Computational Antibody Papers
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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.
2026-01-09
Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties
- developability
- databases
- FLAb2 substantially expands existing antibody benchmarks, introducing the largest public dataset to date with a strong focus on developability rather than binding alone.
- A broad spectrum of models is evaluated, including generic protein language models, antibody-specific models, structure-aware predictors, and simple physics-based baselines such as charge and pI calculations.
- Zero-shot predictions from pretrained protein models are generally weak and unreliable for antibody developability. Surprisingly, simple charge-based features often outperform large models for properties such as aggregation, polyreactivity, and pharmacokinetics.
- Intrinsic properties (e.g. thermostability, expression) are substantially easier to predict than extrinsic or context-dependent properties such as polyreactivity, pharmacokinetics, or immunogenicity.
- Few-shot learning improves performance, but even the best models typically achieve only moderate correlations (ρ ≈ 0.4–0.6) on statistically robust datasets, highlighting the difficulty of the task.
- Incorporating structural information improves predictions, particularly in the zero-shot setting, and helps reduce biases present in sequence-only models.
- Many pretrained models primarily capture evolutionary signal, effectively measuring distance from germline rather than true developability. Encouragingly, this germline bias largely disappears once models are fine-tuned in a few-shot setting.
- Scaling model size alone provides limited benefit. Given sufficient training data, simple one-hot encodings paired with small neural networks can match or outperform billion-parameter protein language models, emphasizing that data quality and quantity matter more than model scale.
2025-12-12
Predicting Experimental Success in De Novo Binder 2 Design: A Meta-Analysis of 3,766 Experimentally 3 Characterised Binders
- protein design
- databases
- Large-scale benchmarking of structural, energetic, and confidence metrics to distinguish protein binders from non-binders.
- Curated 3,766 experimentally tested de novo binders across 15 targets from independent campaigns.
- Of these, 436 were confirmed binders, the remainder non-binders.
- Each design was re-modelled using AF2 (initial guess + ColabFold), Boltz-1, and AF3.
- From these predictions they computed 200+ structural and confidence descriptors.
- AF3-derived confidence scores (especially ipSAE_min) were the best single discriminators, although per-target precision still ranged widely (0.1–1.0), underscoring strong target dependence.
2025-10-16
Revealing bias in antibody language models through systematic training data processing with OAS-explore
- language models
- databases
- Investigation how biases in the Observed Antibody Space (OAS) database, such as overrepresentation of a few donors and limited species or chain diversity, affect the performance and generalizability of antibody language models.
- The authors developed OAS-explore, an open-source pipeline to analyze, filter, balance, and sample OAS data by donor, species, chain type, and publication, enabling systematic assessment of data biases.
- By training 17 RoBERTa models on datasets with different compositions, they found that models struggle to generalize across chain types, species, individuals, and batches, and that even increased donor diversity alone does not guarantee better performance.
- They recommend systematic preprocessing, inclusion of more diverse data, and open sharing of datasets and pipelines to mitigate biases and improve antibody LM robustness.
2025-09-05
AbSet: A Standardized Data Set of Antibody Structures for Machine Learning Applications
- databases
- Introduces AbSet, a curated dataset of >800,000 antibody structures, combining experimental PDB entries with in silico–generated antibody–antigen complexes.
- Adds value beyond SAbDab by standardizing structures, including decoy poses, and providing residue-level molecular descriptors for machine learning.
- Presents dataset profiling and validation, with analyses of structural resolution, antigen diversity, docking quality classification, and descriptor calculation efficiency.
2025-06-24
Benchmark for Antibody Binding Affinity Maturation and Design
- binding prediction
- language models
- generative methods
- databases
- Benchmark of machine learning models for antibody-antigen binding affinity.
- A curated dataset of over 150,000 antibody-antigen complexes with associated experimental affinity values is compiled from literature.
- The benchmark compares a wide range of model types: language models, inverse folding models, graph-based, and diffusion-based generative models.
- Inverse folding models that are globally structure-aware perform best.
- General protein models like ESM-IF and ProteinMPNN outperform antibody-specific models such as AntiFold, DiffAb, and dyMEAN.
- Surprisingly, ESM-3 underperforms relative to ESM-IF, despite incorporating structural signals and improving upon earlier ESM models.
2025-03-11
Fast and accurate antibody sequence design via structure retrieval
- databases
- generative methods
- structure prediction
- Inverse folding and thus antibody design via database search.
- Authors train a vector retrieval database on SAbDab. In this way for a single sequence one can figure out where it falls structurally.
- They benchmark against state of the art inverse folding tools such as AbMPNN, AntiFold, ProteinMPNN and ESM-IF - their tools comes on top in terms of sequence retrieval.
- The database search is orders of magnitude faster than the state of the art inverse folding tools.
- They compare IgSeek versus FoldSeek - their tool gets a higher accuracy in sequence retrieval, for most CDRs, but CDR-H3. Therefore FoldSeek seems like a very good choice alongside IgSeek for such a database-driven inverse folding protocol.
- New (old :) ) therapeutic antibody database, larger than what is available from other sources several times.
- Includes over 2,900 investigational antibody candidates and more than 450 approved or late-stage molecules.
- It tracks molecular format, target antigen, development status, clinical history, and company data, along with antibody isotype, conjugation status, and mechanism of action.
- Analysis highlights a rise in bispecifics, ADCs, and immunoconjugates, with most clinical-stage antibodies targeting cancer and originating from China or the U.S.
- The data are collected from public sources beyond INN lists, including company websites, press releases, clinical trial registries, regulatory agencies, and literature reports.
2025-01-21
Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies
- clinical trials
- databases
- Computational analysis of pK (clearance) of biologics based on a dataset collated for this publication.
- Authors collated a set of 64 therapeutic antibodies and their clearances.
- Here, they defined fast clearance as more than 5.4 mL/day/kg. 48 antibodies fel below this threshold and 16 above.
- They tested whether any single computationally calculated property (e.g. isoelectric point etc.) determines fast vs slow clearance.
- No single computational property was a good discriminator.
- THey constructed a random forest algorithm and showed that the poly specify reagent (PSR), which is an in vitro property and isoelectrip point, which can be computationally calculated are the strong discriminators according to the model.
2024-11-14
On the humanization of VHHs: Prospective case studies,experimental and computational characterization of structural determinants for functionality
- developability
- databases
- Authors perform humanization of VHHs and generate experimental data confirming their designs.
- The protocol involves grafting CDRs1-3 and then systematically modifying Hallmars/Verniers and others to make them more human.
- Positions 49 and 50 (e.g., E49G, H50L in VHH1): These were generally well-tolerated, allowing for humanization without major impact on binding affinity or stability.
- Position 52 (e.g., S52W in VHH2): In some cases, changing this residue even improved affinity.
- Position 42: Humanizing residue F42 to a more human-like amino acid (e.g., F42V) in VHH2 led to a significant reduction in binding affinity. This residue plays a key role in stabilizing the CDR3 loop through interactions with other regions, making it essential for maintaining the bioactive conformation.
- Position 52 (in some contexts): In VHH1, the mutation G52W led to a loss of binding due to steric clashes, demonstrating that this position can be critical depending on the structural context.
- Measured binding affinities, expression yields, and purities of humanized variants. Crystal structures confirmed effects of humanization on binding; non-canonical disulfides stabilize CDR3