ProHiFlo introduces hierarchical coarse-to-fine flow matching with functional guidance from pretrained predictors and an adaptive SE(3)-equivariant architecture, reporting higher success rates and fewer sampling steps than prior methods on protein generation tasks.
Out of many, one: Designing and scaffolding proteins at the scale of the structural universe with genie 2
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
ProHiFlo introduces hierarchical coarse-to-fine flow matching with functional guidance from pretrained predictors and an adaptive SE(3)-equivariant architecture, reporting higher success rates and fewer sampling steps than prior methods on protein generation tasks.
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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.