Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909068387418112 |
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| author | Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Mertelsmann, Roland Kalweit, Gabriel Boedecker, Joschka |
| author_facet | Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Mertelsmann, Roland Kalweit, Gabriel Boedecker, Joschka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05341 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Mertelsmann, Roland Kalweit, Gabriel Boedecker, Joschka Biomolecules Machine Learning 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. |
| title | Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2401.05341 |