A generative foundation model for antibody sequence understanding

ICML 2024 Workshop ML4LMS

Keywords: Foundation models, generative models, antibodies, antigen binding prediction, de novo design

Abstract: Here we introduce FAbCon, a generative antibody-specific language model comprising 2.4 billion parameters. A commonly accepted wisdom in developing large language models is that increasing model scale will translate to higher performance on downstream tasks. Starting from a 144-million parameter setup, we show that progressively larger models achieve greater accuracy in predicting antigen binding and can also be used to design new antibodies with good predicted developability potential.

Submission Number: 44