Computational Antibody Papers

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2026
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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.