Year: 2023
Type: preprint
Abstract: The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies. Such therapies could be particularly beneficial for complex, highly individual diseases such as cancer. However, progress in this field is often constrained by the extensive search space of amino acid sequences that form the foundation of antibody design. In this study, we introduce a novel reinforcement learning method specifically tailored to address the unique challenges of this domain. We demonstrate that our method can learn the design of high-affinity antibodies against multiple targets in silico, utilizing either online interaction or offline datasets. To the best of our knowledge, our approach is the first of its kind and outperforms existing methods on all tested antigens in the Absolut! database.
Source: arXiv (Cornell University)
Authors: Vogt, Yannick, Naouar, Mehdi, Kalweit, Maria, Miething, Christoph Cornelius, Duyster, Justus, Mertelsmann, Roland, Kalweit, Gabriel, Boedecker, Joschka
Institutions: University of Freiburg, University Medical Center Freiburg
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: green
Funder: BrainLinks-BrainTools
OpenAlex ID: W4390832674