3 papers
cs.LG2026
Emyx: Fast and efficient all-atom protein generation
Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla +6
Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the…
cs.LG2025
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…
q-bio.BM2024
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…