Computational Antibody Papers
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2025
TitleKey points
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.