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The field of antibody-based therapeutics has grown significantly in recentyears, with targeted antibodies emerging as a potentially effective approach topersonalized therapies. Such therapies could be particularly beneficial forcomplex, highly individual diseases such as cancer. However, progress in thisfield is often constrained by the extensive search space of amino acidsequences that form the foundation of antibody design. In this study, weintroduce a novel reinforcement learning method specifically tailored toaddress the unique challenges of this domain. We demonstrate that our methodcan learn the design of high-affinity antibodies against multiple targets insilico, utilizing either online interaction or offline datasets. To the best ofour knowledge, our approach is the first of its kind and outperforms existingmethods on all tested antigens in the Absolut! database.

Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design