NVIDIA details RFD4-Proteina: reward-guided inference-time search speeds protein design 3.8x
AllThingsApx · x · 2026-10-21
NVIDIA's technical blog details RFD4-Proteina, an open model that treats protein design as reward-guided search at inference time.
- Protein design is a search problem—an 8-residue sequence has over 25 billion combinations; inference-time search focuses compute on promising candidates while co-designing sequence and all-atom structure.
- Combining explicit backbone coordinates with residue-level latent representations removes the inverse-folding handoff; engineered diffusion cuts sampling from 400 steps to 60, giving 3.8x faster generation and 84% less peak memory than RFD3 at 200 residues.
- CUDA-accelerated oracles (OpenFold3, TMol) score designs on geometry, interaction and confidence metrics; supports tasks like PD-L1 binder design with hotspot constraints.
- Open model and NIM microservice are available, with skills for agents to orchestrate full design campaigns.
Related event: NVIDIA and Baker Lab Open-Source RFD4-Proteina Protein Design Model(35 posts)→
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