BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design

Yannick Vogt, Mehdi Naouar, Maria Kalweit, Cornelius Miething, Justus Duyster, Joschka Boedecker, Gabriel Kalweit

Keywords: Reinforcement Learning, Generative Models, Diffusion Models, Antibody Design

TL;DR: Our novel method combines Variational Autoencoders with offline Reinforcement Learning guided latent Diffusion to generate novel sets of antibody CDRH3 sequences from different data distributions.

Abstract:

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 offline RL guided latent diffusion, can generate novel sets of antibody CDRH3 sequences from different data distributions. 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. Using the Absolut! simulator, we demonstrate that BetterBodies generates sequences with improved binding affinity to the SARS-CoV spike receptor-binding domain and matches or outperforms the state-of-the-art method Generative Flow Network (GFlowNet). In conclusion, our method has the potential for great implications in real-world biological sequence design, where the generation of novel high-affinity binders is a cost-intensive endeavor.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 3663