Computational Antibody Papers
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TitleKey points
- Training specificity models across several domains.
- Models were trained strictly on public 1D sequence data (amino acids, DNA/RNA nucleotides, and chemical SMILES) across six biological domains, requiring no 3D structural information.
- Base sequence encoders were kept frozen while small 5–7 million parameter projection heads were trained in under an hour per fold on a single GPU using thermodynamic contrastive learning.
- Development was driven entirely via natural-language prompts by a domain expert with zero coding experience, with all numerical claims verified by an independent AI auditor.
- Top-1 target retrieval accuracy reached up to 98.0% from pools of 512 candidates, successfully generalizing to unseen rare HLA alleles and non-canonical binding targets that rule-based tools miss.
- Used as re-ranking filters alongside existing computational workflows, the models dramatically boosted precision, such as raising CRISPR off-target prediction precision from 33.2% to 94.0%.
2026-08-21
High-accuracy structural modeling of antibody-antigen complexes
- binding prediction
- structure prediction
- Novel method, DeepAAAssembly, to model antibody antigen complexes, beating AF3.
- DeepAAAssembly introduces a hybrid framework that pairs deep learning inter-chain distance predictions with flexible Monte Carlo sampling, overcoming the lack of strong co-evolutionary signals in antibody-antigen interfaces.
- The pipeline feeds Voronoi tessellation contact geometry, AntiBERTy embeddings, and a Triangular Awareness module into a multi-column CNN, creating continuous multi-peak distance energy landscapes to guide both global orientation sampling and local CDR loop refinement.
- Evaluated on Docking Benchmark 5.5, it surpassed AF3 with a 12.9% higher average DockQ score on best-generated models (0.454 vs. 0.402) and boosted the overall modeling success rate (DockQ > 0.23) from 47.8% to 56.7%.
- Demonstrates strong generalization on CASP15/16 blind targets by successfully correcting severe initial orientation errors, elevating previously failed structural predictions into acceptable, physically plausible models
2026-08-21
AbAgKer: A Unified Semi-Supervised Framework for Antigen-Antibody Binding Affinity and Kinetics Prediction
- binding prediction
- Novel antibody-antigen affinity predictor.
- Evaluated on SKEMPI v2.0 and SAbDab curated into three subsets: AbAgA (1,953 labeled affinity pairs), AbAgI (7,725 unlabeled interaction pairs), and AbAgKoff (272 kinetic pairs).
- Compared against five PLM-based baseline setups, A2binder, AntiBERTa2, DG-Affinity, ESM-F, and AntiBERTY-F, which adapt pre-trained language models with regression heads for affinity tasks.
- Generates pseudo-labels on 7,725 unlabeled pairs (AbAgI) using a teacher model trained on AbAgA; retraining on this expanded pool serves as the main engine for gains, boosting Pearson correlation from 0.45 to 0.68 and reducing MSE by ~35%.
- Infuses explicit biological priors into sequence representations via an MEI module (sequence-based biophysical epitope features), CDR-masked attention (CAttention), and a Mixture-of-Experts (MoE) network to dynamically model diverse binding modes.
- Extends beyond static equilibrium affinity (K_d) by using few-shot transfer learning on learned interaction features to predict dissociation rates (k_off) for target residence time analysis.
2026-08-21
A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability
- binding prediction
- developability
- ngs
- Results from the antibody discovery competition, where ML methods faced off with experimental baselines.
- Participating organizations were evaluated across three tasks on a SARS-CoV-2 receptor-binding domain (RBD) dataset: (1) in silico affinity maturation by modifying non-HCDR3 CDRs using sequencing outputs, (2) affinity ranking of candidates within three HCDR3-clustered sequence datasets, and (3) out-of-library CDR optimization/design to generate novel high-affinity binders not present in the sequencing data.
- For lead optimization, top ML methods matched experimental performance by producing sub-100 pM binders (top ML design: 95 pM vs. best experimental control: 113 pM), though overall ML submissions underperformed relative to experimental sorting libraries. Notably, a simple non-ML statistical consensus baseline also achieved a highly competitive 540 pM affinity without using machine learning.
- For sequence ranking within HCDR3 clusters, almost all ML algorithms performed worse than random clone picking; only 9.8%-13.8% of ML submissions beat the cluster baseline clone compared to 39% from random selection. Additionally, out-of-library ML designs yielded a high proportion of non-binders (30.4%) and top-ranked affinity hits frequently suffered from severe biophysical developability failures.
- ML is genuinely useful for timeline reduction during lead optimization and affinity maturation, replacing 2-3 weeks of physical combination library construction with an in silico step when provided with deep, biologically grounded experimental datasets.
- Pretrained protein language models (PLMs) paired with structure-aware modules (e.g., pairformers) showed utility in exploring novel out-of-library sequence spaces. However, current ML models lack cross-task generalization, struggle with local epistasis, and fail to reliably co-optimize affinity alongside multiparametric biophysical developability.
2026-08-10
Analysing open-source protein folding models for nanobody binding prediction
- binding prediction
- nanobodies
- ngs
- Benchmarking the ability of co-folding models to distinguish nanobody binders and non-binders.
- Evaluated four state-of-the-art structure prediction models (AlphaFold3, Boltz-2, Chai-1, and IntFold) across true binder ranking, out-of-distribution (OOD) sequence detection, and mutational sensitivity in nanobody–antigen complexes.
- Discovered that no single confidence score excels across all tasks: while Boltz-2 achieved the highest median accuracy for true binder identification, local metrics like PLDDT (particularly in AlphaFold3) were far superior at catching OOD alanine-substituted sequences.
- Contributed a original in vivo camelid immunization dataset targeting CD33, revealing that all evaluated models struggle to generalize when discriminating enriched binders from realistic immune repertoire background sequences.
- Demonstrated that commonly used hard filtering thresholds (e.g., pAE < 10) can erroneously discard up to 75% of true binders, underscoring the need to combine complementary global interface and local CDR metrics rather than relying on single confidence scores.
2026-08-10
A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation
- binding prediction
- Benchmark framework for universal antibody-antigen binding affinity prediction evaluated on the AlphaBind dataset.
- Introduces MochiBind, a sequence-based pairwise predictor that compares two antibodies targeting the same antigen to determine which binds stronger.
- Uses pretrained ESM-2 embeddings for antibody and antigen sequences, projecting pooled representations into an MLP to predict relative binding affinity.
- Compared against structural proxies (Boltz-2, GeoDock) evaluated zero-shot without AlphaBind training, and Graphinity fine-tuned on AlphaBind.
- Across the 4 AlphaBind target antigens, average pairwise accuracy reaches ~0.70 for MochiBind, ~0.65 for fine-tuned Graphinity, ~0.58 for Boltz-2, and ~0.50 (random performance) for GeoDock.
- All training, fine-tuning, and out-of-sample evaluation in this study were conducted exclusively on the AlphaBind dataset.
2026-06-24
BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability
- protein design
- binding prediction
- Novel de novo protein binder design pipeline from Boltz that pairs an optimized candidate generation engine with BoltzPPI, a novel, interaction-aware scoring model built to rank designs based on binding confidence rather than just geometric plausibility.
- To train BoltzPPI, the authors created a set of positive labels (PDB and patent complexes) and negative labels (synthetic non-interacting protein pairs co-localized via Boltz-2). This input pool (token, pair, distance, and mask features) is processed through a Pairformer stack using specific optimization tricks (co-trained focal loss, multi-view representation dropping, and Gaussian noise injection) to predict a binary binding-confidence score.
- Significantly improves experimental VHH design performance, boosting the confirmed-binder hit rate from 3.3% to 8.0% on novel targets and successfully discovering screening hits for 7 out of 10 benchmark targets from the Chai-2 dataset.
- Yields highly manufacturable binders, with 58% of its confirmed candidates passing a comprehensive panel of strict biophysical filters (such as thermal stability, purity, and low aggregation Propensity), outperforming both BoltzGen (40%) and clinical-stage VHH controls (21%).
- Generates structurally distinct binders whose CDR loops remain highly distinct from any known entries in the SAbDab database, with the entire pipeline made available to researchers via the Boltz API and Lab platforms.
- Theoretical framework for predicting molecular recognition from sequence alone structures are not required at inference time.
- The selection probability governing which molecules bind which is provably unique, it is the Boltzmann distribution, derived from first principles rather than assumed.
- To use it, we need sequence representations whose dot product approximates relative binding energies, not just higher for binders vs non-binders, but correctly ranking the full competitor pool.
- This separates two problems: learning the embedding geometry (cheap, binary binding pairs) from recovering absolute binding energies (cheap, just two calibration parameters fitted against a small number of experimental KdK_d Kd measurements).
2026-04-24
DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design
- binding prediction
- Case study application of generation of novel HER2 binders using the Herceptin template, with five specific computational properties (HER2 specificity, FvNetCharge, FvCSP, HISum, and MHC II minPR) encoded as constraints.
- The authors train a conditional CDRH3 GPT (based on a mini GPT-2 architecture) using large-scale sequences sourced from the OAS database.
- Sequences are computationally annotated with property labels and refined via reinforcement learning (RL) to satisfy multi-property constraints.
- Target-specific binding predictors (oracles) are used to guide the RL process to generate CDRH3 sequences that exhibit HER2-targeting capabilities similar to Herceptin.
- Wet-lab validation confirms HER2-binding affinity and tumoricidal efficacy; while physical developability assays were not performed in the lab, these traits were primary objectives of the computational design stage.
2026-04-17
AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models
- binding prediction
- Very lightweight method to predict binding affinity of antibody-antigen complexes.
- Local sequence fragments of length 2r+1 are extracted around mutation sites. Distances between these fragments are calculated using the Levenshtein distance to account for sequence shifts.
- Targets are predicted using a k-nearest neighbors (kNN) approach (regression or classification) based on the closest matching fragments in the training set.
- Despite its simplicity, the model achieves results comparable to state-of-the-art machine learning models on datasets like AB-Bind, AbDesign, and Alphaseq.
- It serves as an interpretable benchmark particularly suited for data-sparse, target-specific antibody engineering where experimental data is limited.