Using AI to design antibodies

Abstract

Advancements in antibody engineering are crucial for developing effective and safe therapeutic candidates. Traditional approaches often involve limited screening of sequence space, resulting in drug candidates with suboptimal binding affinity, developability, or immunogenicity. However, recent breakthroughs in deep learning and generative artificial intelligence (AI) offer promising solutions to overcome these challenges. In our work, we utilized deep contextual language models trained on high-throughput affinity data to quantitatively predict binding of unseen antibody sequence variants. Our approach spans a wide range of binding affinities, demonstrating the potential to optimize antibody engineering. Additionally, we introduced a “naturalness” metric that measures similarity to natural immunoglobulins. We found that naturalness is associated with measures of drug developability and immunogenicity, allowing us to optimize it alongside binding affinity using a genetic algorithm. Additionally, we explored generative AI-based antibody design, and achieved successful design of all complementarity-determining regions (CDRs) in the heavy chain of the antibodies. Our designed antibodies exhibit high binding rates, surpassing randomly sampled antibodies from the Observed Antibody Space. Moreover, these AI-designed binders display high diversity, low sequence identity to known antibodies, and favorable naturalness scores, indicating desirable developability profiles and reduced immunogenicity. Collectively, our findings demonstrate the immense potential of deep learning and generative AI in revolutionizing antibody optimization and design. By leveraging large-scale data, predictive models, and high-throughput experimentation, we can accelerate and improve our antibody engineering capabilities. The integration of deep contextual language models and the incorporation of naturalness into the design process provide intelligent screening approaches. Similarly, the application of generative AI enables us to efficiently and precisely design antibodies from scratch, outperforming traditional methods in terms of speed and quality.