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

Researchers have developed ProHiFlo, a hierarchical flow matching framework for de novo protein generation that models backbone geometry before refining to all-atom coordinates, reducing computational cost while maintaining accuracy. The framework incorporates functional guidance from pretrained predictors to steer generation toward desired properties without retraining, and uses an adaptive SE(3)-equivariant architecture for efficient multi-scale processing. In enzyme active site scaffolding, ProHiFlo achieved a 58.9% success rate compared to 41.2% for RFDiffusion, requiring four fewer sampling steps.

arXiv:2606.11243v1 Announce Type: new 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.