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

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.

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2606.11243 [cs.LG]

(or arXiv:2606.11243v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.11243 Focus to learn more arXiv-issued DOI via DataCite

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