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Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict

Michael Chungyoun1, Jeffrey Gray1,2

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Summary

The Fitness Landscape for Antibodies (FLAb2) benchmark reveals current AI models struggle to predict therapeutic antibody developability. Data composition and intrinsic properties significantly impact model performance, highlighting areas for AI improvement in antibody design.

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Area of Science:

  • Biotechnology
  • Artificial Intelligence
  • Immunology

Background:

  • Accurate prediction of therapeutic antibody developability is crucial for successful drug development.
  • Existing protein function benchmarks exclude antibody-specific data, limiting AI model evaluation.
  • Understanding sequence-structure-function relationships (fitness landscapes) is key for antibody design.

Purpose of the Study:

  • Introduce the Fitness Landscape for Antibodies 2 (FLAb2), the largest public benchmark for therapeutic antibody design.
  • Evaluate the performance of 30 AI and biophysical models in predicting antibody developability properties.
  • Identify key factors influencing AI model performance in antibody developability prediction.

Main Methods:

  • Compiled FLAb2 dataset with over 4 million developability assay results across 32 studies.
  • Assessed seven antibody properties: thermostability, expression, aggregation, binding affinity, pharmacokinetics, polyreactivity, and immunogenicity.
  • Benchmarked 30 AI and biophysical models using zero-shot and fine-tuned approaches.

Main Results:

  • AI models showed limited statistically significant correlations (20%) across developability datasets.
  • No single model accurately predicted all properties or across multiple similar datasets.
  • Fine-tuning improved performance, but data quantity (10^3 points) enabled simpler models to match complex ones.
  • Evolutionary signal significantly contributes to protein language model predictions (40% germline edit distance).

Conclusions:

  • Current AI models require significant improvement for reliable therapeutic antibody developability prediction.
  • Training data composition and intrinsic biophysical properties are more critical than model architecture.
  • FLAb2 provides a valuable resource for benchmarking and advancing AI in antibody design.