21 citations · 34 across the 5 of their papers we have counts for
5 papers
Emyx: Fast and efficient all-atom protein generation
Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla +7
Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the…
Transformers trained on proteins can learn to attend to Euclidean distance
Isaac Ellmen, Constantin Schneider, Matthew I. J. Raybould +1
While conventional Transformers generally operate on sequence data, they can be used in conjunction with structure models, typically SE(3)-invariant or equivariant graph neural net…
Assessing interaction recovery of predicted protein-ligand poses
David Errington, Constantin Schneider, Cédric Bouysset +1
The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking…
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…
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…