peleke-1: A Suite of Protein Language Models Fine-Tuned for Targeted Antibody Sequence Generation

Abstract

The discovery of therapeutic antibodies is a traditionally arduous process. Today, the lab-based process of antibody discovery consists of several time-consuming steps that involve live animal immunization, B-cell harvesting, hybridoma creation, and then downstream engineering and evaluation. However, the use of artificial intelligence in drug design has previously been shown effective in the rapid generation of proteinspecific binders, small molecules, and even antibody therapeutics, thereby replacing some of the primary steps of the drug discovery process. Here we present peleke-1, a suite of protein language models fine-tuned from state-of-the-art large language models using curated antibody-antigen complex data. These models generate targeted antibody Fv sequences for a given antigen sequence input at-scale. This suite of models provides a reliable, artificial intelligence-driven approach for in silico therapeutic antibody discovery along with an open-source framework for future antibody language model tuning.