Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
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.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. There…
Bayesian OptimisationBenchmarkingSpecificityBetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-consuming. The use of generative models i…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficiency by leveraging offline datasets, and on…
Offline RLreinforcement-learningReinforcement Learning (RL)Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offline models can further improve performance…
DiversityOffline RLreinforcement-learningReinforcement Learning (RL)Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the quality of the offline dataset, such pre-tr…
D4RLOffline RLreinforcement-learningReinforcement Learning+1