  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.
