Distilling Causal Signals for One-Shot Directed Evolution of Antibodies
Authors: Sai Pooja Mahajan
Natasa Tagasovska
Stefania Vasilaki
Arian Rokkum Jamasb
Andrew Martin Watkins
Rajesh Ranganath
Venue: ICLR 2026 Poster (2026)
Keywords: One-shot learning
matching
directed evolution
antibodies
structure-embeddings
causal learning
OOD generalization
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: AffinityEnhancer boosts antibody affinity from a single sequence, without structure or antigen data, by learning from matched high– vs. low-affinity pairs, capturing generalizable binding features and outperforming SOTA baselines.

Abstract: Improving antibody binding to an antigen without antibody–antigen complex structures or antigen-specific training data is a central challenge in therapeutic protein design. We introduce **AffinityEnhancer**, a framework for one-shot antibody affinity improvement with strong generalization: given a single lead sequence, we propose variants that increase affinity without fine-tuning on the lead and without using antigen information, epitope/paratope labels, or the lead’s structure in complex with the antigen. During training, AffinityEnhancer leverages a pan-antigen dataset of diverse binding environments (antigens) and constructs paired examples of related sequences with higher vs. lower measured binding. A shared, structure-aware module learns to transform low-affinity sequences toward high-affinity ones, distilling consistent, causal features associated with improved binding across environments. By combining pretrained sequence–structure embeddings with a sequence decoder, AffinityEnhancer generalizes to entirely unseen antibody seeds. Across multiple held-out internal and public leads, AffinityEnhancer concentrates mutations on the rim of the paratope, outperforms existing structure-conditioned and inpainting baselines, and achieves substantial in silico affinity gains in true one-shot experiments, despite never observing antigen-specific data at test time.

Published as a conference paper at ICLR 2026
DISTILLING CAUSAL SIGNALS FOR ONE-SHOT DIRECTED EVOLUTION OF ANTIBODIES
Sai Pooja Mahajan 1.f
Natasa Tagasovska if
Stefania Vasilaki
Arian Rokkum Jamash
Andrew Martin Watkins
Rajesh Ranganath²
i equal contribution
AIDD, Genentech
Courant Institute of Mathematical Sciences, NYU maha jsl 70gene. com
ABSTRACT
Improving antibody binding to an antigen without antibody-antigen complex struc- tures antigen-specific training data is central challenge in therapeutic protein design. We introduce AFFINITY ENHANCER, a framework for one-shot antibody affinity improvement with strong generalization: given single lead sequence, we propose variants that increase affinity without fine-tuning on the lead and with- out using antigen information, epitope/paratope labels, the lead's structure in complex with the antigen. During training, AFFINITY ENIIANCER leverages a pan- antigen dataset of diverse binding environments (antigens) and constructs paired examples of related sequences with higher vs. lower measured binding. A shared, aructure-aware module learns to transform low-affinity sequences toward high- affinity ones, distilling consistent, causal features associated with improved binding across environments. By combining pretrained sequence-structto structure embeddings with sequence decoder, AFFINITYENHAL ENHANCER generalizes to entirely unseen antibody seeds. Across multiple held-out internal and public leads, AFFINITYEN- HAANCER concentrates mutations the rim of the paratope, outperforms existing structure-conditee condittone and inpainting baselines, and achieves substantial in silico affinity gains in true one-shot experiments, despite never observing antigen-specific data at test time.
INTRODUCTION
Antibodies are proteins produced by the immune system in response to foreign antigens. In therapeutic settings, antibodies have been developed as drugs against various cancer and autoimmune targets. They detect harmful antigens (such as bacteria and viruses) by the mechanism of binding, attaching to specific patch on the antigen's 's surface, called an epitope, using six hypervariable loops known complementarity-deeee deeermiinng regions (CDRs). A subset of the residues these CDRs form the antigen binding surface known as the paratope.
This ability to form highly specific paratopes which complementary in shape and chemical composition to a extensive repertoire of antigens confers unique therapeutic potential and makes high-affinity antibodies prime drug candidates. Having the therapeutic potential being driven by the binding mechanism, renders structure information as essential in developing solutions for this task. In the typical drug discovery pipeline, lead antibody with reasonably high affinity and specificity to the antigen of interest, is identified from immunized libraries extracted from animals, followed by optimizing the lead for potency and drug-like properties. Optimizing the potency of the lead routinely involves improving its binding or affinity to the antigen. This process is called affinity maturation. Experimentally, affinity maturation involves random or directed mutagenesis to generate large diversified libraries (known diversification or hit-expansion) followed by screening for stronger binding antibodies against the target. Such techniques are common in drug discovery pipelines and have been fairly successful over the last few decades. However, such diversification explores only a minuscule sequence space (~~ order of 108-109) of the entire sequence space (order of 25020, 20 amino acid residues every position of the variable domain which consists roughly of

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250 residues). As consequence, the resulting sets of designs be suboptimal and fail identify sufficient number of antibodies with the desired potency and drug-like properties.
A
Learning general modes of affinity maturation without explicitly modeling affinity
Antigon
Projected aftinity naturation space
paratope
Lower affinity
Higher aftinity
eparnden Learned cirection of affirity shhonoeners
Projected feutuure
Affiity
Affirity unhancing mutationa
Projected feature!
Pan-Antigen Matched dataset
AFFINIYENHANCER
Training
Ab1
Ab2
Matched sequences and predicted structures [Traiiing]
PKD=7.4
ED-4
Universal Protein Sequence-Sruccure Embedder
All Proteins (AF2 D8)
Antigen
Adjacency
Z.
PKD=9.1
Information
ED-2
Reconstruction Module
A: embedding
Dataset SKEMP12.0
Antigen 2
Z: Ab2 embedding
Reconstructed Ab2 enbedding
n
PKD:9.6
Loss f(2
Antigen
Embeddings to Sequence Decoder
POAS
Frozen/ Pre-trained
Affinity matured sequence
Inference and Validation
D
Held out 0OD seed sequence
Inference
Validation
EONL..CARRS DIVA...
Sampled sequences
binder? Improved affinity?
Oracle trained
seed data
AFFINITYNHANCER
Figure 1: One-shot affinity maturation of antibodies with AFFINITY ENHANCEER A) The goal is to implicitly learn modes of affinity maturation by pairing lower affinity antibody with a higher affinity one. B) Matched datasets are obtained by pairing antibodies against the same target/antigen from SKEMPI 2.0 database. C) Architecture for AFFINITY ENHANCER D) Inference and validation pipeline for held-out-seed to determine whether sampled sequences are binders not.
Computational affinity maturation with machine learning offers accelerated alternative random directed mutagenesis. However, the one-shot setting- -here a model must propose improved variants from single lead sequence without antigen context or fine-tuning-pose challenge: the lead may be far from the training distribution in sequence and structural features. This challenge compounded by the limited availability and diversity of paired antibody-antigen structures and affinity measurements, which impedes robust transfer to unseen targets (Hummer
key generalization
al. 2023).
To bypass the challenges associated with explicitly modeling affinity, Tagasovska et al. (2024) proposed Property Enhancer (PropEn), a property-agnostic model which utilizes data matching to implicitly learn the direction of the gradient for property of interest with the goal of proposing new optimized designs. was previously demonstrated that this approach works for a range of tasks, including affinity maturation of antibodies. However, its effectiveness was only demonstrated in sequence-baad based models and in cases where a few hundred sequences related to the lead molecule we wish optimize
already available in the training data, hence, not suitable to one-shot scenarios. In this work, we propose AFFINITY ENHANCER, a model that goes beyond the PropEn framework. namely to the one-shot affinity maturation setup by leveraging structure information and introducing
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novel, diversified matching procedure which allows for generalization and transferabilityy Our main contributions are as follows:
We propose one-shot model for affinity maturation without antigen information (section 3)
We leverage matching in heterogeneous datasets to bolster data-sparse regimes (antibody- antigen interactions)
We provide theoretical analysis supporting distillation and OOD transfer of causal features subsection 3.1)
On held-out datasets, AFFINITYENHANCER outperforms SOTA dructure-condition and inverse-folding baselines, with variants that improve lead-antibody binding without any antigen information (section 5).
BACKGROUND & RELATED WORK
Structure-based design. Most ML models targeted antibody design, including the design of target-specific antibody libraries rely on structure-conditionedd sequence generation, templated on the structure of the lead antibody or, when available, the structure of the antibody-antigen complex
(rreyer et al., 2023 Mahajan et al. [022] Watson et al., (023). Such crunttur-conddttooing is necessary in order to restrain the designed sequences to adhere to the shape of the lead antibody. The sequence space can be further controlled when the structure of the antibody-antigen complex is known. For antibody design, in particular, structure-conditioonnng models such as AbMPNN (Dreyer et a (023). AntiFold (Hoie et al. 2024), FvHallucinator (Mahajan et al. 2022 and MaskedProtEaT Mahajan et al. (2025) have demonstrated impressive performance on in silico benchmarks. On the other hand, de novo models such as RFDiffusion (Watson et al.) (023), follow two-step process. First, they design the backbone of the antibody given the context of the antigen, then follow by sequence design with ProteinMPNN conditioned on that backbone in complex with the antigen.
Sequence-based design. Alternatively, sequence-only models have been proposed to generate protein antibody sequences from a learned distribution near the seed. Examples of such models include discrete Walk Jump Sampler Frey et al. (2023), latent Walk Jump Sampler, ProGen2 (Nijkamp et al. directed evolution methods such Hie et al. )2 Tran & Hy (2024) and Tran et al.) (2025). The latter demonstrate that large protein language models or latent generative models can effectively prioritize mutations during iterative directed evolution campaigns, improving protein function given repeated rounds of target-specific screening. However, there are no existing approaches which address affinity enhancement a one-shot setting, and, in the absence of the antibody-antige antiign complex structure. Even de novo models such as RFDiffusion only guarantee binders (not improved binders) given a binding partner or antigen.
2022), as well language-model and satent-space-guie
Training with matched datasets. We adopt matching- based supervision scheme in which training pairs are formed by selecting, for each anchor the nearest neighbor such that (i) lies within an input-space radius (ii) achieves strictly higher measured affinity. This construction follows the spirit of PropEn which demonstrated that matching, implicitly recovers the ascent directions for a property of interest. Here we extend the matching to the one-shot antibody setting by including structure-aware embeddings and explicit environment control. In other words, in the requirements for matching, we add: (iii) pairing antibodies targeting the same antigen, i.c. same environment. Unlike PropEn, which uses sequence representation only, the AFFINITY ENHAANEESS matching operates in geometry induced by pretrained encoders and a residual graph transformer to map low-affinity embeddings to higher-affinity counterparts.
Conceptually, this pairing induces pairwise preferences (a' > I)). connecting our approach to preference learning (Zhang & Ranganath] 2025) methods such as Direct Preference Optimization (DPO) (Rfailov al. (023) from the LLM literature, where models are updated toward preference winners under KL. regularization. Preference Learning has recently inspired a new direction in protein design. For backbone generation, HHguett et al. (2024) introduce Reinforced Fine-Tuning (ReFT); a supervised fine-tuning pass on dataset filtered by auxiliary rewards to create preferential subset, effectively supervised fine tuning on matched positives. In antibody co-design, Zhou et al.) (2024) leam over paired samples by defining residue- energy preferences and optimize conditional diffusion model with a direct preference objective showing gains via energy decomposition and gradient-surgery to resolve conflicts. For peptide/protein binder design, (Mistani & Mysore 2024
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explicitly formulate multi-objective alignment with DPO curated chosen/rejected receptor- binder pairs, demonstrating that preference learning on matched datasets steers a protein LM toward binders satisfying specificity and developability (e.g., pl) constraints.
Although our setup is related to preference learning, there are two key differences. (1) Data and pairing. Preference learning typically uses (winner, loser) pairs indicating which sample has higher reward, without requiring the pair to be close in sequence space or to have similar measured values. In AFFINITYENIANCEE differ in measured binding-so that the learned transformation corresponds to realistic, stepwise improvements. (2) Optimization target. Preference learning usually updates a generator that maps a conditioning input (e.g., receptor) to an output (e.g.. a binder) to increase reward. In contrast, AFFINITYENHANCEE performs lead-conditioned improvement: given an existing binder (the lead), generates variants that are predicted to bind more strongly.
construct local improvement pairs-nearby sequence variants that
METHOD AFFINITY ENHANCER
We formalize AFFINTYENTIANCER as learning from matched improvements under fixed environ- ments. In what follows, we state the data-generative model from which training pairs are drawn. Then, we derive the constraints that make the signal dominantly causal.
Problem setup & method summary. Let X denote the space of antibody sequences and let y C. R denote measured binding affinities. We assume access to E environments indexed by e = I,..., E, where each environment corresponds to distinct lead antibody (we use the terms leads or "seeds" interchangeably). In environment we observe a small subset of sequences (order of 10) with measured affinities {aaj, vf)). Our goal for held-out environment e* (the "one-shot" seed corresponding to an antigen not seen in the training set), to propose a set of new designs that reliably improve on the lead affinity Viened despite never fine-tuning on e or using its antigen structure. To do so, we propose AFFINITYENHANCE
summarized in |Figure 1
1. Form matched pairs. M (xi, l//ee e') in every environment by finding, for each low-affinity sequence x;. the nearest neighbors x² whose measured affinity is yo > yi. under capped distance threshold &z.
2. Extract embeddings. For each antibody in the matched pairs, extract sequence-structuue embeddings from a foundational model vé X - R 1xxd
3. Learn worse embedding-> better embedding map. Given matched embeddings, learn Graph Transformer G9 acting over residues
f(z) + A,
== (+) Bixd
where A is residue residue adjacency (from predicted structure) and P pos/edge features.
4. simbedding--sequeee decoder. Train hight-weight decoder p : R.xdd - X that maps per-residue embeddings to amino-acid distributions.
5. Sampling for OOD lead. At test time, compute 2tead (((iss))) and apply residual map
E
Zlend Gol Stend; A, P), f = =()).
3.1 FROM MATCHED DATA TO CAUSAL SIGNALS
Data-generative process. We posit that factors into latent components:
I f(s.c) c) y = h(c,e)
where e collects the causal factors that determine affinity and s collects spurious factors (such as batch effects, library or lead/antigen idiosyncrasies, etc.) that influence x but not once the environment fixed. e causally affects y for fixed e. In an idealized world, we would sample independently
c c~ c~q(c), s~ g(s), e~ g(e), x= f(s,)), y= N(cee)
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In practice, selection Pearl 2009) induces dependencies: only some (c. assayed, and not every x is tested in every e. The observable joint is therefore summarized as
(c. p(c, s) f(s,c), c), e ple z), h(c, e),
In particular, depends on the target i.e. environment hence and may spuriously correlate with y through selection rather than causation.
Matched pair selection as targeted conditional. For each anchor assayed in environment with outcome y, we seek nearby variant x that improves the outcome, the same environment:
p( [r, d(x, x') < e, y Ay, e' = c)
with distance d on X, a small neighborhood radius & 0, and an improvement margin Ay > Conditioning on e' e removes environment-driven gains; only changes in x can explain improvements. This conditional represents the data-matching rule that defines our train set. For simplicity we include deterministic analysis free of measurement noise.
Assumption 1 (Property smoothness). For fixed e, the property function is Ky-Lipschitz in the causal latent:
[1(c1, e) h(c2. c)l S Kyd(cı, 02).
Assumption 2 (Responsive observationn/i-lLipcchic renp:h/-----iiicciiieeeeeeerr renderer). There exists K2 such that for all (c, (d,
K₂
and the latent metric decomposes additively,
d([c, s), lc', &')) d(c, d(c,c) d(s,s').
Intuitively, small moves in x, imply small moves in the underlying factors; no large cancellation can hide big change in e by mouttr-movving. Theorem 1 (Improvement Bounds). Consider matched pair x') measured in the same environ- ment with
d(r,x') < and --y-hcc,e M(.,e) > Ay 0.
Then:
1. (Minimum causal movement)
d(c'.c) Ay/Ky.
(3)
2. (Sperinn cap) If. in addition, KBg Ay/ Kg 0. then
d(s's)) < Kg - A Ky-
(4)
Proof. From equation and AI,
A, h(d,e) h(c,e) S d(d,,)) d(d'c) Ay/Kg,
which proves equation 3 Next, by equation and A2,
d(d(,)) d(s', s K₂ (s,,)),(((s) S K.E. Subtracting the lower bound on d(c', c) from the left-hand side yields d(s',s) K₂ d(c',c) K Ay/Kg,
establishing equation 4 whenever the right-hand side is nonnegative.
The matching rule is feasible only if K,s Ay > 0: otherwise no pair can simultancously be K, close in and improve y. From equation 3 and equation each pair enforces minimal step along causal directions and leaves a strictly bounded budget for spurious drift. Hence, the supervision from matched improvements is dominated by causal variation.
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Training AFFINITY ENHANCER Let (xx) be sequence-strructun embeddings (frozen).
The embedding-to- seaeg-to-embeee -o--emeedddingg module learns a residual map fo(2) = 2 Go(z; trained to reconstruct matched targets in embedding space, by minimizing
(()) = |M Feer" - -
At test time, for a held-out lead Itead in unseen environment e" we compute Eload = (D apply the residual map f (Ptea)), and decode x² p(2).
Why this objective isolates causal signals? By equation Jand equation each training pair constrains the model with guaranteed minimum shift in the causal coordinates and tight upper bound on spurious motion. Averaged over many environments, spurious directions fluctuate and cancel, while causal directions align across pairs; minimizing & therefore compels Gø to model the shared environment invariant components that consistently explain affinity gains.
Given the selection rule equation I and the assumptions, every matched pair obeys
d(c', c) Ay/Ky and d(s',s) < a Ay/Ky,
so the training signal is necessarily causal movement plus bounded spurious residue. AFFINITYEN HANCER exploits this by learning residual embedding- space operator that reconstructs matched targets and, at inference steps in the same causal direction on held-out seeds. This "invariance-bl matching" view will underlie experiments that follow.
EFHINITYENHANCER IMPLEMENTATION
Our theoretical formulation proposed above lends direct implementation in AFFINITY EN- HANCER which consists of three main modules (Figure IJ). The structure and sequence embedder (Embedder), the reconstruction module and the embeddings sequence decoder (Decoder) module. The Embedder embeds the antibody sequence and structure to semantically meaningful embedded space. To this end, we utilize GearNet (Zhag al. (2023), a representation learning model trained on 600k sequences and structures from the AlphaFold2 database. To map the embeddings to antibody sequence, we trained a sequence decoder which maps GearNet (frozen) embeddings antibody sequences on the paired Observed Antibody Space (pOAS), (Olsen et al. 2022). Once the sequence decoder is trained, is also frozen. The reconstruction module, Graph Transformer (GT), learns to reconstruct the embedding of the lower affinity antibody to the embedding of the higher affinity antibody. The reconstruction module is trained on the matched datasets prepared from SKEMPI 2.0. These modules allow us to embed sequences massively large database of protein and antibody sequence and residue environments. Utilizing these pretrained modules allows us leverage learned representations from all proteins and antibodies and generalize to blind or unseen test seeds.
general embedding space that is trained
EXPERIMENTS
The main challenge we address is whether is possible to propose sequences of affinity enhanced designs starting from a single lead antibody sequence without any context or structure related to the antigen. Our validation pipeline is included in Figure 1B. We train AFHINIYENHANCEE on a matched dataset that excludes any sequences in the vicinity of held-out seeds. Additionally, we utilize a predictive model, Cortex, (Gruver et al.) (223) (Appendix D) trained and validated on labeled expression and high-quality affinity data in vicinity of the held-out seeds. We then propose designs with ENHANCER and predict the binding and affinity for the proposed designs with the oracle.
Metrics. We evaluate sampled designs by reporting edit distances from the seed sequence, the number of designs that are predicted to be binders, and number of improved binders over the seed. Additionally we include the binding and improved rates as well as the average performance across seeds ease summarizing the performance per baseline.
Baselines. We compare AFFINITY ENHANCER to three baselines PropEn, trained on the same matched dataset AFFINITYENHANCER, AntiFold, an antibody-specifie, structurecoondttioned

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15
No Matching
AlfinitYErhancer
No Embedding
Seed
Framework
L000 0o00
Edl dhlnnc
000
Seed
Edit datanne N
10000
9
TrascuzumaD
1000
restaus
Figure 2: AFFINITYENIANCE identifies distinct and structurally important positions for each antibody. Each residue on the surface representation of the seed antibody is colored on a spectrum ranging from positions modified by the model 0.0 (white) percent of designs 20 percent (red) of designs. For Trastuzumab, we also show the antigen in gray. All antibody structure models were obtained with ESMFold. For Trastuzumab where the crystal structure of the antibody-antige complex structure is available, we aligned the ESMFold structure to the crystal structure to map the position of the antigen.
inverse folding model and IgCraft (Greenig al. 2025) antibody-speche generative inpainting model. For further details on positioning AE wrt baselines, see Table 3
Ablations. We systematically explore the effect of each component in ENHINTYE, dataset matching, embeddings, adjacency information and model architecture (local sequential kernels convolutional neural networks, versus adjacency-informed graph transformers).
Held-out seeds. We evaluate a true one-shot regime on four held-out seeds (three internal antibodies and the public Trastuzumab), each substantially out-of-distribution from training (full-sequence edit
distance 64-87: Table 4). Table quantifies potential germline overlap between train and test sets.
5.1 RESULTS
APPNITYYNHANCE targets edits that retain and improve binding. We asked how each model prior localizes affinity-enhaneine edits across the antibody and, when known, at the untibody-antigen interface. In Figure 2A-D and Figure we compare edit distance (leftmost panels) with the positions of edited residues on the binding surface (top-view CDRs). The model without matching ("No matching") serves as the baseline: it proposes few, nearly uniform edits across CDRs and frameworks, with no clear positional preferences (aside from Seed 2). In contrast, models trained with the matching intervention show distinct spatial patterns. The CNN variant makes more edits overall, spanning both (GT) variants concentrate edits in CDRs; and the "No
CDRs and frameworks; Graph-Transformn Embedding" ablation makes the fewest framework edits. Across seeds, matched models repeatedly target protruding CDR motifs. For Trastuzumab, where the interface is known, many edited positions fall in direct contact with the antigen (Figure 2)).
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A
Crystal
ESMFold
68-
0.-
66-
0.21
Amivantamab
0.0
min of www.iiaaa from www.geo
$
Crystal
ESMFold
24
Figure 3: AFFINITYENHANCER primarily edits positions at the rim of the sauibody-antigen interface. A) Kernel density plots for the distribution of frequency of residue edited by AE versus its distance from the antigen interface for a test set of 96 blind seeds with crystal or ESMFold structures of the starting seed. B) Same A) but zoomed to show the density for the most edited positions C) (Top) Trastuzumab in complex with HER2 (PDB ID: 1N82) Cho et al.) (2003). (Bottom) Amivantamab in complex with human MET protein(PDB ID: 6WVZ) Neijssen et (2021). Most edited positions by the AFFINITY IFFINITYENIINNEE ENIIANCER are colored red. ESMFold structures of the seeds were used for C) and edits were mapped back the crystal structure of the complex. Proposed affinity -enhani-n positions are concentrated in the rim as opposed to the core of the binding surface.
Comparison with inverse folding and inpainting
B
Improved binder distributiion
Seed
Seed 2
Seed 3
Trastuaunab
AFFRMTVENHNC
10-1
CrCrf
ca-o
Peoporto
IgCraft (heavy only)
1011
0.2
0.4
0.6
0.0
ApK0
AptD
4pKD Ap
ápk)
Figure 4: One-shot guided sampling with AFFINITYENHANCEE A) Comparison of AFFINITYEN: HANCER with the antibody-specific druuctrr--coniitioon inverse folding model, AntiFold and inpainting model IgCraft. Distribution of predicted pK D (negative log10 of dissociation constant KD for unique designs with edit distance between [5.12] for 3 internal seeds and the Trastuzumab antibody. We report difference of the predicted pK D from the pK the seed. IgCraft designs were sampled for all-CDRs (IgCraft) or heavy chain CDRs only (IgCraft (heavy-only)) B) Distribution of affinity improvement for AFFINIYYNHANNCER AntiFold and IgCraft for designs with improved affinities (A pKD>> 0.05).
5.1.1 OUTPERFORMING INVERSE FOLDING AND INPAINTING BASELINES.
Figure 4 shows that AFHINTYENHACEE shifts the predicted- afffniity distribution upward and yields more improved binders than baselines across all held-out seeds. Across all seeds, AFFINITYEN: HANCER shifts the predicted-affinig distribution decisively upward relative to AntiFold. Whereas AntiFold-by by conforming to the seed antibody's structure-mosth proposes variants that retain binding with similar or lower affinity, AFFINITYENIANNE
consistently produces affinity-mproving designs for nearly every seed. The inpainting sequence based model IgCraft fails to propose CDR sequences (all CDRs or heavy-only CDRs) which retain or improve binding given the context of the framework residues. This further strengthens claim that models which lear antibody sequence distributions are insufficient to generate CDR sequences that retain binding. The magnitude of these gains also exceeds those from both AntiFold and the inpainting model IgCraft (Figure 3B).
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Table 1: Ablation study for AFFINITYENHANEEN We sample 5,000 sequences for Trastuzumab and for three internal seeds per model. ED = minimum edit distance to the parent; "ED window" counts designs with ED E [5, 12]. "Binders" and "Improved binders" are predicted positives and iffinit--mprooed positives, respectively. AFFINITYENHANCER uses GearNet embeddings, pOAS decoder, an adjacency adjacency-informee iinformed Graph Transformer, and data matching. PropEn is the sequence-only baseline from Tagasovska et al. (2024). For each model, sampling settings were chosen to maximize the number of designs sampled in the ED E [5, 12 range.
Model
window
Binders
Improved
Binder
Improved
AFHNTTEENHNNEER
Trastuzumab
7.9 1:11:.
4,815
3,970
1,575
79.4
%
31.50 %
Seed
6.5 1.6
4,382
1,105
22.
0.04 %
4,672
Seed
6.5 1.7
4.352
1.334
26.7
0.06 %
Meen over seedy
7.08
1,555
2,505
123
50.10 %
8.36 %
Seeds improved (Trastacumab
Seedr I-3)
/4
Trastzzushh
28.6
0.0
0.00
Seed
68.0
0.0
%
0.00 %
Proplin (- Stracture)
Seed
30.9
0.0
0.00
Meon over sends
55.8
0.0
0.00 %
Seculy improved (Trasiacummb Seeds [-3)
/4
Trastuzumab
2.8
3.24
Seed
2.6
253
45
0.91
0.00 96-
APRNITYENHANCER (- Marching)
Seed
3.4
964
838
696
16.8
13.92 %
2.3
47
0.00
Mean over seeds
2.78
138
351
215
6.61
4.29 %
Seeds improved /Trastaqumab Seedr I-3)
2 /4
Trastuzunal
3.1
7:1
2,366
171
134
3.4
2.68 %
AFINTTYENHANCER (- Embredding)
Sced 2
7.6
3. 386
3.457
112
69.
2.21 %
Seed
7.3
1,092
1,737
34.7
0.08
Moon over
2,001
1,351
27.02
1,32
Seeds improved (Trastauumab Seeds I-3)
4
/4
Trastuzumab
8.0
4,570
2,027
121
10.5
2.18 %
3,812
AFRINIYYENHANCER (- Graph Tronsformer)
Seed
17.0
13
13
0.3
0.00 %
Seed
9.4
4,719
89
1.8
0.04 %
seeds
11.40
3,279
16.07
Secds improved (Trasiacumub Seeds [-3)
2
Trastuzumab
6.8
4,196
3,257
1,951
65.9 %
39.02 %
3,139
AFFNITYENHANCER (- Adjacency Matris)
Seed
9.1
4,709
2,873
38
57.5
0.76 %
Sced
T.2
1,123
659
13.2
%
0.00 %
Mean seeds
8.45
4,332
1,752
499
35.04
9.98 %
Seeds impaseed (Thosteummah
Seeds 1-3)
5.1.2 ABLATIONS: WHICH COMPONENTS OF AFFINTYYENHNNEER MATTER AND WHY.
Sequence-Ore Only Baseline: We first compare AFFINIYEENIANCER PropEn (sequence-only) across Seed 1-3 and Trastuzumab (Table 1). PropEn proposes designs more than 25 edits from the seed in every case, i.e., it fails generate variants in the seed's neighborhood; none of its designs predicted binders.
Across all seeds, AFFINITYEHHANCER generates designs close to the seed (Tables 1), with 26-78% predicted binders and non-zero counts of improved binders for each seed, Edit distance is controllable via sampling (fteration//emperatuur)))) enabling edits at higher settings (Tables 2-5, Figure 8).
edits low settings and larger
(- Matching) Autoencoder Without Guidance: Removing the matching intervention reduces the model to an embedding-space autoencoder. This yields low-diversity proposals clustered near the seed and few binders or improved binders (notable exception: Seed 2). Matching therefore critical for shifting probability mass toward functional. higher-affinity regions.
(- Embeddingg Generalization Without the Embedder: Without GearNet embeddings and the POAS decoder, the model still produces some improved binders across all seeds and, for Seeds I and 3, the most improved binders among ablations. This suggests that structural priors plus matching capture useful causal signal even without the embedder. However, sequence diversity and binder counts counts-especialll especially for Trastuzumab lag the full model. Furthermore, edit distances are less controllable and limited to a single iteration (Tables 2-5, Figure 8).

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(CNN) Weaker Structural Prior, Weaker Binders: Replacing the Graph Transformer with CNN (PropEn-style) increases edit distances and weakens edit-distance control (Tables 2-5. Figure 8). Binder and improved-bindee yields drop substantially, indicating that the GT's relational bias is important for localized, functional edits.
(-Adjacency) Losing Contacts, Losing Control: Using fully connected Graph Transformer (no adja- cency) similarly inflates edits and reduces controllability with sampling knobs (Tables 2-5, (Figure 8). This highlights the role of explicit adjacency in guiding compact, physics-aware modifications.
5.1.3 COMPARISON TO EXPERIMENTAL DATA AND BIOLOGICAL INSIGHTS
We probe what AFFINITY ENHANCER (AE) learns beyond improving an in silico oracle score: (i) whether its edits localize to biologically plausible regions of the binding interface without antigen con- text, and (ii) whether the positions and substitutions it prioritizes align with experimentally validated affinity effects. We focus on publically available, structurally characterized, out-of-distribution (OOD) antibody seeds with low similarity to training and no germline overlap (details in Appendix [..).
AE localizes edits to the interface rim without complex structures. Mapping per-residue edit frequency onto solved antibody-antigen complexes shows consistent pattern: AE concentrates edits near the rim of the interface. while making few edits in the core (Figure 3) and non-interface residues. This is biologically meaningful because affinity improvements, particularly from already, strong leads- Seadn-often arise by extending or refining peripheral contacts rather than perturbing core interactions. The same trend holds within CDRs: the most frequently edited CDR residues are enriched at the interface rim (Figure 5). Together, these results suggest AE infers binding-relevant regions from antibody-only input, without using antigen information at train or test time.
AE highlights experimentally high-impact positions. For the G6 antibody, where large-scale single-mutant affinity measurements are available Koenig et al. (2017). positions most frequently edited by AE are associated with larger experimentally observed improvements; in particular, the top edited positions include those attaining the maximum measured gain (Figure 6/A). On three additional seeds from Minot & Reddy (2024) with thousands of experimentally assayed variants (bind/no-bind)) AE's top-edited positions avoid sites where substitution almost always abolishes binding and instead concentrate positions exhibiting informative, context-dependent effects (Figure 6B). This behavior is consistent with learning where edits are both impactful and tolerable.
AE proposes semantically meaningful substitutions. In cases where experiments indicate electrostaticc--diven improvements, AE proposes qualitatively consistent substitutions. For Seed 1, experimentally beneficial mutations include substitutions negatively charged residues (D/E); AE similarly proposes such substitutions and yields variants with correspondingly more negative electrostatic surface at the relevant CDR region (Figure 7A).
6 CONCLUSION
In this work, we tackle the ohe-shot task of affinity maturing a lead antibody for blind or unseen seeds. AFFINTYYEHANCEE combines dataset matching with pretrained Soquence-structuu representations, an antibody-specifte decoder, and lightweight structural priors to propose targeted edits directly from the lead sequence. Empirically, we show it recovers binding-relevant features from sequence alone and generates aftinity-enhancing mutations. Across held-out evaluations, it outperforms sequence- inverse-folding baseline, and requence-inpaintie model,
only PropEn, a dtructure-conditie sampling variants with consistently higher affinity. Unlike reconstruction-driien approaches, AFFINI- TyEnhaNcer is designed to discover causal, iefinit-imprroiigg mutations -iildding practical gains for directed evolution.
Beyond accuracy, ENHANCEE offers practical advantages for directed evolution: it general- izes to new seeds in one-shot regime, provides controllable edit distances for risk-aware exploration, and remains data-efficient by leveraging pretrained biomolecular priors. These properties make it a useful drop-in companion for antibody lead optimization when structural complexes or large creates a clear path for
labeled datasets are unavailable. Looking ahead, AFFINITYNNIIAC further gains. Incorporating epitope antigen context could disambiguate multiple plausible routes to improvement, while expanding labeled affinity resources will broaden coverage of binding modes.
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7 REPRODUCIBILITY STATEMENT
We will release (i) the exact SKEMPI-derived matched pairs (IDs and thresholds), (ii) code to recompute matches from raw SKEMPIpOAS, (iii) all hyperparameters, (iv) pre-trained weights for and Gg. and (v) seripts to reproduce all figures/tables from single make entrypoint. We report complete sampling settings and will upload anonymous artifact with code and models at submission time.
ACKNOWLEDGMENTS
We thank Jan Ludwiczak for expertly curating and quality-controlling cetbody-antigen complex structures from the SAbDab dataset used in this work.
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APPENDIX
CONTENTS
Introduction
Background & Related work
3
Method Affinity Enhancer
4
From matched data to causal signals
AfFiNITYENHANCEE Implementation
5
Experiments
Results
5.1.1
Outperforming inverse folding and inpainting baselines,
8
5.1.2
Ablations: Which components of AFFINTYEEHANCER matter and why.
5.1.3
Comparison to experimental data and biological insights
10
Conclusion
10
Reproducibility Statement
11
Probabilistic bounds for Theorem
14
Affinity Enhancer Model and training
15
Affinity Oracle
15
AntiFold and IgCraft
16
Additional results from experiments
16
Overlap between training set and test seeds
16
E.2 Learned biological features
16
Comparison of Affinity with oracle-guided latent models
18
Model Ablations
19
A
PROBABILISTIC BOUNDS FOR THEOREM 1
We now allow only affinity measurements be noisy, with noise independent of the environment. The sequence/embeddiing path is noise-free. Specifically,
(b)))
Nobs M(c, e) Evy
where Ey is zero-mroee mean sub- Gaussian with proxy of i.i.d. across samples, and independent of (e, s).
(The sub-Gaun Gaussian assumption yields tight, wwvironment-agnosti high-probability margins; weaker moment assumptions possible with looser bounds.)
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Observed matching rule. We form matches using observed improvements in the environment and exact embedding proximity:
(x)x), (b()) <E,
Yoha
Yobs > Dy: 6=d. d.
(5)
bioh--roobbbiltyy causal movement. Since Vots Yoba = (h(c,e) - h(c, e)) + (5g Ey), sub- Gaussianity implies that for any 8 E (0, 1) there exists
T = y 2 (1/6) s.t. By A1, with the same probability,
Ay - (8) 2
Ay -r)(( K
d(c'.c)
(6)
High-probability spurious cap (no z-noise). Assume A2 and denote == 10(x). From equation 5 d(d,c) + +d(s', d(s',8) & K2 Combining with equation yields, with probability 2 5,
A, (0) Ky
d(d'8) kS
Feasibility and interpretation. Non-vacuoue guarantees require Ay > (0) and KGE (A, (o()))/ Ky 2 0. Because E, is independent of the environment, the margin r (6) is uniform across all Equations equation (quution 7 are the noise-robust analogues of the deterministic bounds equation 3 Eequaaioon 4 when is noisy.
B AFFINITY ENHANCER MODEL AND TRAINING
Dataset: The matched dataset was prepared with an edit distance threshold of 5 and a pK D threshold of 1.5.
Inputs and embeddings: All structures are predicted with ESMFold. Per-residue GearNet embed- dings concatenated over all layers (dimension=3072) are obtained for the full fv (heavy and light chains), followed by padding to fixed heavy and light chain length of 151 and 150 respectively.
Sequence decoder:
The sequence decoder a 2-layer multi multi-layer perceptron with hidden dimension of 32 and ReLU activation. The decoder is trained with the GearNet embeddings (frozen) on the paired Observed Antibody Space (pOAS) Table
Model: AFFINITYENHANCEI has 4.2M parameters The Graph Transformer was adapted from has
lucidrains implementation on Github (https://github.ccmm hot.hbubb.oommuuudduuu///gggpppp wttp://dddrriitreraaprrararrnaae coansformer-pytorree 4 blocks. Each block consists of normalization layer, attention layer, gated residual connection with 4 attention heads and a hidden dimension of 256. The model was trained for 200 epochs. Training times on trainset of size 2200 whereas Trastuzumab was trained on trainset of size 1300 (after removing all sequences in the vicinity of the seed). At inference, it takes 3-3.5 mins to generate 5000 samples (batched inference with batch size of 64) on single A 100 or a G5 GPU.
function of dataset size reported in Table Seeds 1, 2 and 3 were trained
C AFFINITY ORACLE
For our in silico validation, we use Cortex Gruver et al. 2023), a multi-task fine-tuning framework that leverages pre-trained language models for Biological Sequence Transformation and Evolutionary Representation (LBSTER) (Frey et al.] (024) to predict multiple properties of interest (e.g., binding and expression). Cortex has been trained on diverse set targets, including the leads and surround- ing data used in this manuscript. On our in-distribution evaluation, Cortex achieves 82.9% mean cassifiiass accuracy with 0.4% standard error, and a Spearman correlation of 0.90 with measured pKD. Per lead, binder accuracy is 72.4% (Seed 1), 62.0% (Seed 2), 70.0% (Seed 3), and 78.4% (Trastuzumab). This oracle has also been recently suggested in an extensive lab-in ieb-in-thee -hethh-loop study for antibody affinity maturation (Frey al. 2025)
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Table 2: Model size and training cost (A100).
Model components
Component
Type
Params
Mode
Embedder
GearNet
20.1M
frozen
Decoder
MLP
99.0K
train
Autoencoder (total)
GearNet_MM MLP
20.2M
eval
Graph model
Graph Transformer
4.2M
train
Training time matched dataset size
#Pairs
Hours
GPUs
1300
2.5
2200
~
5100
14
7640
44
2
Table 3: Positioning of AFFINITYENHANCER with respect to SOTA methods.
optimization optimization
single-shot improved
AFTINTYENHANCER (ours) Property Enhancer [agasovska et al.
2024
AntiFold (Hele
Walk-Jump, aumison
EHEVO, LM-based ura 4024
IgCraft (rreenig et d. 2025
Directed
2025
Tran &
2024
D ANTIFOLD AND IGCRAFT
For AntiFold, for each seed, we sampled 5000 sequences at temperatures 0.2 and 0.5. For IgCraft, we sampled sequences with default parameters and for additional setting (lower sampling temperature of 0.05 and number of steps set to 10).
E ADDITIONAL. RESULTS FROM EXPERIMENTS
E. OVERLAP BETWEEN TRAINING SET AND TEST SEEDS
Table 4: Edit Distance and Sequence identity (SI) to trainset for test set seeds. Seeds 1-3 and Trastuzumab are used affinity enhancement experiments. Amivantamab was used structural comparison in Figure 3B. For reference, typical heavy chain is 120 residues whereas typical light chain is 106 residues in length. sequence identity of 90-95% ( 10-30 residues) is commonly used demarcate out-of-distribatiin samples for antibody sequences in prior works.
Seed
SI
heavy
light
full
L1
L2
L3
H1
H2
H3
Seed1
63
37
84
Seed2
69
28
35
69
3
2
4
6
Seed3
71
30
18
64
4
4
4
5
4
Trastuzumab
72
28
33
64
3
5
Amivantamab (structure comparison)
68
44
19
73
3
3
6
5
E.2 LEARNED BIOLOGICAL FEATURES
OOD seed selection. We selected 96 antibody seeds with experimentally solved autibody-antigen
complex structures from SAbDabl Dunbar et al. (2014). Antibodies selected with the following
criteria: 1) deposited since 2024 2) antigen type is protein 3) conventional antibody 4) resolution of
the complex structure < 3.0 Å5) sequence identity to the closest sequence in trainset < 0.70 and 6)
No v-gene overlap with the trainset. We report similarity statistics of the structural dataset in Table
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Table 5: Overlap between train and test set germlines. Seeds 1-3 and Trastuzumab are used for
affinity enhancement experiments, Amivantamab was used for structural comparison in Figure 3B.
Matching gene in train set
Seed
heavy V-gene
heavy J-gene
light V-gene
light J-gene
Seed
193
0
0
Seed 2
0
0
1785
Sced
85
0
179
Trastuzumab
72
0
813
Amivantamab (Structure comparison)
371
0
279
Table 6: Independent structural dataset with 96 antibody seeds with known antibody-antigee com- plexes and the maximum and minimum sequence identity (SI) and edit distances of the test set from the training set.
Closest/Farthest
SI full
ED full
ED heavy
ED light
Closest
0.70
69
13
19
Median
0.64
82
39.5
32.0
Farthest
0.52
103
63
57
Sampling and structures used at inference. For each seed, we generated designs with AE using antibody-only input. For comparison with experimentally measured affinities from (Minot & Reddy 2024) and Koenig al. (2017). we used the experimentally determined unbound antibody structure; otherwise, we used a predicted antibody structure from ESMFold. For results reported in Figurr 3) we used both experimentally determined holo antibody structures and ESMFold (unbound) structures. For simplicity we sampled 2000 sequences per seed (temperature= 1.0 and iterations= 7 using the same decoding settings as in the main experiments.
Edit- Edit-frreuency computation. For each seed, computed per- per-position edit incidence as the fraction of sampled designs whose amino acid at that position differs from the seed. We optionally report the top-k most frequently edited positions per seed.
Interface distance and core/rim definition. Using the anttgen complex structure, we computed for each antibody residue the minimum heavy-atom distance to any antigen atom. We grouped residues by distance bins and plotted edit incidence as a function of distance (Figure 3). We define interface using distance thresholds (4.5 Åbetween any atom on an antibody residue and the closest atom the antigen) and repeat the analysis restricted CDR residues (Figure 5).
Comparison to single-mutant affinity measurements (G6). For the G6 antibody, we used published experimental measurements of affinity changes for single substitutions (over 4000 mutants). For each position, we computed the maximum observed affinity improvement across tested substitutions and compared this statistic between positions preferentially edited by AE and those not preferentially edited (Figure 6A). We define "prefcrentially edited" as the top 10th percentile of positions by edit incidence.
Comparison to bind/no-bind variant datasets. For three additional seeds with large-scale bind/no- bind measurements (thousands of variants each), quantified for each position the empirical change in binding when that position is mutated relative to the seed, along with the variance of the binding outcome across substitutions. We then examined where AE's top-edited positions fall in this (mean-change, variance) space, verifying that AE avoids positions with near-deterministic loss of binding (high mean-change, zero variance) while emphasizing positions with context-dependent effects Figure 6B).
Electrostatic surface analysis. For Seed 1, we compared electrostatic surface potentials for the seed, an AE-designed variant containing negatively charged substitution in CDR H3, and top experimentally validated single-point mutation with a D substitution in CDR H3. Electrostatic Lerner
surfaces computed using APBS plugin in PyMOL Lerner & Carlson (2006); [Bkker al.) (2001); Dolinsky et al. (2004), using the same structural backbone and standard protonation settings (Figure 7A).
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Crystal
ESMFold
CDRs Crysial
ESMFold
2.0
0.8
0.6
0.4
6.1
8.
30
60
Crysta
ESMFald
CDRs Crysial
CDRs ESMFold
Fraction cwngrreeeteeeeeeeeeee
0.1
Hnterbce Ner Mer-iiaracc
0.6
06
0.6
16 03
0.5
0.5
24
16
16
(R)
ditanicee red frmanrignn
Figure 5: EFHINTYENNANCER primarily edits positions at the rim of the seribody-antiigen interface. A) Kernel density plots for the distribution of frequency of residue edited by AE versus its distance from the antigen interface (minimum distance of any atom on antibody residue to antigen) for test set of 96 blind seeds with crystal and ESMFold structures of the starting seed for all residues and cdrs only. B) Same as A) but zoomed to show the density for the most edited positions.
A
DMS affinity dataset (Koenig et al. 2017)
Binary aftinity dataset [Minot & Reddy 2024] change
measured apKo at postiin
1.0
Position Top 10 percentile model edits
0.9
True
False
change
0.7-
True Position percentilc model
0.0
0.4
Std. dev. change at position
Figure 6: Comparison of AFFINITY AFFNITYENHANCEE edits to experiments. A) Comparison deep muta- tional scanning data from Koenig et al. (217)). Distribution of maximum experimental improvement in pKD over seed for a position edited by AFFINTYENHANCER, B) Comparison to bin/no-bind experimentally measured datasets for seeds from Minot & Reddy (2024). Distribution of the mean versus the standard deviation of the change in measured binding from seed (i.e. bind to no-bind).
Higher mean indicates important of the position to binding. High mean and zero standard deviation indicates a position which upon mutation with respect to the seed amino acid will always lead to loss of binding. High mean and high standard deviation implies position important binding but also available for edits. "True" refers to a position being present in the top 10 percentile of the positions edited by the model for that seed.
F COMPARISON OF AFHINIYEN WITH ORACLE-GUIDED LATENT MODELS
A common class of protein optimization algorithms relies on generative models guided by ora- cles/predictors in latent space, with directed evolution approaches such as Tran & Hy (2024) and Tran et al. (2255) being representative examples. To compare AffinityEnhnneer to this framework, we focus on Tran & Hy (2024) and use the authors' implementation as shared in the public repository. To adapt the method for binding affinity, we first train the ESM2-based decoder the SKEMPI v2 dataset (after removing sequences close to Seed and Trastuzumab for the one-shot setup). We use the default parameters dee hidden dim 1280. batch size=256, Ir=5e-5, and num epochs=50. The validation MAE for the decoder 0.451 for Seed 1 and 1.167 for Trastuzumab. We then sample new sequences with steps=10, population=5000,
proposes per var=4, population ratio per
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Seed
Design (Top design)
Top Mutation (experiment)
Figure 7: Comparison of amino acid substitutions with experiments. Comparison of the electrostatic surface of the Seed, top design and a top experimental single-point mutant for Seed 1. Regions with the amino acid substitution in question is circled. Color scale: Blue-positive +5; white-neutral; red-negative- -.
mask=0.6. We choose lower number of steps than the default (60), since this yields designs closer to the seed, for which we have greater confidence in the oracle predictions.
We were unable to generate sufficiently large number of designs for MLDETTan & Hy (2024) with reasonable edit distances from the seed. For example, default settings yielded edit distances of >30 edits in the seed (Seed 1). We selected lower number of iterations obtain MIDE designs in the vicinity the seed. To match this setting, we also generated AffinityEnhancer designs with lower temperatures and iterations. We also compared predicted affinity values between MLDE and AE and found later to give better affinities. AE also primarily identified edits in the CDRs versus MLDE which made edits both CDRs and frameworks regions to the same extent. In all settings tested, AffinitYEnhane outperforms MLDE. (Table 7).
Table 7: Comparison of AffinityEnhancer (AE) for Seeds I and Trastuzumab with MLDE & Hy
(2024).
Method
ED
ED window
Binders
Improved
Binder
Improved
MLDE (low ED)
0.8
08/128
32/98
14.7%
AE (low ED)
Seed
5.2 0.46
283 497
103/283
36.4%
0.0%
MLDE
Seed
16.2 1.0
6/5000
0/5
0%
0%
MLDE
Trastuzumab
13.6 1.1
816/5000
0/816
0%
0%
AE
Seed
6,5
4382/5000
1.105
22.1%
0.04%
Trastuzumab
7.9
4815/5000
79.4%
31.5%
F.1 MODEL ABLATIONS
19

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Published as a conference paper at ICLR 2026
AFF INIT YENHANCER
matching CNN
Adjjeaency
Embedding
10
10
B
3000
B
100000
30
30
10
M 8
100
2010
.00
MEt A.t.n N
100
400
D-0tto
Figure 8: Edit distance distribution as a function of number of iterations at sampling for different model ablations A) Full length antibody B) CDRs only C) Framework regions only.
20

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Published as a conference paper at ICLR 2026
Table 8: Selecting sampling parameters for Trastuzumab with the maximum number of designs within an edit distance [5,12] from the seed sequence. We sampled 5000 designs for 3 internal seeds and Trastuzumab. AFFINITYEHHNNCH is the base model with GearNet embedder and pOAS sequence decoder, an adjacency-informed Graph Transformer, and data matching. PropEn is the sequence-only model from Tagasovska et al. (2024).
Model
Iterations
Temperature
Edit distance
# in [5,12]
0.7
1.1 0.3
0
1.0
2.9
20
5
0.7
3.9
118
5
1.0
4.8
2544
AFFINITY ENHANEER
0.7
5.7
2427
7
1.0
1.9
4815
0.7
11.3
3543
10
1.0
1.2
805
0.7
1.3
0
2.3
393
5
0.7
1.4
0
2.7
361
AFFINITYENHANCE (No Matching)
7
0.7
1.4
0
1.0
2.3
407
10
0.7
1.4
0
10
1.0
2.3
447
0.7
2.9
2
1.0
3.1
161
ETHNNTYENHANCER (No Embed)
2
0.7
37.2
0
2
1.0
41.0 3.5
0
0.5
7.3
2319
0.7
1.5
4570
1.0
10.8
3988
2
0.5
20.0 1.7
(CNN)
0.7
21.4
2
1.0
25.4
(
0.5
35.3 +
3
0.7
36.7 2.3
0
1.0
40.1 2.8
0.7
1.2
0
1.0
2.5
266
0.7
1.7
0
5
1.0
1.6
1049
AFHINITYENANCEER (No Adj)
7
0.7
2.1
6
7
1.0
4.9
2269
10
0.7
1.0
238
1.0
6.8
6.2
4196
21

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Published as a conference paper at ICLR 2026
Table 9: Selecting sampling parameters for Seed I with the maximum number of designs within edit distance of [5,12] from the seed sequence. We sampled 5000 designs for 3 internal seeds and Trastuzumab. AFFINITY ENHANCER is the base model with GearNet embedder and pOAS sequence decoder, an adjacency-informed Graph Transformer, and data matching. PropEn is the sequence-only model from Tagasovska et al. (2024).
Model
Iterations
Temperature
Edit distance
# in [5,12]
0.7
1.1 = 0.3
0
1.0
2.2
83
5
0.7
3.7
33
5
1.0
4.3
1985
AFFINITY ENHANEER
0.7
4.8
1148
7
1.0
1.6
4382
0.7
6.2
4178
10
1.0
10.4
4182
0.7
1.4
0
2.1
123
5
0.7
1.1
0
2.4
154
AFFINITYENHANEE (No Matching)
7
0.7
+.5
1
1.0
2.2
210
10
0.7
1.2
0
10
1.0
2.6
253
0.7
6.7
703
1.0
1.1
2366
ETHNNTYENHANCER (No Embed)
2
0.7
26.2
0
2
1.0
28.4
0
0.5
10.1
3645
0.7
11.2
3812
1.0
14.7
871
2
0.5
32.6 2 1.7
0
(CNN)
0.7
32.9
2
1.0
34.8 + 2.5
(
0.5
42.8
3
0.7
1.9
0
1.0
45.9 2.4
0.7
1.1
0
1.0
2.2
109
0.7
2.0
0
5
1.0
3.
754
AFHINITYENANCEER (No Adj)
7
0.7
3.9
76
7
1.0
5.2
2960
10
0.7
6.7
3848
1.0
10.7 2.3
3939
22

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Published as a conference paper at ICLR 2026
Table 10: Selecting sampling parameters for Seed with the maximum number of designs within edit distance of [5,12] from the seed sequence. We sampled 5000 designs for 3 internal seeds and is the base model with GearNet embedder and pOAS sequence decoder, an adjacency-informed Graph Transformer, and data matching. PropEn is the sequence-only model from Tagasovska et al. (2024).
Trastuzumab.
Model
Iterations
Temperature
Edit distance
# in [5,12]
0.7
2.7 + 0.7
3
1.0
3.9
1194
5
0.7
1.0
1293
5
1.0
1.8
4672
AFFINITY ENHANEER
0.7
1.2
3515
7
1.0
10.
4130
0.7
1.7
1619
10
1.0
17.6
48
0.7
1.6
0
2.3
443
5
0.7
1.5
0
3.4
557
AFFINITYENHANEE (No Matching)
7
0.7
1.6
0
1.0
3.1
672
10
0.7
1.7
10
1.0
3.5
954
0.7
1.2
1470
1.0
30.1 1.3
3486
ETHNNTYENHANCER (No Embed)
2
0.7
0
2
1.0
32.1 2.6
0.5
16.3 1.5
10
0.7
17.0 1.7
13
1.0
19.5
2
0.5
32.3 + 1.3
(CNN)
0.7
33.4
2
1.0
35.6 2.1
(
0.5
41.6
3
0.7
42.0 1.8
0
1.0
43.1
0.7
1.8
0
1.0
3.0
440
0.7
5.9
840
5
1.0
6.6
4353
AFHINITYENANCEER (No Adj)
7
0.7
7.0
2089
7
1.0
9.1
4769
10
0.7
10.1
3918
1.0
13.6 2.2
1547
23

---

Published as a conference paper at ICLR 2026
Table 11: Selecting sampling parameters for Seed with the maximum number of designs within an edit distance of [5,12] from the seed sequence. We sampled 5000 designs for internal seeds and is the base model with GearNet embedder and pOAS sequence decoder, an adjacency-infe infformed Graph Transformer, and data matching. PropEn is the sequence-only model from Tagasovska et al. (2024).
Trastuzumab.
Model
Iterations
Temperature
Edit distance
# in [5,12]
0.7
1.1 0.3
1.0
1.9
19
0.7
0.9
78
5
1.0
4.6
2106
AFHINITYENAANEE
0.7
1.0
1021
7
1.0
1.7
4352
10
0.7
1.4
4066
10
1.0
10.6 2.2
4062
0.7
1,1
0
1.0
1.9
39
5
0.7
1.2
0
5
1.0
2.1
37
AFFINIYYENANCEER (No Matching)
0.7
1.4
7
1.0
2.1
54
0.7
1.3
10
1.0
2.0
98
0.7
6.1
434
ETHNITYENHANCER (No Embed)
7.3
1992
2
0.7
23.7 2.1
0
0.5
7.1
834
0.7
7.6
2933
1.0
9.4
4719
2
0.5
16.7
SFRNITYENHANCER (CNN)
2
0.7
1.74
2
1.0
1.
3
0.5
26.3 1.5
0
3
0.7
26.9 1.8
0
3
1.0
29.0 2.3
0.7
1.1 0.4
1.0
2.9
31
5
0.7
0.9
5
1.0
1.4
747
AFHINITYENHANCE (No Adj)
0.7
3.2
199
7
1.0
5.4
3274
0.7
7.2
4423
10
1.0
11.3
3466
24

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Published as a conference paper at ICLR 2026
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Figure 9: Distribution of fraction of designs with edits per-residue for each model and seed. Black and red dashed lines mark the 50th and 90th percentile respectively.
25


Reviews:
[{"review_id": "NxXjohGGJU", "reviewer": "Reviewer_trYu", "summary": "The paper introduces AFFINITYENHANCER, a machine learning framework for one-shot affinity maturation of antibodies without requiring antigen or complex structural data. It builds on the concept of matched data (pairs of antibodies with measured affinity differences) and learns a residual mapping in embedding space from lower- to higher-affinity antibodies. The model uses GearNet for structure–sequence embeddings, a graph transformer to model causal affinity-enhancing directions, and a decoder trained on antibody sequence data. Experiments demonstrate strong in-silico improvements compared to baselines (PropEn, AntiFold, IgCraft) on SKEMPI-derived datasets and Trastuzumab.", "questions": "1.\tHave you evaluated how the model performs on antibodies with significantly different scaffolds or germlines (i.e., distribution shift)?\n2.\tHow sensitive is the method to the quality or number of matched pairs — could noisy or synthetic matches degrade performance?\n3.\tCould the model be extended to incorporate weak antigen information, e.g., paratope–epitope contact priors?\n4.\tDo you have plans for experimental or in-vitro validation to verify that the predicted affinity gains translate to real-world improvements?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clearly formalizes one-shot affinity maturation, which is both scientifically and computationally relevant.\nProvides a theoretical causal justification for why “matched improvements” capture meaningful binding signals.\n\nIntroduces a structure-aware residual map leveraging graph transformers and pretrained geometric embeddings.\nThe causal analysis (Theorem 1) connects local smoothness and bounded spurious movement to matched-pair supervision.\n\nComprehensive comparisons and ablations (matching, embedding, adjacency, transformer vs CNN).\nEvaluations on multiple seeds, including a public antibody (Trastuzumab).\nConsistent improvements over strong baselines.\n\nThe motivation and positioning against sequence-only and structure-conditioned baselines are clearly articulated.\nThe one-shot constraint is well justified with biological and data-availability arguments.", "weaknesses": "Experimental validation limited to in-silico predictions.\nThe study depends entirely on oracle models (Cortex) for affinity estimation.\nSome discussion of potential wet-lab verification or validation strategy would strengthen impact.\n\nGeneralization claim could be quantified better.\n“One-shot” evaluation is convincing qualitatively, but out-of-distribution metrics or diversity analyses are limited.\nNo discussion of potential failure cases (e.g., antibodies far from training distribution).\n\nComplexity and reproducibility.\nThe model stack (GearNet + decoder + graph transformer) is nontrivial; ablation on computational cost or scalability would help.\nThe reproducibility section promises artifact release, but details like parameter counts per component or training time are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2yrBjFGeeb", "reviewer": "Reviewer_WQqy", "summary": "The paper proposes AFFINITYENHANCER, a one-shot framework for antibody affinity maturation without requiring antigen structure or fine-tuning on target-specific data. The model learns to enhance antibody binding by training on matched pairs of antibody sequences with known differences in binding affinity, leveraging structure-aware embeddings (GearNet) and a Graph Transformer that maps low-affinity to high-affinity embeddings. The approach is motivated by the lack of antigen-specific data and aims to generalize to unseen antibody seeds through causal learning signals derived from matched improvements across multiple environments. Experimental results on public datasets reportedly show that the method outperforms existing structure-conditioned and sequence-based baselines in improving affinity in silico.", "questions": "How does your model differ in practice from prior ML-based directed evolution frameworks (e.g., LatentDE or LLM-guided directed evolution) beyond adding structure embeddings? Specifically, what unique biological or algorithmic insights does AFFINITYENHANCER provide that cannot be replicated by those prior approaches?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Proposes a clear formulation of \"one-shot affinity maturation\", which addresses an under-explored scenario.\n\n- The matching-based learning framework is well explained and connected to causal inference and preference learning.\n\n- Integration of sequence–structure embeddings and a residual graph transformer is technically sound and leverages state-of-the-art pretrained models.\n\n- The paper includes some theoretical grounding for causal signal isolation in matched-pair training.", "weaknesses": "- Limited novelty compared to previous works such as \"Protein Design by Directed Evolution Guided by Large Language Models\" (IEEE TEVC 2025) [1] and \"LatentDE: latent-based directed evolution for protein sequence design\" (MLST 2025) [2]. Both prior studies also model directed evolution or affinity optimization using pretrained representations and search in learned latent spaces; the main difference here is merely the inclusion of structure-aware embeddings.\n\n- The \"one-shot\" setting is more of a data-split protocol than a conceptual advance: similar generalization settings were already evaluated in LatentDE and related MLDE pipelines.\n\n- No experimental validation (either wet-lab or cross-docking) to confirm the claimed affinity improvement; results are purely in silico.\n\n- The methodological overlap with Property Enhancer (PropEn, 2024) is substantial, with only minor modifications (structure-conditioned matching).\n\n- The causal theory section adds mathematical formality but limited biological insight or empirical validation.\n\n*** References:\n\n[1] Trong Thanh Tran and Truong-Son Hy, Protein Design by Directed Evolution Guided by Large Language Models, IEEE Transactions on Evolutionary Computation, vol. 29, no. 2, pp. 418-428, April 2025, DOI 10.1109/TEVC.2024.3439690.\nURL: https://ieeexplore.ieee.org/document/10628050\n\n[2] Thanh V. T. Tran, Nhat Khang Ngo, Viet Thanh Duy Nguyen, and Truong-Son Hy, LatentDE: Latent-based Directed Evolution for Protein Sequence Design, Machine Learning: Science and Technology, Volume 6, Number 1, DOI 10.1088/2632-2153/adc2e2.\nURL: https://iopscience.iop.org/article/10.1088/2632-2153/adc2e2/pdf", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZCo0w2p8yc", "reviewer": "Reviewer_Mw4x", "summary": "This paper presents AFFINITYENHANCER, a one-shot framework for antibody affinity improvement: given a single lead antibody sequence, the method learns residual mappings in a structure-aware embedding space from matched (low-affinity, high-affinity) pairs collected under the same antigen environment, and decodes improved candidates. The authors report considerable in-silico gains against other baseline methods on several antibody seeds, including Trastuzumab.", "questions": "- The caption for Table 1  states: \"wet-lab positives...\". Please clarify: How was this \"wet-lab positive\" data obtained? Were any wet-lab experiments actually performed as part of this study?\n- Did the authors try using sequence-only inputs (i.e., no predicted structure) for the model? Since predicted antibody structures are often inaccurate.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The one-shot affinity-maturation problem addressed in this paper is practical and challenging in therapeutic antibody development. In real-world discovery scenarios, we frequently have only a single “lead” sequence and lack complex structural data or large matched datasets. \n- The authors propose a novel, matching-based computational framework for antibody affinity optimization that learns from matched low to high affinity pairs. This training strategy avoids relying on antigen–antibody complex structures during training and appears capable of improving performance in low-data antigens.\n- In the reported in-silico evaluations, the proposed model outperforms baseline methods on several held-out antibodies.", "weaknesses": "Major\n- Most of the reported improvements are evaluated using the Cortex predictor alone. Cortex itself is a deep-learning model and may introduce biases in the evaluation. The authors could supplement their validation with some other state-of-the-art predictors to provide a more comprehensive and robust assessment.\n- The method depends on predicted structures (ESMFold). Antibody structure prediction, especially for the highly variable CDR loops, remains a difficult task. If the predicted structure is inaccurate, the residual mapping may learn an incorrect transformation. The paper currently lacks a failure analysis or robustness test for this critical scenario.\n\nMinor\n- Line 48: miniscule -> minuscule", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)