Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Venderley, Jordan
Institution: Eli Lilly (United States)
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4387074774