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AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.

Aditya A Agarwal1, James Harrang1, David Noble1

  • 1Data Science, A-Alpha Bio Inc, Seattle, WA, USA.

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|July 22, 2025
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Summary

Deep learning model AlphaBind accelerates antibody design by predicting binding affinity. This computational approach efficiently optimizes antibody sequences for improved therapeutic properties.

Keywords:
Antibody engineeringcomputational protein designmachine learningyeast display

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Antibodies are crucial therapeutic molecules, but designing optimal sequences is challenging.
  • Predicting antibody sequence-function relationships is key to efficient design.
  • Deep learning offers advanced methods for landscape prediction and optimization.

Purpose of the Study:

  • To introduce AlphaBind, a deep learning model for guided antibody affinity optimization.
  • To demonstrate AlphaBind's capability in improving binding affinity of parental antibodies.
  • To showcase an efficient pipeline for antibody design using computational predictions.

Main Methods:

  • Developed AlphaBind, a domain-specific model utilizing protein language model embeddings.
  • Pre-trained AlphaBind on millions of quantitative antibody-antigen binding strength measurements.
  • Applied AlphaBind to guide affinity optimization of four parental antibodies.

Main Results:

  • AlphaBind achieved state-of-the-art performance in guided antibody affinity optimization.
  • The AlphaBind-powered pipeline delivered candidates with substantially improved binding affinity.
  • Optimized candidates showed sequence diversity allowing for other biophysical characteristic improvements.

Conclusions:

  • Deep learning, exemplified by AlphaBind, significantly enhances antibody design efficiency.
  • AlphaBind enables faster development of therapeutic antibodies with optimal properties.
  • The model facilitates rapid optimization using minimal experimental data generation.