IgCraft: A versatile sequence generation framework for antibody discovery and engineering
This work addresses the problem of antibody discovery and engineering for biologics development by integrating multiple sequence design tasks into a single model, representing an incremental advance in domain-specific applications.
The paper tackled the challenge of designing antibody sequences that resemble natural human repertoires by introducing IgCraft, a unified generative model based on Bayesian Flow Networks, which achieved state-of-the-art performance in CDR motif scaffolding with improved humanness and structural preservation.
Designing antibody sequences to better resemble those observed in natural human repertoires is a key challenge in biologics development. We introduce IgCraft: a multi-purpose model for paired human antibody sequence generation, built on Bayesian Flow Networks. IgCraft presents one of the first unified generative modeling frameworks capable of addressing multiple antibody sequence design tasks with a single model, including unconditional sampling, sequence inpainting, inverse folding, and CDR motif scaffolding. Our approach achieves competitive results across the full spectrum of these tasks while constraining generation to the space of human antibody sequences, exhibiting particular strengths in CDR motif scaffolding (grafting) where we achieve state-of-the-art performance in terms of humanness and preservation of structural properties. By integrating previously separate tasks into a single scalable generative model, IgCraft provides a versatile platform for sampling human antibody sequences under a variety of contexts relevant to antibody discovery and engineering. Model code and weights are publicly available at https://github.com/mgreenig/IgCraft.