most citedInverse folding for antibody sequence design using deep learning

21 citations · 27 across the 2 of their papers we have counts for

collaborators

5 papers

q-bio.BM2024

ABodyBuilder3: Improved and scalable antibody structure predictions

Henry Kenlay, Frédéric A. Dreyer, Daniel Cutting +2

Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their bindin…

q-bio.BM2024

De novo antibody design with SE(3) diffusion

Daniel Cutting, Frédéric A. Dreyer, David Errington +2

We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework which was extended to handle multiple chains. Assessing the…

q-bio.BM20246 cited

Large scale paired antibody language models

Henry Kenlay, Frédéric A. Dreyer, Aleksandr Kovaltsuk +3

Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most succes…

q-bio.QM2023

CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM

Ruben Sanchez-Garcia, Michael Saur, Javier Vargas +2

Cryo-EM is a powerful tool for understanding macromolecular structures, yet current methods for structure reconstruction are slow and computationally demanding. To accelerate resea…

q-bio.BM202321 cited

Inverse folding for antibody sequence design using deep learning

Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider +2

We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically o…