AntiBARTy Diffusion for Property Guided Antibody Design
Jordan Venderley
Over the past decade, antibodies have steadily grown in therapeutic
importance thanks to their high specificity and low risk of adverse effects
compared to other drug modalities. While traditional antibody discovery is
primarily wet lab driven, the rapid improvement of ML-based generative modeling
has made in-silico approaches an increasingly viable route for discovery and
engineering. To this end, we train an antibody-specific language model,
AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) and
use its latent space to train a property-conditional diffusion model for guided
IgG de novo design. As a test case, we show that we can effectively generate
novel antibodies with improved in-silico solubility while maintaining antibody
validity and controlling sequence diversity.