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Over the past decade, antibodies have steadily grown in therapeuticimportance thanks to their high specificity and low risk of adverse effectscompared to other drug modalities. While traditional antibody discovery isprimarily wet lab driven, the rapid improvement of ML-based generative modelinghas made in-silico approaches an increasingly viable route for discovery andengineering. To this end, we train an antibody-specific language model,AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) anduse its latent space to train a property-conditional diffusion model for guidedIgG de novo design. As a test case, we show that we can effectively generatenovel antibodies with improved in-silico solubility while maintaining antibodyvalidity and controlling sequence diversity.

AntiBARTy Diffusion for Property Guided Antibody Design