Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Emily Engelhart, Randolph M. Lopez, Rajmonda S. Caceres, Tristan Bepler, Matthew Walsh
This paper uses a Bayesian language model to design high-affinity scFv antibody libraries, achieving 28.8-fold better binding than directed evolution.
Nature Communications · Jun 12, 2023Peer-reviewedBioinformatics