Inverse FoldDir: Stanford's Dirichlet flow-matching model designs protein sequences from backbone structure
KevinKaichuang · x · 2026-09-16
A Stanford team published the bioRxiv preprint "Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching" (Alp Tartici, Mihajlo Stojkovic et al., with Michael Jewett, Russ Altman, and Microsoft's Bruce Wittmann).
- Task: protein inverse folding — designing amino acid sequences compatible with a target backbone, a core capability for protein redesign and de novo backbone generation with implications across materials, medicine, and energy.
- Method: Dirichlet flow matching conditioned on structure.
- Results: strong computational benchmarks plus several functional nanobody redesigns.
Related event: Stanford team releases FoldDir for structure-conditioned protein design(2 posts)→
More from Research
- Auto-inferring error specs is the next frontier in security, researcher argues — mboehme_ · 2026-11-02
- Security researcher: offense is hard to automate, defending known vuln classes is easier — mboehme_ · 2026-11-02
- Burkov on LLMs' architecture-agnostic low-rank structure over long token sequences — burkov · 2026-11-02
- Adobe & UCLA's Tev: verifiable progress rewards lift long-horizon agent Pass@5 from 0.17 to 0.28 — jiqizhixin · 2026-11-02
- Old trick still works: portal sampling cuts path-tracing noise dramatically at 64spp — ssh4net · 2026-11-02
- Marcel Böhme's paper asks why cybersecurity can never be solved, proposes vulnerability-guided hardening — mboehme_ · 2026-11-02