Computational Antibody Papers

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  • 2026-08-28

    Aligning protein-generative models to experimental fitness with ProteinDPO

    • developability
    • language models
    • Novel way (ProteinDPO) to apply pre-trained models to biophysical readouts.
    • ProteinDPO is the first framework to apply Direct Preference Optimization (introducing a novel scalar-weighted DPO objective) to align protein generative models with experimental biophysical data, without overfitting like traditional supervised fine-tuning.
    • Despite training exclusively on small monomeric stabilities, ProteinDPO generalizes zero-shot to accurately rank the thermal melting temperatures of multichain antibodies and score antibody-antigen binding affinities.
    • Applied to H5N1 influenza hemagglutinin, it generated stabilized variants yielding up to a 32C improvement in thermal stability while retaining strong nanomolar binding affinity to broadly neutralizing anti-HA antibodies.

  • 2026-08-28

    OpenGerminal: an open-source implementation of the Germinal antibody design pipeline

    • protein design
    • generative methods
    • A restriction free reproduction of the antibody design workflow germinal
    • Replaces proprietary dependencies (PyRosetta, IgLM) with an open-source toolchain (OpenMM, AbLang1, sc-rs) and fixes multi-chain bugs, enabling unrestricted academic and commercial deployment.
    • Demonstrates that AbLang1-guided hallucination significantly increases initial cofolding pass rates (e.g., 33.7% vs. 18.6% for PD-L1) with equal or higher structural confidence, at the cost of a ~1.5x increase in per-trajectory compute time.
    • Uses hard-coded placeholder values for three energy metrics which degrades ensemble selection and disables the interface hydrogen-bond filter and lacks wet-lab experimental validation of the generated binders.

  • 2026-08-21

    Benchmarking Antibody Modeling Tools across Structure Prediction, Docking, and Paratope–Epitope Interface Analysis

    • structure prediction
    • Benchmarking of some structural prediction methods on antibody-related tasks.
    • Benchmarked five computational tools across two core tasks: AlphaFold3 (AF3), ImmuneBuilder (ABodyBuilder2/ABB2), and IgFold for antibody variable fragment (Fv) structure prediction, as well as AF3, dyMEAN, and GRAMM for antigen–antibody complex structure prediction and docking.
    • Tools were tested on 50 non-redundant humanized antibody–antigen Fab complexes from the Protein Data Bank (PDB), filtered for resolution < 3.0Å and released after December 19, 2023, to eliminate training data overlap.
    • All Fv predictors achieved high backbone accuracy (mean TM-score > 0.97), but AF3 demonstrated statistically significant advantages in global Fv geometry and hypervariable CDR-H3 loop modeling (median CDR-H3 RMSD of 0.86 Å, compared to 1.34 Å for ABB2 and 1.52 Å for IgFold). In complex prediction, AF3 substantially outperformed dyMEAN and GRAMM, generating reliable docking poses for 46% of complexes, whereas classical rigid-body docking (GRAMM) and epitope-guided modeling (dyMEAN) almost completely failed.
    • Paratope and epitope residue recovery is strictly dependent on initial docking accuracy. When docking succeeds, AF3 reliably identifies interface residues (F1 score ~ 0.85/0.87), salt bridges (84% recall), and non-bonded contacts (75% recall).

  • 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.

  • 2026-08-10

    High-throughput machine learning-aided antibody discovery for cell surface antigens

    • databases
    • experimental techniques
    • ngs
    • ML-Optimized Paratope Representation (ARM).
    • The authors engineered a synthetic yeast-display Fab library centered on a compact "Antigen Recognition Module" (ARM), a <100 nt sequence pairing heavy-chain CDRH3 sequence diversity with a light-chain barcode, providing a computationally lightweight paratope representation tailored for high-throughput sequencing and ML workflows.
    • To overcome physical display biases (such as yeast growth rates or expression disparities), a k-mer-based logistic regression (LR) model was trained on sequence enrichment data from early sorting rounds (MACS to FACS1) to score clones based on sequence motifs.
    • Applying the LR model successfully rescued functional, high-affinity binders for ROBO2N (11 binders) and PD-L2 from early sequence pools that had been depleted or overshadowed by dominant clones in later experimental cell-sorting rounds.
    • The LR model trained on ROBO2N accurately predicted binding potency and epitope cluster preference for the closely related paralog ROBO1, while reliably scoring non-binding clones as low probability.
    • The paper provides a publicly available dataset of over 68,000 unique target-associated ARM sequences alongside comprehensive biophysical characterizations for 486 antibodies, offering a structured foundation for downstream predictive models, affinity maturation, and zero-shot antibody design.

  • 2026-08-10

    Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences

    • language models
    • Autoregressive model for sequence design, with applications to antibody-antigen design.
    • LOGOS was trained autoregressively on 44.87 billion scientific tokens across seven modalities, learning interaction constraints entirely from discrete sequences without natural language text or explicit 3D coordinate inputs (so UniRef, OAS, Smiles etc etc, not scientific ‘text’).
    • Cleaned antibody sequences from the Observed Antibody Space (OAS) were clustered at 70% sequence identity and encoded using a native scientific grammar with chain- and CDR-specific boundary tokens.
    • Evaluated on the SAbDab benchmark for antibody CDR design using Amino Acid Recovery (AAR), refolded structural self-consistency, and AntiBERTy sequence plausibility.
    • LOGOS-8B outperformed specialized 3D structure-conditioned inverse-folding baselines on conserved CDR1 and CDR2 loops, achieving over 85% AAR on CDR-L1 and CDR-L2 from sequence context alone - but if you train on OAS, this is hardly a surprise.
    • Performance on CDR-3 was far worse for LOGOS.