@inproceedings{
dreyer2023de,
title={De novo design of antibody heavy chains with {SE}(3) diffusion},
author={Frederic A Dreyer and Daniel Cutting and David Errington and Constantin Schneider and Charlotte Deane},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=YXchfo0LlX}
}
We introduce VH-Diff, an antibody heavy chain variable domain diffusion model.
This model is based on FrameDiff, a general protein backbone diffusion framework, which was fine-tuned on antibody structures. 
The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution.
We use an antibody-specific inverse folding model to recover sequences corresponding to the predicted structures, and study their validity with an antibody numbering tool.
Assessing the designability and novelty of the structures generated with our heavy chain model we find that VH-Diff produces highly designable structures that can contain novel binding regions.
Finally, we compare our model with a state-of-the-art sequence-based generative model and show more consistent preservation of the conserved framework region with our structure-based method.
De novo design of antibody heavy chains with SE(3) diffusion
Frederic A Dreyer Daniel Cutting David Errington Constantin Schneider Charlotte Deane