This research demonstrates how Large Language Models (LLMs) can predict antibody biological activity against influenza, potentially transforming drug discovery and development.

• Novel application of NLP approaches to protein sequences, treating antibody-antigen interactions as a language problem
• Achieved significant predictive accuracy for antibody effectiveness against influenza hemagglutinin
• Demonstrates potential to dramatically reduce time and cost of therapeutic antibody development
• Opens pathway for AI-accelerated drug discovery across multiple disease categories

Why it matters: This approach could revolutionize medical treatment development by enabling faster, more cost-effective identification of therapeutic antibodies for infectious diseases, autoimmune conditions, and cancers.

Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin