PAR is a multi-scale autoregressive transformer framework for protein backbone generation that uses coarse-to-fine prediction, noisy context learning, and flow-based decoding to achieve high-quality unconditional and zero-shot conditional outputs.
Se (3) diffusion model with application to protein backbone generation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
SymADiT generates stable symmetric materials by enforcing Wyckoff-position and space-group constraints inside a latent generative model built on the prior ADiT architecture.
citing papers explorer
-
Protein Autoregressive Modeling via Multiscale Structure Generation
PAR is a multi-scale autoregressive transformer framework for protein backbone generation that uses coarse-to-fine prediction, noisy context learning, and flow-based decoding to achieve high-quality unconditional and zero-shot conditional outputs.
-
Generating Symmetric Materials using Latent Flow Matching
SymADiT generates stable symmetric materials by enforcing Wyckoff-position and space-group constraints inside a latent generative model built on the prior ADiT architecture.