george church

Professor @Harvard @MIT @PGorg @WyssInstitute tech dev: #CRISPR #NGS #BRAINi

[george church @geochurch]

Rejuvenate Bio spun out of my lab at Harvard, and I co-founded it. One injection reverses cardiac disease in dogs, with three years of safety data and zero safety issues. They’ve raised $3.2M+ and 1,250+ investors are in, and Kingscrowd ranks the round #1 in the country. It closes soon: https://t.co/EDUdl3xq7a

[george church @geochurch]

@amplesa @NatureAging !! Adding this to our short list for age related disease polytherapy.

[Peter H. Diamandis, MD @PeterDiamandis]

Today, I’m launching NeoGenesis Capital with @YianniPsaltis.

Five years ago, I became an early investor in @Colossal Biosciences. Then @BenLamm and @Geochurch built a platform at the intersection of Superintelligence and Synthetic Biology that can build living products… programmable biology.

NeoGenesis invests in Colossal and the spinout companies built on their platform across food, environment, entertainment, security, and species reconstruction.

[george church @geochurch]

Please join me at ARDD 2026, the leading conference on Aging Research and Drug Discovery. At the beautiful Harvard Rubenstein Treehouse October 1–3, 2026. Scientists, biotech pioneers, pharmaceutical executives, investors, and policymakers advancing longevity science. Registration: https://t.co/MGBkHL20Ek#ARDD2026 #AgingResearch #DrugDiscovery

[george church @geochurch]

https://t.co/5TlGkOdfzk

[george church @geochurch]

you should consider joining. Great team.

[Isaak @isaakfreeman]

We're a vibrant and intensely mission-driven team in San Francisco from MIT, Harvard Medical School, Roche, ETH, and Dana-Farber.

We're grateful to be backed early with $12M by:
- 50 Years & HOF
- OpenAI executives Wojciech Zaremba, Kevin Weil, Srinivas Narayanan, and Fidji Simo

Our founding scientific advisory board:
- Prof. George Church at HMS, luminary of modern genetics
- Prof. Luis de Lecea at Stanford, co-discoverer of orexin
- Prof. Bradley Pentelute at MIT, leader in rapid peptide synthesis and brain-targeted peptides

[Lexi Rovner @lexrovner]

We’re launching LV Apex Suite today, our second Apex Suite after AAV. Each new modality adds data on what’s shared, what’s different, and how to engineer production systems in an integrated way. That’s tied to a core view at @64xbio:

Cell lines, reagents, and process aren't separate levers, they're interdependent parts of a single production system. If we want better productivity, quality, and scalability, we need to understand and engineer that system more holistically.

That’s what we’re building toward with CellMap: data-rich experimental maps of production biology that we can learn from computationally, to guide the design of better cell lines and reagents. Biologics are next, and existing suites will keep expanding with additional tools.

With this, we’re also growing our commercial and ops teams. If you’re excited about building next gen biomanufacturing tools, DM me.

Incredibly proud of the team today for all the work behind this launch. Much more to come.

[64x Bio @64xbio]

New product launch: Today we’re launching LV Apex Suite, a new product suite for lentiviral vector production.

This marks 64x Bio’s expansion from AAV into LV — and another step in translating our CellMap-driven VectorSelect platform across new modalities.

LV Apex Suite begins with a high yield engineered HEK293T suspension cell line for transient LV production, with stable systems and additional production-enabling technologies in development.

More broadly, this launch reflects where 64x Bio is headed: expanding CellMap as a data foundation for production biology, and learning from those data computationally to iteratively guide development of better cell lines, reagents, and production systems for viral vectors — and soon, biologics.

To learn more about LV Apex Suite, read our press release below and visit https://t.co/95gQiMpjxk.

Press release: https://t.co/C1BOGTYPnU

[george church @geochurch]

I co-founded @RejuvenateBio and have watched this platform earn every milestone. @Merck Animal Health, the second largest animal health company in the world, just invested in their $6M round and inked a strategic R&D partnership.

The science earned it: cardiac disease reversal in dogs, three years of safety, no safety signals.

Now the biotech community can invest alongside Merck and secure equity at this early stage. Rejuvenate Bio is raising on @Wefunder. @Kingscrowd ranked it the #2 Reg CF deal in the country.

$7M+ raised. Investments range from $250 to $100K+. The round is now oversubscribed, but you can still get in.

https://t.co/EDUdl3wShC

[Nabla Bio @nablabio]

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2.

In a new report (below), we show:

1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability

2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type.

3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input.

4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks.

5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards.

Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

[Eric Nguyen @exnx]

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:
https://t.co/ouikMtRVwf

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: https://t.co/L3f3f1329T

Jobs: https://t.co/EzsHSMcGJ1

[Harlan Stevens @Harlan_Stevens1]

Entropy is random. Its effects on aging are not.

Aging destabilizes the regulatory networks in blood stem cells🩸 But not every part breaks equally.

The hallmarks of blood stem cell aging trace to one thing: fragile TF programs collapse with age, while stable ones expand.
🧵1/8

[george church @geochurch]

https://t.co/3BWIq8mv2F
A Framework for Autonomous AI-Driven Drug Discovery
Douglas W. Selinger, Timothy R. Wall, Eleni Stylianou, Ehab M. Khalil, Jedidiah Gaetz, Oren Levy
@plexresearch

[george church @geochurch]

https://t.co/D9TG2QZjuM

[george church @geochurch]

https://t.co/iDtrYONBim

[PioneerLabs @Pioneer__Labs]

This tube is a library of bacteria with every single-base-pair genome mutation, all DNA-barcoded.🧪Growth + barcode sequencing = data on millions of mutations.

📄 Report: https://t.co/llvs1wUbVk

Retweet if you want more data, and read on if you want to use the library! 🧵

[george church @geochurch]

I co-founded @RejuvenateBio & have enjoyed the science from day one. Cardiac disease reversal in dogs. 3 years of safety data. Zero safety signals.

They’ve opened a unique opportunity for the biotech community to invest & secure equity at this early stage. $250 to $100K+. Everyone can back this: https://t.co/EDUdl3xq7a

[David Sinclair @davidasinclair]

The best advice I ever got was from my colleague George Church

In 2008, I asked him what’s his secret?

He said: Do the research YOU want to do and don’t listen to what anyone else says!

Have never looked back

Thank you, @geochurch 🙏

[george church @geochurch]

https://t.co/EWCsKA7mVY

[João Pedro de Magalhães @jpsenescence]

Excited to share our latest paper on the future of AI in biology 🚀🧬

Machine learning has transformed fields like language through large-scale statistical modelling, yet we are still far from predicting human biology.

In our new work with @geochurch we argue that decoding the human genome and modelling gene interactions across space and time should be the ultimate goal for building predictive models of human biology and disease.

We need computational models that replicate cellular transitions across human life stages (from development to ageing) from the genetic sequence and its dynamic regulation.

Developing models with temporal and spatial resolution is ambitious, but we argue it is theoretically feasible and a long-term vision for understanding the language of life that could reshape biomedical research.

https://t.co/NQWkU71Pxb