Organizations: Arontier Co., Ltd., 241 Gangnam-daero, Seocho-gu, 06735, Seoul, Republic of Korea · Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea · Research Institute of Pharmaceutical Science, College of Pharmacy, Seoul National University, 08826, Seoul, Republic of Korea
Predicting immunoglobulin-antigen (Ig-Ag) binding remains a significant challenge due to the paucity of experimentally-resolved complexes and the limited accuracy of de novo Ig structure prediction. We introduce IgPose, a generalizable framework for Ig-Ag pose identification and scoring, built on a generative data-augmentation pipeline. To mitigate data scarcity, we constructed the Structural Immunoglobulin Decoy Database (SIDD), a comprehensive repository of high-fidelity synthetic decoys. IgPose integrates equivariant graph neural networks, ESM-2 embeddings, and gated recurrent units to synergistically capture both geometric and evolutionary features. We implemented interface-focused k-hop sampling with biologically guided pooling to enhance generalization across diverse interfaces. The framework comprises two sub-networks--IgPoseClassifier for binding pose discrimination and IgPoseScore for DockQ score estimation--and achieves robust performance on curated internal test sets and the CASP-16 benchmark compared to physics and deep learning baselines. IgPose serves as a versatile computational tool for high-throughput antibody discovery pipelines by providing accurate pose filtering and ranking. IgPose is available on GitHub (https://github.com/arontier/igpose).
Figures & tables
Figure 1: An overview of IgPose model architecture and its internal components. ( a ) The data preparation pipeline from data collection, data cleaning, and decoy generation with Chai-1 ( Chai Discovery et al., 2024 ) and Boltz-2 ( Wohlwend et al., 2025 ; Passaro et al., 2025 ) to data preparation and model training and evaluation. ( b ) The IgPose - equivariant message-passing architecture built upon EGNN ( Satorras et al., 2021 ) and a customized GRU module. ( c ) The architecture of the customized GRU module. ( d ) Alternative global pooling operators corresponding to different selective regions.
Dataset
Split
#Positive
#Negative
SID-CA
Train
3668
68907
Validation
1445
9133
Test
928
17092
SID-CB
Test
1001
16436
SID-R
Train
4668
10599
Validation
1921
1448
Table 1: Statistics of our Structural Immunoglobulin Database (SID) and CASP-16 benchmark dataset. Here, positive represents conformations that are either experimentally-resolved ‘native’ complexes or cognate decoys with DockQ scores ≥0.8 ; negative indicates structural decoys with DockQ scores <0.8 . SID-C and SID-R denote datasets for classification and regression tasks, respectively. SID-CA and SID-CB are two classification datasets containing decoy conformations generated by Chai-1 and Boltz-2, respectively.
Method
SID-CA
SID-CB
CASP-16
P
R
F1
AUC
AP
P
R
F1
AUC
AP
P
R
F1
AUC
AP
MIEnsembles
-
-
-
-
-
-
-
-
-
-
-
-
-
0.894
0.326
Prodigy
0.008
1.000
0.015
0.071
0.007
0.012
1.000
0.024
0.093
0.021
0.080
1.000
0.148
0.677
0.129
Rosetta
0.598
0.871
0.709
0.959
0.862
0.435
0.857
0.577
0.939
0.823
0.127
0.983
0.225
0.884
0.268
TRScore
0.249
0.108
0.150
0.623
0.124
0.281
0.113
0.161
0.632
0.138
0.241
0.192
0.214
0.781
0.189
GNN-DOVE
0.040
0.156
0.063
0.380
0.040
0.735
0.136
0.229
0.507
0.239
0.143
0.980
0.250
0.811
0.174
Table 2: Detailed classification performance comparison of our three implemented models (FastEGNN, MACE, IgPoseClassifier) and baselines on two internal test sets and CASP-16 on Precision (P), Recall (R), F1, AUC-ROC (AUC), and AUC-PR (AP) scores. Baseline methods produce unique results for each dataset, IgPoseClassifier’s results are from its deployed version. In IgPC-AbET setting, we perform weighted average on output probabilities of IgPoseClassifier and AbEpiTarget with weights of 0.7 and 0.3, respectively. Bold and underlined text represent the best and second best scores of a metric accordingly.
Figure 2: Model performance comparison across ( a ) classification and ( b ) regression tasks on our internal SID test datasets and CASP-16. Results of our three implemented models (IgPoseClassifier, FastEGNN, and MACE) are averaged over five executions and colored from pale to deep blue. All existing method results are colored from pale to deep green. In IgPC-AbET and IgPS-AbES settings, we perform weighted average on output probabilities/scores of IgPoseClassifier/IgPoseScore and AbEpiTarget/AbEpiScore with weights of 0.7 and 0.3, respectively. FS and FT refer to two IgPoseScore variants: training from scratch and finetuning from a IgPoseClassifier checkpoint. Error bars of our models represent standard deviation of five executions. Please refer to Table 2 for a detailed comparison of methods on five metrics. (Best viewed in color)
Figure 3: Representative decoy structures from our SIDD test dataset and CASP-16 competition submissions. The decoy structures – shown as cartoon – are categorized into four groups based on the IgPoseClassifier prediction: true positive, true negative, false positive, and false negative. Ground truth structures are shown as transparent surface. For SIDD dataset structures, the prefixes ‘Chai1’ and ‘Boltz2’ specify the computational method used for decoy generation, followed by letters that reference the original PDB-ID. For CASP-16 structures ( CASP-16, 2024 ) , ‘H12[number]’ denotes the CASP-16 target ID and ‘TS[number]’ corresponds to the participating group that submitted the prediction. PDB-IDs of ground-truth structures are shown in parenthesis. Each structure is accompanied by four numerical scores: (from left to right) global TMscore, global DockQ score, IgPoseClassifier score, and IgPoseScore. IgPoseScore was computed for true/false positive structures only. Asterisk (*) indicates DockQ scores provided by CASP-16 assessors; dagger ( † ) indicates incomputable DockQ scores, which apply exclusively to non-cognate Ig-Ag decoys. Color-coding scheme used to distinguish antibodies (Ab), nanobodies (Nb), antigens (Ag), T-cell Receptors (TCR), peptide-MHC complexes (pMHC), and single-chain variable fragments (scFv) is indicated at the bottom of the figure.
Figure 4: Comparison of Top-K success rates for IgPose and AbEpiTope on two benchmark datasets. Blue-shaded and green-shaded bars show the SID-R and CASP-16 success rates, respectively. Here, success rate is defined as the precision in Top-K: the proportion of true positive samples among the Top-K ranked predictions. Here, IgPoseClassifier or AbEpiTarget are first used to filter out predicted negative samples; the remaining predicted positives are then ranked by IgPoseScore or AbEpiScore; precision is then calculated among the top 10, 20, 50, and 100 ranked candidates. IgPose+AbEpiTope denotes the weighted ensemble variant of the two methods.
Strategy
Our Test Set
CASP-16
P
R
F1
AUC
AP
P
R
F1
AUC
AP
Pooling over all nodes
0.866
0.723
0.788
0.968
0.846
0.353
0.900
0.507
0.891
0.352
w/o interface nodes
0.627
0.868
0.728
0.959
0.756
0.157
0.908
0.268
0.902
0.525
w/o CDR nodes
0.598
0.824
0.693
0.959
0.713
0.192
0.908
0.317
0.916
0.529
w/o CDR-Epitope nodes
0.954
0.334
0.494
0.966
0.849
0.653
0.868
0.745
0.910
0.682
interface-only nodes
0.675
0.802
0.733
0.958
0.694
0.181
0.894
0.301
0.776
0.158
Table S1: Detailed classification performance comparison of pooling strategies on SID-CA test and CASP-16 on Precision (P), Recall (R), F1, AUC-ROC (AUC), and AUC-PR (AP) scores. Bold and underlined text represent the best and second best scores of a metric accordingly. Best checkpoints are selected based on AP scores on the evaluation set.
Figure S1: A comparison of performance of pooling strategies on SID-CA test and CASP‑16 benchmarks. Labels denote the set of nodes used in the global weighted sum pooling operation. ‘w/o’ denotes exclusion of the specified node set from pooling, while the suffix ‘only’ indicate exclusive use of the specified set of nodes. All node : weighted sum pooling over all nodes in a graph. Ensemble of 3 best : average of output probabilities from the three models corresponding to the pale blue bars. Further detail can be found in Section S1.1 .
Figure S2: Performance comparison of different data augmentation strategies on CASP-16 benchmark in AP score. Here, ‘interface’ and ‘CDR’ mean the selected seed sets for initializing the sampling procedure. ‘Nondocking’ is the setting, where inter Ig-Ag edges are removed from G . For a detailed description of augmentation strategies, please refer to Section S1.3 . ‘3-hop’ refers to a 3-hop BFS sampling procedure starting from a set of seed nodes and finishing once reaching a node threshold (600). All models perform weighted sum over all nodes in graphs.
Figure S3: Performance comparison across graph construction methods on CASP-16. Here, AA (all-atom) denotes the baseline setting, Cα means edges in a graph are established based on Cα distances, and EF represents edge features with a corresponding size. All models perform weighted sum global pooling over all nodes in graphs.
Figure S4: Change of probability distributions over three epochs for SID-CA test set (top) and CASP-16 (bottom). Probability distributions of the two test datasets change differently as training progresses.
Figure S5: AUC Score trends over 15 training epochs for IgPoseClassifier’s GRU variants on SID-CA test set and CASP-16. All models use the all-node global pooling operator in this experiment.
Figure S6: Performance of IgPose and top EMA methods reported in CASP-16 [ CASP-16, 2024 ] . We downloaded predicted results from CASP-16 and computed AUC, AP, and r scores.
Figure S7: Density distributions of Rosetta binding energy and Prodigy binding affinity scores. CASP-16’s structures show narrower distributions concentrated at lower energy values compared to SID-CA, reducing the discriminative power of energy-based scoring functions.
Type
SID-CA
SID-CB
#pos
#neg
#pos
#neg
Ab-Ag
701
679
738
153
Nb-Ag
198
14,230
228
14,126
TCR-pMHC
27
2,139
35
2,157
Table S3: Dataset Statistics for Ab-Ag, Nb-Ag, TCR-pMHC Subsets in SID-CA and SID-CB.
Figure S8: Performance comparison on Ab-Ag, Nb-Ag, and TCR-pMHC subsets.
Protein-protein interaction prediction uses sequence, structure, and biological networks to identify interacting proteins and their binding sites or poses.
Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input. We identify three failure modes that explain this behavior. Antigen blindness arises because models derive predictions from antibody framework context rather than antigen information, producing nearly identical CDRs regardless of the target. Vocabulary collapse reduces predicted amino acids to three to five per position, far below the ground truth distribution in native sequences. Moreover, any model trained with standard per-position cross-entropy converges to the positional marginal distribution, making it provably unable to produce antigen-specific sequence predictions. We propose a novel encoder-decoder architecture called AgForce, that uses a graph neural network (GNN) as the encoder and specialized decoders for sequence-structure co-design. Specifically, we apply framework dropout, gated bottlenecks, and hyperbolic cross attention that prevent the antibody shortcut path. In the decoder, a Mixture Density Network (MDN) sequence head with Potts-like pairwise coupling and annealed Multiple Choice Learning (aMCL) replaces the cross-entropy objective with a multi-component distribution whose optimal solution differs from the positional marginal. An antigen cycle consistency head routes gradients through the sequence decoder, forcing predicted distributions to encode antigen identity. AgForce achieves the best binding quality and sequence recovery simultaneously on the CHIMERA-Bench dataset, improving amino acid recovery by 8% over the strongest sequence baseline while surpassing the baselines across all interface metrics, and nearly doubling the effective vocabulary of GNN methods. The source code is available at: https://github.com/mansoor181/ag-force.git
Mansoor Ahmed, Murray Patterson
1Georgia State University, Atlanta, GA, USA · 2Georgia Institute of Technology, Atlanta, GA, USA
Computational antibody design requires representations that capture the geometric patterns underlying antigen--antibody interactions, yet existing approaches often rely on scalar distances or surface-intrinsic features, leaving cross-molecular geometry largely implicit. We present AbGaze, an end-to-end antibody design framework based on attentive geometric representation learning, which encodes distance, spatial direction, and surface-normal orientation of antigen surfaces relative to antibody-residue local frames, and adaptively aggregates these geometric interactions according to their interfacial context. The learned interaction representation is shared across multi-CDR co-design, complex structure prediction, and affinity optimization, with local-frame geometric supervision further constraining the representation. AbGaze outperforms prior methods across all three tasks: relative to the second-best method, it improves amino-acid recovery by 7.1% and reduces structural error by 14.9% on average over the six CDRs, improves interface docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.
Jiashuo Wang, Siqi Fan, Yizhen Luo +1
Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · PharMolix Inc.
DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid sequences, avoiding the cost and scarcity of resolved three-dimensional structures. The antigen and the antibody heavy and light chains are processed as three independent streams, each embedded with a frozen ESM-2 protein language model and passed through parallel Transformer and convolutional neural network (CNN) branches before late fusion, a decoupled design intended to preserve local complementarity-determining region (CDR) signal that monolithic encoders can dilute. On a sequence-disjoint split of the AbRank benchmark, the model achieves a Pearson correlation of 0.683, an R^2 of 0.460, and a pairwise ranking AUC of 0.895, significantly outperforming single-branch ablations (paired t-test, p < 0.05). Attention-map and gradient-based saliency analyses further show that the model preferentially attends to CDR loops and candidate epitope residues, supporting its use as a scalable, structure-free tool for high-throughput antibody screening.
Aicha Boutorh, Soumia Bouyahiaoui, Manel Kara Laouar +3
National School of Artificial Intelligence (ENSIA) Sidi Abdallah Campus, Algiers, Algeria · Lamine Debaghine University Hospital Center, CHU Bab El Oued, Algiers, Algeria