DTU researcher co-designs AI proteins that protect mice from lethal snake venom
By Timothy Patrick Jenkins (DTU Bioengineering) and David Baker (University of Washington)Built and shared by Timothy Patrick Jenkins (DTU Bioengineering) and David Baker (University of Washington), not by me. Summarised from the public sources below. Check them before you rely on a number.
- Who
- Timothy Patrick Jenkins (DTU Bioengineering) and David Baker (University of Washington)
- Category
- Research & science
- Source
- Web
- Dates
- Published 15 Jan 2025 · Found 17 Oct 2026
Timothy Patrick Jenkins of DTU and David Baker's lab at the University of Washington used deep learning to design new proteins that bind and neutralise cobra toxins. The designed proteins protected mice from lethal doses, which could lead to cheaper and safer antivenoms.
What they built
The researchers used RFdiffusion and other deep learning tools to design proteins from scratch that bind three-finger toxins (short- and long-chain alpha-neurotoxins and cytotoxins) from elapid snakes. Only a small number of designs had to be tested in the lab, and the best ones showed high stability and binding that closely matched the computational models.
The result
80-100% survival in mice depending on dose, toxin and protein. In rescue tests, binders given 15 minutes after the toxin gave 100% protection. Published in Nature.
Sources
Novo Nordisk cuts clinical study documentation from 10+ weeks to 10 minutes
By Novo Nordisk (Waheed Jowiya, Louise Lind Skov)
Novo Nordisk built NovoScribe, an AI documentation platform that writes regulatory-grade clinical study reports and other trial documents. According to Anthropic's case study, producing clinical study documentation went from more than ten weeks to ten minutes.
Swedish MASAI trial: AI-supported mammography finds more cancer with 44% less reading
By Kristina Lång and colleagues (Lund University, Skåne University Hospital)
Lund University ran a randomised trial with about 106,000 women in the Swedish screening programme, comparing AI-supported mammography with standard double reading. AI found 29% more cancers, cut radiologists' reading workload by 44%, and a 2026 Lancet follow-up showed higher sensitivity with no rise in interval cancers.
GPT-5 supplied the key step in a proof in quantum complexity theory
By Scott Aaronson (UT Austin)
Computer scientist Scott Aaronson says that a central technical step in the proof in his new paper on QMA amplification came from GPT-5 Thinking. It is the first time this has happened in his career.
Want this in
your business?
Tell me which case caught your eye. I will tell you straight whether it fits, and what it would take.
Start a conversation →