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arXiv · 2606.11243

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

Chuanzhen Wang·Meade Cleti·Pete Jano
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Abstract

De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and flow matching approaches have achieved progress, they typically operate at single resolution and lack mechanisms for incorporating functional constraints. We introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refining to all-atom coordinates, reducing computational cost while maintaining accuracy; (2) functional guidance leveraging pretrained predictors to steer generation toward desired properties without retraining; (3) adaptive SE(3)-equivariant architecture for efficient multi-scale processing. Experiments on unconditional generation, motif scaffolding, and functional design demonstrate state-ofthe-art performance while requiring 4 fewer sampling steps. On enzyme active site scaffolding, ProHiFlo achieves 58.9% success rate compared to 41.2% for RFDiffusion.

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Chuanzhen Wang, Meade Cleti, Pete Jano. 2026-06-03. ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation. https://arxiv.org/abs/2606.11243

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