NVIDIA and University of Washington Launch RFD4-Proteina for Faster AI-Driven Protein Design
NVIDIA's BioNeMo team, working with the University of Washington's Institute for Protein Design, has introduced RFD4-Proteina, a model that generates both a protein's sequence and its full atomic structure in a single pass instead of handing off to a separate inverse-folding tool. By working with a residue-level representation and a streamlined diffusion process, the model needs only about 60 denoising steps instead of roughly 400, which translates into markedly quicker runs and a much smaller memory footprint than its predecessor RFD3 when generating a 200-residue protein. During generation, the model can also run a reward-guided search that scores and branches partial designs using GPU-accelerated evaluation tools such as OpenFold3 and TMol, steering computation toward the most promising candidates. It ships both as an open-source model on GitHub and as a NIM microservice reachable over HTTP, with toolkit skills available to let automated agents chain generation, scoring and evaluation steps together.