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1. 12h
   RFdiffusion3 paper in Science details atom-level protein design that works in the lab

   An international team from the University of Washington, the University of Oxford and Nvidia published the full design of RFdiffusion3, a generative AI system that designs proteins and nucleic acids at the single-atom level, in Science on the 16th local time; the team had open-sourced the system on December 3, 2025. RFdiffusion3 draws on the AlphaFold3 network architecture, represents each amino acid with up to 14 atoms and designs structures by directly adjusting atomic 3D coordinates, generating proteins that satisfy constraints such as specified reactive-atom positions, hydrogen-bond conditions and allowed atomic distributions. Per IT之家, RFdiffusion3 outperformed RFdiffusion2 at 37 of 41 enzyme active-site comparisons and generated structures about ten times faster, while its designed paraoxonase and peptide-cleaving enzymes completed the intended reactions in real experiments. The study's corresponding author is University of Washington professor David Baker, a 2024 Nobel chemistry laureate.

   IT之家 AI · 🔥 22
2. Oct 12
   DeepMind’s AlphaProof Nexus solves 9 open Erdős problems, published in Science

   Google DeepMind’s AlphaProof Nexus, a Gemini 3.1 Pro-based system that drafts Lean proofs and uses compiler feedback to revise them, solved 9 of 353 open Erdős problems, including two that had stood for 56 years, according to a study published in Science. The system also proved 44 open OEIS conjectures, settled a 15-year-old algebraic geometry question and improved a known bound in min-max optimization, with inference costs of a few hundred dollars per problem. DeepMind says a simpler LLM-plus-Lean loop also solved all nine Erdős problems; an independent audit cited by 36Kr found one solution was already implied by an earlier theorem, which would reduce the count to eight under a stricter definition.

   The Decoder · 🔥 0

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