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Google DeepMind publishes SynthID Bio, a watermarking method for AI-designed proteins

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In what can be called a major breakthrough for synthetic biology and AI-powered biosecurity, Google DeepMind, Google’s AI lab, has developed a new method to watermark AI-designed proteins, establishing a permanent proof of origin directly inside biological code. Published in the journal Nature, the proof-of-concept technology, which is named SynthID Bio, solves a long-standing technical challenge: embedding an non-noticeable, verifiable digital identifier into biological sequences and predicted molecular structures without degrading their physical real-world utility in laboratory tests. The rollout comes amid rapid advancements in biological machine learning, where systems like AlphaFold 3, AlphaProteo and ProteinMPNN allow scientists to predict complex folding patterns and engineer functional binders. Yet these generative capabilities have heightened global biosecurity risks. Unfamiliar, machine-generated sequences can slip past conventional gene-synthesis filters, while unverified synthetic structural data risks contaminating open-access scientific repositories. ‘An urgent challenge for the AI era’ Google DeepMind Chief AI Scientist Demis Hassabis emphasised that guarding against biological misuse must be treated as a frontline safety priority for frontier AI: “Biosecurity is one of the most urgent challenges for the AI era. Bringing SynthID to biology so AI-generated proteins can be watermarked is a critical step - and we’re open sourcing SynthID Bio tools so the research community can build on this work. Published in @Nature today, congrats to the team!” Pushmeet Kohli, Vice President of Science at Google DeepMind and Chief Scientist at Google Cloud, celebrated the practical synthesis of these molecules: “Very happy to announce that our team at Google DeepMind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity.” How SynthID Bio operates SynthID Bio functions as a modular toolkit tailored across two primary dimensions of structural biology: SynthIDBio-sequence that subtly steers the selection of amino acid building blocks du…

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外部发现 · Google DeepMind · Original source ↗ · Site entry ·

Building a new path to make medicines with AI - Isomorphic Labs

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Listen: Solving all disease is one of the most monumentally complex challenges in science. Today, for the first time, AI provides the foundation to make it achievable. Tackling the frontier of human health is our mission, and it means identifying exactly where modern AI can make the greatest difference. We see drug discovery as the critical wedge in solving all disease, and therefore our initial focal point where AI can have a transformative impact on human health. Developing a drug that successfully and effectively treats disease fundamentally involves understanding biological mechanisms and designing exquisitely engineered molecules that can modulate them safely. So we are taking a straight shot at making that a reality. We are generating the data, training the AI models, developing the software, and laying down the physical infrastructure to make new medicines; taking these potential therapeutics all the way to patients in the clinic. We are building this new architecture in a way that will scale across multiple disease areas, and different types of medicines, from small molecules to antibodies, peptides, and beyond. In 2021, when we started Isomorphic Labs, AI drug discovery was just a hypothesis. Conventional approaches were bottlenecked by slow, resource-intensive iterative design-make-test cycles, viewing AI as an experimental tool for narrow tasks. At the same time the AI revolution was well underway across multiple industries. A seismic shift was happening with the emergence of foundation models, moving away from single-task algorithms toward highly versatile systems capable of world-understanding, zero-shot reasoning and generating complex text and images. Up to this point, I had spent my education and career in core AI, building intelligent systems by scaling up neural networks and deep reinforcement learning to solve the hardest problems at a superhuman level. But having grown up in a medical family, I recognised the potential to apply this superhuman new technology to humanity’s most meaningful challenge: human health. Working with Sir Demis Hassabis, we created the founding team at Isomorphic Labs to pivot breakthroughs in AI toward solving the drug discovery problem. We set out on a path to approach drug discovery as an engineering process, stripping back to first principles, to rebuild the process to design medicines. Almost every field of e…

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外部发现 · Isomorphic Labs · Original source ↗ · Site entry ·

Joint Statement about Mathathon

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[This is a guest post by a coalition of Caltech mathematicians: the original organizers of Mathathon (Alvan Arulandu, Andrea Li, Avni Garg, Brian Zhao, Caiman Moreno-Earle, Sathvik Redrouthu), two coauthors of the Open Letter about the Mathathon (Dylan King, Tasmin Chu), and other members of the Caltech community (Mayla Ward, Merrick Hua, Robert Joseph George, Sergei Gukov, Shaowu Zhang, Tony Yue Yu, Vihaan Dheer). This blog post was initially written in a different file format and converted using AI. It is also crossposted at Proofs and Prompts — T.] Mathathon is centered around one question: how can we use AI to augment human understanding of mathematics? This past week, the original organizers of Mathathon, two coauthors of an open letter about the mathathon , and other members of the Caltech community engaged in a conversation to redesign this event. We quickly reached a consensus: problem solving is but one component of mathematics; mathematical understanding and exposition are similarly meaningful. But these components have often been overlooked by grantmakers and hiring committees. We want to celebrate human mathematicians who undertake this work. Thus the new theme of Mathathon is Old Problems, New Proofs . Consider the four color theorem, the ABC conjecture, or the Navier-Stokes problem. Many mathematicians find their proofs—or claimed proofs—difficult to understand or unsatisfying. We invite our participants to pick a problem with an unintuitive solution, learn as much as possible in 40 hours, and present their findings to their peers. Then, they will take two months to develop an alternative proof or exposition. Afterward, they’ll submit an explainer and a GitHub repository. The explainer can take any form: a paper, a blog post, a video, an interactive game, et cetera. The repository will store whatever was used to produce the explainer—LLM chat histories, code used to produce visualizations, and more—so the mathematics community can examine the ideas behind the finished product. By shifting the focus from open problems, we want to encourage Mathathon participants to explore everything else we do to make sense of mathematics: generalizations, new notations and techniques, connections to other problems. These innovations can be more exciting than solving individual problems. Mathathon treats AI as one of many tools available to mathematicians. We…

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陶哲轩 · What's new 博客 · Original source ↗ · Site entry ·

AIhub monthly digest: September 2026 – tracking animal populations, recommender systems, and an interview with Ken Goldberg

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Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet AI pioneer Ken Goldberg, use AI techniques to track animal populations, investigate how to improve recommender systems, and find out how external knowledge is used in AI systems. Combining cultures, from code to canvas: an interview with Ken Goldberg In the latest instalment in our AI pioneers series , we spoke to award-winning roboticist, filmmaker, and artist, Ken Goldberg . We discussed the culture clash within robotics, what art and science have to learn from each other, and how AI will shape the future of art. Understanding animal behaviour and tracking populations Deep learning is a powerful tool for understanding animal behaviour and tracking populations. We caught up with Isla Duporge to find out about her work developing remote-sensing and AI methods to investigate animal movement in the wild. Optimising sensor placement for estimating wildlife populations Hannah Murray is also researching wildlife populations. In this interview , she told us more about work developing optimization methods that help ecologists determine where to place sensors, such as camera traps, to get precise estimates of species population counts from the data they collect. Improving the process for large-scale recommender systems In large-scale recommender systems, platforms often need to handle massive volumes of items at web latency. To meet this requirement, systems often employ a two-stage decision process. We heard from Haruka Kiyohara about her research trying to improve the first part of this decision process – an early-stage ranker, which filters up to billions of items cheaply into a smaller candidate set. How is external knowledge used in AI systems? We kicked off our series meeting the IJCAI-ECAI doctoral consortium participants meeting Yash Saxena . Many AI systems now look through external documents before answering a question. In his research, Yash follows what happens to that evidence as it moves through the system. How much can fair budget-division rules resist manipulation? Haris Aziz, Patrick Lederer and Jeremy Vollen won a distinguished paper award at IJCAI-ECAI for their work “Approximate Strategyproofness in Approval-based Budget Division”. In this blog post , they summarise their pape…

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AIhub · Research news and interviews · Original source ↗ · Site entry ·

OpenAI's Noam Brown on multi-agent systems: 10,000 agents deserve at most 10% of the credit for the Millennium Prize breakthrough

What changed

Noam Brown, an OpenAI researcher and core author of o1, discussed his current focus, multi-agent systems, in a recent podcast. He said OpenAI previously used 10,000 agents over 88 hours and 130 billion tokens to solve a Millennium Prize mathematics problem. Brown was explicit that the result came from having a general, powerful model, and that he would not even attribute 10% of the credit to multi-agent methods, which he feels have received disproportionate praise because they are eye-catching. Multi-agent systems simply scale test-time compute in parallel rather than serially, at some efficiency loss since agents do not share full context. His testing reached only 16 agents: four agents solve a problem about twice as fast for roughly double the cost, and 16 still improve performance with slightly lower efficiency. Speedups are somewhat sublinear and domain-dependent, and ablation experiments at 1,000-agent scale would be unaffordable.

Sources and evidence

量子位 · Original source ↗ · Site entry ·