Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
Authors: Zishen Zhang
Xiangzhe Kong
Wenbing Huang
Yang Liu
Venue: NeurIPS 2025 poster ()
Keywords: Drug Discovery
Geometric Latent Diffusion
Retrieval Augmented Generation
Antibody Design
Peptide Design
Primary area: machine_learning_for_sciences

Abstract: Designing protein binders targeting specific sites, which requires to generate realistic and functional interaction patterns, is a fundamental challenge in drug discovery. Current structure-based generative models are limited in generating nterfaces with sufficient rationality and interpretability. In this paper, we propose **R**etrieval-**A**ugmented **Di**ffusion for **A**lig**n**ed interfa**ce** (**RADiAnce**), a new framework that leverages known interfaces to guide the design of novel binders. By unifying retrieval and generation in a shared contrastive latent space, our model efficiently identifies relevant interfaces for a given binding site and seamlessly integrates them through a conditional latent diffusion generator, enabling cross-domain interface transfer. Extensive exeriments show that
**RADiAnce** significantly outperforms baseline models across multiple metrics, including binding affinity and recovery of geometries and interactions.
Additional experimental results validate cross-domain generalization, demonstrating that retrieving interfaces from diverse domains, such as peptides, antibodies, and protein fragments, enhances the generation performance of binders for other domains. Our work establishes a new paradigm for protein binder design that successfully bridges retrieval-based knowledge and generative AI, opening new possibilities for drug discovery.

Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
Zishen Zhang
Xiangzhe Kong Wenbing Huang Yang Liu 12+
Dept. of Comp. Sci. & Tech., Tsinghua University
Institute for AIR, Tsinghua University "Gaoling School of Artificial Intelligence, Renmin University of China
Abstract
Designing protein binders targeting specific sites, which requires generate alistic and functional interaction patterns, fundamental challenge in drug discovery. Current structure-based generative models are limited in generating nterfaces with sufficient rationality and interpretability.. In this paper, we propose Metrieval-Augmenntee Diffusion for Aligned interface (RADiAnce a new frame- work that leverages known interfaces to guide the design of novel binders. By unifying retrieval and generation in shared contrastive latent space, model efficiently identifies relevant interfaces for a given binding site and seamlessly integrates them through a conditional latent diffusion generator, enabling cross- domain interface transfer. Extensive exeriments show that RADiAnce significantly outperforms bascline models across multiple metrics, including binding affinity and recovery of geometries and interactions. Additional experimental results val- idate cross-domain generalization, demonstrating that retrieving interfaces from diverse domains, such as peptides, antibodies, and protein fragments, enhances the generation performance of binders for other domains. Our work establishes a new paradigm for protein binder design that successfully bridges retrieval-based knowledge and generative AI, opening new possibilities for drug discovery.
Introduction
Designing binders that target specific sites on proteins is a fundamenttl problem with broad im- plications in drug discovery, structural biology, and beyond [54, 45). The core challenge involves engineering molecular interfaces that exhibit desired interaction patterns with a target surface, such hydrogen bonding, hydrophobic packing, and stacking (45). Traditional approaches rely sampling and optimizing within physical or statistical energy landscapes [3] 8, (22.. while recent advances have shifted towards direct structure-basee based generation of binders with deep generative
models, such as diffusion and flow matching [53 40
Despite these advances, current methods remain limited in rationality and interpretability due to their inability to leverage prior knowledge from existing structurally similar interfaces. Most approaches conduct generation solely conditional on the target binding site [30 40). ignoring the wealth of reusable interaction patterns present in existing protein complexes. contrast, it is routine for human experts (e.g. medicinal chemists structural biologists) to rely similar known interfaces to guide rational design [48] 17]
We thus argue that a better generative paradigm should identify and incor- porate analogous interfaces from existing databases improve both rationality and interpretability. However, incorporating interface retrieval into the generation process poses several challenges. First, the retriever should identify relevant interfaces from large database using only the binding site as input. Existing retrieval methods typically assume a known binder structure (e.g., for inverse
*Correspondence Wenbing Huang <hwenbing@ 126.com>, Yang Liu <liuyang201 @tsinghua.ccu.cn>
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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8gpx:HCDR3 Figure 1: Visualization of interface similarity across antibodics, proteins, and peptides, highlighting similar interaction patterns among diverse binder types.
2gkw:Peptide
6mjz:Proteil
Interface Alignment
folding [51] which is inapplicable our setting. Second, while binders may vary in form (e.g.. cyclic peptides, linear peptides, antibodies) due to different biochemical requirements, the underlying interaction motifs are often generalizable across binders different types and domains, as illustrated in Fig. []] Therefore, the similarity metric used in the retriever should capture shared interactions between cross-domain interfaces and binding sites. Moreover, a mechanism is needed to integrate the retrieved information into the generative process, effectively focusing relevant parts for different generated regions.
To address the challenges, this work introduces Retrieval-Augeente Diffusion for Aligned in- terface (rrfaceRRADinnee), novel framework that unifies retrieval and generation shared contrastive latent space. We begin by constructing comprehensive interface database spanning peptides (30), antibodies [14], and protein fragments 30, where each entry comprising a binding site and a corre- sponding ligand interface. We then train all-atom variational autoencoder (VAE) 31 augmented with contrastive learning to derive an interaction-aligned and retrieval-friendly latent space. The encoder maps each binding site and interface independently to separate latent vectors, which supervised by a contrastive loss to align for positive pairs and repel for negative ones. At inference time, the encoder used map database entries of interface pairs into key-value vectors, enabling fast and accurate retrieval using simple fot-produce similarity. A diffusion model is then trained in the same latent space, conditioned on the retrieved interface embeddings. These embeddings act as compressed yet informative prompts, integrated through combination of cross-attention and residual MLPs [50]. This enables the model to leverage relevant priors (such as residue preferences, contact geometries, and binding orientations) from the retrieved examples, resulting in rational, interpretable binder generations with physically plausible interfaces.
We summarize our contributions as follows:
Contrastive Latent Space for Cross-Domain Interface Retrieval. We propose an all-atom VAE that is trained contrastively to align latent representations between binding sites and their interfaces. This latent space enables accurate and a unified similarity metric across diverse binder types such as antibodies, peptides, and protein fragments.
Retrieval-Augmented Latent Diffusion. To the best of our knowledge, we are the first to apply aeurental-ugmmented generation for the aequence-strucinn We introduce latent diffusion model that operates in the same latent space the retriever, seamlessly incorporating retrieved interface embeddings via cross-attention and residual conditioning guide generation.
codesign of protein binders.
Extensive Benchmarking and Analysis. We benchmark RADiAnce on peptide and antibody design tasks, demonstrating significant improvements over strong baselines in recovering sequence, structure, and interaction patterns. Further analysis confirms that the gains stem from the ability of our model mimicing retrieved interaction motifs. We also show that leveraging interfaces from other binder types (e.g., antibodies to aid peptide generation) improves performance, veryfing the rationality of cross-domant transfer.
Related Work
Peptide Design Peptide design has transitioned from classical energy-basee -based sampling
g 22 deep
generative modeling [35]. Recent approaches, such PepFlow (36) and PPFlow 339 employ multi-

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modal flow matching to jointly model residue types, backbone orientations, Ca positions, and side- chain dihedrals, setting new benchmarks in peptide generation. PepGLAD [30] advances this direction by applying geometric latent diffusion models for Sqquence-structiie co-design. Parallel efforts using language models, including PeptideGPT [47], generate functional peptides from sequence space and filter candidates through structure prediction tools like AlphaFold [27] to ensure 3D viability.
Antibody Design Antibody design has similarly evolved from traditional physical sampling 3 to deep learning frameworks [46] 4 41 Early methods focused on CDR loop generation without antigen context, while later works such as MEAN (28) and DiffAb 40) introduced antigen-conditions generation. Diffusion- models like AbDiffuser [41 and GeoAB (38] further improve structure- aware antibody design by incorporating physical constraints, while HERN [26] captures longerange dependencies via hierarchical graph decoding. Additionally, language- coouuge--odel-basee approaches have gained prominence, PALM-H3 [T9] and MAGE [52] adopt encoder-decoder architectures to generate CDR sequences or full variable regions conditioned on antigen features.
Aerrivall-uugmnteed Generation(RAG) RAG integrates external knowledge into generative models to enhance fidelity, interpretability, and controllability. Initially developed for NLP tasks like question answering [4] 18], RAG frameworks pair a retriever with a generator to access dynamic knowledge during inference. In vision, cetrieval-conditione diffusion models [00] [1] improve image synthesis by incorporating powerful paradigm for targeted molecular design. For instance, f-RAG introduces a dynamic fragment vocabulary to improve amlll-molecule generation via fragment-level retrieval 555 Structure based approaches such as IRDiff leverage high-affinity ligands retrieval references to guide diffusion- based 3D molecular generation [2], while READ incorporates pocket-matched scaffold embeddings into 3e(3)-cquvvariant diffusion to ensure both structural validity and affinity optimization (23). RADAb further explored retrieval for antibody design but was limited to sequence inverse folding with fixed structures [5T].
references. Recently, RAG has emerged as a
Despite these advances, existing methods do not address the challenge of protein binder co-design, which requires simultancous modeling of sequence and structure, do they provide unified retrieval framework across modalities such as peptides and antibodies. Our method, RADiAnce. bridges this gap by learning contrastive latent space that aligns interaction interfaces across diverse binder modalities and by introducing a cross-domain motif retrieval to guide joint sequence-struce design.
latent diffusion model that enables
3 Method
We begin the introduction of our method by establishing the notations and definitions ($3.I). Our framework consists of contrastive atomic variational autoencoder ($3.2) to define compressed latent space for target-based retrieval, and a conditional latent diffusion model defined on the same latent space, which learns from the retrieved interfaces (3.3)
3.1 Notations and Definitions
We represent molecular complex pair of graphs (X,)) ))), where X' denotes the binder molecule and y represents the binding site on the target protein. Each graph, either or V, comprises a node set V = (A,, Xx)} and an edge set E = {BB, B.,) i j}, where A; E n and X; E R are the element types and 3D coordinates of the n, atoms in block i, with B; and B,y denoting intra- and inter-block chemical bonds. We treat each standard amino acid as one block, and use principal subgraph decomposition algorithm to decompose other entities (e.g. non-standard amino acids) into blocks of frequently occurring chemical motifs. The resulting vocabulary V thus extends beyond standard amino acids and also includes principal subgraphs derived from non-standard components and we denote the type of block i as 5; (3I). The binding site y is defined residues within 10Å distance cutoff from the binder, based on C3 atoms. We construct key-value database D = (O), Xx) k = 1, 3.... using the binding site y as the key and the corresponding binder interface X' as the value. Given query binding site Y, our framework first retrieves potential reference interfaces T() ID) = (& 10),, Xx) ) E DD, similar to )) from the database and then generates new binder by modeling the conditional distribution pe (X' y, T( D)).
3

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3.2
Contrastive Latent Space
A
Binder Database
Zr-1
Bind sites
Peekeg
Kays
F
&
.....
Add Norm
poptides
Poddro
Feed Forward
Add Norm
antibodles
protfrage
Binder Latents
Nulthead Attention
Contrastive loss
Bind eite Latent
stm(by= 1)
Retrioved
Pooling
Self-Atention
Bind alle
Step L
Bin dert Latene
Zo
(Z Zyz)
Diftusion
Binder
Z
Z
Z
Complec Latent
D
g
2
Z
Denoising Diflusion
Complex
Figure 2: Overview of RADiAnce. (A) Cross-domain binding sites and binders are encoded into key/value latents and trained with contrastive loss to derive a retrievable binder database. (B) Contrastive VAE aligns binding site and binder latents for accurate retrieval and conditional diffusion. (C) Conditional diffusion generator leverages the retrieved latents through cross-attention every reverse step. progressively refining noisy features into sterically and chemically consistent complexes.
To enable reliable retricval and informative conditioning during generation, we construct a compressed contrastive latent space using atomic variational autoencoder [31], which is trained support both interface retrieval and atom-level reconstruction. The encoder projects the binder X' and binding site Y independently into two latent point clouds, 2, and Zy. while the decoder reconstructs the binder X' conditioned on y and Zy:
Z (x()
Zy Eg()),
Dz Zy,
(I)
where latent variable 2z/v (21, consists of scalar embedding z; W.h, Rd derived from trainable linear transformation of the hidden states h; and 3D coordinate each block i. The decoder first predicts the block (i.e. residue) type from 2; and then reconstructs atomic coordinates X; via flow matching process using Gaussian prior centered at Σ, The reconstruction loss includes cross-entropy (CE) over block types and mean squared error (MSE) over time-dependent vector fields:
R for
Liec (1) (EE(p(s)) BA-U((,1) [MSE( v/)].
where 8; and Vi x denote the reconstructed block type and the vector field at time step Note that 2; and Σ, are sampled from the encoded distributions (H,, or.)) and N(ū, d,)), respectively. To regularize the latent space, we apply Kullback-Leibler (KL) divergence to both scalar and coordinate embeddings (3I) with prior distributions N(z,; 0, I) and N(Z; č, I), where d denotes the center of mass for block i:
Ci) A1 DKL. (N [)] diag(o:))) A2 DKL (f,, diag(G.))), (3)
where A₁ and x2 define the strengths for constraining on the latent variables. To further endow the latent space with the ability to retrieve relevant interfaces given the binding site y. we implement contrastive training between the encoded embeddings of the binding site and the binder interfaces. Specifically, the graph- embeddings of the binding site and the binder are derived by averaging the hidden states as k = TDT Σiey hi, and v -|ズ Σiex h,, respectively. Subsequently, the

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the encoder obtain its key embedding training objective maximize the similarity for authentic pairs {(kg. v/y) |p = o) and minimize it for mismatched pairs {(hp: Un p * q} in souble-sided manner [6]
Cretrievat (p) log exp( Up)/T)
exp kTp)
log
Ex=1 exp v/)/T)
Σو= exp((up, kg)
where 7, N, and (t, ) are the temperature, the batch size, and the standard Euclidean inner product.
The temperature т > 0 controls the sharpness of the similarity distribution. The total training objective over the dataset D combines reconstruction, KL regularization, and contrastive retrieval:
L(D) = Σ PED KE
Crec())
+ Σ (i) + frettieval (p))-
(5)
xEX
After training, the database D containing complexes of interest is processed by the contrastive encoder to derive key-value pairs D = {(kk, vx) k = 3...3.... During training and inference of the diffusion model, given arbitrary binding site V/ k, which then used to query TD via inner product similarity. Additionally, to prevent trivial memorization and ensure generalization, we exclude the ground-truth ligand of the query complex from the retrieval database during this querying step. The top-n nearest neighhors are retrieved based on this process and serve as template candidates for subsequent diffusion- -based generation on X' which are denoted as T = {Uk h = 1, 2,.,,... nz..
3.3 Retrieval-Condition Latent Diffusion
Latent Diffusion Given that the variational autoencoder (VAE) encodes the binding site and binder into continuous latent representations, we directly define a diffusion process over these latent variables to model the conditional distribution 2(Zz) 2y, T"), where T contains the value vectors of the retrieved template interfaces. The forward process gradually corrupts the initial latent variables with Gaussian noise across a predefined number of steps T, transforming them into the standard Gaussian prior N(0, I). Conversely, the reverse process denoises these samples step by step to recover the original latent vectors. Let य
denote the state of block timestep the [2], =
forward process can be defined as follows 31
q प N(T;; VI- 11-3.. u. 1
(6)
q x = N(ū); Vāl (1-(1')))
(7)
where a² = 11: 8*), and 8 is the noise scale following the cosine schedule 43 Therefore,
state time & can be sampled given पî पř v ū + VI-vv'a. where E; ~N, I). The reverse process learns to denoise the latent samples 21 with retrieved templates as contexts:
po [
1 Zy, T
= N u
pho (2 Zy, 3,),
(8)
5
pāl 1 Zy, T = (ū)
V1 c0 1 Z,, " tt)li))))/
(9)
where a' = 1 - B The parameterization of the denoising kernel En will be illustrated in the following paragraph. The training process miminizes the discrepancy between the predicted and true noise added during the forward process:
LIDM E.-U(I....T) E, |c, -- Zy, T 1111//111
(10)
where - denotes the size of the latent point cloud.
The denoising kernel co (2), Zy. T t) is instantiated by an E(3)-equivariant transformer (EPT) (25] augmented with cross-attention mechanism to incorporate information from the retrieved templates TV. This cross-attention module is placed between the self-attention and feed-forward layers in the original EPT architecture (Figure 2 Let H E mm denote the invariant hidden states of the m latent nodes (blocks) with hidden dimension h, and let T E m represent the embeddings of the retrieved top-n templates. The queries, keys, and values computed
Template Integration
Q = H
K = TW
V TWV.
(11)
5

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where l w wV E R are learnable matrices for linear transformations. The hidden states are subsequently updated using residual connection with the cross coos-attention output:
QK
H' ( + Softmax
VV)
(12)
Vh
where LN denotes the layernorm 6 This design enables the model to effectively integrate contex- tual information from the retrieved templates T,, guiding the refinement of latent variables toward mimicking key interaction patterns. We have also explored alternative conditioning mechanisms for incorporating template information, but these variants exhibit downgraded performance compared to the current cross-attention strategy, as demonstrated in Appendix D
3.4 Inference Algorithm
During inference, the framework encodes the target binding-site graph y into key ky, retrieves the kop-K latent binders from the database via inner-product similarity to form the prompt set TV, and performs prompt-conditioned reverse diffusion from Gaussian noise to a clean latent 2o, which then decoded into the binder X. Owing to the modularity of retrieval and generation, the latent database ID can be freely replaced at inference. The overall workflow is as follows:
Algorithm Inference Workflow
1: Imput: Binding site structure y
2: ki + -D)
(Encode binding site into query key)
3: for each v05) ED do
4: 8j (ky, v(1)
[Compute similarity by inner product)
5: end for
6: I (opk((s;)] (s;})
[Indices of top-K K highest similarity scores)
7: T + (b(()) k I}
Retrrieve binder latents to form prompt features] (Initialize latent variable from Gaussian]
8: ZT ~ NNO,, I)
9: for = T down to 1 do
10: E - <teg(Z? Zy: T" .))
(Predict noise] )
11: Z,-1 Denoise(Zo Ze, E)
Refine latent variable)
12: end for
13: X (2a)
(Decode final latent molecular graph)
14: Output: Generated binder structure X
Experiments
We evaluate RADiAnce from two key aspects: Retrieval Reliability (Section 4.11 and Generation Performance (Section 4.2), using peptide, antibody, and protein fragments from existing literature. For peptide design, we adopt PepBench (3), which includes 4, 4,157 training and 1 114 validation com- plexes, with 93 test cases from the LNR benchmark [49]. For antibodies, we follow the literature [31 to use 9,473 training and 400 validation entries from SAbDab [4,, and 60 test cases from the RAbD benchmark 3 We further include ProtFrag 330. a dataset of 70,498 monomer-derivel protein fragments, to assess the cross-domain referencing ability of our framework. To prevent data leakage, all retrieval operations are strictly limited to training data. Lastly, we demonstrate the potential of RADiAnce real-world scenarios with a pipeline for de novo antibody design without relying on predefined frameworks (Section 4.3
4.1 Retrieval Reliability
Setup We first assess whether the latent embeddings produced by the variational encoder can reliably retrieve relevant interfaces, accurate retrieval is essential for successful downstream generation. Although the model trained on cross-domain data, retrieval evaluation is conducted using only the in-domain database (i.e., antibody or peptide) to create more stringent testing scenario. Ground-truth binders are also included in the database for this setting, to evaluate whether the models can accurately retrieve the ground truths. We further perform ablation studies to assess

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the effect of cross-domain training and the importance of jointly optimizing the VAE and contrastive objectives. As baseline, we include random retrieval illustrate the degree of structural and biological relevance achieved by our Contrastive VAE (CVAE).
Metries We evaluate retrieval reliability from two complementary perspectives: recall and precision. To assess recall, compute the proportion of cases where the ground-trute binder ranks within the top N% of retrieved candidates (RC), denoted as RC-N%. For precision, we assess the similarity between the top-10 10 retrieved interfaces and the ground-truth binder in terms of shared interaction patterns (e.g., hydrogen bonds, hydrophobic contacts,etc) [2]. This is quantified by Interaction Type Overlap (TTO), which measures agreement between two distributions consisting of interaction types, with details provided Appendix B
Table 1: Results of recovery metrics for retrieval models. Columns "Pep", "Ab", and "ProtFrags" indicate whether peptide, antibody, and protein fragment data are used during training (V for included, for excluded). "All loss" X indicates using contrastive loss only.
Model
Pep
Ab ProtFrag
loss ITO(%)
RC-0.1%
RC-0.5%
RC-5 %
Antibody
Random
x
X
17.71
0.1
0.5
5.0
X
x
28.21
25.00
68.33
100.0
/
x
34.98
60.00
85.00
100.0
CVAE
V
/
X
38.85
5.00
30.00
100.0
/
/
43.93
66.67
96.67
100.0
Peptide
Random
x
36.55
0.1
0.5
5.0
53.39
3,23
5.38
25.81
X
53.64 63.64
4,30
7.53
33.33
CVAE
2.15
8.60
31.18
61.41
11.58
22.58
67.74
Results Table I supports the following three key findings: 1) Our contrastive variational autoen- coder (CVAE) reliably retrieves relevant interfaces, with RC-5% achieving 100% for antibodies and 67.74% for peptides: 2) Exposure to diverse interface types during training significantly enhances generalization, as incorporating cross-domain training, particularly with intra-protein interfaces from the ProtFrag dataset, improves performance to large extent; 3) Joint training with both contrastive and VAE losses is essential, as removing the VAE loss (i.e., using an encoder-only model) signifi- cantly degrades performance, likely because the reconstruction objective reinforces memorization of meaningful interaction patterns between binding sites and binders. These results demonstrate that our CVAE enables effective retrieval with high recall and precision. forming a solid foundation for retrieval-augmmented generation.
Generation Performance
Metrics To evaluate our model's effectiveness in designing peptide-protein and antibody-antigen complexes, we adopt several established structural and biochemical metrics. Amino Acid Recovery (AAR) measures sequence fidelity as the proportion of residues in the generated peptide that match the reference, based on canonical sequence alignment [42] 20). Complex RMSD quantifies structural accuracy via the root mean square deviation of C. coordinates, after aligning binders the target protein. Interaction Site Match (ISM) assesses how well key physicochemical interactions (e.g., hydrogen bonds, hydrophobic contacts) reproduced, reflecting interface realism (2). computation details are Appendix A The change in binding free energy (AAG) captures affinity shifts, where negative values indicate stronger binding. IMP reports the fraction of targets for which generated binders outperform the native ligand in predicted affinity. For each test complex, we sample 100 candidates per model and evaluate all metrics to ensure statistical robustness.
4.2.1 Peptide Sequence-Structure Codesign
Baselines
We compare our method against several representative generative frameworks. RFD- generates peptide backbones, followed by one round of inverse folding using Pro- teinMPNN [13 and Rosetta-based atomistic relaxation [5]. To avoid overfitting physical metrics,
iffusion 53
7

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we restrict this process to single iteration. We also include peptide-speciff models such as PepFlow [36] and PepGLAD [30,, which leverage multi-modal flow matching and latent diffusion to jointly model sequence and structure. In addition, we evaluate UniMoMo [31], unified framework for small molecules, peptides, and antibodies, using best peptide-generation configuration without incorporating the small molecule dataset.
Results As shown in Table our method consistently outperforms all baselines across multiple evaluation metrics. Compared to existing frameworks, our model achieves higher recovery of the ground-truth sequences and structures, effectively leveraging knowledge from existing interfaces. Notably, our RADiAnce surpasses baselines by large margin in terms of ISM, indicating excels at copying relevant interaction patterns from retrieved interfaces. Further ablation shows improved generation when retrieving cross-domain data over single-domain H
Table 2: Results for peptide sequence-structure codesign.
Model
AAR (%)
RMSD (A)
AAG (kI/mol)
IMP (%)
ISM (%)
RFDiffusion
34.68
4.69
24.78
5.38
28.38
PepFlow
35.47
2.87
15.71
14.13
27.83
PepGLAD
38.62
2.74
15.26
16.13
32.63
UniMoMo
38.69
2.31
2.409
40.86
49.13
RADiAnce
39.42
2.29
1.963
41.94
52.15
4.2.2 Antibody Sequence-Structurs Codesign
Setup We define Complementarity templeementrriy-Deeeemnnng Regions (CDRs) according to the Chothia num- bering system [12), as adopted in previous literature Luo et al. [40], Kong et al. [31]. For each antigen-antiboddy complex in the test set, randomly mask one CDR loop and treat the remaining framework and unmasked CDRs as conditioning context. The model is then tasked with generating the missing CDR regions.
Baselines We benchmark our approach against representative state-of-the-art generative mod- els in molecular and antibody design: MEAN (2] uses a multi-channel SEuia-equivariant GNN to iteratively generate CDR sequences and structures. DyMEAN [29] extends MEAN to full-atom generation and handles uncertain antibody docking poses. DiffAb 40 ap- plies diffusion modeling to jointly generate amino acid types, C. positions, and orientations.
GeoAB (37] combines heterogeneous residue encoders with geometric energy priors for realistic structure generation. Uni- MoMo (31) unifies the generation small molecules, peptides, and antibodics within shared representation framework.
Ber
Har
Ber
6scgmmmn
Results As shown in Table 3 our method consistently out- performs all baselines on CDR sgquence-structuur co-design. It achieves notable improvements in sequence recovery, struc- tural accuracy, and binding affinity across all CDR regions, demonstrating the benefits of learning from retrieved interfaces. Moreover, consistent with earlier peptide results, cross-domain retrieval further boosts performance over single-domain re- trieval (Appendix Hj. highlighting the ability our model to generalize shared interaction patterns across different domains. We also provide a qualitative example in Appendix G illustrat- ing how the model leverages retrieved interfaces from other domains to correct and enhance generated interactions.
Figure 3: Free energy changes dur- ing iterative CDR design.
4.3 Antibody Design Without Bound Structural scaffold
To better align with real-world applications, we propose a pipeline for designing all six CDRs of an antibody without access to its bound structure. Beginning with predefined framework, we iteratively
8

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Table Results for antibody CDRs sequence structure codesign.
Model
CDR
AAR
RMSD
AAG
IMP
ISM
CDR
AAR
RMSD
AAG
MEAN
76.78
0.9660
3.194
20.00
66.11
68.84
1.075
4.828
28.30
53
dyMEAN
84.25
0.8061
18.42
1.695
56.10
81.28
17.88
1.695
52.23
GeoAB-R
80.00
0.5472
254.34
25.00
79.00
0.4670
2.757
55.13
DifTAb
III
83.25
0.6278
-3.625
87.93
75.02
LI
83,25
0.6278
-3.424
93.10
75.02
GeoAB-D
85,48
0.4290
-6.925
83.33
74,76
80.65
0.3352
-6.692
78.33
56.32
Unimomo
86.72
0.3728
-6.247
88.33
98.01
80,28
0.3177
2.374
93.33
96.37
RADiAnce
90.83
0.2977
-8.211
96.67
98.36
86,20
0.2907
-7.170
95.00
98.31
MEAN
54.16
0,9928
49.69
6.67
26.98
64.24
0.7657
13.08
10.00
72.37
dyMEAN
73.62
0.9453
18.45
3.390
48.35
84.35
0.7514
17.58
0.00
59.41
GeoAB-R
54.21
0.7125
30.23
16.67
75.67
5,691
72.58
DiffAb
67.51
0,4378
-1895
77.58
29.11
L2
72.87
0.3910
-8.397
86.21
72.12
GeoAB-D
75.04
0.4828
3.982
56.67
28.95
83.06
0,3462
-5.067
76.67
73.16
Unimomo
75.74
0.2207
-2.710
88.33
86,31
84.36
0,1592
-4,779
95.00
96.55
RADiAnce
79.20
0.2135
-2.370
83.33
87.83
86.68
0.1558
-3.857
95.00
98.52
MEAN
26.19
1.076
104.88
8.30
12.81
53.72
1.100
9.835
18.33
49.52
dyMEAN
31.65
1.123
24.23
1.695
41.67
72.86
0.8722
18.39
3,390
57.16
GeoAB-R
32.04
1.682
42.71
0.33
11.67
64.16
0.7213
5.161
35.00
51.27
DiffAb
51.84
1.584
-1.580
56.90
27.40
L3
64.16
0.8005
-6.685
86.21
57.70
GeoAB-D
44.82
1.565
34.73
41.67
22.50
64.16
0.8005
-3,445
80.00
54.76
Unimomo
52.34
1.040
-7.438
65.00
71.52
72.36
0.5003
-8.797
95.00
90.94
RADiAnce
54.66
0.9443
-6.236
71.67
71.64
76.75
0.4478
-9.207
90.00
94.82
alternate between CDR redesign using our RADiAnce and structure prediction with AlphaFold3 [1. progressively refining both the sequence and structure toward an optimal binder.
To design a novel antibody targeting the HIV-1 primary receptor CD4 (PDBID: 302D) 15 we
Ramucirumab (DrugBank ID: DB05578) as the structural scaffold, which originally targets VEGFR2 (24) but with good drug developability. We identify the CD4 epitope via AlphaFold3- guided docking M, and iteratively redesign the complementarity-ddeemmiinn knocking out and regenerating each loop using our model. After each iteration, the antibody is relaxed with Rosetta (5) and re-docked against CD4. This redesign-dock cycle is repeated three times to progressively refine binding. We use PyRosetta to estimate binding free energy (AG), considering
regions (CDRs) by
HIV-1 primarr receptor
8G 452-46.33
33.78
6G 42.11
Sample1
S Sample2
Sample3
Figure 4: Examples of de novo antibody designs targeting the HIV-1 receptor CD4. Each case shows the final docked complex with the redesigned antibody interacting with the target epitope.
designs with AG < 0 as indicative of favorable antigen engagement, even without fixed antibody framework. We visualize the final -ntibdy-anttign complex and highlight hydrogen bonds at the interface (Figure Figure illustrates the AG trajectory over design iterations, showing consistent affinity improvements, with the final design reaching AG < 0. These results highlight the potential

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Table 4: Comparison of retrieval strategies for HCDR3 and peptide codesign tasks.
HCDR3
Peptide
Straregy
Retrieved
Similarity
AAR
RMSD
Diversity
Similarity
AAR
RMSD
Diversity
Top-N
N/A
51.55
0.9525
0.0524
N/A
37.94
2.38
0.5042
Top-N
57.6245.3
53.26
0.9431
0.0404
55.56a4.5
39.03
2.38
0.4846
Top-N
54.65+4.9
54.66
0.9443
0.0484
51.67=3.6
39.12
0.5129
Top-N
50.4324.8
54.63
0.9682
0.0485
50,24=3.3
39.42
2.29
0.5227
Top-N
40
46.04a4.7
54.06
0.9653
0.0494
47.68+3.1
38.16
2.28
0.5273
Reverse-N
10
49,86+16.7
52.31
0.9718
0.0468
-26.09=12.5
36.33
3.09
0.5267
Random
10
17.3426.9
51.72
0.9665
0.0649
11.78#8.16
38.04
2.56
0,5580
Table 5: Performance with adaptive retrieval for HCDR3 and Peptide codesign tasks.
Settings
AAR (%)
RMSD (A)
Diversity
AAG (kJ/mol)
FoldX AG' (kJ/mol)
ISM (%)
DockQ
HCDR3
Fixed Top-N
54.63
0,9682 0,9147
0.0485
-6,236
-8,4250 -8.6180
71.64
0,9582
Adaptive Retrieval
54.71
0.0523
-8,938
71.61
0.9604
Peptide
Fixed Top-N
39.42 42
0.5227 0.5400
1.963 1.427
-7.99 -7.97
52.15 52.89
0.7698
Adaptive Retrieval
39.22
2.27
0.7762
of our framework to generate de novo antibodies capable of strong and specific epitope binding without requiring pre-solved complex.
Analysis
5.1
Effect of Retrieval Quantity and Quality
To better understand how retrieval influences our model performance, we evaluate RADiAnce under different retrieval modes and varying numbers of retrieved samples on codesign tasks.
Results Table summmaizes the relationship between retrieval strategy and model performance. Increasing the number of retrieved samples generally improves performance, but excessively large pools introduce low-similarity samples that add noise and degrade quality. Random and reverse retrieval perform poorly for the same reason, while the no-retrieval baseline is markedly worse than any eetrieval-based setting, highlighting the importance of retrieval. We also find that larger pools and the no no-retrieval case yield higher diversity, whereas retrieving only one sample produces the lowest. This aligns with our expectation, as limited retrieval can overly constrain the generative space.
5.2 Adaptive Retrieval Approach
Building on the results above, we replace fixed top-N retrieval with an adaptive similarity cutoff: only neighbors above task-specific threshold are retained, allowing the model automatically use more samples when many are relevant and fewer when they are not. As shown in Table this adaptive strategy consistently improves multiple metrics by preserving the benefits of larger retrieval pools while avoiding noise from less relevant neighbors.
6 Conclusions and Limitations
We propose RADiAnce, retrieval-augmeentd framework that unifies retrieval and generation in contrastive latent space to improve rational and interpretable binder design. A contrastive VAE enables efficient cross-domain motif retrieval, while cross-attention- nattention-guides latent diffusion enhances sequence structure codesign. Despite its effectiveness, performance depends on retrieval quality, motivating future work on improving structural descriptors and exploring more robust and structure- aware conditional integration strategies. Beyond methodological advances, our framework enables transparent and interpretable binder design process, potentially accelerating biologic development through more controllable generation of therapeutically meaningful molecules.
10

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Acknowlegements
This work jointly supported by the National Key R&D Program of China (No. N0l..20222D011003500 the National Natural Science Foundation of China (No. 61925601, No. 62376276, No. 62276152). and Beijing Nova Program (20230484278).
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A Implementation of Interaction Site Match (ISM)
The Interaction Site Match (ISM) metric evaluates the fine- gre-ggaannd accuracy of predicted molecular interactions by assessing their strict consistency with the ground truth at the atomic level [2]. An interaction is considered strict match only when the predicted interaction has the same interaction type (such hydrogen bond salt bridge), originates from the same residue the target and terminates at the same residue as in the reference. To compute ISM, we iterate over all annotated residue-level interactions in the reference structure and search for corresponding interactions in the predicted structure. If predicted interaction matches a reference one both ligand atom and interaction type, it is counted match. The final ISM score is calculated as the ratio between the number of strictly matched interactions and the total number of interactions in the reference:
In cases where the reference structure contains annotated interactions (i.e., the denominator is zero), the ISM score is defined as NaN and excluded from downstream averaging. This prevents distortion of the overall metric by non-informative cases and ensures that the evaluation focuses structurally meaningful interaction sites. ISM thus captures the model's s ability to recover detailed and chemically precise binding patterns the atomic level.
B Implementation of Interaction Type Overlap (ITO)
The Interaction Type Overlap (TTO) metric measures the global agreement in interaction type distributions between the predicted and reference complexes, regardless of specific atom-level correspondences [2]. focuses on comparing the overall frequency of each interaction type, rather than their precise locations.
To compute ITO, count the number of occurrences of each interaction type in both the reference and predicted structures. For each interaction type, the overlap is defined as the minimum of the two counts. The ITO score is then calculated the total overlap across all interaction types, normalized by the total number of interactions in the reference:
Eyye min mnin(couman (counted, countpred
ITO
Exyp counter
Similarly, if the reference structure contains no interactions, the ITO score is set to NaN and omitted from any aggregate analysis. This design ensures that only informative samples contribute to dataset- level evaluations. ITO captures the model's 's ability to reproduce the overall molecular interaction profile and is especially useful for assessing chemically meaningful binding preferences at global scale.
C Definition of Diversity Metrics
Diversity quantifies the diversity of generated peptides as the ratio of unique clusters to total generations. Sequence and structure clustering thresholds are set at sequence identity above 40% and RMSD below Å, respectively. For instance, a sequence diversity of 0.0593 for HCDR3 means that 100 generated sequences form approximately 6 distinct clusters. The diversity metrics for our model are presented Table
Table 6: Diversity of generated samples
Settings
Sequence Diversity
MEAN (HCDR3)
0.0100
RADiAnce (HCDR3)
0.0593
Pepflow
0.0745
RADiAnce (Peptide)
0.558
The results demonstrate that diffusion based methods achieve a higher degree of sequence diversity compared to non diffusion based methods.
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D Ablations on Condition Integration Strategy
D.1 AdaLN-Zero Conditioning Mechanism
We implement the conditional noise prediction network eg e Z Zy, T by extending the Equiv- ariant Full-Atom Transformer architecture [25] with AdaLN-Zerr conditioning (44. Each layer
simultaneously updates the E(3)-invariant scalar latent features 2, and vector latent features oy through attention and feed-forward stages, all equipped with adaptive layer normalization and resid-
ual scaling.
Let 33xmxx denote the vector features corresponding to 2,, and let T Raxh be the conditioning input. The update rules at layer are:
z (1-0.5) (1) (11-1)
U1 2, + Qatt SelfAun(AdaLN (2(1-11 -)) vi o
(13)
2 2, (150 Offin FFN((AdaNN(2 (-0.5) T), 06 11)
(14)
where AdaLN((. T) applies zero-initialized, conditioning-aware layer normalization to the input, and Outnn; Offin E Rixx are scaling factors computed as:
MΣrlh r()
α = facale
(15)
Here, fscale : R R learnable linear transformation initialized zero, responsible for producing the residual scaling weights.
This conditioning scheme allows each layer modulate feature updates based on template T, while ensuring stable training via identity initialization and structured residuals.
D.2 Contextual Prompt Integration
To further enhance conditional control, we implement a context-aware integration layer within each transformer block. This design leverages prompt features T (e.g., interface representations retrieved from database) to modulate the latent trajectory. The integration process involves attending the prompt features to select the most relevant context and then fusing it with the latent representation. The update of scalar and vector features follows three stages:
Let 2(1-1) m and T E gg denote the input features and prompt templates, respectively. At transformer layer 1, the update rules are:
QK
Tee Softmax
T.
Q
2 Wq.
K = TWK,
(16)
Vh
2 (155.)
Fuse
(2(1_1) Tree
(17)
(1(1) v/00]
FFN
SelfAttn
Z (1150.5)
vi (1-11)
(18)
where Wo, Wx E Rhxc are learnable projections. The attention mechanism computes similarity scores between latent tokens and prompt features to produce a soft selection T enabling each latent position selectively attend to the most informative prompts. The fusion function Fuse(:;) denotes a multilayer perceptron operating on the concatenation the original dimension h.
[Z (1 projecting it back to
This mechanism enables fine-grained control over the generation process by allowing each latent token to dynamically attend to prompt embeddings and modulate its representation accordingly.
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Table Results for antibody CDR-H3 sequence sequence-strueture structure codesign.
Fusion method
AAR (%)
RMSD (Å)
AAG(K//ool)
IMP (%)
ISM (%)
N-eero
50.42
0.964
-7.047
68.33
73.69
In-context
51.03
1.019
-8.102
66.67
72.57
Cross Attention
54.66
0.9443
-6.236
71.67
71.64
D.3 Results
As shown in Table 7 the Cross Attention fusion mechanism demonstrates the most effective perfor- mance among all compared methods. It consistently achieves the best overall results across sequence accuracy, structural aligmment, and interaction quality. This suggests that cross cross-attention enables more precise integration of contextual and structural information, leading improved sequence-struccurr codesign for antibody CDRs. Its superior performance highlights the importance of flexible and fine-grained feature fusion in guiding generative antibody modeling.
E Implementation Details
E.1
Baselines
Peptide For RFdiffusion, since the training pipeline for custom datasets is not publicly available, we directly the official pretrained weights and inference scripts for binder design. For PepGLAD and PepFlow, we adopt the official implementations and retrain them on our peptide dataset, using the default hyperparameters provided in their repositories. For UniMoMo, same cross-domain dataset as our method, using the hyperparameter settings provided in the original publication [31]. All models are evaluated on the same dataset to ensure fair comparison.
retrain the model on the
Antibody For MEAN. DyMEAN, DiffAb, GeoAB-R, and GeoAB-D, we use their official im- plementations and retrain the models on the same antibody dataset our model, using the default hyperparameters specified in their repositories. cross-domain dataset as our method, using the hyperparameter settings provided in the original publication (31. To ensure consistency, we convert all antibody sequences from IMGT [33] to Chothia numbering [12), following the protocol of DiffAb 44)) The Chothia system provides stricter and more structure-consistee
UniMoMo, we retrain the model on the same
definitions of complementtarty-ddeermmnnn conversion may lead to drop in amino acid recovery (AAR) compared to IMGT, the Chothia system avoids overestimating recovery due to trivial unigram patterns [3I] For GeoAB, since the official implementation only supports HCDR3 prediction, following the same processing strategy. Training samples that fail preprocessing are excluded. All models evaluated on the same dataset ensure fair comparison.
regions (CDRs). This
extend it to cover additional CDRs
E.2 RADiAnce
We trained our model using GPUs with GB memory in parallel. The hyperparameter configura- tions for both Contrastive VAE and Diffusion models are summarized in Table
Comparison Between Generated Samples and Retrieved Exemplars
To quantitatively assess how well the generated interfaces recapitulate the interaction patterns their retrieval-based conditioning, report key metrics: Interaction Type Overlap with the Reference (TTO-Gen) and Interaction Type Overlap with the Retrieved Exemplar (ITO-RAG), evaluated for both antibody HCDR3 and peptide cases (Figure
Specifically, ITO-Gen measures the degree of overlap interaction patterns between the generated samples and their corresponding reference complexes, reflecting the fidelity of binding mode re- construction. In contrast, ITO-RAG quantifies the similarity between the generated samples and the retrieved exemplars, indicating the effectiveness of retrieval in guiding the generative process.

---

Table 8: Hyperparameters of RADiAnce
Name
Configuration Description
Contrastive VAE
Encoder Decoder Type
EPT
Backbone architecture for encoder/decoder Dimension of latent state
eatent_size
8
hidden size
512
Feature dimensionality for node and edge embeddings Size of edge type embeddings
edge size
64
n layers
6
Transformer depth encoder/decoder
heads
8
Number of heads for multihead self attention per layer
k neighbors
Graph connectivity for spatial features
cutoff
10.0A
Distance threshold for RBF kernels
Kl._loss_weight
0.6
Weight for KT. divergence of structure sequence latents
atom_com hatp__oord_loss__
Lerrd_losswweigeee weights
1.0
Weight atom coordinate
1.0
Weight for categorical loss on block (residue) types
contrastive_loss
1.0
Weight for contrastive similarity loss
sisal_dittance_lon lacpancceoosssweiigheeee
0.5
Weight for intra-structure distance loss
bond loss weights
0.5
Weight for bond type classification loss
Conditional Latent Diffusion
hidden_size
512
Dimension of hidden states
T
100
Diffusion steps
n_aayers n _hads
6
Number of denoising layers
8
Number of heads for multihead self and cross attention
n_rbf
64
Number of RBF kernels
cutoff
3.0
Cutoff distance for RBF kernels
The overlap with reference complexes reflects the model's ability to reconstruct native binding modes, while the overlap with retrieved exem- plars assesses how effectively information from retrieval guides generation. Our results indicate that the generated molecular interfaces capture substantial proportion of the chemically mean- ingful interactions present in the reference struc- aures-achieving, for example, 60.7% overlap for HCDR3 and 65.0% for peptide cases. At the same time, the retrieved exemplars themselves provide strong and relevant interaction patterns that align closely with the reference, offering reliable and informative guidance for the gener- ative process.
Model
50
Percentage
30
10
HCDR3
Peptide
Figure 5: Interaction overlap analysis.
These results collectively demonstrate that our eetrieval-augmente framework, by leveraging structurally relevant examples, enables the effective transfer and reconstruction of key interaction motifs, thus enhancing the chemical rationality and interpretability of the designed interfaces.
G
Case Study: Qualitative Improvement Through Retrieval Conditioning
GPIIb/IIa (PDB ID: 3NID) is key platelet integrin that mediates adhesion and aggregation during thrombus formation (56). In the 3NID structure, it adopts a closed headpiece conformation when bound to specific antagonist that stabilizes the inactive state and prevents activation. As illustrated Figure 6 we investigate the design of the HCDR3 loop for the GPIIb/IIIa binder under two settings: unconditional generation and retrieval-augmentt augmented generation. Without retrieval guidance, the model struggles to reconstruct the characteristic multi-hyddroge bond interactions observed in the reference structure. In contrast, retrieval from the database yields top-10 candidates that include two peptide complexes and one antibody-antigen complex, all of which exhibit similar interaction patterns to the target. Specifically, these retrieved structures share the distinctive formation of hydrogen bonds
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Antbody. 3nid, H3
Unconditioned Generation
Bad Interactions
Peptide, 7120
Antbody, 7kde H3
Peptide, 7yeh
Retrieved2
Retrieved3
RetrieveAugmented Generation
Good Intaractions
Figure 6: Case study of HCDR3 design for the GPID/III((DBBI:33III) binder. The binding site is shown in pink, binder key residues in blue, and hydrogen bonds light blue dashed lines. Guided by retrieved exemplars, serrival-auggeenn generation successfully preserves these key interaction modes.
between arginine residue the binding site and the binder, as well as between tyrosine residue in the binder and the binding site. Consequently, the RAG-based generation inherits these critical interaction motifs, successfully recovering the hydrogen- and tyrosine residues. This case exemplifies how our retrieval retrieval-augmenne aaggmentedd generation framework effectively leverages retrieved interaction patterns guide and enhance molecular design.
patterns mediated by arginine
H
Cross-Domain Retrieval Ablation Studies
To further elucidate the impact of cross-domain retrieval model's generative performance, conducted a series of ablation studies by selectively restricting the set of available templates during inference. Specifically,
compared the outcomes of conditioning the generation process using retrieval exemplars sourced from either a single domain (e.g.. antibody-only, peptide-only, etc) versus those retrieved from the entire, tross-domain template pool.
The results in Tad Table T reveal consistent trend: models leveraging cross-domain retrieval demonstrate superior capability in reconstructing chemically and geometrically plausible interface interactions, as measured by both interaction recovery metrics and binding affinity scores. In contrast, restricting the retrieval to templates within the same domain the query generally leads diminished performance, manifesting as reduced interaction overlap and less robust generalization to diverse binding interfaces.
These results highlight the critical advantage of our approach: by comprehensively referencing interface templates across distinct molecular domains, our model can flexibly exploit shared principles of molecular recognition and binding site architecture, thereby enabling more rational and reliable binder generation. The empirical gains observed across all tested scenarios underscore the necessity of cross-domain template integration as a core design choice for retrieval-augmeented generative frameworks.
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Table 9: Results of recovery metrics for RADiAnce under different template sources on antibody edrs codesign task. Columns "Ab", "Pep", and "ProtFrag" indicate whether the respective template domain is included for retrieval during inference for included, X for excluded).
CDR
Ab
Pep
ProtFrag
AAR (%)
RMSD (Å)
AAG (kJ/mol)
IMP (%)
ISM (%)
X
90.54
0.295
-7.955
98.33
98.36
III
/
90.35
0.3007
-7.705
95.00
98.36
V
90.83
0.29777
-8.221
96.67
98.36
X
78.64
0.2157
-2.605
88.33
87.83
H2
V
78.70 78.70
0.2158
-2.635
80.0
88,10
V
79.20
0.2135
-2.370
83.33
87.83
X
54.47
0.9475
-5.200
65.0
71.96
H3
V
54.16
0.9557
-4.545
66.7
72.81
54.66
0.9443
-6,236
71.67
71.64
X
86.48
0.2902
-7.59
96.67
97.88
LI
/
86.48
0.2887
-7.50
95.00
97.64
86.20
0.2907
-7.170
95.00
98.31
x
87.13
0.1550
-4.35
96.67
98.52
1.2
86.99
0.1553
-4,647
95.00
98.52
86.68
0.1558
-3.857
95.00
98.52
x
76.71
0.4398
-5.94
86.67
95.08
L3
/
76.41
0.4372
-7.43
93.33
95.08
76.75
0.4478
-9.207
90.00
94.82
Table 10: Results of recovery metrics for RADiAnce under different template sources on peptide codesign task. Columns "Ab", "Pep", and "ProtFrag" indicate whether the respective template domain included for retrieval during inference (V for included, for excluded).
Ab Pep
ProtFrag
AAR (%)
RMSD (Å)
AAG(k//mll)
IMP (%)
ISM (%)
X
38.97
2.27
3.424
40.86
49.82
/
x
39.60
2.41
2,423
38.71
48.33
/
/
39.42
2.29
1.963
41.94
52.15
I Ablation on Single-Domain Data
To ensure that the performance comparison is not biased by the use of multiple binder types, conducted an ablation study where both the training and retrieval data were restricted to a single domain. Specifically,
evaluated RADiAnce and UniMoMo under the antibody heavy chain CDR3 (AbH3) and peptide design tasks, each trained and retrieved using data from only that domain. This setup removes cross-type information and ensures a strictly matched comparison between the models.
Table 11: Ablation study single-domain data. Both training and retrieval were performed within the same domain to ensure a fair comparison. Other single-domain results are omitted as they are the same result with those reported in the main text.
Task and models
AAR (%)
RMSD (Å)
AAG (kJ/mol)
IMP (%)
ISM (%)
UniMoMo (AbH3)
48.78
1.39
-5.781
63.33
65.46
RADiAnce (AbH3)
51.31
1.109
-5,994
68.33
69.71
UniMoMo (Peptide)
37.59
2.48
7.69
29.03
40.08
RADiAnce (Peptide)
38.28
2.37
7.57
27.95
45.37
As shown Table even when trained and retrieved within a single domain, RADiAnce consistently outperforms other models across evaluated metrics. This demonstrates that the performance gain of RADiAnce is not solely due to multi-type data retrieval, rather arises from its intrinsic ability to model contextually aligned binder-target interactions.
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J Discussion
J.1 Reproducibility and Statistical Robustness
To demonstrate the robustness and reliability of our evaluation, we explicitly quantify the statistical variability of our model performance under different random initializations. Our main results are accompanied by standard deviations that measure model variability across independent runs with different random seeds. Specifically, we reran the entire inference and evaluation pipeline three times with distinct random seeds, and computed the mean and standard deviation across these runs. Given that each evaluation aggregates thousands of test instances, the per-run variation remains small, indicating robustness and reproducibility of our evaluation benchmark.
Table Performance metrics with standard deviations across independent runs. The consistently small deviations confirm the stability of our evaluation.
Model
AAR (%)
RMSD (A)
AAG (KJ/mol)
IMP (%)
ISM (%)
HCDR3
54.66=0.0026
0.9443+0.0156
-6.236 0.5862
71.67++0.00% 41.94-100008
71.64+0.0085 12.0007
Peptide
39.42-10.0014
2.29-0
1.963 - 0.2302
the standard deviations across independent runs are consistently small across all metrics. This confimms that the evaluation is highly stable, with negligible sensitivity random initialization or stochastic effects during inference.
As shown in Table 12
J.2 Extended Evaluation Metrics
To provide more comprehensive evaluation of our model, we further introduced several robust and widely used metries, including DockQ, FoldX AG, and Binding Site Recovery. These metrics complement the previously reported ones by assessing structural and energetic aspects of the predicted complexes.
Table 13: Extended evaluation metries for RADiAnce and UniMoMo on antibody and peptide datasets.
Model
DockQ
FoldX AG (kJ/mol)
Binding Site Recovery
RADiAnce (AbH3)
0.9582
-8,4250
0.9964
UniMoMo (AbH3)
0.9491
-7.9950
0.9962
RADiAnce (Peptide)
0.7698
-7.9891
0.9857
UniMoMo (Peptide)
0.7592
-7,4901
0.9822
DockQ DockQ is a continuous quality score for protein-protedd docking models ranging from to 1. It integrates the fraction of native contacts (Faa). ligand RMSD (LRMSD), and interface RMSD (iRMSD) to provide an overall measure of structural agreement with the native complex. Scores above 0.23, 0.49, and 0.80 correspond to acceptable, medium, and high-qualing models, respectively [
FoldX AG FoldX calculates the binding free energy (AG) of pontein-protein complex as the difference between the Gibbs free energy of the complex and the sum its unbound partners. More negative AG values indicate stronger and more stable interactions
9
Binding Site Recovery Binding Site Recovery measures the proportion of true interface residues correctly identified by the model, representing the accuracy of interface residue prediction. Higher values indicate that the generated complex preserves the biologically relevant interaction sites.
As shown in Table 13 the newly introduced metrics are consistent with the previously reported per- formance trends. RADiAnce outperforms UniMoMo across both antibody and peptide benchmarks, achieving higher DockQ scores and more favorable FoldX AG. These results confirm the robustness and reliability of our model across diverse evaluation metrics.
28

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J.3 Dataset Analysis and Benchmark Reliability
Data leakage eerrieal--uggmente generative frameworks can lead to overly optimistic results test samples share high similarity with retrieved templates. To address this concern, we performed a comprehensive verification of our data split.
Established conventions and potential issues Following prior studies in antibody and peptide design, we adopt the RABD and LNR benchmarks, respectively, and enforce 40% sequence-idente clustering to prevent data leakage across train, validation, and test partitions. However, as pointed out in prior discussions, sequence clustering alone may be insufficient, since structural similarity (e.g.. TM-score) can still introduce hidden overlaps across sets.
Verification of data split integrity To rigorously verify the split, we computed both sequence identity and TM-score between each test sample and the entire training set (Table The overall low similarity confirms that the test data are largely distinct from the training examples. Nonetheless, a few rare cases with TM-score > 0.50 were observed, indicating that minor structural overlaps may still occur under conventional data-splitting method.
Table 14: Verification of data-split integrity. All values represent mean + standard deviation.
Test Set TM-score with Train Set
Seqld with Train Set
RABD
0.2421 + 0.053
0. 1.7227 + 0.021 0.0983 + 0.017
LNR
0.2503 + 0.069
Re-evaluation after removing overlapping cases To completely remove any potential confounding factor, we excluded any test sample that had least one training neighbor with a TM-score > 0.50. On the LNR dataset, this excluded 43 samples, leaving 50 for re-evaluation. Table [3 summmarzzze the resulting performance across multiple baselines.Al models experienced performance degradation, which may result from both inadvertent data overlap in previous works and the inherently more challenging or biased characteristics of the remaining test cases. Importantly, our model (RADiAnce) not only maintained the strongest overall performance after this correction but also exhibited the smallest performance drop among all methods, further demonstrating that its advantage does not stem from data leakage but from genuinely robust retrieval guided generation.
Table 15: Baseline performance drop after removing overlapping test cases (TM-score > 0.50). Numbers in parentheses show relative change; lower RMSD/AAG and higher AAR/ISM are better.
Model
AAR (%)
RMSD (Å)
A&G (kJ/mol)
ISM (%)
PepFlow
31,41 (-11.4%)
3.63 (+26.6%)
18.84 (+19.9%)
22.22 (-21.7%)
PepGLAD
33.85 (-12.4%)
3.43 (+25.2%)
18.66 (+22.3%)
27.67 (-15.2%)
UniMoMo
35.00 (-9.36%)
2.87 (+24.2%)
3.52 (+46. 1%)
38.69 (-21.2%
RADiAnce
35.79 (-9.20%)
2.80 (+22.3%)
2.21 (+12.7%)
42.26 (-19.0%)
Implications for future benchmarks This analysis reveals that even widely used benchmarks may inadvertently allow structural redundancy across data splits. Our findings suggest that future dataset construction should incorporate stricter filtering, combining both sequence and structure based similarity constraints (e.g., TM-score thresholds) to ensure fair evaluation. We encourage the community to establish standardized, publicly verifiable data splitting protocols to enhance reproducibility and avoid unintended information leakage further research.
K
Code Availability
The source code of the RADiAnce is available htttps://gittiii httpa//llllat///gaaaoo/////////a
29


Reviews:
[{"review_id": "xHOAocqtTv", "reviewer": "Reviewer_yozy", "summary": "The paper presents a new method for generating protein binders using the retrieval-augmented generation (RAG) technique. A dataset includes pairs of binding sites and binders, which are encoded into a shared latent space. Contrastive learning is employed to align the representations of binding sites and binders. A generator, trained as a VAE model, takes latent space vectors and decodes binders conditioned on the binding site. Additionally, the top n binders are retrieved from a key-value database to perform a latent diffusion process that produces block types and their positions. The model is tested in two different setups. First, the retrieval capabilities are evaluated by querying antibodies and peptides. Then, the generative capabilities are assessed, demonstrating promising results both in terms of predicted binding strength and interactions at the binding site.", "questions": "1. What is a block type? Is it one of the standard amino acids? Can this method generate other amino acids?\n2. Why is $\\Delta\\Delta G$ positive in Table 2? Do you believe that designing peptides is more challenging than designing antibodies?\n3. What is the relationship between the number of retrieved examples and the model's performance? For example, is a single instance enough to significantly improve results?", "limitations": "One limitation of the method is discussed in the “Conclusions and Limitations” section, which is its reliance on retrieval quality. Other limitations may include the small number of amino acids that can be produced, even though unconventional amino acids are sometimes preferred in real-world peptide design cases (see Question 1).", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The choice of the model architecture and the way retrieved examples are integrated appears well-justified.\n- Multiple models for peptide design are included in the benchmarks, including UniMoMo, which is the same core model but without RAG.\n- The paper is very clear, and the illustrations help make the method easier to understand. Especially Figure 2, which I find extremely helpful.\n- The proposed latent RAG improves the quality of the generated protein binders, outperforming other methods.\n- The proposition is original because there are only a few papers that employ RAG for molecule generation, and to the best of my knowledge, this is the first paper to apply RAG to protein design.\n\nWeaknesses:\n- Experiments should be repeated multiple times (at least three) to report error bars of the resulting performance. The checklist (point 7) states that error bars are reported, but in reality, they are not. If the authors claim that the results are stable and representative, this should be confirmed with some experiments, such as repeated training or evaluation runs.\n- The related work should include a discussion about RAG in molecule generation. At least f-RAG should be covered [1], but during the review period, more notable papers appeared, including READ [2] and Rag2Mol [3].\n- Multiple datasets are combined to form a training dataset for the proposed model. The methods used to clean the data and verify its quality should be described. If standard test sets are used for these datasets, are similar molecules removed from the training data across data sources?\n- “Block type” should be defined.\n- $\\mathcal{Z}_x^t$ should also be defined. In the text, only $\\mathcal{Z}_x$ is used before Section 3.3. In Figure 2, $\\mathcal{Z}_t$ is used instead.\n\nMinor comments:\n- On page 5, the sentence “The layer is inserted between the self-attention layer and the feed-forward layer in the original EPT” is repeated.\n- A typo on page 6:: “accurate retrieval is is essential”\n- A typo on page 8: “we randomly mask one CDR loops”\n\n[1] Lee, Seul, et al. \"Molecule generation with fragment retrieval augmentation.\" Advances in Neural Information Processing Systems 37 (2024): 132463-132490.\n\n[2] Xu, Dong, et al. \"Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion.\" arXiv preprint arXiv:2506.14488 (2025).\n\n[3] Zhang, Peidong, et al. \"Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation.\" Briefings in Bioinformatics 26.3 (2025): bbaf265.", "was_revised": true, "final_justification": "All issues I raised were resolved in the rebuttal and during further discussion with the Authors. I believe this is a strong paper with valuable insights on using retrieval-augmented generation for molecular data. I recommend acceptance because the Authors properly addressed my concerns, and the inclusion of confidence intervals makes the results more reliable. All additional experiments, metrics, and related work discussed with the reviewers will need to be incorporated into the paper.", "rebuttal": "Thanks for your appreciation and the positive comments!\n\n>**W1: Experiments should be repeated multiple times (at least three) to report error bars of the resulting performance. The checklist (point 7) states that error bars are reported, but in reality, they are not. If the authors claim that the results are stable and representative, this should be confirmed with some experiments, such as repeated training or evaluation runs.**\n\nThank you for raising this important point. We apologize for the confusion caused by omitting explicit error bars in the original submission. Each evaluation in our study aggregates results over several thousand samples, leading to highly stable metrics. To substantiate this claim, we report the standard deviation of all key metrics as the table below. The resulting deviations are consistently small, confirming the robustness and reproducibility of our evaluation.\n\n**Table S1: Performance metrics with standard deviations**\n|Model|AAR (\\%)|RMSD (Å)|$\\Delta\\Delta G$ (kJ/mol)|IMP (\\%)|ISM (\\%)|\n|-|-|-|-|-|-|\n|HCDR3|54.66±0.0026|0.9443±0.0156|-6.236±0.5862|71.67±0.0096|71.64±0.0085|\n|Peptide|39.42±0.0014|2.29±0.0043|1.963±0.2302|41.94±0.0108|52.15±0.0007|\n\n>**W2: The related work should include a discussion about RAG in molecule generation. At least f-RAG should be covered [1], but during the review period, more notable papers appeared, including READ [2] and Rag2Mol [3].**\n\n\nThank you for drawing our attention to the recent lines of work on retrieval-augmented generation for molecular design. \nRetrieval-augmented generation (RAG) has emerged as a powerful paradigm for targeted molecular design, with **f‑RAG** introducing a dynamic vocabulary of molecular fragments to improve small molecule generation through fragment-level retrieval [1],[2]. Structure-based RAG methods such as **IRDiff** incorporate high-affinity ligands as retrieval references to steer diffusion-based generation in protein-specific 3D molecular design[3], and **READ** further integrates pocket-matched scaffold embeddings via SE(3)-equivariant diffusion to ensure valid, affinity-optimized ligand generation[1]. The **ChemRAG‑Bench** benchmark demonstrates that RAG strategies yield an average relative improvement of about 17% over direct generation across diverse chemistry tasks[4]. However, none of these approaches address the challenges of protein binder co-design, which requires simultaneous modeling of sequence and structure, nor provide a unified retrieval framework across modalities such as peptides and antibodies. Our method, **RADiAnce**, addresses this gap by learning a contrastive latent space that aligns interaction interfaces across diverse binder modalities, and uses a retrieval-augmented latent diffusion model to enable cross-domain motif retrieval guiding joint sequence–structure binder design.\n\n[1] Xu D, Yang Z, Wong K, et al. Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion[J]. arXiv preprint arXiv:2506.14488, 2025.\n\n[2] Lee, Seul, et al. \"Molecule generation with fragment retrieval augmentation.\" Advances in Neural Information Processing Systems 37 (2024): 132463-132490.\n\n[3] Huang, Zhilin, et al. \"Interaction-based retrieval-augmented diffusion models for protein-specific 3d molecule generation.\" Forty-first International Conference on Machine Learning. 2024.\n\n[4] Zhong, Xianrui, et al. \"Benchmarking retrieval-augmented generation for chemistry.\" arXiv preprint arXiv:2505.07671 (2025).\n\nIn the revised version we will expand the *Related Work* section to cover these approaches and clarify how our framework differs and advances beyond them. \n\n\n>**W3: Multiple datasets are combined to form a training dataset for the proposed model. The methods used to clean the data and verify its quality should be described. If standard test sets are used for these datasets, are similar molecules removed from the training data across data sources?**\n>\n\nThanks for your concern about the data splits!\nWe follow established practice by adopting RABD as the antibody benchmark and LNR as the peptide‑design benchmark.\nTo prevent data leakage, we clustered all sequences at 40 % sequence identity and assigned the train, validation, and test splits by clusters. Consequently, no sequence in test or validation shares a cluster with any training sequence.\nWe further verified the split by computing (i) the sequence‑identity and (ii) TM‑Scores between every test‑set structure and the training set. Both metrics confirmed low similarity, indicating that our split is stringent and leak‑free.\n\n**Table S2: Data split verification metrics**\n|Testset|TMscore with Trainset|SeqId with Trainset|\n|-|-|-|\n|RABD|0.2421±0.053|0.1272±0.021|\n|LNR|0.2503±0.069|0.0983±0.017|\n\n>**W4: “Block type” should be defined.**\n>**Q1: What is a block type? Is it one of the standard amino acids? Can this method generate other amino acids?**\n\nWe apologize for the confusion on the block type. Following UniMoMo, we treat each standard amino acid as one block, and use principal subgraph decomposition algorithm to decompose other entities (e.g. non-standard amino acids) into blocks of frequently occurring chemical motifs. The resulting vocabulary $V$ thus extends beyond standard amino acids and also includes principal subgraphs derived from non-standard components.\nWe will incorporate these revisions in the final version of the paper. Thank you for your valuable feedback.\n\n>**W5: Zxt should also be defined. In the text, Zx only is used before Section 3.3. In Figure 2, Zt is used instead.**\n\nWe apologize for the confusion. In our notation, $Z_x$ denotes the equivariant features of the binder side, whereas $Z_t$ in Fig. 2 represents the latent embedding of the entire graph (binder + binding site), including both the invariant feature and the equivariant feature. We will update the figure and provide precise symbol definitions to make this distinction clear.\n\n\n\n>**Q2: Why is $\\Delta\\Delta G$ positive in Table 2? Do you believe that designing peptides is more challenging than designing antibodies?**\n\nThank you for the insightful comment. We clarify that the reported $\\Delta\\Delta G$ refers to the difference in binding free energy between the sampled structure and the reference structure, where a negative value indicates that the sampled structure exhibits stronger binding. Notably, peptide binders possess fewer conformational constraints compared to antibodies, leading to greater flexibility and a significantly larger search space[1][2][3], which makes identifying effective binders more challenging. However, we would like to emphasize that despite these difficulties, our model still achieves state-of-the-art performance compared to existing baselines.\n\n[1] London, Nir, Dana Movshovitz-Attias, and Ora Schueler-Furman. \"The structural basis of peptide-protein binding strategies.\" Structure 18.2 (2010): 188-199.\n\n[2] Lee, Jessica H., et al. \"Structural features of antibody-peptide recognition.\" Frontiers in Immunology 13 (2022): 910367.\n\n[3] Makabe, Koki. \"Molecular basis of flexible peptide recognition by an antibody.\" The journal of biochemistry 167.4 (2020): 343-345.\n>**Q3: What is the relationship between the number of retrieved examples and the model's performance? For example, is a single instance enough to significantly improve results?**\n\nThank you for raising the valuable question! Indeed, the number of retrieved samples does have an impact, and even a single retrieved sample can lead to noticeable performance improvements, as shown in the tables below.\n\nFor HCDR3:\n\n**Table S3: Ablation study on the number of retrieved samples for HCDR3**\n|Retrieved Samples|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|0|51.55|0.9525|0.0524|\n|1|53.26|0.9431|0.0404|\n|10|54.66|0.9443|0.0484|\n|20|54.63|0.9682|0.0485|\n|40|54.06|0.9653|0.0494|\n\n\nAnd for Peptide:\n\n**Table S4: Ablation study on the number of retrieved samples for Peptide**\n|Retrieved Samples|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|0|37.94|2.38|0.5042|\n|1|39.03|2.38|0.4846|\n|10|39.12|2.31|0.5129|\n|20|39.42|2.29|0.5227|\n|40|38.16|2.28|0.5273|\n\n\nWe could find that increasing the number of retrieved samples generally leads to slightly better results, albeit with a modest increase in computational cost. However, overly increasing the number might include samples with lower similarity score, introducing noise into the generation and thus harming the performance. Notably, using no retrieval sample performs significantly worse than using any retrieval.\n\nAn additional observation is that the diversity of generated outputs also varies with the number of retrieved samples. Specifically, both the no-retrieval setting and the setting with many retrieved samples exhibit higher diversity, while retrieving only a single sample results in the lowest diversity. This aligns with our expectation, as limited retrieval can overly constrain the generative space.\n\nThese findings suggest that practitioners may choose an appropriate top‑N retrieval size based on the trade-off between performance, diversity, and efficiency."}, {"review_id": "J4MEwNoL1q", "reviewer": "Reviewer_J8pu", "summary": "This paper presents RADiAnce (Retrieval-Augmented Diffusion for Aligned interface), a novel framework for protein binder design that combines interface retrieval with generative modeling. The authors introduce a contrastive latent space that aligns binding sites and interfaces across diverse domains (e.g., peptides, antibodies), enabling accurate cross-domain interface retrieval. A latent diffusion model then generates binders conditioned on these retrieved interfaces, integrating prior knowledge through cross-attention and residual MLPs.", "questions": "### Questions for the Authors\n- Is there a measurable **correlation between the binding affinity of retrieved binders and that of the generated binders**?\n- The baseline comparison may be biased, as the proposed model retrieves binders from multiple types while baselines are trained on a single binder type. Could the authors report performance in a **more directly comparable setting**, where the training data and binder types are matched?\n- How do the model determined the number of residues during inference?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- Rather than focusing solely on architectural modifications or new generative frameworks, this paper explores retrieval-augmented generation, which is applicable to real-world protein design scenarios.\n- The proposed framework enables **cross-domain generalization** of intermolecular interactions across different binder types, such as antibodies and peptides.\n- **Experimental results demonstrate superior performance** of the model in generating binders with higher computational binding affinities compared to strong baselines.\n### Weaknesses\n- The paper’s **formatting and presentation require improvement**;  Figure (1) are not rendered correctly.\n- The main text describes diffusion defined on a continuous latent space, yet Figure 1 illustrates diffusion applied to Euclidean coordinates, which is confusing.\n- The model’s generalizability may be **limited to targets that have strong binders represented in the retrieval database**, potentially restricting applicability to novel targets.", "was_revised": true, "final_justification": "The authors have addressed all my concerns. The experiments of running retrieval-based generation on a synthesized dataset are really promising for hard targets that have few available data. I have increased my rating.", "rebuttal": "We thank the reviewer for the valuable comments, and answer the reviewer’s questions as follows.\n>**W1: The paper’s formatting and presentation require improvement; Figure (1) are not rendered correctly.** \n\nWe sincerely apologize for the confusion caused by the figure rendering issue in the paper, which was unexpected on our end. This appears to be a bug introduced in Microsoft Edge Stable version 136.0.3240.50 (released after the paper submission deadline). Currently, there are several workarounds: (1) disable the new PDF viewer in Edge, (2) use any other browser engine (e.g. Chrome) or standalone PDF reader. We are actively working on fixing the formatting to ensure full compatibility across different PDF viewers for the revision. Thank you for your understanding.\n\n\n>**W2: The main text describes diffusion defined on a continuous latent space, yet Figure 1 illustrates diffusion applied to Euclidean coordinates, which is confusing.** \n\nSorry for the unclear description. Our model performs diffusion over a *joint* latent pair $Z_t = (H_t, X_t)$, where $H_t$ contains SE(3)-**invariant** features (e.g., sequence or chemical embeddings), and $X_t \\in \\mathbb{R}^{N \\times 3}$ contains SE(3)-**equivariant** spatial coordinates. In the figure, each point cloud represents a block: its position corresponds to $X_t$, while its color encodes $H_t$. The current caption only mentions $Z$, without clearly distinguishing the roles of $H$ and $X$, which understandably led to confusion. We will revise the caption to clarify this distinction.\n\n\n\n>**W3: The model’s generalizability may be limited to targets that have strong binders represented in the retrieval database, potentially restricting applicability to novel targets.**\n\n\nThank you for raising this insightful question! Our experiments indicate that even a single retrieved binder can provide meaningful guidance for generation. Furthermore, the cross-domain retrieval is exactly designed to enhance generalization by increasing the likelihood of discovering similar interaction patterns across different contexts. \n\nIn rare cases where even no suitable retrieval data is available across all the databases and modalities, we propose constructing an artificial retrieval database using a protein design pipeline. As shown in the table below, we used Trop2 as the target—currently lacking any experimentally resolved binder structures—and evaluated two settings: (1) no retrieval and (2) retrieval using a synthetic database constructed with RFdiffusion. \n\n**Table S1: Ablation study on the retrieval stage**\n|Settings|DockQ|Rosetta $\\Delta G$|Foldx $\\Delta G$|\n|-|-|-|-|\n|（1）|0.549|-24.68|-6.54|\n|（2）|0.574|-25.74|-6.86|\n\n\nThe results clearly show that retrieval using the artificial database yields significantly better performance. This suggests a practical solution for extremely novel cases where not a single structurally similar binders is available.\n\n\n>**Q1: Is there a measurable correlation between the binding affinity of retrieved binders and that of the generated binders?**\n\n\nThank you for the insightful comment. We computed the Pearson correlation coefficient between the Rosetta dG scores of generated binders and those of the retrieved binders, which yielded a value of 0.6620—indicating a strong correlation. As a control, we also calculated the correlation between generated binders and randomly selected samples from the retrieval database, which resulted in a much lower value of 0.0536. These findings further support the effectiveness of our RAG framework in improving the binding affinity of generated samples.\n\n\n>**Q2: The baseline comparison may be biased, as the proposed model retrieves binders from multiple types while baselines are trained on a single binder type. Could the authors report performance in a more directly comparable setting, where the training data and binder types are matched?** \n\n\n\nThank you for highlighting this point. First, we note that UniMoMo also involves multiple data types, making it a direct and fair comparison. Additionally, we performed an ablation study using single-domain data for both training and retrieval. The results are summarized in the table below:\n\n**Table S2: Ablation study on single-domain data**\n|Task and models|AAR (\\%)|RMSD (Å)|$\\Delta\\Delta G$(kJ/mol)|IMP (\\%)|ISM (\\%)|\n|-|-|-|-|-|-|\n|UniMoMo(AbH3)|48.78|1.39|-5.781|63.33|65.46|\n|RADiAnce(AbH3)|51.31|1.109|-5.994|68.33|69.71|\n|UniMoMo(pep)|37.59|2.48|7.69|29.03|40.08|\n|RADiAnce(pep)|38.28|2.37|7.57|27.95|45.37|\n\nWe observe that even when trained and retrieved within a single domain, RADiAnce still outperforms other methods under the same conditions. We will include the full ablation details in the supplementary material. We appreciate your thoughtful comments and suggestions!\n\n>**Q3: How do the model determined the number of residues during inference?**\n\nThank you for the question, and we apologize for not detailing this in the main text due to space limitations. For benchmarking evaluations, we follow common conventions by using the sequence length as the reference for fair comparison[1]. For real-world applications on *de novo* design, our framework allows users to manually select the binding pocket and specify a desired sequence length range with flexibility.\n\n[1] Luo, Shitong, et al. \"Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures.\" Advances in Neural Information Processing Systems 35 (2022): 9754-9767."}, {"review_id": "VLSsI9fOPA", "reviewer": "Reviewer_Lic4", "summary": "This paper proposes a novel framework to design binders to targeting on specific sites. By aligning known interface in the database, the diffusion model enables accurate and interpretable sampling for binder structures. In the experimental stage, there're three main experiments--recovery analysis based on different binding type and percentage the database knowledge is used, AAR and RMSD for sequence-structure co-design, and the antibody-CDR co-design recovery. In each experimental system, RADiAnce achieves the best performance.", "questions": "It is a great work, but please address the following questions. Thanks. \n\n1. Why the binder molecule variables are defined with arrows? I think it somehow causes confusions when understanding the method. \n2. In detail (line129-132), what is the motivation to regularize s_i and \\arrow{s}_i into different prior distribution?\n3. It seems that Figure 2 has many '?', maybe it brings more cmofusion to understand the method.\n4. Results shown in Table seems promising. However, mentioned in line 202-210, how to prove that the information retrival stage does not contain data leakage? In other words, if the samples are very very similar to the template, that would be meaningless. So I think there would be a demo about how the framework performs without the retrival stage and an analysis about how the retrival information changed z_t during sampling. Alternatively, it is worthy to provide a similarity test between the test protein and the ones in the retrival database. Though there are performances shown in Table 6 and Table 7, I still believe there should be more detailed analysis. \n5. Would there be some failure sample cases for further analysis? It may include how the poor information retrival affect failure design and the poor latent repersentation cannot match with the retrivaled information.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength: \n\n1. The motivation--using analogous to improve rationality and interpretability is clear and reasonable. \n2. The proposed unified framework for generative protein binder co-design (together with latent diffusion training) is a challenging task, which improves the significance of the work. \n3. The comprehensive evaluation system from protein-binding peptide to antibody demonstrates wide range of applications. \n\nWeakness: \n\n1. Clarity needs to be improved for the method section. \n2. The experimental setting may exist problems.", "was_revised": true, "final_justification": "I will maintain my score as 4. The rebuttal is good, but i still believe the application value is not so promising. That is why I keep score as 4.", "rebuttal": "Thanks for your appreciation and insightful feedback, which is very helpful in improving our paper!\n\n>**Q1: Why the binder molecule variables are defined with arrows? I think it somehow causes confusions when understanding the method.**\n\n>**Q2: In detail (line129-132), what is the motivation to regularize s_i and \\arrow{s}_i into different prior distribution?**\n\nWe apologize for the insufficient explanation in the paper. In our work, arrows represent 3D coordinates, which correspond to SE(3)-equivariant features, while the elements without arrows denote SE(3)-invariant features, such as sequence embeddings. To preserve geometric relations in the latent space, the coordinate features should retain their original spatial shape and are therefore regularized with a prior Gaussian at the original block positions. In contrast, the invariant features can be modeled with a standard Gaussian prior.\n\n\n>**Q3: It seems that Figure 2 has many '?', maybe it brings more cmofusion to understand the method.**\n>\nWe sincerely apologize for the confusion caused by the figure rendering issue in the paper, which was unexpected on our end. This appears to be a bug introduced in Microsoft Edge Stable version 136.0.3240.50 (released after the paper submission deadline). Currently, there are several workarounds: (1) disable the new PDF viewer in Edge, (2) use any other browser engine or standalone PDF reader. We are actively working on fixing the formatting to ensure full compatibility across different PDF viewers for the revision. Thank you for your understanding.\n\n\n>**Q4: Results shown in Table seems promising. However, mentioned in line 202-210, how to prove that the information retrival stage does not contain data leakage? In other words, if the samples are very very similar to the template, that would be meaningless. So I think there would be a demo about how the framework performs without the retrival stage and an analysis about how the retrival information changed z_t during sampling. Alternatively, it is worthy to provide a similarity test between the test protein and the ones in the retrival database. Though there are performances shown in Table 6 and Table 7, I still believe there should be more detailed analysis.** \n\nThanks for the insightful question! To address the concern, we first analyzed the similarity test between the test set and the database with both sequence and structure metrics, which exhibited low similarity. Second, we conducted analysis on the number of retrieved samples to see how it affects the sampling, including dropping the retrieval stage, which showed a trade-off effect performance, diversity, and efficiency. Third, we compared the trajectory of sampling with and without retrieval, and found the model automatically identifying a few key residues for imitation, and actively exploring other regions.\n\n1. We follow established conventions in this field by using RABD as the benchmark test set for antibody design and LNR for peptide design. To ensure a clean split, we perform clustering based on 40% sequence identity, ensuring that samples from the same cluster do not appear across the train, validation, and test sets. During the retrieval process, we strictly use only the training set as the retrieval database. Furthermore, we computed both sequence identity and TM-score between the test and training sets to confirm that our data split is sound and free from data leakage.\n\n**Table S1: Data split verification metrics**\n\n|Testset|TMscore with Trainset|SeqId with Trainset|\n|-|-|-|\n|RABD|0.2421±0.053|0.1272±0.021|\n|LNR|0.2503±0.069|0.0983±0.017|\n\n\n2. For the setting *without the retrieval stage*, we conducted an ablation study on the number of retrieved samples. The results are summarized in the tables below:\n\nFor HCDR3:\n\n**Table S2: Ablation study on the number of retrieved samples for HCDR3**\n|Retrieved Samples|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|0|51.55|0.9525|0.0524|\n|1|53.26|0.9431|0.0404|\n|10|54.66|0.9443|0.0484|\n|20|54.63|0.9682|0.0485|\n|40|54.06|0.9653|0.0494|\n\n\nAnd for Peptide:\n\n**Table S3: Ablation study on the number of retrieved samples for Peptide**\n\n|Retrieved Samples|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|0|37.94|2.38|0.5042|\n|1|39.03|2.38|0.4846|\n|10|39.12|2.31|0.5129|\n|20|39.42|2.29|0.5227|\n|40|38.16|2.28|0.5273|\n\nWe could find that increasing the number of retrieved samples generally leads to slightly better results, albeit with a modest increase in computational cost. However, overly increasing the number might include samples with lower similarity score, introducing noise into the generation and thus harming the performance. Notably, using no retrieval sample performs significantly worse than using any retrieval.\n\nAn additional observation is that the diversity of generated outputs also varies with the number of retrieved samples. Specifically, both the no-retrieval setting and the setting with many retrieved samples exhibit higher diversity, while retrieving only a single sample results in the lowest diversity. This aligns with our expectation, as limited retrieval can overly constrain the generative space.\n\nThese findings suggest that practitioners may choose an appropriate top‑N retrieval size based on the trade-off between performance, diversity, and efficiency.\n\n3. In addition, to better understand the dynamics of the sampling process, we visualized the diffusion trajectories and compiled them into a video, which we will provide in the revision, as we are not allowed to post external links during the rebuttal phase. One mechanism we observed is that the diffusion model does not simply copy a single retrieved sample. Instead, in the final few denoising steps, it tends to preserve the positions and properties of a few key residues from the retrieved examples, while actively exploring other regions of the sequence.\nIn contrast, when we disable RAG by skipping the conditioning step, this effect disappears, and performance metrics degrade accordingly. These observations suggest that the benefit of RAG does not stem from directly copying retrieved samples, but rather from identifying and transferring meaningful interaction patterns of key residues.\nOur additional analysis on interaction recovery from the generated structures further supports this explanation.(Appendix E)\n\n\n>**Q5: Would there be some failure sample cases for further analysis? It may include how the poor information retrival affect failure design and the poor latent repersentation cannot match with the retrivaled information.**\n>\n\nYes, thank you for the question. As addressed in our response to Q3, we have conducted a thorough evaluation of the model's performance without retrieval augmentation. Additionally, we present an ablation study analyzing the dot-product similarity between the embeddings of the retrieved binders and the test binding sites, and its influence on model performance.\n\nFor HCDR3:\n\n**Table S4: Ablation study on retrieval quality for HCDR3**\n|Settings|Similarity|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|-|\n|Reverse Ranking|-49.86±16.68|52.31|0.9718|0.0468|\n|Random Ranking|17.34±6.90|51.72|0.9665|0.0649|\n|Top 10|55.81±5.01|54.66|0.9443|0.0484|\n\n\n\nAnd for Peptide:\n\n**Table S5: Ablation study on retrieval quality for Peptide**\n|Settings|Similarity|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|-|\n|Reverse Ranking|-26.09±12.54|36.33|3.09|0.5267|\n|Random Ranking|11.78±8.16|38.04|2.56|0.5580|\n|Top 10|53.78±4.06|39.12|2.31|0.5129|\n\n\nThese results confirm that model performance degrades when the retrieved information is poorly aligned (e.g., reverse ranking), but the degradation is moderate rather than severe. This suggests that while our framework benefits from retrieval to enhance generation quality, it remains reasonably robust under suboptimal retrieval conditions and does not rely excessively on retrieval quality."}, {"review_id": "72mdhdDi7p", "reviewer": "Reviewer_KBot", "summary": "The paper introduces **Retrieval-Augmented Diffusion for Aligned Interfaces (RADiAnce)**, a novel framework that leverages known interfaces to guide the design of novel binders. By unifying retrieval and generation within a shared contrastive latent space, the framework efficiently identifies relevant interfaces for a given binding site and integrates them via a conditional latent diffusion generator, enabling cross-domain interface transfer. Experimental results demonstrate that RADiAnce outperforms baseline models across multiple metrics, including binding affinity and recovery of geometric configurations and molecular interactions.", "questions": "1. How does the framework address Out-of-Distribution challenges for proteins without analogous binding modes?  \n2. Can authors measure similarity between designed binders and the training set?  \n3. Does the current dataset splitting strategy overestimate model performance?  \n4. Could authors adopt more reliable performance metrics?  \n5. Can results without retrieval components be provided?", "limitations": "The framework's retrieval - based conditioning restricts generalizability to unseen proteins and novel binders, and evaluation metrics may not fully reflect model performance.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths  \n1. **Novel Generative Framework**: RADiAnce integrates retrieval mechanisms with generative processes to design high-affinity binders,.  \n2. **Clarity of Presentation**: The manuscript is systematically structured, with logical derivations and clear explanations that facilitate reader comprehension.  \n3. **Reproducibility**: The authors provide open-source code, enabling independent validation of results.  \n\n### Weaknesses  \n1. **Limitations in Out-of-Distribution Generalization**:  \n   RADiAnce relies on retrieving binders with similar binding modes from the training set to condition generation, which may lead to designs that exhibit structural/sequential similarity to training data. The experiments (e.g., peptide and antibody design) primarily use sequence similarity for filtering, which insufficiently demonstrates the model’s generalization capacity. In practical applications, generative models are often required for uncharacterized proteins without known binding modes in the training set, where RADiAnce’s performance may be compromised.  \n\n2. **Dataset Splitting Methodological Flaws**:  \n   The current dataset splitting strategy, which relies solely on sequence similarity, may introduce bias and additional information due to the retrieval process incorporating structural and sequential information from the training set. To ensure fair evaluation, the study should filter training dataset based on structural and sequence similarity simultaneously and exclude data featuring the same target protein in the test set, as overlapping targets could artificially inflate performance metrics.  \n\n3. **Inadequacies in Evaluation Metrics**:  \n   Most experimental metrics assess similarity between designed binders and references, which fails to appropriately measure the true design capability. The goal of protein design is to generate plausible binders with enhanced binding affinity, not merely reproduce similar structures as refernce. Metrics such as ISM (Interaction Similarity Measure) and ITO (Interaction Topology Overlap) may also be insufficient, as they prioritize reconstruction of reference interactions over prediction of novel, functional and plausible interactions.  \n\n4. **Performance Validation**:  \n   The improvement over UniMomo is marginally significant. To thoroughly evaluate the generative capacity of RADiAnce, the authors should provide experiments demonstrating design performance without the retrieval component, isolating the contribution of the diffusion generator.", "was_revised": true, "final_justification": "In my opinion, I recommend that the paper be accepted. All my concerns have been resolved.", "rebuttal": "Thanks for your constructive comments! We provide more explanations to address your concerns as follows.\n>**W1: Limitations in Out-of-Distribution Generalization**\n>**Q1: How does the framework address Out-of-Distribution challenges for proteins without analogous binding modes?**\n\nThank you for raising this question! Our experiments indicate that even a single retrieved binder can provide meaningful guidance for generation. Furthermore, cross-domain retrieval enhances generalization by increasing the likelihood of discovering similar interaction patterns across different contexts. \n\nIn cases where no suitable retrieval data is available, we propose constructing an artificial retrieval database using a protein design pipeline. As shown in the table below, we used Trop2 as the target—currently lacking any experimentally resolved binder structures—and evaluated two settings: (1) no retrieval and (2) retrieval using a synthetic database constructed with RFdiffusion. \n\n**Table S1: Ablation study on the retrieval stage**\n|Settings|DockQ|Rosetta $\\Delta G$(KJ/mol)|Foldx $\\Delta G$(KJ/mol)|\n|-|-|-|-|\n|（1）|0.549|-24.68|-6.54|\n|（2）|0.574|-25.74|-6.86|\n\nThe results clearly show that retrieval using the artificial database yields significantly better performance. This suggests a practical solution for cases where no structurally similar binders are available.\n\n>**W2: Dataset Splitting Methodological Flaws**\n>**Q2: Can authors measure similarity between designed binders and the training set?**\n\nThanks for your concern about the data splits!\nWe follow established practice by adopting RABD as the antibody benchmark and LNR as the peptide‑design benchmark.\nTo prevent data leakage, we clustered all sequences at 40 % sequence identity and assigned the train, validation, and test splits by clusters. Consequently, no sequence in test or validation shares a cluster with any training sequence.\nWe further verified the split by computing (i) the sequence‑identity and (ii) TM‑Scores between every test‑set structure and the training set. Both metrics confirmed low similarity, indicating that our split is stringent and leak‑free.\n\n**Table S2: Data split verification metrics**\n|Testset|TMscore with Trainset|SeqId with Trainset|\n|-|-|-|\n|RABD|0.2421±0.053|0.1272±0.021|\n|LNR|0.2503±0.069|0.0983±0.017|\n\nBased on the experiments above, we observe that there is no high structural or sequence similarity between the test set and the training set. Regarding the generated samples and their retrieved complexes, due to differences in sequence length and pocket configuration, we evaluate similarity using interaction recovery (see Appendix E). This analysis shows that the model effectively leverages information from retrieved samples without simply copying them.\n\nWe further support this conclusion from two additional perspectives. First, the average sequence identity between the top-10 retrieved samples and the ground truth is 26.60% for HCDR3 and 15.92% for peptides—both significantly lower than the similarity between our designed binders and their respective references. Second, the diffusion-based method produces binders with high sequence diversity.\n\nTaken together, these results strongly indicate that our model is not a “copy-cat,” but rather generalizes from retrieved interactions to generate novel and diverse binders.\n\n>**W3: Inadequacies in Evaluation Metrics**\n\nWe thank the reviewer for highlighting the limitations of using only reference-similarity metrics. We acknowledge that metrics like ISM and ITO, which emphasize similarity to a reference complex, do not fully capture a method’s ability to create novel, high-affinity binders. However, we respectfully note that recovering key reference interactions is often a prerequisite for functional binding rather than a flaw. In practice, many successful binder designs intentionally mimic critical interactions from known binders to preserve essential binding hotspots.[1][2][3][4]\n\nThus, a high ISM/ITO can indicate that a design retains these crucial interactions needed for binding activity, which is a desirable feature and not merely rote reconstruction of the reference. Nevertheless, we agree that the ultimate goal is to generate binders with enhanced affinity and new interactions beyond the original template. To address this, we evaluated our designs using an affinity-based metric (predicted ΔG) in addition to ISM/ITO. Encouragingly, our model’s designs show significantly more favorable binding free energies (lower ΔG) compared to the baseline, indicating improved predicted binding affinity. This result demonstrates that our method is indeed producing more plausible and stronger interactions, not just copying the reference structure. In other words, the improvements in ΔG suggest that the designed binders are likely to bind their targets more tightly than those from the baseline method, validating the functional relevance of the generated interactions. Finally, we would like to emphasize that our approach does allow for novel interaction discovery. The designs are not constrained to only reproduce reference contacts. In fact, many interactions in our designed binders are new(Appendix E). \n\nAdditionally, our framework can be tuned to encourage more novelty by adjusting the number of retrieved templates used during design. A smaller reliance on retrieved reference complexes naturally leads to more exploratory designs, at the cost of fewer pre-defined interactions – this trade-off can be controlled by the user based on the specific design goals. We will clarify this in the revision. \n\n[1] Wakui, M., Fujimori, Y., Nakamura, S., Kondo, Y., Kuroda, Y., Oka, S., … Murata, M. “Distinct features of bivalent direct thrombin inhibitors, hirudin and bivalirudin, revealed by clot waveform analysis and enzyme kinetics in coagulation assays.” *Journal of Clinical Pathology* 72.12 (2019): 817–824. \n\n[2] Skov, M. J., Beck, J. C., de Kater, A. W., & Shopp, G. M. “Nonclinical safety of ziconotide: an intrathecal analgesic of a new pharmaceutical class.” *International Journal of Toxicology* 26.5 (2007)\n\n[3] Karoyan, P., et al. “An hACE2 peptide mimic blocks SARS‑CoV‑2 pulmonary cell infection.” *bioRxiv* (2020): preprint. \n\n[4] Lechner, A., Jordan, P. A., da Cruz, G. C. M., Burk, M., et al. “Overcoming Immune Checkpoint Inhibitor Resistance via Potent and Selective Dual αvβ6/8 Inhibitors Based on Engineered Lasso Peptides.” *bioRxiv* (Feb 2025). \n\n>**W4: Performance Validation**\n>**Q5: Can results without retrieval components be provided?**\n\nThank you for the suggestion regarding validation. To evaluate the no retrieval setting, we conducted an ablation study to assess the impact of using RAG at the sampling stage. The results are summarized in the tables below:\n\nFor HCDR3:\n\n**Table S3: Ablation study on the retrieval stage for HCDR3**\n\n|Settings|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|Without Retrieval|51.55|0.9525|0.0524|\n|With Retrieval|54.63|0.9682|0.0485|\n\nAnd for Peptide:\n\n**Table S4: Ablation study on the retrieval stage for Peptide**\n|Settings|AAR (%)|RMSD (Å)|Codesign Diversity|\n|-|-|-|-|\n|Without Retrieval|37.94|2.38|0.5042|\n|With Retrieval|39.42|2.29|0.5227|\n\n\nWe could notably find that using no retrieval sample performs significantly worse than using any retrieval, although it does not cause catastrophic failure.\n\n\n>**Q3: Does the current dataset splitting strategy overestimate model performance?**\n\nWe appreciate your concerns regarding dataset splitting and potential overestimation. Based on our analysis and responses to Q2 and W2, we would like to clarify that our dataset splitting strategy follows the established conventions in this field. We employ a standardized dataset splitting approach for benchmarking, consistent with prior work. Furthermore, we have conducted extensive analyses to demonstrate that our evaluation is unbiased and does not overestimate model performance.\n\n>**Q4: Could authors adopt more reliable performance metrics?**\n\nThank you for your attention to our model evaluation methodology. We agree that assessing the generated binder is a complex issue. To provide a more comprehensive evaluation, we have introduced several robust metrics, as shown in the table below.\n\n**Table S5: Extended evaluation metrics**\n|Model|DockQ|Foldx $\\Delta G$(KJ/mol)|Binding Site Recovery|\n|-|-|-|-|\n|HCDR3|0.9582±0.0011|-8.4250±0.0415|0.9964±0.0002|\n|Peptide|0.7698±0.0003|-7.9891±0.1407|0.9857±0.0003|\n\nAnd here are brief explanations of each new metric:\n* **DockQ**: DockQ is a continuous quality score for protein–protein docking models ranging from 0 to 1, integrating F\\_nat, LRMSD, and iRMSD to assess agreement with native complexes, where values ≥ 0.23, ≥ 0.49, and ≥ 0.80 correspond to acceptable, medium, and high‑quality respectively [1].\n* **FoldX $\\Delta G$**: FoldX computes the binding free energy of a protein–protein complex as complex $\\Delta G$ minus the sum of individual $\\Delta G$ values of its partners, with negative values indicating favorable (stabilizing) interactions[2].\n* **Binding Site Recovery**: Binding Site Recovery denotes the proportion of true interface residues correctly predicted by the model, i.e. the fraction of real binding-site residues recovered in the prediction (typically expressed as a percentage) — this metric is commonly used to evaluate interface residue prediction accuracy.\n\n[1] Basu, Sankar, and Björn Wallner. \"DockQ: a quality measure for protein-protein docking models.\" PloS one 11.8 (2016): e0161879.\n\n[2] Bheemireddy, Sneha, and Narayanaswamy Srinivasan. \"Computational Study on the Dynamics of Mycobacterium Tuberculosis RNA Polymerase Assembly.\" Prokaryotic Gene Regulation: Methods and Protocols. New York, NY: Springer US, 2022. 61-79.\n\nAs can be seen, the new metrics exhibit strong consistency with the previous ones. Moreover, our model also demonstrates robust performance on these extended metrics."}]

Author rebuttal:

Meta-review: The paper presents RADIANCE: a RAG-based method for generating protein binders. Briefly, the method aligns pairs of binders in a latent space through contrastive learning. A generator, trained as a VAE model, takes latent space vectors and decodes binders conditioned on the binding site. Additional information about (top-n) related systems is integrated in latent diffusion process to generate binders. 
The paper is methodologically interesting and evaluation is done carefully. Especially after the review, rebuttal and discussion, several issues concerning for instance, data-leakage, was quite convincingly resolved.
Decision: Accept (poster)