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01Discovery · Google DeepMind
Google DeepMind watermarks AI-designed proteins, and the first binders still bind Google DeepMind has taken its SynthID watermark into biology, and says the first watermarked protein binders still bind. In wet-lab testing against three target proteins, VEGF-A, the SARS-CoV-2 spike protein RBD and PD-L1, the watermarked designs “matched the hit rate, binding affinity, and natural sequence diversity of unwatermarked versions”, the company says. It calls those “the first-ever watermarked and biologically functional protein binders”. The method is published as a peer-reviewed paper in Nature, “Function-preserving watermarking of AI-generated proteins”, and Google describes the work as a proof of concept. The mark goes into the sequence, not into a file SynthID Bio “embeds an imperceptible signature directly into the biological code”, Google says, so the watermark is “verifiable not just on a digital model but on the synthesized, physical protein itself”. A design that leaves the model and gets made in a laboratory carries the mark with it. For binders, molecules built to latch onto other proteins, the team used AlphaProteo, DeepMind’s own binder design method, alongside a SynthID Bio-enabled version of ProteinMPNN, a widely used protein sequence generator. Adaptyv Bio helped with the in vitro validation. AlphaFold 3 carries the watermark in its weights The second target is 3D structures. Google says SynthID Bio fine-tunes “a small part of AlphaFold 3’s diffusion network” so that predicted coordinates “inherently carry a detectable signature regardless of who runs the model”, and says prediction accuracy is preserved alongside near-perfect detectability. Google gives two reasons for wanting any of this. Novel AI designs “can bypass traditional DNA synthesis screening”, and mislabelled synthetic 3D structures “risk polluting public databases and misleading downstream research”. Tampering is the part that does not work yet “Moving forward, key challenges include making the watermark more robust against deliberate tampering,” Google says. It suggests pairing the watermark with provenance metadata, along the lines of C2PA for digital media, or with central repositories of AI-generated biological data. The work on viruses is further back. With the Hie lab at Stanford University and the Arc Institute, DeepMind put SynthID Bio into the genomic model Evo 2 and watermark…
Allen Institute for AI · AI for Science
Language models can already help researchers search the literature, synthesize evidence, and work through complex questions. But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports rather than quietly broadening a study’s conclusions, and researchers need to be able to verify the final outputs. We see that in how scientists use Asta, our agentic platform for scientific work. Instead of simple keyword searches, users often bring substantial context and many constraints—for example, asking Asta to compare approaches across a body of literature while accounting for a particular method, population, or setting. Many also return to generated reports later, treating them as working research artifacts rather than one-off answers. We wanted to help scientists generate cited reports faster, with a model they could download and run themselves. To do that, we tested whether a small, open model trained specifically for scientific report generation could match the report quality of the proprietary models we were using, while reducing generation time and serving costs. We built AstaBrief 8B, a model that turns a research question and retrieved literature excerpts into a cited report. AstaBrief is available in Asta’s Generate a report feature today as Fast mode alongside Claude-powered Thinking mode, and we’re also open-sourcing it and the training data so others can study, reproduce, and build on our approach. Developing AstaBrief required tens of thousands of real research queries, citation-focused filtering, preference data, and a redesigned report-generation pipeline that writes the full report in one pass rather than section by section. The result is nearly an order-of-magnitude reduction in report generation time compared to the proprietary models we tracked—across the full Asta pipeline, Fast mode averages 51.1 seconds per report compared with 178.5 seconds for Thinking mode, about 3.5× faster. Together, those efficiency gains made AstaBrief a useful test case for a broader goal: building open language models that can be adapted to the specific demands of scientific work. Open weights will also let institutions run AstaBrief on their own infrastructure, which is necessary when research questions reveal sensitive or unpublished work. Alongside the model we…
Discovery · Google DeepMind
October 1, 2026 report Google DeepMind develops invisible watermarks for AI-designed proteins Paul Arnold Author Gaby Clark Scientific Editor Robert Egan Senior Editor Watermarks have long protected everything from bank notes and fine art to digital photographs and software, helping prove authenticity and trace an object's origin. Their main function is to protect copyright and verify authenticity. That same traceability is needed in synthetic biology, where AI is now a useful research tool. To address this, Google DeepMind has introduced SynthIDBio, a method for embedding invisible signatures directly into biological sequences and 3D structures without interfering with their function. AI is actively designing new functional proteins and predicting their three-dimensional shapes. But along with the benefits come a host of potential problems. These include biosecurity risks, such as the potential misuse of AI-designed biological molecules, and the spread of fake or misleading scientific data. The team released details of its protein watermarking in a paper published in the journal Nature. How to watermark a protein There are two kinds of watermarks, digital patterns built directly into the protein design data. Which one is used depends on what the AI is generating. If the model is writing a protein sequence, the watermark is a secret code hidden in the order of the amino acids. If the model is predicting a 3D biomolecular structure, the watermark is an invisible pattern hidden in the 3D coordinates of the atoms. Then came the jump from computer models to the real world. Researchers made watermarked protein binders in the lab to target three specific molecules. These included part of the coronavirus spike protein, a human protein involved in blood vessel growth and a human protein involved in regulating immune responses. They then needed to find out two things. The first was whether the watermarks changed how the proteins work, and the second was whether a detector could spot them. The watermarked proteins attached to all three targets with comparable strength to nonwatermarked versions. Meanwhile, the detection tool correctly identified all of the watermarked protein sequences and more than 99.8% of the watermarked 3D structures. Adding the watermarks did not significantly affect how well the proteins worked or the accuracy of the 3D structures. There are st…
Hugging Face Daily Papers
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this signal to decide which tokens to train on: they typically train on the final text or assign rewards to whole denoising steps, rather than selecting the individual commitments that shape the response. We introduce Pivot-SD, an efficient offline self-distillation framework that supervises only these high-impact commitments (pivots). Pivot-SD selects pivots using an information-gain metric measuring uncertainty reduction over the remaining masked positions. Pivots from successful trajectories are trained with cross-entropy, and pivots from failed trajectories with targeted unlikelihood, leaving the rest of the failed trajectory untouched. Using only 200 questions and four rollouts each, Pivot-SD improves LLaDA-8B-Instruct over full-sequence SFT and budget-matched diffusion RL baselines across math and code benchmarks.
Nature Portfolio · Scientific Data
The development of machine olfaction (digitization of odor) is currently hindered by data scarcity and the lack of standardization in descriptive labels. Datasets integration faces the challenges of descriptor-level conflicts, including cross-source labeling inconsistencies, synonym redundancy, skewed distributions, etc. To address the semantic confusion inherent in multi-source data, we developed a SEA (Statistical co-occurrence, Expert correction, and AI alignment) taxonomy framework that integrates statistical co-occurrence analysis, expert knowledge (perfumery taxonomy), and AI-assisted semantic alignment based on the definitions from both encyclopedia and odor-related scientific literatures. A unified-labeled olfactory dataset, namely OdorNet, is further presented, which is by far the largest molecule dataset with unified olfactory labels comprising over 20,000 entries and 9,000 unique molecules originated from academic literatures (1960–2021) and open databases. This process consolidates cluttered labels into a hierarchical system with the most extensively effective vocabulary coverage in the field to date. Technical validation demonstrates that a baseline model on OdorNet achieves a Macro F1 score of 0.42, substantially higher than the scores obtained on commonly used odor datasets and on OdorNet itself using the same model but without the SEA-based label taxonomy. This indicates that OdorNet allows to better capture learnable structure-odor relationships, laying a solid fundamental for digital odor editing and human-computer interaction.
