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1/ 🚨 Excited to share my Postdoc research with @geochurchlab at @harvadmed! Dive into our discoveries on the central dogma of biology, antibiotic resistance, #compbio designs, and #synbio. 🧬🔬 Check our paper: biorxiv.org/content/10.1101/…
Anush Chiappino-Pepe retweeted
MIT announces the MIT for America initiative. The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI. news.mit.edu/2026/mit-americ…
Anush Chiappino-Pepe retweeted
Our latest paper uses gene deletions to shed light on the specificity of bacterial nitroreductases and to unlock new applications in whole-cell biocatalysis, metabolic engineering, and GCE
nature.com/articles/s41557-0…
3-min podcast summary in the comments
“My concern is that this information was used to train future versions of the models,” Dr. Mestre said of his own research. “I think it’s important to raise this possibility.”
In a statement on Sunday, Anthropic said: “We are not aware of any previously published work describing the ART system we recently found. Claude was also not trained on any user transcripts, and our molecular biology team has no such access, either.”
Anthropic said that Claude’s key contribution was identifying an array of RNA molecules and a protein associated with reverse transcriptase in a system. But Dr. Maestre said that he and his colleagues had made that discovery over a year ago.
nytimes.com/2026/09/27/scien…
Anush Chiappino-Pepe retweeted
MIT published a brutally honest report on what AI is doing to students.
A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet.
Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle.
The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable.
The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust.
Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates.
The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output.
The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.
Anush Chiappino-Pepe retweeted
New PET upcycling work in Science Advances:
BHET/MHET readily enter E. coli, letting us directly couple PET glycolysis to whole-cell biocatalysis.
Fun collaboration with Dion Vlachos, and congrats to first author Roman Dickey!
science.org/doi/10.1126/scia…
Anush Chiappino-Pepe retweeted
Just an incredible paper released by George Church's lab @geochurch absolutely worth reading!!!
nature.com/articles/s41586-0…
Anush Chiappino-Pepe retweeted
Thrilled to post thread re: new single-cell lineage of mouse embryo reconstructed w/ DNA Typewriter. One animal, zygote to late organogenesis (E13.5). Tree has 1,340,794 transcriptionally profiled, annotated tips (cells), 1,142,588 dated internal nodes, rooted at zygote
1/n
Anush Chiappino-Pepe retweeted
New paper from the @nablabio team expanding generative drug design to multispecifics, the intracellular proteome, and pushing the limits of atomic precision.
A tour de force of computation + wet-lab and some incredible results, including zero-shot design of a KRAS G12V pMHC-targeting bispecific that recruits primary T-cells from human donors and achieves picomolar cell killing.
Much more in our post and report
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.
Anush Chiappino-Pepe retweeted
Thrilled to share that our paper, “Deep learning reveals antimicrobial peptides within prions,” is now published in @NatureMicrobiol @NaturePortfolio.
In this work, we used AI to ask an unexpected question: could proteins best known for their role in fatal neurodegenerative diseases also encode molecules that fight infection?
Prions and prion-like proteins are usually viewed through the lens of misfolding, aggregation, and disease. But biology is often more layered than our labels suggest. Our study shows that these proteins can contain encrypted antimicrobial peptides, which we call prionins.
This connection did not come out of nowhere. Previous studies had shown that certain amyloid-associated protein sequences, including amyloid-β and the cellular prion protein, can have antimicrobial or host-protective activity.
But until now, this had not been explored systematically across prion and prion-like proteins at scale.
Using our deep-learning platform APEX 1.1, we screened 19.3 million peptide fragments from 2,897 prion-related proteins. This search identified 1,179 candidate antimicrobial peptides, moving from scattered observations to a global AI-guided search across millions of possible protein fragments.
We then moved from prediction to experiment. Of 75 synthesized prionins, 59 inhibited at least one bacterial pathogen, including multidrug-resistant strains. Forty-two showed activity at 16 µM or lower against at least one pathogen.
Many prionins disrupted bacterial membranes, and a subset showed encouraging early selectivity, including minimal hemolysis and limited cytotoxicity. Two lead prionins also reduced Acinetobacter baumannii burden in a mouse skin-infection model, with efficacy comparable to polymyxin B in the model tested.
This is still early-stage work. We do not show that prionins are naturally released during infection or that they function physiologically in host defense, and these findings do not represent a treatment ready for patients.
But the study raises a provocative idea: proteins long viewed mainly as biological “villains” may also encode useful molecular functions.
More broadly, it shows how AI can become a discovery engine for biology: helping us search hidden molecular spaces, connect seemingly distant fields such as neurodegeneration and innate immunity, and uncover new starting points for medicine in places we might never have thought to look.
At a time when new antibiotics are urgently needed, expanding where we search matters.
Deeply grateful to @mdt_torres, Fangping Wan, and everyone who made this work possible, including @Penn, @CBE_Penn, @PennBioeng, @PennEngAI, @PennEngineers, @PennMedicine, @PennChemistry, @PennMicro, @PennPsych, and @PennSAS.
nature.com/articles/s41564-0…
Anush Chiappino-Pepe retweeted
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/radi…
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/exclu…
Jobs: radicalnumerics.ai/join-us
Anush Chiappino-Pepe retweeted
Generating E. coli 0.5 controlled by a half-sized genome biorxiv.org/content/10.64898…
Anush Chiappino-Pepe retweeted
I have spent my entire life working on this and thinking about this for the past 4 years. I don't know what will happen in 20 years, but I can promise you that on the 5-10 year timescale, scientists are not out of their jobs. AI is going to massively accelerate the pace of science, increase productivity, let individual scientists make way more discoveries way faster, and is going to make science overall more fun. But the model is going to be collaboration between humans and AI, not replacement.
The key difference here between science and e.g. software engineering is that science is not verifiable in any rapid/convenient way (unlike software), unlike programming. We still need humans for their scientific taste.
Today we all lost our jobs.....
Three Nature papers showing that scientists in the conventional sense are obsolete
At least read the first one.... the AI replaced all things that the scientist does ....
nature.com/articles/s41586-0…
Thank you very much @Schmidt_Center for the opportunity to present my research and for this recognition!
And thank you to my collaborators, including @pgmikhael @Itamarchinn, and mentors, @geochurch @BarzilayRegina G Stephanopoulos for their support throughout this project.
🎉Congratulations to our symposium poster and short talk winners!
🥇First: @AdaFang_ (poster 34)
🥈Second: @AnushChP (poster 24)
🥉Third: @JoYatesResearch (poster 13)
🥉Third: Mahshid Heidari, Mina Karimpour (poster 42)
🏆 Best Short Talk: Maile Hirschmann, @dawnchenx
Anush Chiappino-Pepe retweeted
1/ Excited to share our new paper in Science: “Toward life with a 19-amino acid alphabet through generative artificial intelligence design.” @ColumbiaSysBio @ColumbiaBME @Columbia
science.org/doi/10.1126/scie… 🦠🧬🛠️🖥️💥
Congratulations to @harriswangnyc and his team for this breakthrough! science.org/doi/10.1126/scie…
Anush Chiappino-Pepe retweeted
The synthetic biology community lost one of its founding giants today. Craig Venter - scientist, provocateur, and relentless force of nature - has passed away. synbiobeta.com/read/a-synthe…
Anush Chiappino-Pepe retweeted
I came back to the United States, hearing about a very sad news: Craig Venter passed away today. He is a pioneer, successfully sequenced the first Human genome, and tried to create Synthetic Cells. We lost a giant in Science. RIP.
@JCVenterInst
Thank you, Craig Venter, for all your contributions to science and humanity. You leave a huge legacy behind. ❤️🕯️
Anush Chiappino-Pepe retweeted
Thrilled to share our review paper on @TrendsBiochem. We summarize the latest breakthroughs in expanding the genetic code beyond single noncanonical amino acids (ncAAs) — from orthogonal translation systems and quadruplet codons to rare codon recoding — enabling the synthesis of sophisticated “new-to-nature” biopolymers with multiple distinct functionalities.
authors.elsevier.com/a/1m%7E…


























