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Updated: Mar 19, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for

Marc Hoffstedt1, Jannis Wowra1, Hermann Wätzig1

  • 1Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Germany.

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|March 18, 2026
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Summary

A new distance-based model, AbDist, predicts antibody affinity using local sequence environments. It matches complex models in performance while being faster, more interpretable, and suitable for early-stage antibody engineering.

Keywords:
Antibody AffinityBenchmarkingDistance-BasedMachine Learning

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • Complex models for antibody affinity prediction exist.
  • Simple distance-based models show promise in related fields like T-cell receptor epitope prediction.
  • These simpler models offer advantages in interpretability and computational efficiency.

Purpose of the Study:

  • To develop a novel distance-based model for antibody affinity prediction.
  • To assess the effectiveness of local sequence environments for featurization.
  • To provide an interpretable and efficient tool for antibody engineering.

Main Methods:

  • Developed AbDist, a distance-based computational model.
  • Utilized fragments around mutation sites for sequence featurization.
  • Applied AbDist to classification and regression tasks on public datasets.

Main Results:

  • AbDist performance matches state-of-the-art machine learning models.
  • The model demonstrates that local sequence environments are sufficient for effective featurization.
  • AbDist is computationally efficient and interpretable.

Conclusions:

  • AbDist is a viable, efficient, and interpretable alternative for antibody affinity prediction.
  • The model is well-suited for data-sparse, early-stage antibody engineering.
  • It shares limitations in out-of-distribution generalization with current models.