惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

I
InfoQ
Spread Privacy
Spread Privacy
GbyAI
GbyAI
F
Fortinet All Blogs
小众软件
小众软件
B
Blog RSS Feed
博客园_首页
量子位
Y
Y Combinator Blog
美团技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Google DeepMind News
Google DeepMind News
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Vercel News
Vercel News
Last Week in AI
Last Week in AI
F
Full Disclosure
Stack Overflow Blog
Stack Overflow Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
宝玉的分享
宝玉的分享
Microsoft Azure Blog
Microsoft Azure Blog
有赞技术团队
有赞技术团队
A
About on SuperTechFans
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
The Cloudflare Blog
Hugging Face - Blog
Hugging Face - Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
T
Tenable Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Project Zero
Project Zero
C
CXSECURITY Database RSS Feed - CXSecurity.com
Engineering at Meta
Engineering at Meta
博客园 - 叶小钗
S
SegmentFault 最新的问题
T
Threat Research - Cisco Blogs
博客园 - 司徒正美
MyScale Blog
MyScale Blog
云风的 BLOG
云风的 BLOG
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
The GitHub Blog
The GitHub Blog
V
Vulnerabilities – Threatpost
S
Schneier on Security
Latest news
Latest news
I
Intezer
A
Arctic Wolf
T
Threatpost

IEEE Spectrum

How a Spinning Drone Exploits Your Eyes to Become Nearly Invisible Why Indonesia’s Fisheries Future Hinges On Data Integrity and Trust Inside ELIZA’s Source Code and Its Multiple Personalities How Darth Vader Taught Me Card Counting and AI Security Got Weird The AI Arms Race in Technical Interviews Is Escalating Inside X Square Robot’s Bold Plan for Real Home Robots Large Tabular Models Excel Where LLMs Fail The Hidden Overthinking Flaw That Could Drag AI Services Down There Why Small AI Models Could Power Health Care Where Big Tech Cannot AI’s Wild Power Demands Are Quietly Rewriting Grid Rules Is Melbourne the Place Where AI and Clean Energy Finally Align? The Orbital Data Center Hype Machine Is Already in Orbit What Emily Bender Really Meant by "Stochastic Parrots" Poetry for Engineers: Nine Lives of Nikola Tesla How a Forgotten Wire Turned a Cheap Chip Into a Brainlike Neuron AI Model ConlangCrafter Dreams up Entire New Languages Why Does a Bank Need a Chief Scientist? What it Means to Be a Mathematician When AI Does the Math AI Learns the "Dark Art" of RF Chip Design Can AI Learn to Read the Room? Commemorating 70 Years of Artificial Intelligence IEEE Rolls Out Large Language Models Virtual Training Course Can Sound-Driven Synapses Make AI Both Faster and Greener? How AI Attribution Could Finally Pay Musicians for Training Data Inside GM’s AI Push to Speed Up the Design of Cars and Moon Rovers Are Emotion Reading Robots Still Missing What Matters Most? The Google DeepMind Spinoff Chasing Hidden Drug Targets Save 14 Percent of Energy Used in LLM Training With This Trick AI Can Help Track the World’s Shrinking Glaciers Nvidia’s AI Hardware Comes to Windows in RTX Spark PCs Why Quantum Computers Need a ‘Healthy Chunk’ Of Classical Power How Young Engineers Can Turn AI Into Career Leverage Why Aren’t We Measuring How AI Affects Humans? Majestic’s 128TB AI Server Aims to Smash the LLM Memory Wall Finding Success in Industry as a Chip Designer Why South Africa’s AI Policy Leverage Is Slipping Away Unused AI and Thermal Cameras Help Ships Steer Clear Of Gray Whales Why Reclaiming ‘Social Engineering’ Could Protect Your Autonomy AI with Model-Based Design: Virtual Sensor Modeling - Wiley Science and Engineering Content Hub Millimeter Waves Turn Tiny Insects Into Trackable Data Māori AI Voice Puts Language Ownership Back In Community Hands Open-Source AI Could Make It Easier to Build Smart Robots The Future of Physical AI Isn’t Smarter Robots, It’s Smarter Interfaces Agentic AI for Robot Teams How Melbourne’s AI and Data Center Flywheel Is Accelerating Research Innovation Hidden Voice Glitches Could Hijack Audio AI Tools AI Rings Turn Sign Language Into Text In Real Time Graphene Leaf Tattoos Turn Plants Into Living Moisture Meters Accelerating Chipmaking Innovation for the Energy-Efficient AI Era Can AI Chatbots Reason Like Doctors? General AI Outruns Specialized Tools at Transcribing Handwriting Neutralizing the Gigascale Problem: How to Solve the Physical Power Paradox of Extreme AI Training Loads Tiny Data Centers at Substations Aim to Keep AI Power Usage In Check Orbital Bets On a Mesh Of GPU Satellites for AI Inference Can AI Really Build Better AI? AI Chatbot Safety Guardrails for Mental Health Ten Key Enablers for 6G Wireless Communications - Wiley Science and Engineering Content Hub
AI Turns DNA Into Tiny Dogs and Mona Lisa Nanostructures
https://www.facebook.com/48576411181 · 2026-07-15 · via IEEE Spectrum

Shaped like dogs, stars, and the Mona Lisa, you could mistake these DNA structures for fun-shaped macaroni if they weren’t only nanometers wide. South Korean scientists made the constructions using a technique called DNA origami that can bend genetic material into any form. Designing DNA strands so they’ll fold into a specific shape typically requires tedious manual work, but the researchers behind the playful fabrications have developed a shortcut using generative AI.

The AI model, called Generative SNUPI (short for Structured Nucleic Acids Programming Interface, and yes, inspired by the dog), was created by research teams at Seoul National University (SNU) and Hanyang University. The work behind it, which was accepted for publication in Nature Communications, shows the model can conjure DNA origami designs that work in the real world for user-requested shapes. For a design like the Mona Lisa, that doesn’t mean simply tracing an outline; the model considers the chemical rules of DNA to tell researchers how unpaired DNA strands should be sequenced so that molecular forces will cause them to self-contort into the required shape.

DNA origami techniques have been around for two decades now, with potential applications ranging from nanoscale robots to therapeutic structures that interact with cells. But these innovations have been slowed by how time-consuming and expensive the DNA structure design process can be.

“Traditionally we need some expertise, background knowledge, and know-how to design the proper nanostructures that we intend to make,” says Kyounghwa Jeon, a PhD candidate at SNU. The work requires humans running algorithms and tweaking results until the desired shape is achieved and structurally stable. With Generative SNUPI, she says, users could, in theory, go straight from drawing a target shape to physically assembling the DNA.

Rebecca Taylor, a professor of mechanical engineering at Carnegie Mellon University who was not involved in the research, says the new generative platform is exciting for researchers. “The entire field is sort of enabled and held back by its tools. When you make a new tool that enables a new tech, a new capability, that’s just such a big advance for the field.”

Traced outline of a Pug, surrounded by several dozen microscopic DNA structures bearing its exact shape. Generative SNUPI designs DNA sequences that, when synthesized, fold into nanoscale replicas of user-requested shapes.Source images: Chien Truong-Quoc, Kyounghwa Jeon, et al.

How AI can design DNA origami

Designing DNA origami using Generative SNUPI begins with a target shape. That could be something with complex curvature, like the outline of a dog’s face, or a more simple geometric pattern. Next, the new tech comes into play: Generative SNUPI applies a diffusion model, which adds and refines noise to the input shape to create the desired output in DNA form. Diffusion models are how platforms like DALL-E and Midjourney create AI-generated imagery.

“What it looks like is one of those kids crafts where you decorate something with glue and then put glitter all over it,” says Taylor. When the noise is removed—or, the glitter is shaken off—the design is revealed. “They’re basically just saying ‘populate this guide that I have with the DNA,’ but they also know how DNA comes together … that’s the thing that it’s really been trained on.”

The arts-and-crafts metaphors only continue once Generative SNUPI returns the DNA sequences that form the target shape. Scientists chemically synthesize short DNA strands called staples and used biological methods to produce a long strand called a scaffold. The staples pull the scaffold into shape in a way that Jeon says is “very similar to stapling paper.” The staple-scaffold relationship exploits DNA’s imperative to bond guanine to cytosine and adenine to thymine; the exact positions of each of these molecule are dictated by Generative SNUPI during the design process.

Researchers were able to produce a variety of DNA origami structures, but some did not hold their shape at first, notes Do-Nyun Kim, an assistant professor of mechanical engineering at SNU. “This occurred not because Generative SNUPI had an error, but because the drawn shape was, in fact, structurally unstable,” he says. In response, they added a step before actually designing the DNA sequence to predict the structural integrity of the input shape.

To expand Generative SNUPI’s capacity for real world applications, Kim says that DNA origami designs will need to be less rigid than what the model is currently able to produce. The technology reaching its full potential could mean life-saving uses like drug delivery and immunotherapy, but these uses often require flexibility.

“Most molecular structures are dynamic and reconfigure in response to external stimuli to perform their designated functions,” he says. “So, we plan to extend the current work to the design of dynamically reconfigurable structures in future research.”