Proteina-Pronto: Fast De Novo Protein Design and Structure Prediction via Distillation

Accurate flow-matching protein design models and diffusion-based structure prediction models are bottlenecked by the hundreds of network evaluations required at inference time. This restricts large-scale discovery campaigns that require generating and scoring between $10^4$ and $10^6$ candidates per target. We introduce Proteina-Pronto, a framework for accelerating atomistic generation and folding-based evaluation. For generation, we replace low-temperature stochastic sampling with a deterministic ODE using Temporal Score Rescaling, then couple the modality-specific denoising schedules to a shared global time for diffusion distillation. This enables a 16x reduction in sampling steps for both unconditional sequence-structure co-design and target-conditioned binder design, with design performance comparable to the original teacher models. For folding-based evaluation, we distill Boltz-2 diffusion module and accelerate its computationally expensive Pairformer trunk through representation distillation, achieving a 3.9x end-to-end wall-clock speedup while retaining approximately 99% of its original lDDT on the PDB test set. Together, these accelerations enable more efficient inference-time scaling with best-of-N sampling and guided search, yielding up to 6x as many unique successful designs on hard targets within the same total GPU budget as the original pipeline.