Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design

TL;DR

This paper presents a novel reinforcement learning approach for designing high-affinity antibodies, capable of learning from online interactions or offline data, and outperforming existing methods across multiple antigens.

Contribution

Introduces the first reinforcement learning method tailored for antibody design that works with online and offline data, improving performance over existing techniques.

Findings

Successfully designed high-affinity antibodies in silico

Outperformed existing methods on all tested antigens

Effective with both online and offline datasets

Abstract

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.