool for pairwise comparisons and obtain some observations. Based on these observations, we develop deduplication algorithm for these samples. Below, present several typical examples comparisons found in this process, focusing only on the CDRs sequences. First, consider the following pair of examples:
VH sequence of Chain H in PDB 1bj1:
FR1
GYTFTNY
FR2
NTYTGE
FR3
YPHYYCSSHWYFDV
FR4
VH sequence of Chain in PDB 1bj1:
FR1
GYTFTNY
FR2
NTYTGE
FR3
YPHYTGSSEWYFDY
FR4
Both samples are associated with the same PDB structure, and their CDRs sequences are completely identical. For such samples, we believe that retaining just one sufficient. We choose to retain the one with the longer VH sequence, as it provides more complete information. Now, consider the next set of examples:
VH sequence of Chain J in PDB 6tyl:
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FR1
GCIVIIS
FR2
PSNGD
FR3
FLRITASASYANE
FR4
VH sequence of Chain in PDB 6ty1:
FR1
GGIVHIS
FR2
PSNGD
FR3
FSTENY
FR4
In the second set, the CDR-HI and CDR-H2 regions are identical, but differences exist the CDR-H3 sequence, where the lower sequence is noticeably shorter than the upper one. Upon closer inspection, it can be observed that the lower sequence lacks some amino acids present in the upper sequence. This situation may arise due to some unexpected uncertainties during the data collection process of the PDB structure. In our study, we regard these two sequences as essentially the same, and it is evident that the upper sequence provides more complete information, so we discard the lower sample. Now, consider the third set of examples:
VH sequence of Chain K in PDB 7xj6:
FR1
GFIFSSS
FR2
VVG3GN
FR3
PACHSITCHDGFDI
FR4
VH sequence of Chain E in PDB 7xj6:
FR1
GCTFSSY
FR2
IPILAI
FR3
GTEYCDYDVSHD
FR4
In the third set of examples, the sequences of CDR-HI and CDR-H2 have the same length, but the amino acid types differ at certain positions. In the CDR-H3 sequence, although the lower one is shorter than the upper, this is not case of missing parts as the second example but rather indicates significant sequence differences. Thus, we consider these two chains as fundamentally different antibody sequences, and we retain both.
Based on these findings, we design the following algorithm to deduplicate the data obtained after processing in Section A.1. First, we use the Levenshtein distance to measure the differences between the CDRs sequences of two chains. The Levenshtein distance [Levenshtein, 1966] between two strings defined as the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other. In the examples above, the Levenshtein distance values for the three sets of sequences 0, and 20, respectively, illustrating varying degrees of similarity and difference.
We concatenate the CDRs sequences of each sample's 's VH sequence into single string and then calculate the Levenshtein distance between these concatenated strings for each pair of samples when assessing differences. In our study, we set threshold of for the Levenshtein distance; if the distance does not exceed 9, the sequences are considered not significantly different and likely represent the same sequence, in which case one should be discarded. As some samples are nanobodies without light chain, we only compare the CDRs of the VH sequence for all samples.
The algorithm is as follows: group the samples by their associated PDB ID. For each group, we maintain a list to store the samples that are ultimately retained within the group. We first sort the samples in descending order based on the length of the VH sequence and add the longest sequence to the list, as it is considered to provide the most comprehensive information. Then, we iterate through the remaining sequences: if the Levenshtein distance to any sample in the list does not exceed the threshold, it is considered a duplicate; if the Levenshtein distance to all samples in the list exceeds the threshold, it is retained and added to the list. This process is detailed in Algorithm S1.
Following the processing detailed in Section A. we initially have 11,009 samples associated with 5,762 PDB structures. Upon completing the deduplication step, we retain 6,347 samples.
A.3 Train & Validation & Test Sets Curation
As discussed in Section 4 of the main text, it is essential to consider the release date of the data to prevent data leakage when partitioning it into training, validation, and test sets.
We begin by applying MMSeqs2 [Steinegger and Söding, 2017] to cluster all antibodies based on their CDR-H3 sequences, using a 50% sequence identity threshold. These clusters form the basis for subsequent data partitioning and filtering.
Clusters that contain any structures with release dates prior to the cut-off date of September 30, 2021, entirely included in the training set. This ensures that all pre-cut-off date data is used for training
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Algorithm S1 SAbDab Deduplication
Input: a dataframe DF gathering samples sampled from Sec. A.I, dussaance_thrshheee 1: all reserved =
# Group samples according to related ID and then iterate over each group
2: for group in DF groopbyyy (PDEE "'PD_ID' ID") do
# Sort the samples descending order within current group based on length of the sequence group
3: sorted -roepp = group.sort, values(by='VHL " key=lambda x:x.str.len(). ascending-False)
# Reserve the sample with the longest VH sequence within the group
4: (ssered=[sorted, Seerreedgrruupdtteeeee group.iloc[O]]
# Iterate over every sample after the first one within the group do
for entry corted_groupiloef]]
6: should add flag = True
7:
for reserved _etireerrrrrre in reserved do distance=levenshtein. htrprrasheeiinddssttneeeettt if distance I distance threshold then
8:
distance(entryl (HH_trryyYYYSSSUUEECCCCCC reserved SHLLy("VV_SEUUBBAR
distance_threshold
10:
should_add_ftae = False break 12: end if
11:
13:
end for should add add_alag then reperved.apeenndeentr) end if
15:
16:
17:
end for
# Merge reserved samples
18:
all rerreseveed += reserved
19:
end for
20:
filtered_DP DataFrame(all reserved)
Output: dataframe filtered_ DD composed of remaining data after Sec. A.2;
and avoids any risk of leakage into validation or test phases. For those clusters with data released on or after the cut-off date, we further divide them into validation and test sets ensure these sets contain completely unseen data. The overall target ratio for the training, validation, and test clusters approximately 9:0.5:0.5.
Due to Boltz-1's [Wohlwend al., 2024] memory limitations, which do not support structures with more than 2000 residues/tokens on standard GPUs with 80GB memory (refer to this GitHub issue: wwtp./oowww/////oooooooooo
?
we also filter out samples in the validation and test sets where the combined antibody-antige complex exceeds 2000 residues.
Moreover, we incorporate two specific filters inspired by AlphaFold3 [Abramson et al., 2024] to improve data quality:
Remove any sample where a complex contains a single chain with all residues marked as unknown.
Exclude protein chains with consecutive C. atoms that are more than Äapart. This filter applied only to the antibody chains and not to the antigen chains.
As result of this process. we obtained 5,843 training samples from 2,570 clusters, 187 validation samples from 142 clusters, and 204 test samples from 144 clusters.
A.4 MSA Construction
The Boltz-1 repository has already provided a way to obtain MSA results through the Colab- Fold [Mirdita et al., 2022] API. However, with limited computational resources, such public remote server needs to queue incoming requests when handling multiple concurrent users, resulting in unstable processing speeds. To address this issue, we choose build our own local server that exclu- sively processes our requests. Consistent with Boltz-1, 1, we adopt these two databases for MSA search: uniref30 2302,, colabf cll_bfold__eeee envddb___bb____ee 202108. After the setup, we can simply replace the default _ta_server__ url parameter in Boltz-1 with our local server URL to obtain paired and unpaired MSA results from the local MSA server. The server built following the local setup guide provided the ColabFold repository, available at: ? http://htw....oooooooooooooeeeee
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Filtering. We have already discussed in the main text the need to filter MSAs to prevent data leakage during both the training and inference stages. The filtering is done as follows: The MSA results provide an amino acid-by-amino acid alignment of the retrieved sequences with the query sequence. For each VH or VI. query sequence, we examine each sequence retrieved one by one. We extract the segments that correspond to the three CDRs of the query sequence. If any of these segments have a sequence identity greater than or equal to a specified threshold with the corresponding CDR sequence, we remove the entire sequence from the final MSA results. In both the training and inference stages discussed in the main text, this threshold is set to 0.2.
B Supplementary Details about the Our Method
Due to space limitations in the main text, this section presents supplementray details on the method- ological aspects of MFDesign. For implementation specifics, we defer to Appendix D.2.
B.1 Token Cropping
As discussed in the Sec. 3.3, our model training consists of four stages, involving three token cropping strategies. The first strategy, employed in the first stage, is straightforward, involving inputting only the VH and VL tokens of the antibodies.
The other strategies are used in later stages. One of them, used specifically in the fourth stage, is a random token sampling method. For this, we directly adopt the cropping technique from the Boltz-1 [Wohlwend al., 2024] paper, specifically utilizing the approach detailed in their Algorithm 2. This cropping method effectively handles complex of varying size by interpolating between spatial and contiguous cropping strategies. It defines "neighborhoods" around specific tokens, which are incrementally added based on their distance from randomly selected crop center. By varying the size of neighborhoods, the algorithm can seamlessly transition from spatial to contiguous cropping.
The last strategy. used in the second, third, and fourth stages, focuses on cropping tokens around the antibody. This method, based on the above random token sampling method (Algorithm 2 from the Boltz-1 paper), is formalized in Algorithm S2 of our work, where we provide a comprehensive description of its tailored implementation for our specific need. In brief, this cropping approach first includes the VH and VL. sequences of the antibodyy Then, it randomly selects a token from the six CDRs the center token. Tokens located at the antigen epitopes are then sorted in ascending order based on their distance to this selected center token. In this work, we define a residue on the antigen surface as an epitope residue if any internal atom is within a distance of less than a threshold of 10 Åfrom the atoms of the antibody CDRs. The remaining process follows Algorithm 2 from Boltz- 1.
B.2 Training Objective
In this work, we aim to tackle the task of co-designing antibody sequences and structures. For this purpose, we define two separate loss functions: one for the sequence diffusion training and another for the structure diffusion training. The structure diffusion loss, Laructure is directly taken from AF3, as defined by Equation 6 in AF3's supplementary materials. The sequence diffusion loss function is denoted as Leequeece- Our final loss L simply the sum of these two components:
L = Lxequence + Lerreturre
Next, we give the detailed definition of Esoquence for the version using discrete diffusion for sequence. We adopt the D3PM- abbsobbin [Austin et al., 2021a], discrete diffusion framework, for our sequence diffusion here. We denote the clean sample as Jo and we perform sequence diffusion only the CDRs within (kr The forward diffusion process p ([k,). (o) gradually corrups CDRs tokens by replacing them with special token <X> at each timestep t, via a discrete transition matrix that defines the probability of mutating a residue into <X> The prior distribution at T a sequence where residues within CDRs are all <x> tokens. Our denoising model, TokenDenoiser, parameterized
by 0, po (o: (ki),, t)). is trained to recover the original sequence from the absorbing version [k:)z- To optimize the parameter set 0, we maximize the likelihood of the denoising process on ground-truth tokens:
28

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Algorithm S2 Cropping Strategy Adopted in Our 2nd & 3rd & 4th Training Phases
Input: tokens, max token number N Nimax max atom number N Noos. neighborhood sizes = [0, 2, ..., 40];
1: cropped_ tooeeottoes---tt =
2: Add all tokens in VH and VL sequence of the antibody to cropped_tokes tokens Sample neighborhood size uniformly at random from neighborhood sizes Sample center token uniformly within the tokens
CDRs of VH and VL.
5: sorted sorted_epinee cpitope_tokems tokens +- sort tokens in the epitope of antigen by ascending their distance to center token do 7: Let chain tokens be the entries in the same chain token (including token)
6: for token in sorted spitope_tokeee
len(chain tokens) neighborhood size then
9: selected tokens chain tokens
10: else
11:
selected_tokens [token]
12: min_ids + index token in chain_tokke tokens
max idx <- index token chain tokens
14:
while len(selected tokens) < neighborhood size do
15:
min idx min _dx_idx-- -
16:
max idx max idx +
Let selecfed_tokk tokens be the entries chain tokens whose index in chain_ tokens [min idx, max idx]
18:
end while
19:
end if
20:
Let new_tokens he the entries selected _tokens that present in cropped propped_tokens tokens
21:
adding new tokens to eropped tokens would exceed N inken or max atom number atto then
22:
Break the for loop
23:
else
24:
Add tokens cropped tokens
25:
end if
26:
end for
Output: toopped_tokens.
Lxequence
[k. |- log ((kiool (k:)))), where (ki) ((kk) (ks)o).
B.3
Sampling
In this paper, two sampling methods are employed: one for the antibody sequence and structure co-design task, and the other for structure prediction. We present our two sampling algorithms for the method employing discrete diffusion for sequence in this section.
B.3.1 Sampling w/ Replacement Technique
The structure follows the sampling method from AF3 [Abramson al., 2024] (refer Algorithm 18 in AF3), which adopts the EDM [Karras et al., 2022] sampling method. For sequence sampling, we use the standard D3PM-absorbing [Austin et al., 2021a] sampling method.
In particular, when designing antibody CDRs, experimentally resolved co-crystal structures are provided, where all non-CDR regions (antibody framework sequences/struutturss, antigen se- quences/structures, and the antibody-antige binding pose) are given. This means our sampling must be conditioned on these fixed components.
In this work, we utilize a replucement sampling technique to allow the design our CDRs to be conditioned on the given co-crystal structures. This technique is initially proposed and widely adopted in the image inpainting task [Lugmayr et al., 2022]. has also been used molecule and protein generation tasks [Schneuing et al., 2024, Trippe et al., 2023] to condition the sampling motifs. In simple terms, it involves replacing the generated contents corresponding to the fixed parts with their forward noised counterparts. In our sampling, we need to align the given ground-truth co-crystal structure to the denoised structure and then perform replacement operation.
given
Assuming the input has already been processed by the conditioning trunk to yield {s: inputs {s!nunk) {2]j)nk we then focus the subsequent sampling process. We formalize our

---

Algorithm S3 Sampling with Replacement Method
Impul: naw inpuis (f trunk's ourpus {s."""), y. (e?" (xly I] given ground tuth co-erysial strucuuee {x(
input sequence (k. with marked
agpo-ganmetess requiedd by EDM [Karms et 2022 sumpling mathed (oress Traas Maans "Inies A. 1))
1: for [0, 1, 2 199]
Define moier wheddade for strurtson sompling
maass (/1/p 200
1/p
1/e
min
Define noise achedaale for seguence sumpling
3: m. 200-1
end For
buttalice atructrr via sompting from Ganssion moise
5: X; -N (6.1a)
Ixpuc repoocess nnttonally srrw as inttinligation because CDDs are all already in the abopthing state
(k = (k!r"))
for 21.2,...: 199)
8: {x ContreRusdom.Aggmeetttiio ((x (xx)
Alguritim
9:
of Tinin elso
10: == +
Same as AF3, which tollows EDM sampling method
11: & ==A/o o- - (6.Is)
12: xi
13: [xfromish) ). (Lefeessed) CoDiffusloMModull (xx) ). (Deg J.a, a.")( Leltony (s!"ul) (utjhh))
Alg.
14: Repincement compling rechnique 14-15) [Lugmayr 2022, Solnewing 2024! RRgidAlign (x<²), (xfemnned)
Alporithm
15: xi Xemmia =xtrrannn if stom is within CDRs che algged
16:
4 (xL x((ruisa)) y/a
17:
di
Same AF1, which follows EDM sampling mathed
18: xi solyy + dt 6t
Hondonil simple profubility from suiforr distribution betwver O and for vich toden
19: PL ~ Le(U.
tremnn if residue is within CDRs ebs itper 20: kenoia le.
Follow D'SPM-absautinn sampling method
21: k one bot encuding of <x> if <t mas und residue within CDRs else k gemmsted
22: end for
23:
RigidAlign (xx(") }, (xx))
Refer Alguristm AF3
24: Xi = x, if stom I within CDRs else Giodlggo
25: [k:] [1deesiodd]
Output: predicted tinal sequence (k; predicted tinal structure
sampling method for antibody sequence and structure co-design in Algorithm S3. In fact, the raw inputs (f*) include (k"")u)), but choose highlight them separately.
B.3.2 Sampling w/o Replacement Technique
For the structure prediction task, we sample from scratch. Therefore, we do not need to input a given structure in Algorithm S3, nor do we use the replacement sampling technique. As result, the sampling algorithm used here omits given structure as input and also skips Lines 14-15 from Algorithm S3, which correspond the replacement technique, while the remains consistent with Algorithm S3. If the input antibody sequence contains the <X> token, we will simultancously predict its amino acid type while predicting the structure.
B.4 Sequence Diffusion in the Continuous Space
In the main body of this paper, we present an implementation utilizing D3PM-absorbing [Austin al., 2021a], discrete diffusion method, for sequence diffusion. Additionally, also implement a continuous version. Following the methodology of prior research [Hoogeboom et al., 2022], we introduce Gaussian noise directly the one-hot encoding of residue types to handle discrete data through continuous diffusion.
As noted in Sec. 3.2, the noise level for AF3's structure diffusion training is sampled from Jaata exp(- -1. + 1.5 (O, 1)). For inference, AF3 defines a sequence of decreasing noise levels:
{ (01/ 200-1 (o m/d ) t= 0, 1...., 199} dataa Tmxxs T and p are hyper- parameters. For structure diffusion, adhere to the default settings in AF3 for these parameters.
Since our diffusion now also based on continuous diffusion, we can also apply the EDM [Karras al., 2022] training method to sequence diffusion. Similar structure diffusion, directly sample
30

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the noise level instead of first sampling time step and then using predefined noise scheduler to obtain the corresponding noise level. By sharing the noise level between structure and sequence, we can easily align their training. We define scaling factor and set it to 1/4 in our experiments. In our implementation, we initially set the scaling factor to 1/4. Empirical results show this value already delivers significantly better performance than baselines. Due to limited computing resources, we do not perform extensive hyperparameter tuning. The noise level for the structure is sampled from data exp(-1.2 + 1.5 - (O 1)), and then this sampled noise level is multiplied by to serve the noise level for the sequence. This ensures that the noise levels for the sequences are proportional to those for the structure. Once the noise level for the structure is determined, the noise level for the sequence diffusion can be obtained using w, and noise is then added to the one-hot encodings of paixy
J. Diffusion is still applied only to the CDRs. tokens to obtain {k2
In this following, present how to input the corresponding noisy sequence into the CoDiffusion- Module at each sampled timestep. During training, we add noise to the one-hot encodings of tokens to obtain (k Similar to the method used in discrete sequence diffusion, we then replace the
specified dimension in {s|"Dus] } with (k j. For each denoising step in inference, we directly add noise to the token-level probability distributions, [h,}. which represents the probability distribution of each amino acid type for each token. This is obtained by inputting previously predicted [kfenoked after last step into the SOFTMAX function, Then, we replace the specified dimensions in {s. [ssppuss with }
B.5 Limitations and Future Work
One limitation of our current approach is that only generate backbone atoms for CDRs and lack support for non-standard residues, requiring an additional side-chain packing step to obtain full-atom structures. Accordingly, we aim develop end-to-end model capable of direct all-atom structure prediction in the future. Additionally, we currently use full-parameter fine-tuning and plan to explore more efficient strategies reduce computational costs while maintaining result quality.
On the other hand, a methodological limitation arises from our use of a discrete approach, which requires forcing continuous processes into discrete time steps for alignment. This discretization can impair model performance. promising future direction is to adopt a discrete diffusion model capable of operating within a continuous time/schedule. This would allow sequence and structure generation to be perfectly synchronized within a unified, continuous training framework, enabling each component operate in its optimal modality.
B.6 Broader Impacts
In this work, adapt AlphaFold3-like folding models with specific modifications enable the co-design of antibody sequence and structure, in addition to their existing structure prediction capabilities. Our societal impact lies in potentially accelerating the development of new antibodies. which could improve healthcare by providing more efficient treatments. So far, we have not identified any significant negative impacts, but we remain attentive any potential concerns that may arise.
C Details about the Adopted Metrics
In this section, we provide more details about the metrics we used in this paper for evaluation.
C.1 Metries Adopted in Sec. 4.1
AAR. It measures the accuracy of the generated CDRs sequences by comparing the amino acid identity with native sequences. It calculates the percentage of amino acids in the generated sequence that match the reference sequence.
RMSD. RMSD measures the deviation of the C. atom coordinates between generated and native CDRs. Following DiffAb [Luo et al., 2022], the antibody frameworks are aligned prior to RMSD calculation. In this work, this alignment is specifically based on the C, atoms.
Loop-AAR, This metric evaluates the accuracy of amino acid recovery specifically for the central loop residues of CDR-H3, the region most critical for antigen binding. Consistent with [Zhu et al.,
31

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2024], we exclude specific residues from our analysis. For the Chothia numbering scheme [Chothia and Lesk, 1987], we exclude the initial and terminal two residues of CDR-H3. contrast, for the IMGT scheme [Lefranc et al., 2003], we exclude the first four and the last two residues.
Loop-RMSD. This metric assesses the structural deviation of the central loop residues within CDR- H3 between generated and native structures, focusing on the region critical for antigen binding. Consistent with Loop-AAR, we follow the loop region definitions specific each numbering scheme. Similar to the RMSD calculation, the antibody frameworks are aligned before computing Loop- RMSD.
IMP. The Improvement Percentage (IMP) is determined by evaluating the binding energies of auttbody-antiigen complexes, calculated using the InterfaceAnalyzer with the ref2015 score function in PyRosetta toolkit [Chaudhury et al., 2010]. IMP represents the fraction of designed complexes that achieve lower binding energies compared to their natural counterparts.
Metries calculated from structural data are computed after we perform side-chain packing and relaxation.
C.2 Metries Adopted in Sec. 4.2
RMSD. Here, RMSD measures the root-mean-square deviation of all C. atom coordinates between generated and native protein structures. For this calculation, the entire protein structure is aligned, focusing on C, atoms to provide an overall measure of structural accuracy.
Loop-RMSD. evaluates the structural deviation of C. atoms specifically within the central loop of the CDR-H3 region. This metric is calculated after aligning the entire protein structure and focuses on prediction accuracy in this highly variable and critical region for antigen binding.
TM-score. TM-score assesses the similarity between predicted and native protein structures, with scores ranging from 0 to A higher TM-score indicates better global similarity and is particularly sensitive to the overall fold of the protein, rather than local variations.
Given that these metrics only require backbone predictions, and as noted by AlphaFold 3 that that the relax postprocessing step is rarely needed when using AF3, we do not perform side-chain packing or relaxation here. The models' prediction results used directly for evaluation.
D
More Details about the Implementations
Due to our use of different data splits from those the original papers of the baselines, it is necessary to retrain these baseline models to ensure a fair comparison. In this section, we provide detailed information on the implementation of both the baseline models and MFDesign. All experiments run on single node consisting of 8 H100 GPUs with 80 GB HBM3 each (aggregated GPU memory of 640 GB), 2 x Intel Xeon Platinum 8468 processors comprised of 48 CPUs each (total 96 cores, 192 threads).
D.1 Details about the Implementations of Baselines
DiffAb [Luo al., 2022] We implement the baseline using the code provided by the authors, and we adhered to the hyper-parameter settings detailed in the podesign_mullicdss_s file from the repository for our training configuration. DiffAb employs the Chothia numbering scheme, which is consistent with our approach.
[Kong al., 2023a] We utilize the code provided by the authors and set our training parameters according to the prpee-pprameters specified in the multi multi_cdr__a citi__dd_eeee ddddiigninjoon file in the repository. dyMEAN employs the IMGT scheme [Lefranc et al., 2003] for numbering antibodies. We choose to adhere to its original selection regarding the scheme as Zhu et al. do.
dyMEANS
Remark. The official dyMEAN code does not support nanobody processing, which results in reduced number of samples for evaluation in dyMEAN as compared to the full test set. contrast,
https///ghhaa.cco//uss22///aiii M https//ggthubbccmm/HUUUPPPPPMTT
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both DiffAb and our method are capable of effectively handling nanobodies, allowing for evaluation on the complete test set.
D.2 Details about the Implementations of Our Method
Since AlphaFold3 has not open-sourced its training scripts, in this work, we base our approach on Boltz-1, reproduced version of AF3 by Wohlwend et al., which provides these scripts. Addition- ally, Boltz-l maintains consistency with AlphaFold3 in terms of variable and function definitions, facilitating a clearer understanding of the implementation alongside the original AlphaFold3 paper.
Our primary modification compared to AF3 is the addition of a denoising network for sequences, which we implement using a simple MLP composed of several LINEAR layers connected by GELU activation functions. We observe that in the implementation of DiffAb [Luo et al., 2022], an additional feature is encoded for each amino acid to indicate its region. We adopt this approach in our implementation and find it beneficial for accelerating early convergence of the model. Specifically, we include a feature indicating which chain residue belongs to, specifying whether it is the heavy chain, light chain, or antigen. For antibody sequences (VH and VL.), another feature specifies the region within the chain, such FRI, CDR1, FR2, CDR2, FR3, CDR3, or FR4. For antigen residues in the antibody co-design task, where the baseline provides epitope information, we include a feature to identify if antigen residue is part of the epitope region. However, in complex structure prediction, we do not differentiate based on epitopes, as incorporating such information does not align with the bind-ng setting. These additional features, along with the previously obtained token-level representations, are fed into the sequence denoising network.
For our models used in the co-design of antibody sequences and structures, the implementation of both discrete and continuous diffusion for sequence follows the same training approach:
1st-stage: warm-up strategy is employed. where the learning rate increases linearly from 0 to a predefined value 5 x 10 over 10 steps. Subsequent to this warm-up phase, the learning rate decays by factor of 0.999 every 100 steps. The max token number is set to 256. The batch size for single GPU is 6, thus amounting to 48 samples per training step. This stage consists of a total of 5, 000 training steps.
2nd-stage:
Training continues for a total of 2, 000 steps, starting with an initial learning rate that is set directly. The learning rate then decays by factor of 0.999 every 100 steps. The max token number is set to 384, and the batch size for a single GPU is set to 3, resulting in 24 samples per training step.
3rd-stage:
Training continues for total of 5, 000 steps, starting with an initial learning rate that set directly. The learning rate then decays by factor of 0.999 every 100 steps. The max token number is set to 512, and the batch size for a single GPU is set to 1, resulting in 8 samples per training step.
4th-stage:
Training continues for a total of 9, 000 steps, starting with an initial learning rate that set directly. The learning rate then decays by factor of 0.999 every 100 steps. The max token number is set to 512, and the batch size for a single GPU is set to 1, resulting in 8 samples per training step.
E
More Experimental Results
E.1 Results of Generating One CDR A Time
Some prior works [Luo et al., 2022] have adopted single-CDR design settings, where individual CDRs are designed independently while treating other regions as fixed structural contexts. While this approach simplifies computational complexity, compared to our simultaneous design of all six CDRs at once, such setting presents two fundamental limitations for real-world antibody development:
The setting of designing only one CDR at a time actually simplifies the real problem. In this simplified setting, for example when we want to design CDR-H3, the other CDRs are directly given as ground-truth context, so a separate model needs to be trained for the design of each corresponding CDR region. However, antibody-antigen binding relies the cooperative action of
33

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Table S1: Comparison of designed CDR-H3 for typical antibodies in terms of AAR and RMSD metrics under two settings, with the best and second-best results highlighted. The comparison evaluates CDR-H3 generation under two approaches: (1) separate CDR-H3 generation versus (2) simultaneous generation with other CDRs. Due to limited computational resources, we only trained the discrete version of our model for this analysis.
Separately Design
Simultaneously Design
CDR
Method
AAR/ %
RMSD
CDR
Method
AAR/ %
RMSD À
DiffAb
24.41
3.89
DiffAb
22.26
4.29
H3
dyMEAN
39.03
3.82
H3
dyMEAN
34.69
6.15
Ours
67.79
3.44
Ours
65.04
3.71
all six CDRs. real-world scenarios, when designing antibodies against an unseen antigen, all six CDR regions need to be designed, where ground-truth exists for any CDR.
Furthermore, each CDR is generated separately one by one, there would be an order dependency. It's difficult to determine in what order the six CDRs should be generated sequentially.
In contrast, simultaneously generating all six CDRs is more practically valuable for real-world applications.
But we still supplement the results for single-CDR design in this study. Due to limited time and computational resources, we only train one CDR-HS--pecific design model for both method and baselines. We specifically select this CDR because it plays the primary role in antibody-antigen binding. The results are presented in Table S1. We can see that all methods perform better when generating CDR-H3 alone compared to jointly generating CDR-H3 with other CDRs, indicating that the joint-generation setting more challenging, as requires simultancously modeling the structural and sequence distributions of all six CDR regions, which is more complex. Particularly, our method outperforms baselines by notable margin in both scenarios (CDR-H3-only and full six-CDR generation) across all metrics.
E.2 Comparison against More Baselines: RFantibody and IgGM
In this study, provide comparison results against additional baseline methods: RFanti- body [Bennett et al., 2024] and IgGM [Wang et al., 2025]. We exclude them from our main analysis for the following reasons:
RFantibody. We would like to humbly point out that RFantibody follows sequential two-stage approach: initially generating structures using RFDiffusion [Watson et al., 2023], followed by sequence generation with ProteinMPNN [Dauparas et al., 2022]. This differs fundamentally from our co-design setting. Therefore, since its setting differs from ours and does not fall under the scope of antibody sequence and structure co-design studied in this paper, do not compare this work in the main paper.
IgGM. IgGM accepted ICLR 2025 (conference date: late April). making it essentially contemporaneous with our NeurIPS 2025 submission deadline. Furthermore, the authors only release the inference code and model weights. The critical training framework remains unavailable. This partial disclosure prevents scientifically rigorous evaluation, as we cannot retrain the model standardized data splits.
Despite these above constraints, we include supplemental comparisons against them and summarize the results in Table S2, Table S3 and Table $4. The results still demonstrates superior performance of method, further validating its advantages.
E.3 Metric beyond the AAR and RMSD: Binding Energy AG
The evaluation metrics employed in our main text AAR and RMSD, are standard evaluation metrics in machine tearning-based antibody design literature [Luo et al., 2022, Kong et al., 2023a, Martinkus al., 2024]. In Sec. 4, we have reported results using the IMP metric, another energy-related evaluation indicator, where we outperform basclines by notable margin.
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Table S2: Comparison of simultaneously designed CDRs for typical antibodies in terms of AAR and RMSD metrics, with the best and the second-bess highlighted for each CDR. We additionally compared our results with RFantibody and IgGM. Since neither method has released their training code publicly, we directly used the checkpoints provided by the authors for testing. It should be noted that these comparisons are not entirely fair because the models were not trained the same dataset as ours. Therefore, these results should only be considered as providing limited reference value. In particular, IgGM was just accepted at ICLR 2025, making essentially contemporaneous with our NeurIPS submission deadline. Findings: The results show our method maintains leading performance, outperforming all baselines in both metries except for CDR-L3's RMSD where IgGM performs better. Specifically, for CDR-H3, which is the key region determining antibody-antigen binding. our method achieves substantially better AAR and RMSD results than all baseline methods by notable margin.
CDR
Method
AAR
RMSD A
CDR
Method
AAR/S
RMSD/ A
DiIAbb
60.92
1.52
Dillbb
56.72
1.43
dyMEAN
70.31
1.65
dyMEAN
73.05
1.58
RFantibodl
3.01
REantihody
21.82
3.23
1gGM
73.01
1.70
IgGM
74.02
1.44
MFDesign-C
74,89
1.48
MFDesign-C
82.98
1.41
MFDesign-D
MFDesign-D
1.65
DiffAb
33.53
1.44
DiffAb
55.08
1.21
dyMEAN
66.38
1.47
dyMEAN
76.04
1.23
RFantibody
1gGM
63.89
1.73
1.2
RFantibody
IgGM
21.66
73.11
2.19
1.26
MFDesign-C
65.59
1.26
MFDesign-C
88.84
1,00
MFDesign-D
MFDesign-D
$7.81
1.15
DilTAb
22.26
4,29
DiffAb
43.03
1.80
dyMEAN
34,69
6.15
dyMEAN
$2.69
1.59
RFantibody
REantihodly
3.33
1gGM
32.45
4.07
L3
IgGM
61.02
1.51
MFDesign-C
63.13
3.54
MFDesign-C
78.93
1.55
MPDesign-D
MFDesign-D
80.15
1.69
Table S3: Results on typical antibodies with simultaneously designed CDRs, highlighting the best and the second-best. We additionally compared our results with RFantibody and IgGM. Since neither method has released their training code publicly, we directly used the checkpoints provided by the authors for testing. It should be noted that these comparisons were not trained on the same dataset as ours. Therefore, these results should only be considered as providing limited reference value. In particular, IgGM was just accepted ICLR 2025, making it essentially contemporaneous with our NeurlPS submission deadline. Findings: The results show that RFantibody performs slightly better than our method on the IMP metric, while method significantly surpasses all baselines on other metrics. Recall that in Table S2, although RFantibody achieves better IMP scores, it performs poorly on both AAR and RMSD metrics these two being the standard evaluation metrics in machine learning-bas bbased antibody design literature. We would like note again that RFantibody does not fall under the sequence the focus of our paper.
not entirely fair because the models
sequence-structtuee structure co-design methods that are
Metric
DiffAb
dyMEAN
RFantibody
IgGM
MFDesign-C
MFDesign-D
Loop-AAR/ %
15.86
20.80
9.69
25.41
61.71
63.38
Loop-RMSD
144
5.03
7.84
6.94
4.68
4.13
4.28
IMP7 7%
53.35
5.60
68.07
45.34
65.37
59.16
In this section, we have included additional results on another energy-related metric, i.e. binding energy AG. The related results summarized in Table S5.
E.4 Diversity Analysis
While prior antibody co-design work (including DiffAb) omitted sequence diversity evaluation, we argue that both quality and diversity metrics are crucial. The tradeoff between the quality and diversity of generated samples can be manipulated by adjusting the temperature parameter during sampling. We design the following protocol to evaluate the diversity:
We fist implements temperature parameter ((0.1, 0.5, 1)) scale sampling logits.
We then define diversity metric with its details as follows:
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Table S4: Comparison of generated nanobodies with simultaneously designed CDRs, assessed based on AAR and RMSD for CDR-H1, H2, and H3, as well as Loop-AAR, Loop-RMSD, IMP metrics, with the best and the second-best results highlighted. Note that we do not compare against dyMEAN here, as its source code does not support processing nanobodies. We additionally compared our results with RFaatibody and IgGM. Since neither method has released their training code publicly, we directly used the checkpoints provided by the authors for testing. should be noted that these comparisons are not entirely fair because the models were not trained on the same dataset as ours. Therefore, these results should only be considered as providing limited reference value. In particular, IgGM was just accepted ICLR 2025, making it essentially contemporaneous with our NeurIPS submission deadline. Findings: The results clearly show that our method maintains superior overall performance. The only exceptions are marginally lower performance on CDR- 1OR- RMSD compared to DiffAb. Crucially, for the most challenging CDR-H3 region, which is the key determinant of suubodd-antigen binding, our method achieves significantly better results than all baselines both AAR and RMSD metrics.
Method
CDR-HI
CDR-H2
CDR-H
LoppAAR/ /
Loop-RMSDD Á
IMP /
AAR7%
RMSD7A
AAR/S %
RMSD/
AARTS
RMSD7
DITAbb
15300
3.50
KFantibuly
1967
3.68
EgCM
3618
236
4019
222
1724
477
11.43
3.49
25.38
MFDesign-C
6571
248
55.00
160
61300
371
61.01
4.26
43.14
MrDrsgg-DD
67.3
262
503
1.7
AL31
134
00.00
4.41
38.25
Table S5: Results on typical antibodies with simultaneously designed CDRs in terms of binding energy AG (keal/mol, J). Reference corresponds the results for the ground-truths. The best and the second-best are highlighted respectively. We additionally compared our results with RFantibody and IgGM. Since neither method has released their training code publicly, we directly used the checkpoints provided by the authors for testing. It should be noted that these comparisons are not entirely fair because the models were not trained on the same dataset as ours. Therefore, these results should only be considered as providing limited reference value. In particular, IgGM was just accepted ICLR 2025, making essentially contemporaneous with our NeurlPS submission deadline. Findings: Our experimental results demonstrate that the antibodies designed by our method achieve significantly better average binding energy compared to other baseline methods. terms of best binding energy, only RFantibody shows marginally better performance than ours. We would like to emphasize that R RFantibody does not belong to the sequence-structus -trucccue co-design paradigm that the primary focus of this study, representing fundamentally different technical approach.
AG +
Method
best
avg.
Reference
-60.08
-6.60
DifrAb
-64.51
16.97
dyMEAN
-13.10
3542.55
RFantibody
-77.26
2.44
IgGM
-52.81
-5.04
MFDesign-C
-67.98
-21.72
MFDesign-D
-62.47
-20.83
We compute pairwise CDR sequence identity across candidates.
We average the identity scores per sample, then compute the mean across the entire test set.
We use 1 score (since lower original values indicate better diversity) as final metric.
The results are demonstrated in Fig. S1. We can see that temperature (no logits scaling). our method achieves significantly higher AAR than DiffAb while showing lower diversity. We maintain that quality outweighs diversity, as highly diverse but low-quality samples would be biologically meaningless. At temperature = 0.1, our method achieves comparable diversity to DiffAb, while delivering substantially better overall AAR (12.59% vs DiffAb's 5.68%) more than two-fold improvement, clearly demonstrating our method's 's superior generation quality.
36

---

DiffAb
0.8
Ours
60
Overall AAR
Diversity
0.6
40
0.4
DiffAb
20
Ours
0.1
0.5
Temperature
Temperature
(a)
(b)
Figure S1: Figure: (a) Comparison of diversity variation with temperature; (b) Comparison of overall AAR variation with temperature.
Table S6: Performance comparison on the structure prediction task, evaluated by RMSD, Loop- RMSD, and TM-score.
Given CDR?
Fine-tuned?
H1
H2
H3
L1
L2
L3
X
X
3.72
2.21
4.79
3.55
1.51
2.83
X
2.01
1.79
4.22
1.54
1.32
1.59
X
1.64
1.40
3.04
1.12
1.08
1.16
1.91
1.70
3.91
1.38
1.23
1.39
Table S7: Comparison of antigen structure prediction performance, with and without the fourth training stage, evaluated using RMSD and TM-score, with best results highlighted.
w/ 4-th training stage?
RMSD / À |
TM-score %
X
9.87
74.91
9.59
76.72
E.5 Per-CDR Structural Accuracy Analysis
To complement the integrated RMSD metrics in Table 4, we conduct additional per-CDR evaluations and summarize the results in Table $6. It can be observed that the RMSD values for individual CDRs are consistent with the overall RMSD results presented in our main text: when amino acid types of CDRs not provided in the input sequence, than the original model; when CDR sequences are provided, the fine-tuned version performs slightly worse than the original model, but still achieves good results. We have already provided explanations for both observations in Section 4.2.
fine-tuned Boltz-1 performs significantly better
E.6 Ablation of the 4th Training Stage
Recall that we adopt four-stage training approach. The first three stages primarily focus on selecting tokens from regions around the antibody and its nearby areas, which could result in inadequate training for antigen regions far from the antibody. To address this limitation, we introduce a fourth training stage specifically designed to ensure adequate learning of these distant antigen regions.
Here, we use our model predict the structures antigens, comparing the performance with and without the fourth stage of training. For each antigen in the test set from Sec. 4.1, we generate predictions. We employ RMSD and TM-score as evaluation metrics and summarize the results in Table S7. We use the MF-Design with discrete sequence diffusion for this study. We find that, after undergoing the fourth training stage, the model shows improvements both metrics compared the model trained with only the first three stages.
37

---

Table S8: Antibody design results the RAbD dataset (tda all CDRs. Higher AAR and lower RMSD are better. Best and second-best are highlighted.
CDR
Method
AAR %
RMSD
CDR
Method
AAR/ /
RMSD
DiffAb
67.03:12.25
1.1010.07
DiffAb
1.795=1.755
0,96:10.04
dyMEAN
79.15+2..32
1.03+0.08
dyMEAN
1..00+1.81
0.97+00.05
H1
111
MFDesign-C
79.06+1.116
1,08+00066
MFDesign-C
86.80
0.87++0.04
MFDesign-D
7..5
1.15+0.07
MFDesign-D
86.31=1.52
1.12:00.05
DifAAb
15.16:21.36
1,13+0.00
DifTAb
55.84+1.80
0.92+00.04
dyMEAN
73.87+22.82
0.97+:0.05
dyMEAN
88.30-3.288
0.79-10.04
H2
1.2
MFDesign-C
70.93+1.91
0.89+0.04
MFDesign-C
92.25+1.20
0.00
MFDesign-D
72.31:12.01
1.0410.04
MFDesign-D
90.46:11.39
0.8010.02
DifTAb 26.66:11.30
2.88
DifTAb 45.15
1.21
dyMEAN 38.46:12.01
4.44+0.20
dyMEAN
57.88-22.70
1.20:10.09
H3
L3
MFDesign-C
77.87=1193 2.2610.13
MFDesign-C
86.17=1477 85.26:11.73
1.12:00.07
MFDesign-D
78.61
2.35:0.12
MFDesign-D
1.24:0.07
Table S9: Performance loop regions and overall improvement (IMP) the RAbD dataset
(tt) Higher AAR/MMP and lower RMSD are better.
Method
Loop-AAR %
Loop-RMSD /
IMP %
DiffAb
1..2
3.44
22.29
dyMEAN
24.28+22.68
6.00-10.24
0,00-10.00
MFDesign-C
73.76::2.82 75.27+2.43
2.69:10.15
48.14=10.07
MFDesign-D
2.75+10.14
41.95=66.22
E.7
Results RAbD Dataset
To further assess the generalizability of our method, we conducted additional experiments on the RAbD benchmark, widely used dataset for antibody sequence and structure co-design. While our main experiments were performed on custom data split to ensure strict training-test separation (due potential data overlap with Boltz-1's pretraining set), include this additional benchmark to provide a broader evaluation. To ensure a fair comparison, all baseline models were retrained using our training data, so that any potential pretraining-related data leakage equally accounted for across methods.
As shown in Table S8-S9, our model achieves consistently superior performance over all base- line methods on the RAbD benchmark across multiple evaluation metrics. These results further demonstrate the robustness and general applicability of our approach beyond the initially test set.
E.8 Statistical Results
To assess the statistical reliability of our results, we report the standard error of the mean (SEM) for all main metrics in Tables S10-S12. The reported SEM values are consistently small across metrics, and the observed performance improvements of our method exceed the SEM margins by clear margin, supporting the statistical significance and robustness of the reported gains.
E.9 General Protein-Proten Structure Prediction
We evaluated MFDesign-C on 128 pure protein complexes from the Boltz-1 1 test set to assess its generalization. The model achieves comparable performance (as shown in Table S13) to Boltz- in terms of TM-score, RMSD, and DockQ.
Table S13: Structure prediction performance on protein complexes from the Boltz-1 test
Method
TM-score
RMSD /
DockQ>0.23/
Boltz-1 77.43 75.24
9.55 10.79
35.16
MFDesign
34.38
38

---

Table S10: Results typical antibodies with simultaneously designed CDRs, reported as -SM. Best and second-best results are highlighted.
Table S11:
Loop-region per- formance on typical antibodies, shown SEM. Standard errors support the robustness of the reported improvements.
RMSD
CDR
RMSD
1521006A
1AR1Q.11
III
MFDhsigg-C
GYMEAN
70.31=1 1454917A
L48:9.0A
L
MFDeiga-C
1.529014
Metted
Larp-AA
Lecp-IXX5D
IMP
MFIhelgg--
1611007A
MFDedgge-D
1.N6-000
IMITbD
33.55:1 1616
1,850000
DRTAb
5518-11
1.21
DHEAD
501-0004
53.888500
4yMEAN
65.38.21.700
14729.166
d:MEAN
76:4420099
1.23400J4
MPDasige-C
65.59 19.99-11 53%
1.26:0..6A
MFDosggeeC
1.00 BUSA
MFDesign-I
67.54:1.261
1.455000
MEDedgge-D
MMBAN
31.9011311
78:40MA
3.0c11 BYS5
115
dyMEAN
DETAb
34,99=1000 02%
02.36:0.0615
4 4.950.18A
6.15
13
dsMEAN
DITAb
15.0=1104
52 7396
1. 1.5
1,59 12A
MFDolgg-
6177111445
413:8166
65.57:3775
MK
03:35-1 16%6
3.54:034AA
7831
1.55-B
MFDeign-
6504411007
3.110034
MFDesga-D
90.1521.245
1.95000
MrDeignD
63.8:13345
3916688875
Table S12: Nanobody results with simultancously designed CDRs, reported as S dyMEAN is excluded as it does not support nanobody inputs. Best and second-best results are highlighted.
Method
CDR-ITI
CDR-11
CDR-II
Losp-AAR/ 16
Laog-RMSD A
IMP/S
RA
RMSD
AAR/S
RMSD7A
DREAD
22.
10:3
130 1.2-1.24
MPDDsige
071-0.11
451457.55
MEDesigaDD
1781011
633
E.10 Inference Speed
We report the average inference time of our model by evaluating 10 randomly selected samples on a single NVIDIA H100 GPU. The -1 and -5 suffixes denote whether the model generates I or 5 predictions per sample, respectively. As shown in Table S14, the inference time of our method is comparable to that of Boltz-1. with minimal differences observed. Notably, generating multiple predictions per sample benefits from parallel decoding, leading to a reduced average time per prediction.
Table S14: Average inference time per sample for different methods.
Method
Time /
DiffAb dyMEAN
4.0
1.1
Boltz1-1
84.7
Compared to other antibody co-design baselines, our model incurs higher computational cost due its larger number of parameters and more complex architecture. However, this overhead is accom- panied by improved performance, reflecting a practical trade-off between computational efficiency and generation quality.
Boltz115
32.8
MFDesign-1
85.4
MFDesign-5
33.0
E.11 Visualization of More Examples
In this section, we provide more visualizations of our results. In Fig. S2, we show the outcomes from simultaneously designing all CDRs of antibodies conditioned on co-crystal structures, as discussed in Sec. 4.1, with a focus on our designed CDR-H3. Fig. S3, we present additional cases from Sec. 4.2, where sequences with unknown amino acid types in the CDRs of nanobodies are used as inputs, and our model able to predict the structure while also predicting the sequences of the nanobody CDRs. Additionally, in Fig. S4, we illustrate some predicted complex structures by model when given the complete antibody-antigen complex sequence as input. These structures here are all predicted using the model with a discrete implementation of sequence diffusion.
39

---

PDB DD: 9e6k
PDB ID: 9df0
Heavy Chain:
Heavy Chain:
Light Chain:
Light Chain:
Ansigen:
Ansigen:
Ground-truth GWLDV Predicted I3: SWMDV
Ground-truth KIVNWEDP
Predicted IE: QVINWFDP
H3-RMSD:
HB-RMSD:
TM-score: 98.57
TM-score: 98.29
Stq7
Suky
Heavy Chain: F
Heavy Chain: II
Light
Light Chain:
Antigen: C
Antigen:
Ground-trahh H3: STAAWFPY
Ground-truth H3: PSI YGSEDY
Predicted STDAWFAY
Predicted PSLYNSEDY
H3-RMSD: 1.31
H3-RMSD: 0.90
Ground-truth
Predicted
Figure S2: Examples of generated typical antibodies with simultaneously designed CDRs.
PDR TD: Rum
PDB ID: Sqpt Heavy Chain:
Lleaay Antigen:
Antigees:
Ground truch H3: SYLSLNFPDDL Predicted H3. SYLSVNFPODE
Ground sruth H3: LARKAAYGSRRSADDDU
Predicted 113: WIRRTAEYSSSMACE
HE-RRMDD:2.31
H3-RMSD:
TM-spoee: 94,14
IMMscare:33.19
PDB Seat
PUB ILY 9tvb Heavy Chain: C Antiges: Ground-ruth DIFIVTHRKYDY Predicted DREHVTNRQYDY IIB-MMD: 2.72 95.66
Heavy Clain:
Antige: A
Ground-muth H3: DIYSYGTRDI
Prodicted 113: DLYSYATRDUSDI
TM score: 94.44
Figure S3: Examples of predicted nanobody structures using sequences with unknown CDRs as input.
JOR th sua Chan wee's Dioene 1oe
FORTD SHEf Ligs than begger TRCOORORLAT
FORUD DUW LaRCCRREK ARRIE TINNOR 15.0
FOR SaN Litichate ARRRIA TM
Ground truth
Predicted Antibedy
Prodicted Antiges
Figure S4: Examples of predicted untibody-antigen complex structure using complete sequences.
40

---

Correspondence of Modules Referenced in This Paper to AlphaFold 3 Algorithms
For direct technical cross- cross-refe,,ncinn, This section explicitly maps the AlphaFold3's modules refer- enced in this paper their original algorithm numbers in the AlphaFold3's supplementary materials in Table S15.
Table S15: Mapping of referenced modules this paper to AlphaFold3's algorithms.
Module
AF3 Algorithm
InputEmbedder
Algorithm
TemplateModule
Algorithm 16
MSAModule
Algorithm
PairFormerStack
Algorithm 17
DiffusionCCndditionnn
Algorithm 21
EonmnnttenionnEecoueer
Algorithm 5
DiffussionTransformmrr
Algorithm 23
AtomAftenntooDDecoder
Algorithm 6
DiffusionMMduule
Algorithm 20
CentreRandom Augmentatttt
Algorithm 19
Algi
Algorithm 28
G
Notations
We summarize the notations used in this paper in Table S16 facilitate reading.
Table S16: Notations.
Notation
Description
correspending residuc's type
requires spelicied
(k)]
X
Ihreiniimemioral counlimtes of strm
sinnccure disenctriin by a sar of stome coondinsaes
1
ripuls
inpuss, generated by Iaputtmmhodder conditionnng noken-layel singl reprosenatinss the ouiput of the conditioning bunk
basic encodines
[
Jeyel repesssttaions, the ouipus of the conditianing trank
[s.)
token level single represetiations generated by Diffusion DiffusionCoeditiiiinng which embed information from the noise level. (5***) ) and (graa)
loken-leysl repessentatioss generatedd by Diffasiont onditioning, whach embed information from relative position encoding and (2)
noisy structure
scaled (x] 1 appcoximately
(g]
stom level single representatons generated by AtomAttentieeneudeer which encode basic atomic features and incorporate information from
[0]]
stam-leval single representtioss gencrated by AtomAttienieekaoddee which encode bask atomic Fentures and incoqporate intormation hrom sg
generated by AtomAttention
encode basic
and incopparate information
[.]
reprosentaions
opdinal
[x [x
dencised stracture
hyper parameters for siructre sampling
hyper parameters for sinccure sampling
lyper-paraneters for sinuciare sumpling
for
sampling
70
typrr-prammcios
simpling
Tea
byper parametens foc sinccure sampling
A
hyper parameeers for sinucure samplig
R
lyper paramsters lor stlucare seapling
sampled timestep
The koaal muilber of limosopp for sequemee and siietuee diffeiio training
seguence diffasson tanstep
poisy sequence
desoised soquence
(xPT)
given groudd-mulh coserystal
41


Reviews:
[{"review_id": "OrbRoHNaf5", "reviewer": "Reviewer_unJA", "summary": "This paper proposes adapting a pretrained Boltz1 for the codesign of antibody binders conditioned on antigens, making one of the first very performant breakthroughs (concurrent with chai2) in antibody co-design, The progress made by this work has the promise to be very impactful to the community in particularly if everything is open-sourced; thus I provide my positive score on the condition that everything is opened-sourced on camera-ready.", "questions": "1. My understanding is AF3 is an all atom model , meaning it predicts the side chains , once one has all the side chains the sequence/ aa type can be obtained, so why do we chose to go for co-design here ? is boltz-1 not all atom? \n\n2. Whilst I agree that antibodies are very sparse in protein datasets such as the PDB, one could also try to pretrain a diffusion model on not only monomers but all available complexes in the PDB and then finetune on a smaller antibody dataset. In spirit this feels as data efficient as what the authors have done (I dont see the issue here other than the fact that maybe no such diffusion model is openly available at the moment). \n\n3. As the work mentions Boltz1 and AF3 are conditional diffusion models typically conditioned on sequence (in this use case the sequence of the target antigen) they are then able to generate the structure, however these key and clear detail has been somewhat ommited from the way the authors discuss AF3 like models , they jump straight into talking about the trunk making it harder to parse for a wider ML auidience. \n\n4. In theory AF3 like models have and can be used to generate antibodies conditioned on a antigen? from what I understand the model can take as input the sequence of the antigen ? happy to be corrected here , so why not include an AF3 like baseline with boltz1 ? why is the sequence codesign modification necessary ? Im a bit confused here", "limitations": "I think in their appendix section on limitations this has been well addressed.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strenghts\n\n1. Ignoring extremely concurrent work (which this work should not be compared to) I think this method makes breakthrough strides in the problem of antibody design\n2. The methodology /adaptations are mostly well explained, and the work requires a substantial piece of ML engineering / empirical work. \n\nWeakness \n\n1. This is a small writing preference, but why keep calling things AF3-like? It sounds a bit like PRy, Boltz1 is a well-known implementation with some of its own additional features which distinguish it from AF3 (e.g. Inference time steering, among other things).\n\n2. Some details in the main should be a little clearer without needing to dive into the appendix (See questions)", "was_revised": true, "final_justification": "I'm happy with the reviewer's response, and I maintain my positive score.", "rebuttal": "Thank you for acknowledging our work. Your rating has provided us with great encouragement, and your detailed feedback and suggestions have been immensely helpful. Below is our detailed response.\n\n---\n> **W1: Why call it AF3-like**\n\n- We sincerely appreciate the reviewer for this valuable feedback on our terminology. We fully agree that Boltz-1 is an excellent model with its own distinguishing features, and our decision to use the term \"AF3-like\" is based on two primary considerations:\n\n  - As mentioned in our introduction, a series of models including Chai-1, Boltz-1, and Protenix have recently emerged. They share the core diffusion-based architecture pioneered by AlphaFold3. We use \"AF3-like\" to refer to this class of models that share a common technical foundation.\n  - Our core contribution — the integration of sequence-structure co-diffusion — is an architectural modification that applies to this general framework, not to any feature specific to Boltz-1. We chose Boltz-1 as it is an excellent open-source implementation with available training scripts. Therefore, the term \"AF3-like\" more accurately reflects the general applicability and potential impact of our work, rather than confining it to a single model.\n\n> **W2 & Q3: Clearer details in main article**\n\n- We sincerely appreciate the reviewer for this invaluable suggestion. Due to space limitations, we had to place some key details in the appendix. Following NeurIPS' standard practice, accepted papers are permitted one additional page - we would use this to incorporate the highlighted content. If accepted, we will add a clear introduction to more details, improving accessibility. Thank you again for your constructive feedback, which will significantly improve the quality of our paper.\n\n> **Q1: All-atom generation**\n\n\n- Thank you for this insightful question, which touches upon a core consideration of our methodology. We would like to clarify this with the following points:\n\n  - We have acknowledged this limitation in Appendix B.5 and consider it as one of our future research directions.\n  - Regarding the premise that sequence can be derived from predicted side chains, this inverts the actual operational mechanism of models like AF3. These models are conditioned on a known amino acid sequence to predict an all-atom structure, including the side chains. When the sequence is provided, the model knows which side-chain atoms to generate. However, in our co-design task, the CDR sequences are initially unknown and are represented by the special `<X>` token. In this context, the model cannot determine which side chains to build for these unknown residues, making direct end-to-end all-atom design a non-trivial problem.\n  - Our backbone-first approach is an established paradigm. Many existing works, including the recent AbX[1] (ICML 2024), also generate backbone atom coordinates first, followed by side-chain packing and relaxation using tools like PyRosetta.\n\n- We hope this addresses your concerns. In summary, while Boltz-1 is capable of all-atom prediction, our specific task setting justifies the rationality of our backbone-first strategy.\n\n> **Q2: Pretrain a diffusion model using all PDB and finetune on antibody dataset**\n\n- Your insight is exactly right – this is precisely what we do in our work! In fact, the paradigm used in this paper exactly follows a pretraining-finetuning approach as you proposed and we have already used a pretrained diffusion model. Boltz-1 is actually a pretrained AF3-like diffusion model, trained on the entire PDB, a large dataset containing all available complexes, and thus possesses rich pre-trained information. Our work involves fine-tuning these types of models on a subsequent antibody dataset. It is precisely because pre-training on the entire PDB dataset is very costly that \"repurposing\" already pre-trained models is much more convenient and cheap.\n\n> **Q4: Include an AF3-like baseline and necessary of sequence codesign modification**\n\n- Thank you for your question. We would like to clarify that sequence co-design is the main work of this paper, which transforms a structure prediction model into a generative model, rather than just fine-tuning a structure model on antibody data. Therefore, the sequence codesign modification is necessary.\n\n- On the other hand, AF3 itself, as a structure prediction model, does not have the capability to generate sequences in co-design. So we can only compare it on the structure prediction task. In fact, in Table 4, we have already listed the comparison between our fine-tuned model and an AF3-like structure prediction model like Boltz-1 itself, where the model with \"Fine-tuned?\" as '✗' is Boltz-1. It can be found that our model achieves better results on CDR-masked input and maintains comparable accuracy on CDR-unmasked input. \n\n- Moreover, to demonstrate that our fine-tuned model still maintains good performance on general protein structure prediction, we conducted an additional evaluation. For this test, we selected a set of 128 protein-only complexes from the original Boltz-1 test set. We present the comparison of the relevant performance metrics below. This comparison shows that our fine-tuned model's capability is well-preserved.\n\n| Method   | TM-score | RMSD(A) | DockQ>0.23(%) |\n| -------- | -------- | ------- | ------------- |\n| Boltz-1  | 77.43    | 9.55    | 35.16         |\n| MFDesign | 75.24    | 10.79   | 34.38         |\n\n> **Reference:**\n\n- [1] Zhu, Ren, et al. \"Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints.\" ICML (2024).\n\n---\n\nWe hope this response could answer your questions and address your concerns, looking forward to receive your further feedback soon."}, {"review_id": "OWXHKf30BU", "reviewer": "Reviewer_ppEN", "summary": "The authors propose a method to repurpose the diffusion module part of AlphaFold3-like models for sequence-structure co-design of antibodies. In detail, the authors add a residue-type denosing part to the original diffusion module and using mask tokens as input to CDR regions to enable sequence-structure co-design. The authors provide a 4-stage training strategy to fine-tuning Boltz-1, an open-source version of AlphaFold3. The authors provide extensive experiments on antibody benchmarks, and a detailed ablation study alsat the same time.", "questions": "1. The demonstration of methodology looks quite heavy in the current manuscript. I hope the authors could help me make their contributions more clear.\n2. One concern about fine-tuning AlphaFold3-like models on specific domains is the training strategy. The authors provide a very detailed description for their strategy, but I wonder how is this strategy determined? Will changing the strategy harm the performance of robustness of the model? Especially, will the structure prediction of antigens be harmed?\n3. There are multiple choices for discrete token diffusion, and I wonder why did the authors choose the current version? It will be very interesting if there could be an ablation study about this.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The paper provides a very nice method to finetune AlphaFold3-like models. This approach is meaningful as it can maintain the good structure prediction ability of protein folding models while smoothly adding design ability. Such methods have strong potential in many protein-related domains other than antibodies. The experimental analysis is quite extensive within its scope, and addresses most concerns. While the paper is clearly written, the demonstration could be further improved. I think the methodology part is a bit heavy, and it is best to move the detailed model structure algorithm into the supplementary while keeping this paper's critical contribution clear.", "was_revised": true, "final_justification": "The authors have addressed all my questions, making comprehensive ablation studies. Thus, my scores will remain unchanged as 4.", "rebuttal": "Thank you for acknowledging our work and the valuable feedbacks. In the following, we have carefully crafted detailed responses to address your comments.\n\n---\n> **W & Q1: Heavy methodology writing and clearer contribution**\n\n- We sincerely appreciate your suggestion. We apologize for the issues with the writing. We promise to thoroughly revise the main text in future version, move non-essential parts to the appendix, and highlight our key contributions in the main paper.\n\n- We would like to humbly clarify that our contribution lies in transforming a well pre-trained AF3-like model (leveraging its learned general protein interaction knowledge) into an antibody sequence-structure co-design model, which effectively addresses the scarcity of antibody-antigen complex structure data while achieving state-of-the-art performance with original folding accuracy maintained, resulting a multi-purpose method. In addition, such paradigm may offer insights for other protein-related fields where similar specific data limitations exist beyond antibodies.\n\n> **Q2: Impact of fine-tuning strategies**\n\n- Thank you for this insightful question. Our training strategy is guided by the principle of Curriculum Learning, which is a well-established and effective methodology for training large-scale models. We were inspired by the multi-stage training paradigm of models like AlphaFold3, where the core idea is to present data from easy to hard, allowing the model to first learn local, simpler features before progressing to global, more complex structures.\n\n- Unlike a general-purpose model, we have tailored this curriculum specifically for the task of antibody co-design. Since our primary goal is to design the CDRs that bind to an antigen, we have designed the training to expand its focus outwards from a CDR-centric perspective. Specifically:\n\n    - Stages 1-3: The cropping strategy we have designed for the first three stages always includes the complete CDR regions of the antibody. We first let the model learn the antibody structure in isolation (Stage 1), and then gradually introduce the context of the antigen epitope and its surrounding areas (Stages 2 & 3), while progressively increasing the maximum number of input tokens. This design forces the model to continuously focus on the antibody-antigen interface, which is most critical to our task.\n\n    - Stage 4 & Robustness: We fully anticipated the risk you astutely pointed out: an excessive focus on the CDR-proximal regions might degrade the model's ability to predict the structure of antigen parts that are distant from the binding interface. To mitigate this, we have specifically designed a fourth training stage. In this stage, we have implemented a mixed-strategy cropper: there is a 50% probability of using the aforementioned CDR-centric cropping, and a 50% probability of performing completely random sub-structure sampling across the entire antibody-antigen complex. This ensures that while the model masters local design, it also revisits and maintains its pre-trained capability for global complex structure prediction.\n\n- The necessity of this strategy is empirically validated. As shown in our ablation study in Appendix G.2 (Table S1), without the fourth training stage, the model's prediction performance on the antigen structure worsens, as measured by both RMSD and TM-score. Therefore, our carefully designed four-stage strategy is crucial for the model's robustness.\n\n- Finally, given the substantial computational cost of fine-tuning models of this scale, an exhaustive search of all possible strategies was not feasible. However, we believe our proposed and empirically-validated curriculum learning strategy strikes an effective balance between task-specific adaptation and the preservation of the model's general capabilities.\n\n> **Q3: Ablation study with other discrete token diffusion method.**\n\n- Thanks for the valuable suggestion. First, we will explain why we chose D3PM-absorb as the version for sequence diffusion.\n\n    + D3PM [2], as a typical method for discrete data diffusion, has several different types, including uniform, absorb, gauss, etc. D3PM-absorb is the most suitable one for amino acid sequence design. Specifically, D3PM-absorb can be seen as a Masked Language Model, suitable for handling the generation of text sequence modality. Since amino acid sequences can be regarded as text modality information, they are applicable to D3PM-absorb.\n\n    + In addition, in the original D3PM paper, Table 1 also compared the performance of different types on text generation, and the effect of absorbing was significantly better than other types of discrete diffusion. This proves the effectiveness of D3PM-absorb in terms of performance.\n\n    + Furthermore, many previous protein sequence design works have adopted D3PM-absorb. For example, LaMBO-2 [1] from NeurIPS 2023 used D3PM-absorb for antibody sequence generation. Our specific implementation also referenced the implementation of LaMBO-2.\n\n- Next, we follow your valuable suggestions and choose another discrete token diffusion model D3PM-uniform to conduct further ablation studies. We have supplemented the results on the test set after retraining with D3PM-uniform for sequence diffusion, as shown below:\n\n- Typical antibody results:\n\n| Method              | H1 AAR(%)/RMSD(A) | H2 AAR(%)/RMSD(A) | H3 AAR(%)/RMSD(A) | L1 AAR(%)/RMSD(A) | L2 AAR(%)/RMSD(A) | L3 AAR(%)/RMSD(A) | Loop-AAR(%)/RMSD(A) | IMP(%)    |\n| - | - | - | - | - | - | - | - | - |\n| MFDesign-C          | 74.89/1.48            | 65.59/**1.26**        | 63.13/**3.54**        | **82.98**/1.41        | **88.84**/1.00        | 78.93/**1.55**        | 61.71/**4.13**      | **66.24** |\n| MFDesign-D(absorb)  | **74.95**/1.61        | **67.54**/1.44        | **65.04**/3.71        | **82.98**/1.65        | 87.81/1.15            | **80.15**/1.69        | **63.38**/4.28      | 59.66     |\n| MFDesign-D(uniform) | 71.82/**1.41**        | 55.74/1.29            | 50.15/3.70            | 77.09/**1.38**        | 82.47/**0.98**        | 73.06/**1.55**        | 48.99/4.30          | 64.72     |\n\n- Nanobody results:\n\n| Method              | H1 AAR(%)/RMSD(Å) | H2 AAR(%)/RMSD(Å) | H3 AAR(%)/RMSD(Å) | Loop-AAR(%)/RMSD(Å) | IMP(%)    |\n| - | - | - | - | - | - |\n| MFDesign-C          | 65.71/**2.48**    | 55.60/**1.62**    | 61.40/3.71        | **61.01**/4.26         | 42.67     |\n| MFDesign-D(absorb)  | **67.34**/2.62    | **58.93**/1.78    | **63.51**/3.84    | 60.80/4.41         | 38.26     |\n| MFDesign-D(uniform) | 60.08/2.51        | 46.29/1.65        | 45.45/**3.60**    | 43.88/**4.14**     | **43.84** |\n\n\n- The results show that D3PM-uniform is significantly weaker than D3PM-absorb in terms of AAR. On the other hand, its RMSD results do not show a clear advantage over continuous diffusion methods. This ablation study demonstrates the effectiveness of our chosen method.\n\n> **Reference**:\n\n- [1] Gruver, Nate, et al. \"Protein design with guided discrete diffusion.\" NeurIPS (2023).\n\n- [2] Jacob Austin, Daniel D Johnson, et al. \"Structured denoising diffusion models in discrete state-spaces.\" NeurIPS (2021).\n\n---\nWe hope this response could answer your questions and address your concerns, looking forward to receive your further feedback soon."}, {"review_id": "KsDgO18AoM", "reviewer": "Reviewer_jMg6", "summary": "This work proposes MFDesign, a method for modifying a AF3-like model for antibody sequence and structure co-design. MFDesign is equipped with an additional sequence diffusion head so that structure and sequence of antibody CDRs can be co-generated. For this, the authors re-use AF3's structural RDM diffusion, add a discrete (MFDesign-D) or continuous (MFDesign-C) sequence diffusion path, align both paths, and fine tune on ~6k antibody-antigen complexes from SAbDab.\nThe authors show that MFDesign outperforms compared-to methods on antibody co-design tasks on SabDab. While no benchmarks on non-antibody complexes were conducted, MFDesign maintains the original folding accuracy of AF3 for antibody complexes.", "questions": "In addition to the questions/points in the Strengths And Weaknesses section:\n19. Please provide estimates for statistical confidence for the reported improvements.\n20. It would be beneficial for practical application to include a rough estimate of the computational effort with a comparison to the compared-to methods. Please include that if possible.", "limitations": "The authors do address some limitations (MSA leakage) but do not discuss that in-silico metrics may not translate to actual binding affinity and that MFDesign inherits AF3's limitations regarding novel/non-standard residues.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Quality\n## Strengths\n\n1. The paper filters and splits the data well, with meaningful considerations for train-test leakage. In my opinion, the time-aware split of SAdDab is a very good approach and the other filters, e.g. deduplicating using Levenshtein distance and MSA scan, make sense. I also appreciated the clear description of the filtering steps in the appendix e.g. (Algorithm S1).\n2. The method is transparent and the 4 stages of the curriculum are outlined well with supporting evidence for their usefullness via abalation study.\n3. The paper reports multiple metrics: AAR, RMSD, Loop metrics, IMP, and Rosetta.\n\n\n## Weaknesses\n\n4. Missing statistical uncertainty estimates make it difficult to put the results into perspective. E.g. if the large AAR gain is truly significant.\n5. While the data curation is well done, the paper seems to rely on a single benchmark - which is restricted to the authors' SABDab split. I can understand why RAbD60 was discarded but this still opens the door for unaccounted affects that may distort the results. In my opinion, it would benefit the paper to have another benchmark.\n6. It would have been great to have proper comparison to AbDiffuser, AbX, IgGM, and RFdiffusion-Antibody. I appreciate the authors reasoning why they were omitted (and missing code is a hard-stop for complex models) but I will list it here for completeness.\n7. There seem to be inherent resource limitations, which led to filtering out antibody-antigen complexes that exceed 2k residues. How sever is this? It's only mentioned in the appendix (I honestly might have by chance overread it), is it justified to only mention it there?\n\n# Clarity\n## Strengths\n8. The list of contributions is clear and easy to read.\n9. The Algorithm 1 part clarifies the implementation.\n10. The authors seem to be transparent and provide information on training objective, continuous and discrete diffusion, as well as hyperparameters in the appendix.\n11. Similar for the data pipeline - documented step-by-step in the appendix.\n\n\n# Significance\n## Strengths\n12. I think this work is significant because of the potential practical use in therapeutic design. Co-design of CDR sequence/structure, conditioned on antigen pose, seems relevant for industry (if results hold in real-world applications).\n13. The reported improvements over the compared-to methods seem substantial.\n14. The preserved folding accuracy is interesting and might serve as a nice evidenced example for the benefits of transfer-learning.\n\n## Weaknesses\n15. Unfortunately, the restriction to in-silico experiments only does take away from the significance of the results (which is not uncommon in DL papers for budget reasons).\n16. As mentioned above, the reliance on a single dataset is in my opinion reducing the strength of the results.\n17. Given the maintained folding accuracy, it would be interesting to see the performance of MFDesign on general protein–protein-interface data.\n\n\n# Originality\n## Strengths\n18. To my knowledge, the modification of AF3-style diffusion with discrete sequence diffusion for antibodies is novel.\n\n## Possibly relevant missing literature references\nI'm not affiliated with any of these missing references but they seem relevant to the paper:\n\nIngraham, John B., et al. \"Illuminating protein space with a programmable generative model.\" Nature 623.7989 (2023): 1070-1078.\n\nLisanza, Sidney Lyayuga, et al. \"Multistate and functional protein design using RoseTTAFold sequence space diffusion.\" Nature biotechnology (2024): 1-11.", "was_revised": true, "final_justification": "## Resolved\n1. Added uncertainty estimates (SEMs), a second benchmark on RAbD with retrained baselines, per-sample runtime on H100, and clarified/quantified the >2k-residue limit (rare; one test case). These directly address my main concerns about robustness, fairness, and practicality.\n2. Partially resolved: For generalization beyond antibodies the authors report comparable folding on 128 non-antibody complexes. Full co-design beyond antibodies remains future work but this is helpful, so I view it as \"partially resolved\".\n\n## Unresolved\n3. No wet-lab validation.\n4. Current sequence diffusion supports only standard residues.\n5. Paired statistical tests (e.g., bootstrap CIs over the same complexes) would be helpful despite small SEMs.\n\n## Weighting/Conclusion\nThe consistent gains on key antibody co-design metrics and maintained folding accuracy, plus improved transparency (data curation, curriculum, compute), in my opinion, outweigh the unresolved limitations (which are also common in the area).\nThis leads me to recommend \"Accept\", assuming the rebuttal changes are integrated in the final version.", "rebuttal": "Thank you for dedicating your time and providing valuable feedback. In the following, we have carefully crafted detailed responses to address your comments.\n\n---\n> **W4 & Q19: Statistical Confidence**\n\n- Thank you for your suggestion. To ensure the credibility of our results, we have added the standard error of the mean (SEM) to our main results as you suggested. Here are the updated results for Tables 1-3:\n\n- Typical antibody results:\n\n| Method| H1 AAR(%)/RMSD(Å) | H2 AAR(%)/RMSD(Å) | H3 AAR(%)/RMSD(Å) | L1 AAR(%)/RMSD(Å) | L2 AAR(%)/RMSD(Å) | L3 AAR(%)/RMSD(Å) | Loop-AAR(%)/RMSD(Å) | IMP(%) |\n| - | -|- | - | - | - | - | - | - |\n| DiffAb | 60.92±1.31/1.52±0.08 | 33.53±1.16/1.44±0.05 | 22.26±0.66/4.29±0.18 | 56.72±1.16/1.43±0.11 | 55.08±1.19/1.21±0.09 | 43.03±1.04/1.80±0.27 | 15.86±0.58/5.03±0.20 | 53.35±3.93 |\n| dyMEAN | 70.31±1.40/1.65±0.17 | 66.38±1.70/1.47±0.16 | 34.69±1.02/6.15±0.23 | 73.06±1.81/1.58±0.14 | 76.04±2.01/1.23±0.14 | 52.69±1.73/1.59±0.12 | 20.80±1.21/7.84±0.26 | 5.60±1.81 |\n| MFDesign-C | 74.89±0.88/**1.48±0.08** | 65.59±1.33/**1.26±0.06** | 63.13±1.16/**3.54±0.14** | **82.98±1.10**/**1.41±0.06** | **88.84±0.92**/**1.00±0.05** | 78.93±1.27/**1.55±0.05** | 61.71±1.44/**4.13±0.16** | **65.37±3.75** |\n| MFDesign-D | **74.95±0.81**/1.61±0.07 | **67.54±1.26**/1.44±0.06 | **65.04±1.07**/3.71±0.14 | **82.98±1.15**/1.65±0.06 | 87.81±0.96/1.15±0.05 | **80.15±1.24**/1.69±0.05 | **63.38±1.34**/4.28±0.17 | 59.16±3.87 |\n\n- Nanobody results:\n\n| Method     |      H1 AAR(%)/RMSD(Å)       |    H2 AAR(%)/RMSD(Å)     |      H3 AAR(%)/RMSD(Å)       |  Loop-AAR(%)/RMSD(Å)  |     IMP(%)     |\n| - | - | - | - | - | - |\n| DiffAb     |     45.17±2.24/2.52±0.17     | 31.11±2.04/**1.54±0.12** |     15.39±0.98/4.71±0.25     |   10.51±0.60/5.50±0.29   |   30.93±7.05   |\n| MFDesign-C | 65.71±2.59/**2.48±0.16** | 55.60±2.93/1.62±0.13 | 61.40±1.86/**3.71±0.21** | **61.01±2.20**/**4.26±0.24** | **43.14±7.55** |\n| MFDesign-D |     **67.34±2.34**/2.62±0.16     |   **58.93±2.35**/1.78±0.14   |     **63.51±1.72**/3.84±0.21     |   60.80±2.15/4.41±0.24   |   38.26±7.41   |\n\n- The standard errors (SEM) are small across all metrics, and our performance gains significantly exceed the SEM, confirming the statistical significance of our improvements.\n\n> **W5 & 16: Another benchmark**\n\n- We thank you very much for your valuable feedback regarding benchmark diversity. We completely agree that validation on multiple benchmarks greatly enhances the reliability and generalizability of research findings.\n\n- Our initial decision to create our own data split was driven by a strict consideration for experimental fairness. As you understood, since our method is based on a model (Boltz-1) with a specific pre-training data cut-off date, data from many public benchmarks (like RAbD) have already been \"seen\" by it. A direct test would lead to an unfair evaluation due to data leakage.\n\n- Nevertheless, we recognize the limitations of a single benchmark. Therefore, in response to your suggestion, we have supplemented our work with a full experiment on the RAbD benchmark. For fairness, all baselines were retrained on our training set, ensuring a comparable level of potential data leakage for all models.\n\n- The new experimental results on the RAbD benchmark are as follows:\n\n| Method | H1 AAR(%)/RMSD(Å) | H2 AAR(%)/RMSD(Å) | H3 AAR(%)/RMSD(Å) | L1 AAR(%)/RMSD(Å) | L2 AAR(%)/RMSD(Å) | L3 AAR(%)/RMSD(Å) | Loop-AAR(%)/RMSD(Å) | IMP(%) |\n|  - |  - |  - |  - |  - |  - |  - |  - |  - |\n| DiffAb | 67.03±2.25/1.10±0.07 | 35.16±1.36/1.13±0.06 | 26.66±1.30/2.88±0.17 | 57.95±1.75/0.96±0.04 | 55.84±1.80/0.92±0.04 | 45.15±1.35/1.21±0.07 | 20.21±1.32 / 3.44±0.19 | 22.29±5.42 |\n| dyMEAN | 79.15±2.32/**1.03±0.08** | **73.87±2.82**/0.97±0.05 | 38.46±2.01/4.44±0.20 | 77.00±1.81/0.97±0.05 | 88.30±3.28/0.79±0.04 | 57.88±2.70/1.20±0.09 | 24.28±2.68 / 6.00±0.24 | 0.00±0.00 |\n| MFDesign-C | 79.06±1.16/1.08±0.06 | 70.93±1.91/**0.89±0.04** | 77.87±1.93/**2.26±0.13** | **86.80±1.38**/**0.87±0.04** | **92.85±1.20**/**0.68±0.02** | **86.17±1.47**/**1.12±0.07** | 73.76±2.82 / **2.69±0.15** | **48.14±0.07** |\n| MFDesign-D | **79.55±1.13**/1.15±0.07 | 72.31±2.01/1.04±0.04 | **78.61±1.69**/2.35±0.12 | 86.31±1.52/1.12±0.05 | 90.46±1.39/0.80±0.02 | 85.26±1.73/1.24±0.07 | **75.27±2.43** / 2.75±0.14 | 41.95±6.42 |\n\n- As shown in the table above, our model remains comprehensively and significantly superior to all baseline methods. This added result further demonstrates the robustness and superiority of our method. We will include this experiment and its discussion in the final version of the paper. Thank you again for your suggestion, which has made our work more complete.\n\n> **W6: Another baseline**\n\n- Thank you for your recognition. As we pointed out in the paper, among the mentioned papers:\n\n    * AbDiffuser has no open-source code.\n    * IgGM and AbX only released inference code and model weights, without training code.\n    * RFAntibody adopts a two-stage method: first generating the structure using RFDiffusion, and then generating the sequence using ProteinMPNN. This is fundamentally different from our co-design settings.\n\n- Therefore, we did not include them in the main results in the main text. However, to provide results that are as rich as possible, we still provided a comparison of the results with IgGM and RFAntibody in Tables S2, S3, and S4 in appendix, which you can refer to. Our model still has an advantage in the results.\n\n> **W7: Filter out complexes that exceed 2k residues**\n\n- We thank you for your meticulous review and valuable suggestion. Regarding the severity of this limitation, we believe its impact is minor. In our final test set, only a single antibody-antigen complex (PDB ID: 8sak) was filtered out due to this limit. We consider its impact negligible for the following key reasons:\n\n    * Data Rarity: Antibody-antigen complexes larger than 2,000 residues are very rare in the entire PDB database. In practical applications, one could also significantly reduce the input length by using only the antigen's epitope region.\n\n    * Hardware Limitation: The limit stems from GPU memory constraints, not an intrinsic flaw in our algorithm. On hardware with larger GPU memory, our model could process these very long complexes without any modification.\n\n    * Existing Solutions: This limitation is inherited from our base model Boltz-1. It is worth noting that subsequent updates to Boltz have effectively mitigated this issue by introducing techniques like chunking. Migrating our method to these updated versions would likely support longer sequences even on existing hardware.\n\n- We agree and will move this disclosure to the main text in the final version for better visibility.\n\n> **W15 & L: Limitation of in-silico experiments and novel/non-standard residues**\n\n- Thank you for your suggestion. Due to cost issues, we are indeed unable to conduct wet-lab experiments to verify the actual results and can only measure the results through computer simulation experiments and related metrics. We want to state frankly that this limitation is common in the field.\n\n- Regarding the limitation of AF3 on novel and non-standard residues, we sincerely accept it. Our current discrete diffusion only supports the 20 standard types of amino acids. However, to our knowledge, Boltz has added support for Modified Residues in subsequent updates, thus being able to predict the structure of non-standard residues. Therefore, this limitation can be solved by migrating our method to the latest Boltz model and solving the sequence generation problem for non-standard residues. We will list it in the \"Future work\" section of our paper.\n\n> **W17: Performance of general protein–protein-interface data.**\n\n- Thank you for your suggestion. As our work primarily focuses on the sequence-structure co-design of antibodies, it cannot support the design of general proteins. We would like to clarify that our method can indeed maintain a certain folding accuracy. To test the structure prediction capability of MF-Design on general proteins, we took a total of 128 pure protein complexes from the Boltz-1 test set for testing and compared them with Boltz-1 itself. We chose MFDesign-C because it has better structure prediction ability. The following are the comparison results:\n\n|Method|TM-score|RMSD(A)|DockQ>0.23(%)|\n|-|-|-|-|\n|Boltz-1| 77.43 | 9.55 | 35.16 |\n|MFDesign| 75.24 | 10.79 | 34.38 |\n\n- It can be found that on TM-Score, RMSD, and DockQ, our model still maintains folding accuracy comparable to Boltz-1.\n\n> **W20: Estimate of computational effort**\n\n- Thank you for your suggestion, we will add this result in the final version of paper. Below is an estimation of our running time, which is the average time to generate a single prediction by running 10 different sample inputs in single NVIDIA H100 GPU. The suffixes -1 and -5 represent predicting 1 result or 5 results per sample, respectively.\n\n|Method|DiffAb|dyMEAN|Boltz1-1 | Boltz1-5 | MFDesign-1 | MFDesign-5 |\n|-|-|-|-|-|-|-|\n|Time(s)|4.0|1.1|84.7|32.8 | 85.4 | 33.0 |\n\n- It can be found that the difference in running time between our model and Boltz-1 is almost negligible. Furthermore, when predicting multiple results per sample, it supports parallel prediction, which greatly speeds up the average running time for a single prediction result. \n\n- Compared to other baseline methods for antibody codesign, our method does not have an advantage in computational efficiency due to the larger number of model parameters and a more complex architecture. However, the advantages in parameter count and architecture also bring us better results, which we believe is a reasonable performance-efficiency tradeoff.\n\n> **Possibly relevant missing literature references**\n\n- Thank you for your additions. We will add these two related articles to our citations.\n\n----\nWe hope this response could answer your questions and address your concerns, looking forward to receive your further feedback soon."}, {"review_id": "2KPAbW09Ce", "reviewer": "Reviewer_RhHw", "summary": "This paper presents a method for antibody co-design by repurposing a large, pre-trained protein folding model (Boltz-1). The authors' approach, named MFDesign, augments the native structure diffusion module of the folding model with a new sequence diffusion head, enabling simultaneous co-design of antibody CDR sequence and structure. After fine-tuning this modified architecture on a relatively small dataset of antibody-antigen complexes, the model achieves dramatic performance improvements over state-of-the-art baselines while retaining its original structure prediction capabilities.", "questions": "* Discrete vs. Continuous Diffusion Trade-off: The results show a clear and interesting trade-off: the continuous diffusion model excels at structure (RMSD), while the discrete version excels at sequence (AAR). This is a valuable insight. Could you elaborate on this? Further, have you considered a hybrid approach that might leverage the strengths of both to achieve superior performance on all metrics?\n\n* Impact of MSA Filtering: Your rigorous filtering of MSA data to prevent leakage is a major strength. To better quantify the importance of this step, could you report the model's performance on the test set without this filtering? This would highlight the extent of the data leakage problem in this domain and underscore the validity of your evaluation protocol. A strong response here would further increase my confidence in the work's contribution.\n\n* What is the purpose of using AlphaFold3 if only modeling backbone?", "limitations": "Yes. The authors provide a clear and honest \"Limitations and Future Work\" section in the appendix. They appropriately identify the need for a separate side-chain packing step and the high computational cost of full-parameter fine-tuning as key limitations. They propose clear and reasonable directions for future research to address these points.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "**Strengths:**\n\nThe paper is very clearly written, presenting a complex idea in an accessible way. The methodology is well-explained, building logically on the known architecture of AlphaFold3. The evaluation is comprehensive, covering both typical antibodies and challenging nanobodies, and it includes an important analysis showing that the model's original folding capability is preserved after fine-tuning.\n\n**Weaknesses:**\n\n* A  weakness is that the method generates backbone atoms only, requiring a separate step with PyRosetta for side-chain packing and relaxation. While this is common practice, a fully end-to-end all-atom generation would be ideal.\n\n* The novelty of the approach is questionable. The benefits of using AlphaFold 3 are not apparent for the task of backbone generation alone, and the core concept of applying discrete diffusion to protein structure prediction has already been demonstrated by DiffAb.", "was_revised": true, "final_justification": "I have raised my score and now recommend acceptance for this paper. The authors provided an exceptionally thorough and insightful rebuttal that successfully addressed my main technical concerns.\n\n**Technical Soundness:** My primary concerns regarding the methodology have been fully resolved. The authors' new experiments (e.g., on MSA filtering) and their deep analysis (e.g., on the discrete vs. continuous diffusion trade-off) were highly convincing and demonstrated the technical solidity of this work.\n\n**Positioning of Contribution:** A minor point remains regarding the precise positioning of the paper's core contribution. While I agree with the \"foundation model\" paradigm, this concept was already well-established in the AF2 era (e.g., by RFdiffusion). It would be beneficial for the paper to further articulate the specific, fundamental advantages of using an AF3-like model as the foundation (beyond general accuracy improvements), especially given the acknowledged limitation of not achieving all-atom generation.", "rebuttal": "We appreciate your valuable feedback. Below are our detailed responses. We hope they address your concerns and welcome your favorable reconsideration.\n\n---\n> **W1 & L: All-atom generation**\n- Thank you for your question. Regarding all-atom generation, we recognize this limitation (Appendix C.1) and consider it a future research direction.\n- We would like to clarify that models like AF3 can directly determine the atoms contained in the side chain because the amino acid type is explicitly known at input time. However, in our model's input, the amino acid types of the CDRs part are unknown, so it is non-trivial to directly design an all-atom result when design sequence and structure when co-designing the structure and sequence.\n- Furthermore, many works, including AbX [1], also generate only backbone atoms and use Rosetta for side-chain packing and relaxation.\n\n> **W2 & Q3: Novelty**\n- Thanks for the critical question regarding the novelty of our work. This allows us to clarify our core contribution, which lies not in an innovation in a single technical component, but in proposing and validating a new and, we believe, more efficient paradigm for protein design.\n- We would like to clarify that our purpose for using AF3-like models is not merely to leverage their ability to generate protein backbones. Our core motivation is to \"repurpose\" large-scale folding models, which have been pre-trained on vast amounts of general protein data, as \"Foundation Models\" to solve the problem of data scarcity in specific downstream tasks, such as antibody design.\n- This idea is analogous to the paradigm in the field of Large Language Models (LLMs), where foundation models like GPT are fine-tuned for various downstream tasks. We posit that AF3-like models are, in effect, the foundation models for the \"AI for Science\" domain, and our work serves as a successful application of this \"pre-train and fine-tune\" paradigm in the field of antibody design. This is fundamentally different in principle from previous methods which train models from scratch on limited antibody data. We believe that this approach of \"repurposing\" protein folding foundation models holds great potential for the broader field of protein design in the future.\n\n> **Q1: Discrete vs. Continuous Diffusion Trade-off**\n- Thank you very much for the question and attention to this interesting finding in our work. We completely agree that this is a valuable observation and are happy to share our understanding and speculation on it.\n- First, we would like to clarify a premise: the \"discrete\" and \"continuous\" methods we compare are applied only to the generation of the sequence. For structure generation, we consistently use the native, continuous diffusion-based EDM method from the AF3-like model. Based on this, our explanation for the trade-off phenomenon you observed is as follows:\n    - **Why is discrete diffusion superior for sequence (AAR)**? We believe this primarily stems from data modality matching. Amino acid sequences are fundamentally discrete data (a choice from 20 categories). Discrete diffusion models are specifically designed to handle such data, allowing them to more accurately capture the underlying distribution and thus achieve better results in sequence recovery accuracy (AAR).\n    - **Why does using continuous diffusion (for sequence) lead to superior structure (RMSD)?** We speculate this is due to training schedule alignment, not a direct benefit to the structure.\n      - When we use continuous diffusion for the sequence, we can perfectly reuse the training schedule from the base model's (Boltz-1) pre-training, where the noise level is sampled from a continuous distribution. This maximally preserves the original model's structure prediction capability, hence resulting in better RMSD performance.\n      - When we use discrete diffusion for the sequence, we must \"adapt\" the structure module's continuous training schedule to discrete time steps for synchronization. This means the sampling of the noise level σ degenerates from a continuous space to a discrete set. This \"non-native\" training approach has a slight mismatch with the base model's pre-training, which we speculate leads to a minor degradation in structure prediction capability, manifesting as a slightly higher RMSD.\n- Based on the analysis above, an ideal \"hybrid method\" is not a simple combination of the two models, but rather one that resolves the training schedule mismatch while maintaining the advantage of discrete diffusion for sequence modeling. \n- **A promising direction is to adopt a discrete diffusion model that can operate in continuous time/schedule.** This would allow sequence and structure generation to be perfectly synchronized within a unified, continuous training framework, with each component operating in its optimal modality. We acknowledge that this is an advanced and challenging research direction with few available references at present. We have included the exploration of such methods as an important part of our future work and will state this in the paper. Thank you again for your insightful question.\n\n> **Q2: Impact of MSA Filtering**\n- Thank you for your insightful suggestion. We have conducted the corresponding test according to your feedback, and the results have led us to a more nuanced understanding, which we will elaborate on below.\n- Our methodology employs a 0.2 similarity threshold to filter the MSA. We would like to humbly clarify our rationale for this protocol. Our primary goal was to establish an academically rigorous and conservative benchmark from the outset. This strict filtering serves two purposes: 1) to preemptively prevent any potential for data leakage to interfere with our results, and 2) to ensure a consistent data distribution is used for both training and testing, which is fundamental for a reliable evaluation. In fact, we believe that even without filtering, the impact and risk of data leakage are minimal for the following reasons:\n  - Our statistical analysis shows that sequences with high similarity to the ground truth are extremely rare. For instance, sequences with >0.6 similarity constitute only about 7% of the raw MSA, and those with >0.8 similarity are a mere 2%.\n  - The `MSAModule` in our architecture internally subsamples the vast MSA for each computation. This mechanism further diminishes the already low probability of the model actually utilizing these few highly similar sequences.\n  - This concern is primarily relevant to the controlled setting of academic benchmarking to ensure fair comparison. In practical, real-world antibody design applications, leveraging all available homologous information is standard practice.\n- Therefore, we hypothesized that **data leakage would not have a significant impact** and the model would have difficulty learning from such a minuscule amount of \"leaked\" information. Consequently, we predicted that using unfiltered sequences as input would likely not lead to a substantial improvement in results. \n- We must be transparent that this is a theoretical conclusion based on the model's mechanics, as experimentally verifying it across various thresholds would be prohibitively expensive due to high training costs. After establishing our perspective, we performed the test as you suggested. The AAR(%) results are as follows:\n\n|Total|H1|H2|H3|L1|L2|L3|\n|-|-|-|-|-|-|-|\n|32.31±0.58|7.52±0.63|21.09±1.35|43.65±1.10|40.96±1.01|30.13±1.18|31.33±1.14|\n\n- As shown, the overall performance is indeed far worse than reported in our paper. We believe the primary and most plausible reason for this decline is **distribution shift**. To illustrate, the total unfiltered MSA is, on average, nearly **twice** as large as the one filtered with our strict 0.2 threshold. This dramatic increase in sequence quantity does not merely add more data; it also alters the information density of the input. It fundamentally alters the statistical properties of the input distribution our model was trained on, consequently impacting the distribution of key latent variables within the model.\n- However, it is crucial to note that the overall AAR is still above our 0.2 threshold, and the performance on the key CDR-H3 region remains at a respectable 43.65%. This strongly suggests that our model learns from complex patterns and multiple data (e.g., antigen's information), rather than simply \"cheating\" via leakage.\n- This drop demonstrates that violating distribution consistency hurts performance. This is precisely where the effectiveness and importance of our filtering method lie: it is the mechanism that establishes and guarantees this crucial consistency. While the 0.2 threshold may not be theoretically optimal—as a comprehensive search is too costly—our results in our paper empirically prove that it creates a sufficiently effective distribution, while also preventing potential leakage at source.\n- Tackling this sensitivity to distribution shift is a valuable direction for future work. We plan to explore potential solutions, such as training the model with filtered MSAs using random threshold to improve its robustness. This requires substantial experimentation. Due to time constraints, we are committed to pursuing this in our future research. \n- On the other hand, we do not view this distribution shift as entirely negative. The fact that respectable performance is maintained on key metrics suggests that this shifted distribution contains rich, relevant information, which could potentially be harnessed to generate more diverse protein designs, although this too would require further experimental validation.\n\n> **Reference**:\n- [1] Zhu, Ren, et al. “Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints.” ICML (2024).\n---\nWe hope this response could answer your questions and address your concerns, looking forward to receive your further feedback soon."}]

Author rebuttal:

Meta-review: This paper proposes to repurpose AF3-like protein folding models for antibody sequence-structure co-design. Specifically, the framework uses a sequence diffusion head and finetune the protein folding model. With experiments on antibody sequence-structure co-design tasks, the results show great improvements.

Reviewers all agree the paper is well presented and well motivated, more importantly, the performance show strong strengths compared to existing works, especially that the results can compare to Chi or related models. As for improvements, the authors are encouraged to add more discussions and revised points as discussed in rebuttal.
Decision: Accept (poster)