IgGM: A Generative Model for Functional Antibody and Nanobody Design
Authors: Rubo Wang
Fandi Wu
Xingyu Gao
Jiaxiang Wu
Peilin Zhao
Jianhua Yao
Venue: ICLR 2025 Poster (2025)
Keywords: de novo antibody design
complex structure prediction
protein design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Efficient and accurate design methods for antibody and nanobody sequences and structures tailored for real-world design scenarios.

Abstract: Immunoglobulins are crucial proteins produced by the immune system to identify and bind to foreign substances, playing an essential role in shielding organisms from infections and diseases. Designing specific antibodies opens new pathways for disease treatment. With the rise of deep learning, AI-driven drug design has become possible, leading to several methods for antibody design. However, many of these approaches require additional conditions that differ from real-world scenarios, making it challenging to incorporate them into existing antibody design processes. Here, we introduce IgGM, a generative model for the de novo design of immunoglobulins with functional specificity. IgGM simultaneously generates antibody sequences and structures for a given antigen, consisting of three core components: a pre-trained language model for extracting sequence features, a feature learning module for identifying pertinent features, and a prediction module that outputs designed antibody sequences and the predicted complete antibody-antigen complex structure. IgGM effectively predicts structures and designs novel antibodies and nanobodies. This makes it highly applicable in a wide range of practical situations related to antibody and nanobody design. Code is available at: https://github.com/TencentAI4S/IgGM.

Published as a conference paper at ICLR 2025
IGGM: A GENERATIVE MODEL FOR FUNCTIONAL ANTIBODY AND NANOBODY DESIGN
Rubo Wang 2,,3g Fandi Wur*, Xingyu Gao Jiaxiang Wu', Peilin Zhao', Jianhuuaa Ya IIstitute of Microelectronics, Chinese Academy of Sciences, Beijing, China
2University of Chinese Academy of Sciences, Beijing, China
Teencett AT Lab, Shenzhen, China
wangrubo @ hotmail.com, gxy9910@ gmmil.cmmmmmmmm {fandiwu, masonzhao, jianhuayao) @ @tencent.com
ABSTRACT
Immunoglobulins crucial proteins produced by the immune system to identify and bind to foreign substances, playing an essential role in shielding organisms from infections and diseases. Designing specific antibodies opens new pathways for disease treatment. With the rise of deep learning, Al-driven drug design has become possible, leading several methods for antibody design. However, many of these approaches require additional conditions that differ from real-world sce- narios, making it challenging to incorporate them into existing antibody design processes. Here, we introduce IgGM, a generative model for the de novo design immunoglobulins with functional specificity. IgGM simultaneously generates antibody sequences and structures for a given antigen, consisting of three core components: a pre-trained language model for extracting sequence features, a fea- ture learning module for identifying pertinent features, and prediction module that outputs designed antibody sequences and the predicted complete antibody- antigen complex structure. IgGM effectively predicts structures and designs novel antibodies and nanobodies. This makes it highly applicable in a wide range practical situations related to antibody and nanobody design.
INTRODUCTION
Antibodies, also known as immunoglobulins (Ig), are Y-shaped proteins secreted by B lympho- cytes, primarily found in blood and lymphatic fluid (Silverthorn, 2015; Akkaya et al., 2020). As shown in Figure ((A), they consist of two heavy chains and two light chains, each containing a variable domain (VH or VI.) and constant domain (CH or CL.). The variable regions include three eecemmenttartyyddemmmmme
regions (CDRs), which are crucial for antigen binding and determine the antibody's 's specificity. Additionally, the variable regions contain four framework regions (FRs). The FRs provide structural support for the VR and exhibit relatively low variability. Antibodies play critical role in the immune system by recognizing and binding to specific foreign substances such as bacteria, viruses, fungi, and parasites, and tagging them for clearance (Schroeder Jr & Cavacini, 2010; Litman et al., 1993). Their importance extends medicine, scientific research, and biotech- nology, where they are used disease treatment, personalized medicine, vaccine development, and new drug development (Nelson al., 2010; Weiner, 2015; Sliwkowski & Mellman, 2013).
Despite their significance, traditional methods for antibody production face challenges such as long production cycles (Georgiou et al., 2014), batch-to-batch variations (Bradbury et al., 2018), and the need for humanization (Safdari et al., 2013) to reduce immunogenicity, These challenges limit the widespread application and therapeutic efficacy of antibodies. To address these issues, researchers have turned to artificial intelligence for antibody design. Early approaches, such as energy-based computational methods (Li et al., 2014; Adolf-Bryfogle et al., 2018), were limited by the expressive capacity of statistical energy functions. Language models trained on sequences (Liu et al., 2020;
"Equal contribution.
CCoreesonning author.
Code is available

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CDR.
H5
Hg
VL
Heavy Chain
GDR
Light Chain
Ansgon
VH
Hg
11
Antigen
Ig
Novo Design
Existing Co-desgn Methods
Figure 1: (A) An antibody consists of symmetric Y-shaped structure, which includes variable gions (VH, VI.) and constant regions (CH, CL.. In practical antibody design, the focus is on the vari- able regions, which comprise the framework regions (FRs) and the complementarity- determiiityddrrriiingnngg regions (CDRs). (B) De novo antibody design refers to the process of creating a novel antibody that can bind to given antigen, where the framework regions can be selected based sequences with favorable physicochemical properties. Existing co-design methods require the simultaneous provi- sion of both the structure and sequence of the framework regions; however, in practical antibody design, the structures are often unknown.
Saka et al., 2021: Akbar al., 2022: Shin et al., 2021; Jing al.. 2020: Cao et al., 2021) also showed suboptimal performance due to lack of structural knowledge. Recently, co-design methods that simultaneously design protein sequences and structures (Anishchenko et al., 2021; Wang al., 2022: Anand & Achim, 2022; Shi et al., 2023) have demonstrated the feasibility of using AI for antibody design (Jin et al., 2021: 2022: Luo al., 2022; Kong et al., 2023a;b; Wu Li, 2024). Many existing co-design methods rely known experimental structures of antibody-antigen complexes and the modification of existing antibodies. However, these structures and antibodies are not always available for the design of novel antibodies targeting specific antigen. As shown in Figure 1(B), most approaches depend on actual framework region structures or templates derived from datasets. Unfortunately, such information is often missing when targeting
antigen.
To overcome these limitations, we proposed IgGM, generative model that performs simultaneous co-design of antibody sequence and structure. The overall process is shown in Figure 2. IgGM employs a multi-level network architecture. It first utilizes a pre-trained protein language model to extract evolutionary features of sequences. Then, feature encoder studies the interactions between antigens and antibodies. Finally, prediction module outputs the structures and sequences of the an- tibodies. IgGM leverages the interplay between sequence and structure generate accurate antibody designs, even when only partial sequences of the framework region are available. This capability aligns with practical application scenarios and offers new possibilities for antibody design. IgGM excels at generating the CDR regions and their structures and can dock the generated structure to the corresponding epitope. supports multiple design scenarios and can adapt to various conditions without the need for retraining, such predicting antigen-antibode complexes, designing the CDR HB region of antibodies, and designing multiple CDR regions. Furthermore, it can be extended to nanobodies, which are small single-domal antibodies that exhibit strong binding affinity to antigens and high stability (Cai et al., 2020). The experimental results indicate that IgGM achieves superior performance in multiple design tasks, demonstrating accuracy in structure prediction tasks that is
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Forward Process
Heavy Chain
Chain
Wrong Seqs
Eptapo
EXPOPSREA....
GOYYSSPLT.
Single stop
(Sr, Xx)
(s,)
(8l 1)
(8e, Xa)
Reverse Process
Figure 2: The forward process and reverse process for Co- Co-Design of Antibody Sequences and Struc- tures: The figure illustrates example trajectory in the design of antibodies, incorporating specific antigen and antibody framework region sequences. depicts the input data (ST, aT) to the model, along with the corresponding predicted outcomes (sg, 2o)) The consistency model is capable of generating antibody sequences and structures across diverse noise levels, demonstrating the gradual refinement from noisy inputs to well-defined antibody structure.
comparable existing structure prediction methods. In the Appendix G, we discussed the future research directions and existing limitations.
2 BACKGROUND
2.1 PRELIMINARIES
Since nanobodies can be considered as single heavy chain an antibody, we will use antibodies as example in the following discussion. Proteins are composed of 20 different amino acids. For a given protein of length N, the protein sequence can be represented as = { }
where each 8; denotes residue. The three-dimensional structure of the protein can be represented by the three- dimensional coordinates of the backbone atoms, denoted as X' = { | where each ær,, E R and w E (Co,N, O). Antibodies are specialized proteins that consist of two distinct chains, while nanobodies contain only single heavy chain. Each chain is composed of four framework regions (FRs) and three cumplementarrty-deeermii
regions (CDRs), The CDRs of each chain can be further divided into three segments: CDR HI, H2, H3 for the heavy chain, and CDR LI, L2, L3 for the light chain. An antigen-antie antibody complex can be represented
H: C - = {(s,, ((,)) (1,..,,4,.... ..., IH + Lc...,, +1
where H:C of the antigen, | represents the length of the amino acid sequence for each chain. In the case of nanobody-antigee complex, the light chain is not included.
C - A represents the heavy chain and light chain of the antibody, as well as the chain
Previous studies have defined the antibody design problem as selecting framework region (Shin et al.. 2021; Akbar et al., 2022) to design the CDRs to create antibodies that can bind to specific antigens. Since the influence of the framework region on the antigen-antibody interaction rela- tively minor, earlier research has primarily focused on designing the CDRs, often assuming that the structure of the framework region fixed and unchanging. However, when faced with a completely new antigen, the structure of the resulting antibody, including the framework region, is unknown and cannot be predetermined. IgGM considers structural changes in the framework regions during the binding process, enabling the design of the whole antibody structure even without experimental structures. Given the role of framework regions in providing support and certain sequences having favorable physicochemical properties for practical reuse (Bennett et al., 2024; Vincke et al., 2009),
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ESM-FP Modure (Preirain)
Siggle represematton
ENEGELTIOE
Single represemtation
COLYSSPUZ
Far representttio
EVCROKL....
Iter-chain Featine Enteacking Mocule
Sylormer (16 alocks)
Predict Module (8 blocks]
Pair representation
Structure
Encoder
Par rppresentatin
Figure 3: IgGM Model Framework Diagram: Before being input into the model, the antibody quence and structure are pre-noised. The model includes a pre-trained ESM-PPI module, Inter- chain Feature Embedding Module, a Structure Encoder, the Feature Embedding Module, 16 layers of Sgformer, and an layers of Prediction Module.
there's no necessity to design entirely new framework region sequences. Therefore, our focus re- mains on designing CDR sequences.
2.2 PROBLEM FORMULATION
In practical antibody design, existing work has utilized the sequences of known framework regions to guide antibody design (Bennett et al., 2024). However, the absence of the complementarity- determining region (CDR), the structure the antibody not fixed. Therefore, we investigate the problem in real-world application scenarios, specifically designing antibody structures and se- quences that can bind to specific locations (epitopes) on the antigen, given the framework region sequences. We separate the residues of CDRs and the framework regions for easier identification. The set of residues contained in the CDRs given by:
Rc == (Se, (Se, Xcw,) E {(SCDRs {cDRS)}
the set of residues representing the framework regions is denoted as
RF (SF, (SP, E {(SRRs, x
The entire design problem can be simplified to the task of designing the sequences of CDRs &c and the overall structure (Fw) of the antibody, given the sequence and structure of the anti- gen RA (SA, XA.w) as well as the sequences of the antibody's framework regions Sp.
3 METHODS
We introduce IgGM, versatile antibody design model suitable for designing antibodies that can bind specific antigens. The model aims facilitate the flexible design of various tasks related to antibody sequences and structures. We first discuss the denoisng network architecture in Section 3. 1, followed by the training methods and objectives of IgGM in Section 3.2, and finally, we address the sampling methods in Section 3.3.
3.1 DENOISING NETWORK ARCHITECTURE
The overall network architecture is illustrated in Figure 3, which includes a pre-trained protein language model, multi-level feature encoder, and sequence and structure design module. Given an antigen structure and an initialized sampled antibody sequence, the pre-trained protein language model first extracts features from the sequence, which encompass evolutionary information about the
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protein. These features are then fused through feature encoder (Sgformer), and finally, sequence and structure design module (prediction module) is employed to generate the antibody sequence and the structure that binds to the antigen.
Feature extraction from protein language models. Inspired by the success of pre-trained lan- guage models in natural language processing, we employ pre-trained protein language models (Lin et al., 2022; Chen et al., 2023; Hayes et al., 2024) as a feature extractor. We select ESM-PPI (Wu al., 2024) as our sequence feature extractor due to its ability handle inter-chain relationships. ESM-PPI is an extension of the ESM2 model (Lin et al., 2022), which has been further refined to improve its proficiency in capturing the structural and functional characteristics of multi-chain protein complexes. The antigen and perturbed antibody data are processed by the PLM, with the features from the final layer being meticulously extracted to serve as input feature encoder. To preserve the integrity of the learned features and to conserve computational resources, we maintain the PLM's parameters in a frozen state throughout this process. The enhanced capabilities of ESM- PPI for multi-chain protein structure prediction are detailed in (Wu et al., 2024), where readers can find a more in-depth discussion of the model's architecture, training procedure, and its application.
Multi-level feature encoder. To fully leverage the interactions between different features, we utilize a multi-level feature encoder for feature encoding, as illustrated in Figure 3. This approach allows the model to develop an understanding of the distinct chains that comprise the antibody structure. We incorporate chain-specific representations into the output features of the pre-trained language model (PLM). Furthermore, to address the critical aspect of antigen epitopes, we augment the antigen feature set with specialized representations that emphasize the interactions between the antibody and the antigen. We employ structural encoder to provide the model with a means to identify the precise positions of individual amino acids. Subsequently, these extracted features are input into Sgformer consisting of 16 blocks for further feature fusion and encoding. The sequence features extracted at this stage are pivotal for the subsequent recovery of the original sequence from the perturbed input, while the pair-wiss wise representations encode the relational information essential for understanding the complex folding of the antibody-antigen complex.
Sequence and structure design module. IgGM employs 8 layers of Predict modules, as illustrated in Figure 6. The Predict modules utilize invariant point attention to optimize the structure while simultaneously outputting the designed sequences. Due to the invariance of the Predict modules, this ensures that the model's predictions remain consistent regardless of the orientation or position of the antibody in space. The Predict modules fully leverage the sequence features and pair- wise representations learned by the Sgformer modules, while also incorporating the structures obtained from the initial sampling as input. By integrating these features with the invariant point attention mechanism, the Predict modules able to iteratively refine the coordinates of the amino acids, ultimately revealing the precise spatial arrangement of the antibody's 's three-dimensional structure.
Inter-chain Feature Embedding Module and Structure Encoder. IgGM utilizes two components to leverage the distinct characteristics of different chains and epitope information, as illustrated in Figure 3. The Inter-chain Feature Embedding Module integrates positional information of amino acids and inter-chain information to fuse features, thereby capturing the distinct positional charac- teristics of the chains while also obtaining chain-specific features. The Structure Encoder primarily encodes the protein structure; this module employs distance information to derive spatial features between pairs of amino acids, converting them into features through a discretization process. To ef- fectively utilize epitope information, we implement a specialized processing approach. Specifically, we encode the sequence epitope and spatial contact information into Single representation and Pair representation, respectively, to facilitate the effective generation of structures near the epitope. As shown in the Figure 8, IgGM can design antibodies that specifically bind to designated epitopes.
3.2 TRAINING DETAILS
We train our model on a structural dataset using a distillation approach to train a consistency model. First, we pre-train diffusion model, which consists of two phases. Ablation studies (Appendix E) demonstrate that this two-phase training approach is crucial for the successful training of the model. In the first phase, we focus training the structural component while preserving the information of the original sequences, specifically by conducting training tasks solely for structure prediction. During the training process, we sample pair of antigen-antihody complexes x from the dataset
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{s,z} and add noise at different time steps. We randomly select time step to introduce noise, resulting in 2;. The model is then trained to recover the overall antibody structure. Our objective is to ensure that the recovered structure closely resembles the true structure. For the protein structure, we utilize a combined loss function. Below, we provide a brief introduction, and for more detailed information, please refer to the Appendix D. The overall loss is follows:
[ Leeo + Liaame + Li + Ovill
Here, Lgco is designed to provide more direct supervision in the subsequent stack. Four auxiliary heads, implemented feed-forward layers, are added to the top of the final pair features to predict inter-residue distances and angles, as described in trRosetta (Yang et al., 2020). The term Ltaae is intended to provide direct supervision in the prediction module to recover the antibody structure, as proposed in RFDiffusion (Watson et al., 2023), and we extend to multi-chain scenarios as LiFrame to enhance the model's focus on the structural stability of the binding site. The formula can be expressed as follows:
Lraae
Pramme (z) +(00)))3
(5)
T-11
V²
LiFrame
1 di (0) 5 (0),))
(6)
E1=0 T²
where i represents the output of the i-th layer of the structural module, while y can increase the weights of the subsequent layers. The term Lviol serves penalty term correct incorrect bond lengths, bond angles, and spatial conflicts, as introduced in AlphaFold-Multe Notably, we do not penalize the bond length and angle between the last residue in the heavy chain and the first residue in the light chain, as there is no peptide bond between them.
(Evans et al., 2021).
Lviol Lengd-length
length + Lound ungle
Leassh-
Next, we move into the second stage of training, expanding on the groundwork laid in the initial phase by focusing on sequence design. The model is trained using the following objective:
C = Lavev + Lgan + FFaame + LiFrame + 0.02
Here, Laev is designed to supervise the model in recovering the amino acid sequence using a cross- entropy loss function. This loss function encourages the model to predict the correct amino acid probabilities for each position in the designed sequence, thereby enhancing the overall performance of the sequence design. It is noteworthy that during the second phase of training, we employed mixed training approach. In the model training process, for the sequences. we assigned probabilities of 4 2 2 : 2 for the model to design CDR H3, CDR H, and all CDRs, due to the greater variability observed in the heavy chain CDRs. Ablation studies (Appendix E) indicate that this approach effectively enhances the performance of IgGM and equips the model with the capability to design various regions, such as predicting structures and designing sequences for all CDR regions.
After completion of the diffusion model training, we employed distillation training to obtain the final consistency model. We followed Song et al. (2023) training methodology, aiming to minimize the consistency distillation loss:
LCD (0,0 v) = Ez,,, [d (fo(Ztamus e, lat1) ; So- (2%, e, c.ln))]
Algorithm IgGM Sampling
Input: Model fe(:,). sequence of time points > 72 ... TN initial noise (ST, &T), antigen S,A, xx)
(s. fo((S)) (,) T, (SA,
for n = 1 to N - do
Sample Q2 Q Q1Q2..00 .T q(It = j| =)), Xx X,~ (N((0,) I), (nifomm(S((3))
$_ +sQ &,, x
V Th Exx
x x fo((Sr., Xr,), Tn, xA))
end for
Output: (s. x) =0

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In this context, 2,,, represents estimate of the evolution of the PF-ODE from tn+1 to t, utilizing the ODE solver V/:
L.mm. (1U)2z++
g2(() 60 (ze, e, dt vo (2 tn+1, & C),
(10)
1nt1
20,
where the integration from 1n+1 to tn is approximated using the solver (()))))
3.3 DIRECTLY GENERATE ANTIBODIES
As shown in Algorithm 1, when designing antibodies against specific antigen, we first randomly sample one type of amino acid from the 20 standard amino acids to serve as the initial amino acid. We then sample the translation coordinates from Gaussian distribution and the initial rotation from the standard SO(3) group. These initial variables are subsequently sampled and generated using trained model. Due to the characteristics of the consistency model, it is capable of recovering real data from different time points. Therefore, we can also replace the initial coordinates, for instance, by using structural prediction tools such as AlphaFold3 to initialize the structure, thereby achieving higher quality antibody generation. During the generation process, the advantages of the consistency model allow IgGM to either generate in single step optimize through multi- step sampling to enhance the stability of the generated results. In single-step sampling, taking the collaborative design of sequences and structures as an example, once the initial sequence and structure are obtained from noise, the model can generate the final sequence and structure in one step. In contrast, multi-step sampling enables the generation of relatively more stable structures and sequences. We compared the performance differences between multi-step and single-step generation in Appendix Table 4, where we selected 10 steps to achieve balance between quality and speed. For proteins, structure determines function, and this is particularly true for antibodies, where the stability of binding is crucial for effective interaction (Majewski et al., 2019). In this context, we have chosen the DockQ score
selection criterion, it measures the quality of interactions. The DockQ score provides a comprehensive assessment of the binding interface, taking into account factors such as the accuracy of the predicted complex structure and the stability of the interaction.
EXPERIMENTS
We constructed our training, validation, and test sets from the SAbDab database, employing the widely used method of dividing the dataset based on time, as previously established in other works (Jumper al., 2021; Ruffolo et al., 2023; Wu et al., 2024; Abramson et al., 2024). We removed antibodies from the second half of 2023 that exhibited high sequence similarity to those in the training set to construct the test set, resulting in a test set comprising antibodies (SAb- 23H2-Ab) and test set of 27 nanobodies (SAb-23H2-Nano)) More details can be found in the Appendix C.I. Due to the limitations of AlphaFold 3, we generate five samples for each example to ensure a fair comparison. The details of the evaluation metrics can be found in Appendix C.4. Moreover, an ablation study of IgGM in Appendix E.
Table I: Complex structure prediction. Methods with superscript antigen structure input, while methods with superscript * utilize epitope information as input; methods with superscript use multiple sequence alignment (MSA). (AF3) indicates that the structure predicted by AlphaFold is used as the initial input. The root mean square deviation (RMSD) in CDR H3 is reported. Bold
indicates the best performance, while underline indicates the second-best performance.
Antibody Structure
Docking iRMS}
Method
TM-Scoret
IDTT
RMSD,
DockQ
LRMS
SR
IgFold >Hoock
0.9577
0.9019
2.1715
0.0218
16.6519
48.15 48.1571
0.0000
1Fold-Ag"!
0.9634
0.9142
1.9489
0.2522
6.7957
21.0346
0.4068
AlphaFold 3
0.9729
0.9305
1.5063
0.2951
10.9645
32.4080
0.3684
dyMEAN
0.9572
0,8882
2.2521
0.1005
8,9227
27.4234
0.0667
lgGMt-
0.9591
0.8956
2.1997
0.2986
6.2195
19.4888
0.4667
IgG3)
0.9580
0.8941
2.1422
0.3630
3,8635
11.2647
0.6667

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4.1 COMPLEX STRUCTURE PREDICTION
Complex structure prediction involves predicting the structure of the complex based on a given antibody sequence and antigen. IgGM can achieve structure prediction without the need for se- quence design. Following the evaluation criteria of tFold-Ag (Wu et al., 2024), we assess the re- sults using the TM-Score (Zhang & Skolnick, 2004), DockQ (Basu & Wallner, 2016), and success rate(DockQ> 0.23) of the predicted overall structure of the complex. We evaluated the structural prediction performance on the SAb23H2 test set, comparing three methods: the antibody structure prediction methods IgFold (Ruffolo et al., 2023) and tFold-Ag (Wu et al., 2024), as well as the latest protein structure prediction method AlphaFold (Abramson et al., 2024), alongside the antibody design method dyMEAN (Kong et al., 2023b). Since IgFold only supports the prediction of an- tibody structures, we utilized HDock (Yan et al.. 2020) to dock the predicted antibody structures with the original antigen. For tFold-Ag and AlphaFold 3, which can directly predict the structure of antigen-antibody complexes, we used the antigen-antibody sequences as input. In the case of dyMEAN and IgGM, we predicted the structure of the -adieen-antibody complex given the antigen, using the antibody sequence as input.
As shown in Table 1, for the predicted antibody structures, IgGM outperformed dyMEAN, which uses templates for initialization, in terms of antibody structure prediction. Although there is still a gap compared to specialized structure prediction methods, the results are overall quite close, in- dicating that our method has learned the distribution of antibody structures. In terms of docking performance, our method surpassed both structure prediction methods and antibody design meth- ods on DockQ, demonstrating that IgGM can achieve high docking performance. Additionally, it showed improved accuracy on iRMS and LRMS, with a success rate of 0.4667, significantly higher than dyMEAN's s 0.067. This indicates that IgGM is capable of capturing the interactions between antigens and antibodies effectively. When using the structures predicted by AlphaFold 3 as the ini- tial input for IgGM instead of randomly initialized structures, IgGM demonstrated improved perfor- mance across all metrics, particularly in docking-related indicators. Specifically, utilizing AlphaFold 3 structures increased the success rate by 20% compared using randomly initialized structures. As shown in Figure 9, for the inaccurately predicted structures by AlphaFold3, IgGM is capable of making corrections to yield more suitable structures.
4.2 DE NOVO DESIGN OF ANTIBODIES FOR SPECIFIC ANTIGEN
Due to the limitations of previous methods in achieving end-to-end antibody design, we employed two pipelines to evaluate these approaches. Specifically, the first pipeline follows the dyMEAN process (structure predictio==<occing---e
packing). For a given an- tibody sequence, we use IgFold (Ruffolo et al., 2023) to predict the antibody structure, followed by docking with the antigen using HDock (Yan et al., 2020), and then apply design methods for CDR generation. The second pipeline utilizes AlphaFold 3 (Abramson et al., 2024) for complex structure prediction (replacing Hgookd_ after which the antigen structure is aligned, and the predicted antigen structure is replaced with the native antigen structure to ensure that the anti- gen structure does not influence subsequent evaluations. In terms of antibody design, we evaluated several methods, including MEAN (Kong et al., 2023a), which employs graph neural networks to simultaneously generate sequences and structures for CDR H3; DiffAb (Luo et al., 2022), which uses diffusion models to generate sequences and structures for the CDR regions of antibodies; and dyMEAN (Kong et al., 2023b), which utilizes an end-to-end model for antibody design, allowing for novel structural designs through template utilization.
generation=xside-chie
We tested various methods for the collaborative design of antibody sequence structures, with results presented in Table 2. It is noteworthy that MEAN (Kong et al., 2023a) is limited to designing the CDR H3 region. Therefore. we only tracked the performance of these two methods in this spe- cific region. In contrast, DiffAb (Luo et al., 2022), dyMEAN (Kong et al., 2023b), and IgGM are capable of simultaneously designing all six CDR regions in both the light and heavy chains of anti- bodies. IgGM outperformed all other methods across nearly all metrics, such as sequence recovery rate and accuracy of generated docking positions, demonstrating the effectiveness of our approach. In comparison to DiffAb and MEAN, which do not support the design of overall structures, their performance varied when using different structures for initialization. The structures predicted by Al- phaFold were more accurate, leading to better docking position accuracy than dyMEAN as shown in Figure 4(A), although still lower than that of lgGM. Notably, IgGM was the only method with
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Table 2: Results of the novel antibody design SAb-2023H2-Ab. (IgFold) (AF3) indicates that the antibody structure predicted by IgFold or AlphaFold is used as the initial input. Backbone RMSD different CDR regions are reported. H1-H3 indicate the CDRs heavy chain while LI-L3
indicate the CDRs of light chain. Bold indicates the best performance, while underline represents the second best.
DiffAb
DiffAb
MEAN
MEAN
IgGM
Model
dyMEAN
IgGM
(IgFold)
(AF3)
(IgFold)
(AF3)
(AF3)
L1
0.597
0.608
0.633
0.750
0.737
1.2
0.598
0.599
0.634
0.743
0.735
L3
0.421
0.424
0.570
0.635
0.602
AART
H1
0.642
0.637
0.742
0.740
0.739
H2
0.363
0.394
0.627
0.644
0.639
H3
0.214
0.226
0.248
0.246
0.294
0.360
0.330
L1
0.783
0.749
0.864
0.589
0.659
1.2
0.471
0.466
0,481
0.378
0.395
L3
1.002
1.017
0.941
0.847
0.903
RMSD
0.650
0.623
0.633
0.555
0,590
H2
0.641
0.586
0.705
0.486
0.566
H3
2.741
2.646
2.357
2.300
2.454
2.131
2.155
DockQt
0.022
0.208
0.022
0.207
0.079
0.246
0.326
iRMS}
17.034
9.731
16.838
8.968
9.698
6.579
4.030
Docking
LRMS
48.163
27.559
48.104
27.557
28.764
19.678
11.229
SRT
0.000
0.368
0.000
0.354
0.049
0.433
0.627
iRMS below and an LRMS below 20, while achieving a docking success rate of 43.3%, high- lighting its generalizability. terms of sequence and structural fidelity in the CDR regions, IgGM achieved higher sequence recovery rate, with 36% recovery in the highly flexible CDR- H3 region, representing 22.4% improvement over the previous state-of-the Additionally, the generated structures exhibited smaller deviations from the structures. We also implemented a method using structures predicted by AlphaFold 3 as the initial input. The gener- ated results demonstrate significant improvement in docking-relann related metrics, with an increase in success rate of nearly 20%. Although there was slight decline in performance regarding the CDR sequences and structures of the antibodies, the decrease was minimal. This indicates that utilizing powerful structural prediction methods to pre-estimate structures can effectively enhance the quality of the designed structures.
method, dyMEAN.
A
dyNENN
8OM
DITAb
Ground Truth
Heavy Chain
Light Chain
Figure 4: (A) Structures of the designed antibodics (PDB: 8hpu), designed by IgGM (left, DockQ 00.824) and dyMEAN (right, DockQ = 0.029). (B) Structures of the designed nanobodies (PDB:
8q93), designed by IgGM (left, DockQ 0.766) and DiffAb (right, DockQ = 0.495).

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Table 3: Results of structure prediction and novel nanobody design 2Ab-2023H22-Nmm. (AF3)
signifies that the structure predicted by AlphaFold 3 is utilized as the initial input. Backbone RMSD in different CDR3. CDRI-CDR3 indicate AAR of the rigons. Bold indicates the best performance.
Structure prediction of Nanobody
Method TM-Scoref
IDDTT
RMSDJ
DockQt
iRMS.
LRMS
SRT
(Fold-Ag
0.9344
0.9303
L6722
0.2881
6.3490
15.0810
0.4296
AlphaFold 3
0.9519
0,9286
1,1885
0,2867
11.2194
32.6760
0,3885
lgGM
0.9318
0.8931
1.9925
0.2907
7.9879
22.0168
0.4400
Design of Nanobody
Method DiffAb (AF3)
CDRI1
CDR21
CDR31
RMSD]
DockQt
iRMS.
LRMSL
SRT
0.533
0.291
0.156
2.274
0.211
13.265
35.805
0.346
IgGM
0.565
0.330
0.183
1,980
0.267
6.927
14.966
0.415
4.3 STRUCTURE PREDICTION AND DE NOVO DESIGN OF NANOBODIES
Nanobody sources are type of single-domain antibody known VHH fragments (Harmsen & De Haard, 2007). Nanobodies offer several significant advantages over traditional antibodies. Their structure is simpler, yet they possess a longer CDR3 region, making them particularly suitable for modifications and fusions with other proteins or biomolecules, thereby enabling the creation of multifunctional therapeutic and diagnostic agents based on nanobodies. Thanks the extensibility of IgGM, it can also be used to design nanobodies. We evaluated the performance of IgGM in predicting nanobody structures using the SAb-2023H2-Nano dataset, comparing with DiffAb and using structures predicted by AlphaFold 3 as the initialization.
As shown in Table 3, we assessed IgGM in terms of structural prediction and novel design for nanobodies. In the structural prediction of nanobody complexes, the relevant metrics for nanobody structures show an overall improvement compared to traditional antibodies. This enhancement is due to the fact that nanobodies consist of single chain, making them relatively easier analyze than the two-chain structure of conventional antibodies. However, for docking-relatee metrics, the performance of nanobodies is somewhat inferior to that of antibodies. This decline is attributed to the more flexible binding modes of nanobodies, which complicate the accurate prediction of the correct binding positions. IgGM achieved the best performance in terms of success and DockQ metries, demonstrating a success rate of 44%. For nanobody design, IgGM outperformed DiffAb (AF3) across various metrics; however, there was noticeable decline in the sequence recovery rate of the CDR regions compared to Antibody. This decline can be attributed to the longer CDR regions of nanobodies, which complicate the design process. In terms of structure, IgGM surpassed DiffAb (AF3), benefiting from the robust structural prediction capabilities of Alphafoold 3 as shown in Figure 4(B). While DiffAb (AF3) achieved success rate of 0.346, the structures predicted by IgGM were more accurate, resulting in a success rate of 0.415.
CONCLUSION
In this study, we introduce IgGM, generative model for antibody design that leverages consistency models to jointly design CDR sequences and the entire antibody structure. Unlike conventional approaches that target specific regions, such CDR H3, IgGM considers the whole antibody, quiring only the target antigen and an antibody framework sequence. By integrating structural data, IgGM enhances specificity and quality, resulting in higher success rates for predicting binding posi- tions. Our experimental results demonstrate that IgGM outperforms traditional methods in terms of accuracy and efficiency. Furthermore, IgGM holds potential in the design of nanobodies and has the ability to achieve the design of CDRs with specific lengths. With the accumulation of experimental data and advancements in structural prediction methodologies, IgGM's s capabilities are anticipated to be substantially augmented, thereby establishing it as a formidable instrument for practical antibody design and expediting the development and application of antibodies.
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ACKNOWLEDGMENTS
This work was supported in part by Science and Technology Innovation (STT) 2030 2030-Major Projects under Grant 20222D0208700, and National Natural Science Foundation of China under Grant 62376264.
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A
RELATED WORK
Protein design. Protein design aims to create new protein molecules with multiple func- tions (Dahiyat & Mayo, 1997). Traditional methods utilize energy functions, such as molecular dynamics simulations, for protein design; however, these energy function-based approaches are time-consuming and not suitable for practical protein design. In recent years, various machine learn- ing methods have been applied to protein design, focusing on either sequences (Madani et al., 2023; Ferruz et al., 2022; Munsamy et al., 2022; Alamdari et al., 2023) structures (Watson et al., 2023; Ingraham et al., 2023; Lutz et al., 2023). ProGEN (Madani et al., 2023) uses Transformer (Vaswani al., 2017) for protein sequence design. ProtGPT2 (Ferruz et al., 2022) deploys a GPT model (Rad- ford et al., 2018) to generate protein sequences. Evodiff (Alamdari et al., 2023) implements protein sequence design through an autoregressive diffusion model. Lutz et al. (2023) utilizes reinforce- ment learning to design the structure of proteins. RFDiffusion (Watson et al., 2023) uses a diffusion model to generate the structure of proteins and then obtains the corresponding sequences through ProteinMPNN (Dauparas et al., 2022). AlphaProteo (Zambaldi et al., 2024) employs generator to design high-affinity binders, achieving a high success rate. We focus on the design of antibodies, which are important proteins for disease treatment.
Antibody design. Early antibody design methods relied on Monte Carlo iterations to update and generate antibody sequences and structures through handcrafted functions (Pantazes & Maranas, 2010: Lapidoth et al., 2015; Adolf-Bryfoole et al., 2018; Warszawski et al., 2019; Ruffolo et al., 2021). However, this approach is resource-intensive and typically produces antibodies that conform strictly to the energy function, heavily relying on the design of the energy function itself. Sequence models (Alley et al., 2019; Saka et al., 2021; Shin et al., 2021; Akbar et al., 2022) were widely used in the early stages of deep learning for antibody design. RefineGNN (Jin et al., 2022) was the first to propose the co-design of antibody CDRs and structures, utilizing an autoregressive approach to generate the CDR regions of antibodies. DiffAb (Luo et al., 2022) employs diffusion models to de- sign the sequences and structures of CDR regions for specific antigens. MEAN (Kong et al., 2023a) utilizes graph neural networks to iteratively generate the sequences and structures of antibody CDR regions. dyMEAN (Kong et al., 2023b) further expands the capabilities of MEAN, enabling end-to- end antibody design. However, these methods are limited to the design of CDR regions, specifically the CDR H3 region, while the other regions of the antibody must be predefined, rendering them unsuitable for practical antibody design.
Generative models. Generative models are becoming increasingly popular in the field of biomolec- ular generation (You et al., 2018; Liu et al., 2018), particularly due to the high quality of samples produced by diffusion models (Song& & Ermon, 2019; Ho et al., 2020; Leach et al., 2022: Austin et al., 2021). Consequently, growing number of studies have introduced diffusion models into biomolecular generation. Many works have successfully utilized diffusion models to design multi- functional proteins (Wu et al., 2022; Trippe et al., 2022; Gao et al., 2023). To address the limitations of inference steps in diffusion models, Song et al. (2023) proposed the consistency model. This model can map any point back to the initial point at a given time step while maintaining high qual- ity. Several studies (Luo et al., 2023; Sauer et al., 2023; Xiao et al., 2023; Wang et al., 2023) have demonstrated the effectiveness of the consistency model. In this work, we utilize the consistency model to accelerate sampling.
B DIFFUSION MODELS AND CONSISTENCY MODELS
B. DIFFUSION MODELS
Diffusion models (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al., 2020) are a type of generative model that have been successfully applied in various fields, including image gener- ation (Dhariwal& & Nichol, 2021; Nichol & Dhariwal, 2021; Rombach et al., 2022), protein de- sign (Anand& & Achim, 2022; Trippe et al., 2023; Komorowska et al., 2024), among others. Dif- fusion models gradually add noise to the data through a forward process until it becomes random noise, and then learn a reversible backward process to progressively recover the original data from the noise.
Continuous Diffusson To model a distribution p(w), an effective method involves first embedding into continuous variable vo using an embedding matrix Up and then adding Gaussian noise.
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For the Ca coordinates of antibodies, the values in three-dimensiones space continuous, and therefore a continuous diffusion model can be used to generate the Ca coordinates (Watson et al., 2023). In practical applications. Gaussian noise can be gradually added to the coordinate values until they approach a Gaussian distribution. The prior distribution is set as (()) = (0, 7), and the forward process is defined by:
p(a,(10) (2as vārzo, (1- 1))
(11)
where ãg ranges from 0 to 1. The values of ae are determined by predefined noise schedule.
The reverse process aims to learn function pel ( that reconstruct the sequence from the noisy data points z₁. This is achieved by minimizing the following loss function with respect to 8:
L( = Emp. |-
log pe
~ p(z,p({ Ugwo).
(12)
Using (o t), we can define the reverse process distribution follows:
po(21-1/01) Σ p (1 2o Upw) p( x.. L
(13)
where p(z;-1/ is also a Gaussian distribution. During inference, the learned reverse process can be used to transform samples from (2) into samples from the learned distribution (zz)) This done by iteratively sampling from po ( (2x) and then sampling w ~ po 0).
Discrete Diffusion As proposed by Austin et al. (2021), for scalar discrete random variables with K categories, denoted as 8, and 8--1 where 8t, 8t-1 (1....., K). For antibody sequences, there are 20 classes of amino acids. For specific antibody sequence, the amino acid type each position can be regarded as a categorical distribution. For a random variable at certain position, there 20 classes, denoted as 8t....,5--1 E {1,.... 203. The forward transition probabilities can be represented using matrices: [Qrli = q($, = j/s==11 = 1). Representing s in its one-hot form as a row vector s, the forward transition can be expressed as:
(14) where Cat(s; p) denotes categorical distribution the one-hot vector with probabilities from p. The term 1 represents a row vector-matrix product, with Q, applied independently to each pixel or token.
(( = (at = p 1Q-))))
Starting from so, the I-step marginal and posterior at time / are given by:
(5p//5)) = Cat(p:;p== = SoQi), with Φ. = Q1Q22 .t.
1s5, ((sr|s:-1,,0)ee 50) 1ss0) Cat(s,-1: siQi O 00
(15)
(()
Su S
SO(3) Diffusion For each amino acid direction, the orientation within the local coordinate system of each amino acid can be considered continuous value in the SO(3) space, analogous uniform distribution both forward and backward diffusion processes on the three-dimensional rotation group SO(3). The forward process diffusing the direction data oo into pure noise, following the specific formula:
polar coordinates (Leach et al., 2022). Consequently, we can construct
((or(on) = I3SO oo);
: (1 - aa))
(16)
where IGSO(3) denotes the isotropic Gaussian distribution SO(3), and À represents the scalar product along the geodesic from the identity rotation matrix to oo. Conversely, the backward process designed to transform noise back into data, guided by the following probability distribution:
p(or-alos ou)
IGSO (3) (ī(o,, ou), Bt)
(17)
where Ça op) denotes the mean of the backward process, which is calculated as the product of two rotation matrices.
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B.2 CONSISTENCY MODELS
Consistency Models(CMs) (Song et al., 2023; Song & Dhariwal, 2024) leverage the PF-ODE frame- work to create a direct relationship between data and noise distributions. The primary objective of CMs is to develop consistency function f(x,, 1) that effectively transforms noisy image x, back into its clean counterpart Xo. adhering to a boundary condition at t O. This is accomplished through a specific parameterization:
fo (x), = Csxip(t) X; + Cou (t) Fo(x,,1 t),
(18)
where the conditions Gakip (0) and Coar (0) ensure compliance with the boundary requirement. During training. the PF-ODE is discretized into N - segments and a loss function is minimized to quantify the difference between adjacent points along the sampling path:
arg min E (o( (nt1; (xtt, [m))]] 9
(19)
In this equation, d(:;-) represents chosen metric, fo- denotes exponential moving average of previous outputs, and & is calculated based on the noise gradient. The selection of the metric and the sampling strategy is vital for effective model training.
C EXPERIMENT DETAILS
C.I DATASET
We selected all experimentally determined antibody structures published in the database up De- cember 31, 2022, as our training set. The final training set consisted of 6,448 antibody-antigen complexes with both heavy and light chains and 1.907 single-chain antibody-antigen complexes. During the training process, we used CD-Hit (Li & Godzik, 2006) to cluster the training set, with each cluster containing antibodies with sequence similarities above 95%, resulting in a total of 2,436 clusters. To ensure the utilization of available data, we randomly sampled one sample from each cluster for training in each epoch. The validation and test sets included antigen-antibody complexes determined experimentally and published between January 1, 2023, and June 30, 2023, and between June 30, 2023, and December 30, 2023, respectively. More details can be found at Appendix C.3. We removed sequences that were similar those in the training set to eliminate redundancy in the data, ensuring fair evaluation. This process resulted in 101 validation samples and 60 test sam- ples, both of which were completely unrelated the training set. The validation set was used for hyperparameter tuning and model selection, while the test set, named SAb-23H2-Ab, was utilized for subsequent performance evaluation. We also utilized a nanobody dataset released in the second half of 2023 to construct test set for nanobodies, referred to as SAb-23H2-Nano.
C.2 MODEL DETAILS
C.2.1 INTER-CHAIN FEATURE EMBEDDING MODULE
The input to the Inter-chain Feature Embedding Module includes a list of chain information (chn-infos) an asymmetric ID vector (asymidd), the number of embedding dimensions (n_dims,, and the maximum relative index (ridx max). The algorithm begins by initializing a linear layer with an input dimension of 2x ridx. max +3 and an output dimension of n._ dims. Next, an asymmetric matrix is computed based on the asym_id and converted into floating-poit ppont tensor. An index vector then generated to calculate the relative index matrix. After being trimmed and adjusted, the final relative index matrix is obtained and one-hot encoded. Subsequently, the asymmetric tensor and the one-hot encoded tensor are concatenated along the last dimension and processed through the linear layer to produce updated feature tensor. Finally, the algorithm outputs this updated tensor for subsequent feature processing. This process effectively integrates the relative positions the chains and asymmetric information, providing the model with rich contextual information.
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C.2.2 STRUCTURE ENCODER
The structural module consists of two components: one for encoding spatial information and the other for encoding the interaction information between the antigen and antibody based on epitope information. For spatial encoding, the pairwise distances between Ca atoms in each sample are calculated using the three-dimensional coordinates of each alpha carbon atom, resulting in the gen- eration of the corresponding distance mask. The distance values in the distance tensor are then mapped to their respective bin indices, effectively discretizing the distances into equidistant variable indices. By embedding these indices and incorporating the distance mask, the structural encoding is obtained. Epitope information encoding is utilized to generate embedded representations of the interaction and contact map features between antigens and antibodies. Initially, the input epitope information is mapped to a specified feature dimension through a linear layer, followed by further projection to the target dimension. During the transmission of epitope information, the module pre- processes the input epitope data generate the corresponding feature representations. Specifically, an appropriate feature generation method is selected based on the dimensionality of the input fea- tures, ensuring that the type of features is consistent with the weight type of the projection layer. The preprocessed features are then passed through the embedding layer and the projection layer to produce the final feature embedding. Through these steps, the epitope information encoding module effectively processes and transforms the input epitope data, generating features that enable the model to recognize epitope locations, thereby facilitating the effective generation of antibodies targeting specific epitopes, as illustrated in the Figure 8.
C.3 TRAINING DETAILS
The training process of IgGM is divided into two stages, following the concept of curriculum learn- ing. Initially, we train the model for structural design, we use the Adam (Loshchilov & Hutter, 2017) optimizer and set the batch size of the training process to 32. We also maintain an EMA (Exponential Moving Average) decay of 0.999 for the model parameters and evaluate the model, selecting the best complex,
TM-Score the validation subset as the optimal model. Given perturb the antibody structure to introduce noise, and then have the model reconstruct the perturbed antibody structure. This process lasted for 5 days 8 A100 GPUs. Once the first-stage model has converged, we use the parameters from the first-stage training to proceed with the second stage. In the second stage, we perturb the sequence and structure of the antibody's s CDR regions and have the model reconstruct the perturbed antibody. This perturbation is aimed at introducing greater com- plexity and variability into the training data, thereby challenging the model to generalize better to unseen data. It is noteworthy that during the second phase of training, we employed mixed training approach. In the model training process, we assigned probabilities of 4 design CDR H3, CDR H, all CDRs, and to refrain from sequence design, respectively. This process also lasted for 5 days on A100 GPUs. Throughout both stages, we use self-conditioning (Chen et al.) to enhance the stability of the training. This self-conditioning technique involves feeding the model with additional information derived from the original unperturbed structure, which helps the model to better learn the underlying patterns and regularities in the data. Ensuring that the model learns robustly and can generalize well to new and challenging data. The two-stage training process allows the model to first learn basic patterns and then progressively build that knowledge to han- dle more complex scenarios. After training the generative model, we distill the consistency model using the method proposed by Song et al. (2023). For specific details, please refer to Song et al. (2023).
antigen-antiboun
2: for the model to
Table 4: Different sample steps for IgGM.
AARt
Method
DockQt
iRMS.
LRMS
SR
CDR3
Step=1
0.362
0.240
6.474
19.694
0.383
Step=2
0.363
0.244
6.570
19.616
0.383
Step=5
0.363
0.242
6.610
19.932
0.400
Step=10
0.360
0.246
6.579
19.678
0.433
Step=20
0.361
0.232
6,840
20.980
0.400
Step=50
0.348
0.225
6.809
20.905
0.383
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C.4 EVALUATION METRICS
For the sequence portion, we employ metrics that have been widely used in previous work (Luo et al., 2022; Jin et al., 2021; 2022; Kong et al., 2023a;b): AAR, the amino acid recovery rate represents the proportion of similarity between the designed antibody sequence and the actual antibody sequence. A higher value indicates that the model has greater ability to generate specific antibody.
For the evaluation of antibody structure design, we employ several established protein structure assessment metrics, including the RMSD, TM-Score (Zhang & Skolnick, 2004), GDT-TS (Zemla, 2003), DockQ (Basu & Wallner, 2016), and SR.
Root Mean Square Deviation (RMSD): This metric obtained by calculating the mean of the squared differences between the coordinates of aligned CDR H3 backbone atoms and then taking the square root. The CDR H3 region is the most flexible, making it more challenging to predict.
TM-Score (Template Modeling Score): This metric measures the structural similarity be- tween the predicted antibody structure and a reference structure. A TM-Score of indicates an exact match between the two structures, while a score closer indicates a poor match, a high TM-Score suggests that the predicted structure closely resembles the native struc- ture.
GDT-TS (Global Distance Test-Total Score): This metric provides an overall assessment of the model's accuracy by comparing the predicted structure to the native structure based on the global distance test. It takes into account both the accuracy of the model's predictions and the conformational similarity to the native structure. A higher GDT-TS score indicates a better match between the predicted structure and the native structure, suggesting a higher quality design.
This metric is specifically designed for evaluating the quality of protein-protce docking predictions. assesses the interface complementarity and the conformational ac- curacy of the predicted complex structure. A high DockQ value indicates that the predicted interface is likely to be functional and stable, suggesting well-designed multi-chain inter- face.
DockQ:
DockQ Fnat + RMS (1 (LRMS, di) + RMS (iRMS, d2)
(20)
where RMS represents the scaled RMS deviation corresponding to either LRMS or iRMS, de is scaling factor, d1 used for LRMS, and d2 is used for iRMS. Fnat is defined as the fraction of native contacts retained in the predicted complex interface.
SR (Success Rate): Indicates that the quality of the designed multi-chain interface posi- tioning is within acceptable range when DockQ is greater than 0.23. High SR indicates that more structure of antibodics is good.
D THE OBJECTIVE OF IGGM
For amino acid sequences, given that there are only 20 types of amino acids, different amino acids exhibit similar backbone atoms. We treat sequence recovery classification problem. We employ cross-entropy loss to guide the model in learning the correct sequence.
When it comes the structure of antibodies, as previously mentioned in Appendix B. our objective is to recover the spatial coordinates of alpha carbons and the orientations of backbone atoms. To this end, we utilize the residue Frame Mean Squared Error (FMSE) loss, which has been demonstrated to be effective in (Watson et al., 2023). This loss function is specifically designed to measure the discrepancy between the predicted and actual frames of protein residues, which are essential for accurately modeling the three-dimensional structure of antibodies. We have further developed an enhanced loss function termed inter-chain Frame Mean Squared Error (iFMSE) loss. This loss function is specifically tailored to impose constraints on the differences between different chains within the antibody structure. The iFMSE loss is designed to ensure that the model accurately captures the relative orientations and positions of the various chains that make up the antibody, which is crucial for maintaining the integrity of the quaternary structure. Additionally, we employ the inter-residue distance and angle metrics reconstruct the orientations of backbone atoms. By
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incorporating these loss functions, model is trained to not only accurately predict the amino acid sequences but also to reconstruct the intricate -ieee-iinnnioonal structures of antibodies, thereby enhancing the potential of our approach in the fields of structural biology and antibody modeling.
The overall objective the model is to recover the correct sequence and structure from the denoised antibody data. The overall loss function can be represented follows:
C Sieev + geo + Lraame + Lirrame + 0.022
(21)
Amino-acid Sequence Recovery Loss Larev is designed to supervise the model to recover the amino-acid sequence s; the position A total 20 classes for common amino acid types are considered. Sequence embedding {s;} are linearly projected into the output classes and scored with the cross-entropy loss:
20 vf logp?.
1
Sarev
(22)
Σ
Eddsiinn c=1
where of predicted class probabilities, yf are one-hot encoded ground-truth values, and averaging across the masked positions.
Inter-residue Geometric Loss Lgen is designed to provide more direct supervision in the following stack. Four auxiliary heads, implemented as feed-forward layers, are added to the top of the final pair features for predicting inter-residue distances and angles, as described in trRosetta (Yang et al., 2020). These include:
Diy: Distance between Cp and C²
Pay: Dihedral angle formed by C.. C3. C'3, and Ca
Oiy: Dihedral angle formed by N, Co. C3, and C's
Piy: Planar angle formed by Ca. Cg. and C²
Each prediction head outputs probabilistic estimation of the aforementioned distance and angles, denoted logits,, logits logits and logitsf,- The cross-entropy loss is caleu- lated for each term and summed up to form the final inter-residue geometric loss:
Egen En, CE llgitss";: Dis) +CE (logits"); P4y)) +CE (Iogits,):0. +
+CE (logits tj & (Pij)
(23)
Residue Frame MSE Loss is designed provide more direct supervision in the predict module to recover the structure of antibody. The formula can be expressed as follows:
L
drrame (z) (0))a)).
(24)
Σl-
where derame (x (0) .000 represents the distance about both rotation and translation, and it can be expressed in the following form:
drrame (0) (0))=
wi min (1:50- - 2(00 detamp
+ we
r1
ri
(0)
2
F
(25)
where u; and w, weights the translation and rotation distances, and detamp is maxi- mum distance to avoid the numerical overflow.
Interface Residue Frame MSE Loss is designed to provide more direct supervision in the predict module to recover the structure of antibody between interchain. The formula can be expressed as follows:
LiVerane
Σ
2
datrame (I) (00))33
(26)
Σ1= 7d i=1
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Triangle update usina pungoing nooming edges
Triangle attention around starting ending
Fair transition
Pair Impresentatio
node
Pair representaaiio
Outer product mean
Single repesintation
Single representatin
Soromrr (16 blacks)
Figure 5: The SgFormer blocks update the single and pair representations through sequence attention and triangle update module.
where dirrame (0) ,:()) represents the distance about both rotation and translation, and can be expressed in the following form:
Lemerisez
dirrame (x (()))
Wi min (1=10) 2/011² detamp
+ W2
13
(0)T
r1
Linterface
(27)
where Wi and wy are weights on the translation and rotation distances, and detamp is maxi- mum distance to avoid the numerical overflow, Linterface represents the amino acid sequence of the contact region.
Structure violation loss Lvist: Similar to AlphaFold2 (Jumper et al., 2021), we introduce penalty terms for incorrect peptide bond length and angles, as well as steric clashes between non-bonded atoms. For multimer structure prediction, we do not penalize the bond length and angle between the last residue in the heavy chain and the first residue in the light chain, since there is peptide bond between them. Besides, normalize the steric clash loss by the number non- non-bonded atom pairs in clash to stabilize the model optimization, as suggested in MohaFoodd-uutimme (Evans al., 2021).
L Lroond length + Lband angle + Lelash
(28)
E ABLATION STUDY
We conducted ablation experiments on parts of the model and evaluated the test shown in Table 5. The removal of the two-stage training resulted in significant decline in all metrics, particularly with the SR dropping to indicating that the two-stage training crucial for model performance. The absence of epitope information led to marked deterioration in the DockQ, iRMS, and LRMS metrics, highlighting the importance of epitope information on the docking quality and bias of the model. After removing ESM-PPI, there was slight decrease in AAR and DockQ, while iRMS and LRMS increased, suggesting that ESM-PPI has a certain impact on the bias of the interface and ligand. The removal of mixed training also resulted in a slight decline in AAR and DockQ, with increases in iRMS and LRMS, indicating that mixed training has a notable effect on the overall performance of the model. Overall, the various components and training strategies of the IgGM model significantly influence its performance, with the two-stage training and epitope information being particularly critical for the docking quality and success rate of the model.
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Sequence head
TWFGELYFDY... DOYYSSPLT.
Align
representation
Invariant Point Attention
Singla Impresentation
Single repr ixpresentstion
Predict reletive rotations transtations
Xaatt
Predict Module blecks)
Appaae
Figure 6: Antibody Sequence-Structure Prediction Module: The features input into prediction module that includes an IPA module and a Sequence head predict the sequence and structure of the antibody.
Table 5: Ablations performance for IgGM.
AARt
Method
DockQt
iRMS..
LRMSJ
SR
CDR3
IgGM
0.360
0.246
6.579
19.678
0.433
w/o two stage training
0.160
0.072
10.260
30.961
0.000
w/o epotipe
0.326
0.069
14.609
36.967
0.050
w/o ESM-PPI
0.322
0.233
7.444
20,996
0.426
w/o mixed training
0.334
0.231
7.524
22.713
0.350
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Ground Heavy
Light
8k5g HB AAR: 0.769
863
H3 AAR: o 1825
H3 AAR: 0.786
RMBD: 52000
RMSID: 0.487
RMSD: 0.802
DocaQ: 0.841
DockQ: 0.780
DockQ: 0.806
pct: Bly5
pob: Bbgg
pdb: Shrd
AAR: 0.700
AAR: 0,400
HB AAR: 0.500
RMSD: 0.573
RMSD: o 524
RMSD 0.785
DockQ: 821
DockQ: 0.746
DeexQ: 0.671
páb: 0a7s
páb: 8he1
pob: 0q94
Ha AAR 0.263
HB AAR: 0,300
H3 AAR: 0.158
RMSD: 2.043
RMSD: 599
RMSD: 2.187
DockQ: 0.675
DoskQ: 0.657
DockQ: 0.557
Figure 7: Samples of generated antibodies and nanobodies by IgGM.
F EXAMPLES
We present additional examples of the de novo designs generated by our IgGM Figure As depicted in the Figure 8, by employing IgGM, we are capable of generating specific antibodies tailored to various lengths and different epitopes. Moreover, as illustrated in Figure 9, IgGM has the ability rectify the incorrect positions predicted by AlphaFold3, bringing them close the epitope.
G LIMITATIONS AND FUTURE DIRECTIONS
With the advancement of antibody design methods, the feasibility of Al-designed antibodies has become a reality (Khetan et al., 2022). However, existing experimental metrics are insufficient to effectively evaluate whether the antibodies designed by these models can truly bind to the antigen.
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Figure 8: IgGM designs nanobodies targeting different epitopes of (PDB ID: 7MMN), with different colors representing the various designed nanobodies. The original binding entity of this antigen antibodies.
Ground
After WGM
igDM Input
pohi DodeC:
DuckQ:
Figure 9: Examples of deviations in the complex structures predicted by AlphaFold3 can be cor- rected using IgGM, resulting in more accurate structures.
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A key reason for this limitation is the significant gap between in silico evaluations and wet lab experimental results. Bridging this gap is crucial, as it would allow in silico evaluations to signifi- cantly accelerate the antibody development process. This approach holds promise across all fields of Al-driven drug discovery. Currently, in silico evaluations rely on comparisons with existing an- tibodies; however, for entirely new antigens, such references unavailable. Therefore, efficiently and accurately screening for effective antibodies remains a critical research challenge.
Beyond the realm of antibody design, AlphaProteo (Zambaldi et al., 2024) has achieved a high suc- cess rate in binder design by utilizing a generator to produce candidate binders, followed by screen- ing with a discriminator. This method has significantly improved success rates. AlphaProteo em- ploys a generator-discriminn discriminator approach, where IgGM can serve as the generator. However, there currently no tool capable of effectively identifying binding antibodies, and the unique characteris- ties of antibodies make the development of relevant tools even more challenging. Nevertheless, this remains one of the future research directions: designing an efficient discriminator for screening and evaluating the affinity and specificity of antibody candidates. By effectively filtering out inefficient or unsuitable antibodies during the computational simulation phase, this approach could greatly re- duce the number of candidates requiring wet lab validation. This not only enhances the efficiency of antibody design but also accelerates the drug development process and reduces research and de- velopment costs. In the future, we aim develop discriminator utilize existing discrimination matrices to perform screening, applying these methods in practical scenarios for wet lab validation.
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Reviews:
[{"review_id": "oK9koX3MpY", "reviewer": "Reviewer_nUM9", "summary": "The authors present IgGM, a novel generative model designed for creating antibodies and nanobodies.", "questions": "The authors did not explicitly mention the threshold used for redundancy reduction in the summary provided. This information is typically included in the methods section, where they would specify the sequence identity cutoff or other criteria used to filter out redundant sequences.\n\nWhile the authors have shared the test data, any plans for sharing the training data have not been clearly outlined.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The authors curated a high-quality dataset that includes recent data from 2023, ensuring the model is trained on up-to-date antibody and nanobody structures. \n\nBy employing diffusion models—a powerful approach in generative modeling—the authors introduce a novel solution to antibody and nanobody design. This choice is especially suited to capturing complex structural and functional patterns in biological data. The study includes ablation studies and comparisons with multiple alternative models.", "weaknesses": "The manuscript could benefit from a clearer and more detailed explanation of the features and methods. Improved clarity in this section would enhance readers' understanding of the model’s construction and functionality.\n\nWhile the authors have effectively applied diffusion models, the novelty is somewhat limited since diffusion models have already been established in various generative tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i0GhQFRSQT", "reviewer": "Reviewer_VRyT", "summary": "The article discusses the challenges in practical applications where obtaining the structures of antigens and antibodies (including the framework region) is often unfeasible. To address this, it introduces an end-to-end algorithm called IgGM, which simultaneously predicts the sequences and structures of the CDR regions, performs docking based on the epitope, and predicts the structure of the antigen-antibody complex. Additionally, the article employs a two-stage training method to enhance the model's performance in predicting both structures and sequences.", "questions": "1. It was observed in Table 1 that HDock does not have an asterisk (*) symbol. Does this mean that Hdock did not use the information of the epidemic, resulting in lower indicators such as DockQ?\n2. ESM-PPI is a model retrained based on Sabdab data. Will the training data of ESM-PPI appear in the validation set of IgGM? Has this part been considered?\n3. As shown in Table 1, IgGM (AF3) uses the structure predicted by AF3 as the initial state. However, the authors do not clearly specify whether the sequence input to AF3 is the ground truth sequence. If it is, this would indirectly leak the answer, leading to inflated metrics for both the subsequent structures and sequences. A more rigorous approach would be to use 'initial state sequence + AF3 (random initial state sequence)' instead of 'random initial state sequence + AF3 (ground truth sequence)'", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 4, "strengths": "The innovation of the article lies in further extending the scope of co-design. The model can not only design amino acids in the CDR region, but also complete antigen docking, which is unprecedented. IgGM not only has excellent performance in predicting antibody structure, especially in the CDR region, but also outperforms existing algorithms in docking. IgGM not only shows SOTA performance on conventional antibodies, but also performs well on nanobodies, further demonstrating the application value of IgGM.", "weaknesses": "1. The core architecture of the algorithm is very similar to AF2, and the accuracy of structural prediction seems to be attributed to AF2. However, the innovation in this part is not strong enough.\n2. The article did not elaborate on the introduction and explanation of the Inter chain Feature Embedding Module and Structure Encoder.\n3. In line 343, the authors define the success rate as DockQ > 0.23. The authors should either provide a reference to justify the selection of this threshold or elaborate on the rationale behind it.\n4. From the overall text, it appears that the length of the antibody CDR regions is also specified; however, the authors do not clarify this in the problem formulation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0BMRZS2NvI", "reviewer": "Reviewer_wZ8n", "summary": "This paper introduces IgGM, a generative model designed for creating functional antibody and nanobody structures. It integrates sequence and structural generation using a multi-level network approach comprising a pre-trained language model, feature encoder, and prediction module. Experimental results demonstrate its applicability in antibody and nanobody design tasks.", "questions": "1.  It would be valuable to explore why AlphaFold 3 (AF3) shows stronger performance in structure prediction than in docking-related metrics, perhaps due to limitations in capturing finer antigen-binding dynamics.\n2. Why did the authors opt not to use RAbD as the test set, as has been customary in prior studies?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. IgGM successfully establishes an antibody co-design pipeline that integrates several key elements previously deemed essential by the research community.\n2. IgGM shows promising docking success rates over AF3, suggesting that it effectively captures essential antibody-antigen interaction patterns.", "weaknesses": "1. Although the IgGM framework demonstrates its effectiveness on various antibody design tasks, it relies heavily on components and algorithms established in prior work, limiting the originality and impact of its contributions to the field.\n2. The study lacks direct comparisons with RFDiffusion while they perform similar tasks\n3. The authors didn't report the variance/robustness of their performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "n2hxSr7mfz", "reviewer": "Reviewer_R8Qh", "summary": "This paper introduces IgGM, a diffusion model for simultaneously generating the structure and sequence of antibody CDR regions, as well as the structure of antibody FR regions. By employing a two-stage training approach (folding -> design), IgGM is capable of conducting antibody design and docking tasks, and achieves commendable performance.", "questions": "1. In line 278, the probabilities of 4:2:2:2 are assigned to the model to design CDR H3, CDR H, and all CDR. So, do you mean CDR-H3 only for 4/10, all CDR-Hs for 2/10, and all CDRs for 2/10 ?\n\n2. I am confused as to why you ultimately trained a consistency model. I comprehend that the key advantage of a consistency model lies in accelerating the generation process, enabling completion in fewer steps. However, in a scientific problem such as antibody design, is acceleration truly significant? Related to this, you have employed 10 different sampling steps in the appendix, but aside from the success rate (SR), the other four metrics seem to decline with the increase in the number of steps. Even SR shows a decline after surpassing 10 steps. These experimental results differ from what we might anticipate; I would appreciate a detailed study and explanation. Additionally, how does the original model perform without using the consistency model?\n\n3. Some physical metrics (like energy) are needed for a more refined evaluation. \n\n4. The model name (DiffIg) in Figure 4 has not been updated. \n\n5. The experiment in Section 4.2 seems to be the most important one in this paper, but I am somewhat unclear about the experimental setup here.\n  \n    5.1. 'Structure prediction⇒docking⇒CDR generation⇒side-chain packing' is not the process of dyMEAN. On the contrary, this is the 'Existing Works Pipeline' shown in Figure 1 to highlight the end-to-end approach of dyMEAN.\n  \n    5.2. In the stage of structure prediction, how is the sequence of the CDR part defined? Are random sequences used, or are special symbols like [MASK] employed?\n  \n    5.3. The AAR of dyMEAN on CDR-H3 is much lower than reported in the literature. Although the training and test data used in this paper differ from the original dyMEAN article, such a significant disparity needs to be explained (43.65%->29.4%).\n  \n    5.4. In both Section 4.1 and 4.2, there is a version of IgGM that uses AF3-predicted structures for initialization, making the prior distribution inconsistent with N(0, I). How does IgGM predict the structure in this scenario? Was an additional IgGM trained with AF3 predicted structures as prior? If it's the latter, how were the noise addition and denoising processes conducted?\"\n\n6. Finally, for any researcher working on AI-driven antibody design, aligning in silico evaluation with wet-lab experimental results is an essential consideration. Unfortunately, I did not find any discussion related to this in the main paper. If IgGM were to be eventually validated experimentally, do you believe the advantages of IgGM over other methods would still persist? Also, what improvements could be made?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Compared to most existing antibody design works, this study undertakes the design of the antibody CDR region without providing the antibody framework, consistent with dyMEAN. This setting is closer to real-world requirements and poses a greater challenge. \n\n2. The two-stage training approach brings a significant performance improvement. \n\n3. Additionally, targeted designs focusing on inter-chain interactions were conducted.", "weaknesses": "1. It seems that the work does not fundamentally differ from DiffAb in terms of diffusion algorithm, and it bears some resemblance to ESMFold in model design. \n\n2. Having only five samples for each example is somewhat limited. \n\n3. The main paper does not provide an explanation of the diffusion algorithm, nor does it detail the representation of proteins and the prior distribution associated with each modality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tf47Fc9Sze", "reviewer": "Reviewer_ar8p", "summary": "This paper introduces IgGM, a generative model for designing antibody/antigen structures and sequences for a given antigen. IgGM uses features from a pre-trained language model and a diffusion framework to generate antibodies and nanobodies. The model shows great performance in both structure prediction and antibody design.", "questions": "1. How is epitope information encoded in the model, and to what extent can the model follow the guidance of a specific epitope?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The authors made a comprehensive comparision between IgGM and other baseline methods cross multiple metrics, demonstrating impressive results.\n2. This paper is well-structued, with carefully prepared figures that enhance the reader's understanding.", "weaknesses": "Major comments:\n\n1. One major limitation is that the generation of antibodies depends on their framework regions, which predetermine the length of CDRs and may not always be available when handling a new antigen. Since the optimal CDR lengths are not known a priori, the authors should demonstrate the capability of IgGM in designing antibodies with varying CDR lengths.\n\n2. However, in line 147, the authors state that their method \"...enables the design of the whole antibody structure even without experimental structures.\" This claim requires clarification, as the results presented only demonstrate the ability to design CDRs.\n\n3. I suggest that the authors should compare their method with other antibody deisgn methods, such as:\n - Co-design models: AbX [1] (very similar framework with IgGM)\n - Structure-only models: RFdiffusion for antibody design [2]\n - Sequence-only models: AbLang [3] and IgLM [4]\n\nMinor comments:\n\n1. In figure  6, \"Aligin\" should be \"Align\"\n\nReferences\n\n[1] Zhu T, Ren M, Zhang H. Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints[C]//Forty-first International Conference on Machine Learning.\n\n[2] Bennett N R, Watson J L, Ragotte R J, et al. Atomically accurate de novo design of single-domain antibodies[J]. bioRxiv, 2024.\n\n[3] Olsen T H, Moal I H, Deane C M. AbLang: an antibody language model for completing antibody sequences[J]. Bioinformatics Advances, 2022, 2(1): vbac046.\n\n[4] Shuai R W, Ruffolo J A, Gray J J. IgLM: Infilling language modeling for antibody sequence design[J]. Cell Systems, 2023, 14(11): 979-989. e4.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)