UNAAGI: Atom-Level Diffusion for Generating Non-Canonical Amino Acid Substitutions
UNAAGI is a generative model that reconstructs residue identities from atomic structures using an E(3)-equivariant diffusion framework. It aims to improve prediction of effects from non-canonical amino acid substitutions by modeling amino acids at the atom level rather than as discrete tokens. The model shows promising predictive power across both natural and non-canonical substitution benchmarks, suggesting a link between structure generation and variant effect prediction.