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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2409.16298 |
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| _version_ | 1866914956705792000 |
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| author | Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Boedecker, Joschka Kalweit, Gabriel |
| author_facet | Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Boedecker, Joschka Kalweit, Gabriel |
| contents | 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 in designing antibodies therefore holds great promise, as it can reduce the time and resources required. Recently, the class of diffusion models has gained considerable traction for their ability to synthesize diverse and high-quality samples. In their basic form, however, they lack mechanisms to optimize for specific properties, such as binding affinity to an antigen. In contrast, the class of offline Reinforcement Learning (RL) methods has demonstrated strong performance in navigating large search spaces, including scenarios where frequent real-world interaction, such as interaction with a wet lab, is impractical. Our novel method, BetterBodies, which combines Variational Autoencoders (VAEs) with RL guided latent diffusion, is able to generate novel sets of antibody CDRH3 sequences from different data distributions. Using the Absolut! simulator, we demonstrate the improved affinity of our novel sequences to the SARS-CoV spike receptor-binding domain. Furthermore, we reflect biophysical properties in the VAE latent space using a contrastive loss and add a novel Q-function based filtering to enhance the affinity of generated sequences. In conclusion, methods such as ours have the potential to have great implications for real-world biological sequence design, where the generation of novel high-affinity binders is a cost-intensive endeavor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design Vogt, Yannick Naouar, Mehdi Kalweit, Maria Miething, Christoph Cornelius Duyster, Justus Boedecker, Joschka Kalweit, Gabriel Biomolecules Machine Learning 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 in designing antibodies therefore holds great promise, as it can reduce the time and resources required. Recently, the class of diffusion models has gained considerable traction for their ability to synthesize diverse and high-quality samples. In their basic form, however, they lack mechanisms to optimize for specific properties, such as binding affinity to an antigen. In contrast, the class of offline Reinforcement Learning (RL) methods has demonstrated strong performance in navigating large search spaces, including scenarios where frequent real-world interaction, such as interaction with a wet lab, is impractical. Our novel method, BetterBodies, which combines Variational Autoencoders (VAEs) with RL guided latent diffusion, is able to generate novel sets of antibody CDRH3 sequences from different data distributions. Using the Absolut! simulator, we demonstrate the improved affinity of our novel sequences to the SARS-CoV spike receptor-binding domain. Furthermore, we reflect biophysical properties in the VAE latent space using a contrastive loss and add a novel Q-function based filtering to enhance the affinity of generated sequences. In conclusion, methods such as ours have the potential to have great implications for real-world biological sequence design, where the generation of novel high-affinity binders is a cost-intensive endeavor. |
| title | BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2409.16298 |