Samuel King

Today in @Nature, we introduce SynthID Bio, a family of watermarking methods for protein sequences and structures, that we believe will contribute to responsible development of AI for biology. Learn more in our blog and this thread 🧵 - deepmind.google/blog/introduci…

Cool to have our cryo-EM structure of Evo-Φ36 highlighted on the "Molecule of the Month" article for the 55th anniversary of the PDB!

Getting this structure was incredible work by @_David_Li @samuelhking @driscoll_cl @maxewilkinson.

pdb101.rcsb.org/motm/322

Excited to share Minerva, our approach using genome language models for biological discovery! Using Minerva, we find that UG27 reverse transcriptase systems encode variable arrays of diverse ncRNAs with a shared structure, each templating a short DNA hairpin. With @garykbrixi.

Following the publication of our phage gen paper, I wrote some personal thoughts on biosafety and biosecurity as generative biology improves.

Given the trajectory of progress, there should be concern and urgency -- but also optimism.

brianhie.com/present_and_fu…

Excited to share the final version of ProteinDPO is out today in @naturemethods! New in the paper is application of the model to stabilization of the pre-fusion state of hemagglutinin, the primary component of flu vaccines. 1/8

🔗nature.com/articles/s4159…
📑rdcu.be/fzUc2

While I may have been the only person at @SynBioBeta to wonder aloud "What's the difference between a genome and DNA?" 🫣 ... I did also manage to ask some more nuanced questions about the nature of cutting edge bioengineering to Stanford University PHD candidate @samuelhking: youtu.be/i-XxyjW3fO0?si…

None of this would have been possible without the support of @BrianHie, my brilliant co-first authors @dan_guo_ and @bviggiano_13, and the rest of the Proto team, @leba1127, @ev_hur, @tinabmai, @peteryin21, and @samuelhking! Incredibly grateful to have spent the last few months working alongside these incredible people in our small corner of Arc!

Biological engineering has long aimed to design living systems with the programmability of software, but the tools to do so rarely fit together.

Today, we’re sharing our preprint introducing Proto, a high-level programming language for generative biology that composes models and design tools across DNA, RNA, and proteins to optimize towards user-specified functional objectives.

Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence.

We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date.

Omnii preview link:
radicalnumerics.ai/blog/radical-n…

At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense.

This is our dual mandate, which comes from something our own team helped make possible.

Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics.

Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point.

It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself.

That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime).

The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations.

We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms.

We founded Radical Numerics to turn the tide.

And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins.

The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life.

Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now).

1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer).

2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated.

We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up.

Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives.

Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap
, and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs.

Fortune article: fortune.com/2026/06/15/exc…

Jobs: radicalnumerics.ai/join-us

Tomorrow, at the #SystemsVirologyJournalClub, @samuelhking will present his work with @BrianHie using AI genome language models to generate novel, high-fitness bacteriophages.

Met @samuelhking, who was in our UBC lab, and @utnk1, who was in our University of Tokyo lab in Palo Alto. Amazing time…

What if we could autocomplete DNA based on function?

Today in @Nature, we share semantic design—a strategy for function-guided design with genomic language models that leverages genomic context to create de novo genes with desired functions.🧵

nature.com/articles/s4158…

Closing the AI-to-lab loop is hard, especially if you want to test your WHOLE GENOME generator..

Viruses are the only genomes cheap enough to print en mass, but raise biosafety flags

So @ArcInstitute chose phages!

We went deep w/ @samuelhking & @driscoll_cl on how they did it:

Samuel King, @BrianHie Lab discusses his research on generative design of novel bacteriophages with genome language models @bioe_stanford 🧫 🧬

🚨 Systems Virology Journal Club – 8th Series! 🚨
@wm_schneider and I are delighted to announce another round of cutting-edge talks in #SystemsVirology! 🦠💡
Join us and an outstanding lineup of speakers, starting Oct 30.
Free registration: shiraweingartengabbay.com/systems-virolo… 🔬✨

This Tues (10/14) at 4PM ET, @samuelhking will present "Generative design of novel bacteriophages with genome language models"

Paper: biorxiv.org/content/10.110…

Then, on Wed (10/15) at 4PM ET, @r_krishna3 will give an early-career talk on his research!