Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
This work addresses the challenge of designing personalized antibody therapies for diseases like cancer, which is incremental as it applies RL to a specific domain.
The authors tackled the problem of designing high-affinity antibodies by introducing a novel reinforcement learning method tailored for antibody CDRH3 design, demonstrating that it outperforms existing methods on all tested antigens in the Absolut! database.
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.