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.

  • 2025-09-30

    Advancing large-molecule discovery with a unified digital platform for data analysis and workflow management

    • experimental techniques
    • ngs
    • annotation/numbering
    • server/webapp
    • Review of currently available large scale software for antibody analysis.
    • Today’s biologics R&D is slowed by fragmented tools and manual data wrangling; the paper proposes a unified, open-architecture platform that spans registration, tracking, analysis, and decisions from discovery through developability.
    • Key components are end-to-end registration of molecules/materials/assays; a harmonized data schema with normalized outputs; automated analytics with consistent QC; complete metadata capture and “data integrity by design.”
    • The platform should natively interface with AI, enable multimodal foundation models and continuous “lab-in-the-loop” learning, and support federated approaches to counter data scarcity while preserving privacy.
    • Dotmatics, Genedata, and Schrödinger each cover pieces (e.g., LiveDesign lacks end-to-end registration), and the authors stress regulatory-ready features.

  • 2025-09-30

    mBER: Controllable de novo antibody design with million-scale experimental screening

    • binding prediction
    • generative methods
    • protein design
    • experimental techniques
    • Novel de novo antibody design method with massive experimental testing.
    • The computational method involves integration, not retraining, of existing tools. It combines AlphaFold-Multimer, protein language models (ESM2/AbLang2), and NanoBodyBuilder2 with templating/sequence priors to design/filter antibody-format binders.
    • They perform massive testing. >1.1 million VHH binders designed across 436 targets (145 tested); ~330k experimentally screened.
    • Hit rates look low per binder (~0.5–1%) but that’s ~50× above random libraries, and still yields thousands of validated binders.
    • Target-level success is 45%, for how many targets we got binders; some epitopes reached 30–38% hit rates after filtering.
    • The big caveat is the specificity of epitopes- it really makes a difference, with some epitopes producing nought.

  • 2025-07-03

    Developing drug-like single-domain antibodies (VHH) from in vitro libraries

    • experimental techniques
    • ngs
    • Novel library design technique for VHHs that produces developable and humanized antibodies without the need for further optimization.
    • The authors built a humanized VHH phage display library using four therapeutic VHH scaffolds, incorporating CDR1 and CDR2 sequences from human VH3 germline genes (filtered for sequence liabilities) and highly diverse CDR3s from CD19⁺ IgM⁺ human B cells.
    • CDR1 and CDR2 libraries were filtered via yeast display for proper folding and protein A binding, while CDR3s were refined to remove poly-tyrosine stretches to reduce polyreactivity.
    • An improved library version incorporated CDR1/2 variants selected for heat tolerance and further depleted CDR3s with poly-tyrosine motifs, increasing stability and developability.
    • VHHs were tested for expression, thermal stability, aggregation, hydrophobicity, and polyreactivity, showing that the V2 library yielded a higher proportion of drug-like antibodies with favorable biophysical properties.

  • 2025-03-11

    Redefining antibody patent protection using paratope mapping and CDR-scanning

    • paratope prediction
    • experimental techniques
    • Proposal how to make antibody patents reasonable via mutational scanning.
    • If you develop a therapeutic antibody you want to claim a space around it so that no-one piggy backs off your effort by doing one substitution.
    • If you claim a ’homology space around your mabs’, then even a small amount of substitutions can circumvent 90-95% sequence identity of either CDRs or variable region.
    • Claiming that you own all antibodies that bind some protein (e.g. like Amgen did with pcks9) is too broad. That goes back to the ‘enablement’ of patents, as it needs to allow a skilled person to reproduce it. If you claim a handful of abs against pcks9, you do not exactly give a way to make ‘all others’.
    • Authors propose to make broader claims by point mutations in the CDRs in strategic paratope positions and characterizing binders. For a single lead you are looking at a ballpark 1,000 mutants, which is experimentally feasible. This would give hard data for a broad spectrum of binders around your candidates, giving wider protection.

  • 2024-08-15

    Active learning for affinity prediction of antibodies

    • binding prediction
    • experimental techniques
    • An active learning framework is proposed to efficiently identify antibody mutations that enhance binding affinity, minimizing wet-lab experiments.
    • From the paper: “Active learning is a framework from experimental design that focuses on making informed decisions about which experiments to perform next”. That’s important these days to effectively generate prediction first data to maximize the effectiveness of models.
    • Bayesian optimization is used with relative binding free energy (RBFE) methods to iteratively propose and evaluate new antibody sequences, improving binding affinity predictions.
    • Various encoding schemes, including one-hot, bag of amino acids, BLOSUM, and AbLang2, are tested. The best performing methods are identified through validation with pre-computed data.
    • The study uses two RBFE methods: NQFEP for accurate but costly simulations, and Schrödinger Res Scan for faster but less precise results. The active learning loop consistently finds better binding sequences using these methods.
    • AbLang2 encoding with the Tanimoto kernel consistently outperformed other methods in the validation phase, indicating its effectiveness in predicting improved binding affinities.
    • NQFEP method provides more accurate but computationally expensive simulations, while the Schrödinger Res Scan offers quicker but less precise results. Despite the higher computational cost, the NQFEP method might be preferable when high accuracy is crucial.

  • 2024-03-13

    Language models enable zero-shot prediction of the effects of mutations on protein function

    • non-antibody stuff
    • language models
    • experimental techniques
    • They contrast ESM to some other language models and show that in zero shot fashion some correlations can be made with experimental measurements of variants.
    • They compare performance of ESM and DeepScan on 41 deep mutational scanning datasets collated in a single paper. They claim ESM has better overall correlations but it is not crystal clear from the graph and by their own admission by paired t-test.
    • They find that pretraining the data on Uniref30 gives worst performance. An ok performance is given for Uniref50 or Uniref70 with a dip again at Uniref100.
    • Binding sites have much higher conservation.
    • Core of the protein also appears to have lower conservation.