Computational Antibody Papers

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  • 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.

  • 2026-01-09

    Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2

    • protein design
    • All-atom, zero-shot generative model that designs antibody sequence and structure directly in complex with a target from epitope-conditioned prompts.
    • One specifies target, epitope, modality and the algorithm produces designs.
    • They tested 4–24 designs per target, achieving 50% target-level success, producing VHHs and scFvs with pico- to nanomolar affinities (best ≈ 26 pM).
    • Designed antibodies show therapeutic-grade developability (expression, aggregation, hydrophobicity, polyreactivity, stability) without optimization via wetlab validation.
    • Human PBMC assays (10 donors) show no detectable immunogenicity for representative de novo nanobodies.