Kat (@katyenko) header
Kat (@katyenko) profile picture

building @muni_bio AI ∩ bio | in vitro to in silico, always an experimenter

SF
Joined August 2024
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coolest email i have gotten since starting at @OpenRouter: "the compute you sponsored helped identify hits against Bundibugyo ebolavirus in our assays" you check out what @katyenko and her team were able to accomplish here: nitter.cf/katyenko/status/211622…
Binding is nice, but it’s getting boring. If you’re not thinking about biological function, you’re ngmi. At our last @muni_bio hackathon co-hosted with @onepot_ai, we set one ambitious goal: neutralize the latest threat of Ebola using small molecules. The wet lab data is in, and we’re excited to announce that teams were successful, including our agent that repurposed... an anti-itch gel?
 muni.bio/research/hits-from-…
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Great to see teams using the Boltz API at the Ebolathon successfully designing novel molecules! 140 molecules tested, 11 selective hits in Ebola pseudovirus assays. Exciting to see hackathon ideas turn into experimental results! 🧪 muni.bio/research/hits-from-…
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The factorioization of biology is happening. Expect faster/cheaper experimental validation and more novel drugs developed at a fraction of todays cost. My sense is that many people inside the drug development industry are further behind in understanding what's becoming possible than those on the outside looking in. Which leads to an interesting arbitrage, bound purely by ambition. Teams that embrace agentic workflows will soon look like an entirely different species of biotech, one that the old guard will struggle to comprehend. Some will interpret this negatively as the removal of human creativity from biological research, my view is opposite. I think this gives people with deep biological insight far more leverage. Freeing them from the tedious, repetitive work that consumes most of their context. Exciting times.
Binding is nice, but it’s getting boring. If you’re not thinking about biological function, you’re ngmi. At our last @muni_bio hackathon co-hosted with @onepot_ai, we set one ambitious goal: neutralize the latest threat of Ebola using small molecules. The wet lab data is in, and we’re excited to announce that teams were successful, including our agent that repurposed... an anti-itch gel?
 muni.bio/research/hits-from-…
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Kat retweeted
In July, @muni_bio and @onepot_ai co-hosted Ebolathon to find small molecules that could block Bundibugyo ebolavirus from entering cells without harming the cells themselves. Teams designed 140 molecules in just one Saturday in San Francisco. All were synthesized in 11 lab days, followed by 22 lab days of testing. The results are in: 26 of the 140 molecules were preliminary hits—an 18.6% hit rate. In part, powered by Modal.
Binding is nice, but it’s getting boring. If you’re not thinking about biological function, you’re ngmi. At our last @muni_bio hackathon co-hosted with @onepot_ai, we set one ambitious goal: neutralize the latest threat of Ebola using small molecules. The wet lab data is in, and we’re excited to announce that teams were successful, including our agent that repurposed... an anti-itch gel?
 muni.bio/research/hits-from-…
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A2A payments will be more important than P2P payments.
Introducing 'stripe pay', for instant business-to-business transfers. Find companies using their Stripe profile handle and send funds for free. Available in the Dashboard and CLI.
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Force fields and folding models finally merging, nice
The latent space is physics-informed: we train on physics-based data (synthetic PDB with glycan conformers, DFT small molecules from OMol) and supervise the autoencoder with force-field backprop via TMol (more soon!). New tasks then just need new input conditions. 5/30
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Worse than actual gore
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We're launching 30B-scale virtual screening at Rowan today in partnership with @onepot_ai. By combining Rowan's high-throughput computational infrastructure with onepot's autonomous lab, we can go from target to experimental data in weeks, not months.
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Excited to introduce @ReactorfieldAI Cohort 1: @merge - brain-computer interfaces with no implants, $250M seed led by OpenAI and Bain @episteme - a new R&D institution, cofounded by Louis Andre and Sam Altman @RetroBio_ - unicorn longevity startup, adding 10 healthy years to human life. In phase 1 trials @NorthwellHealth - NY's largest healthcare system, with Chief Biotech Officer @itchdoctor @StrandTx - programmable mRNA medicines out of MIT, lead cancer drug already in patients @MayoClinic - turning leading clinical science into new ventures represented by @vera_mucaj @xiliotx - publicly traded biotech making cancer therapies to activate inside tumors @marathonfusion - producing the world's most valuable isotopes from compact fusion reactors @KopraBio - viruses that turn cancer cells into factories for their own destruction @radifymetals - hydrogen-plasma reactors that turn rare-earth oxides directly into metal @FoundationManf - rapid domestic CNC machining for frontier hardware @MassMagnetics - US-made high-performance magnets from recycled rare earths @algabiosciences - turning cattle methane into calories, peer-reviewed cuts of up to 63% @lumehealth - building a wearable that measures cortisol continuously from sweat @liu_liu_lemon - smart materials for med tech and robotics, @Harvard PhD, Fulbright Fellow
AI transformed coding, science is next @ReactorfieldAI makes scientists and deep tech startups AI-native Built by me and @berkbuilds to accelerate science with frontier AI
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ʙɪɴᴅᴄʀᴀꜰᴛ2 is out, and we're not waiting for the paper. The full code drops today, free for academic and industry use. We're releasing it early so you can start designing right now, and bring its full power to the current Adaptyv competition. github.com/PacesaLab/BindCra…
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Also the technology to even measure most of it at a reasonable scale doesn't exist yet.
taps sign most of the data AI needs to predict human biology doesn’t exist yet.
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Every experiment should inform the next. @katyenko and @derekalia are building @muni_bio: a platform for agents to design hundreds of drug candidates, run wet-lab validation, and use that knowledge for future discoveries. Kat and Derek are joining Reactorfield to show our scientists how to connect autoresearch with real-world experiments.
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Introducing Transfyr. Always Learning.
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The closed-loop research flywheel is here. 🚀 @muni_bio's autoresearch agent combined NVIDIA’s Proteina-Complexa model with @adaptyvbio's automated wet lab to generate 3 sub-nanomolar TREM2 binder! The autoresearch agent outperformed previous human & agent hackathon leaderboards!
Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs. @adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity. The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘muni.bio/research/closing-th…
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Very exciting to see how Proteina-Complexa in the hands of muni's agentic autoresearch pipeline produces sub-nanomolar binders for TREM2, scoring at the top of the leaderboard. More about NVIDIA's Proteina-Complexa here: research.nvidia.com/labs/gen….
Replying to @muni_bio
@muni_bio now gives agents access to the wet-lab. Using @NVIDIAHealth's Proteina-Complexa + @adaptyvbio validation, muni found 3 sub-nanomolar binders, all of which were better than our hackathon winners. Even if assays take days or weeks, the results automatically feed back into the agent, allowing you to orchestrate autonomous scientific research in parallel.
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Very exciting to see our work part of this report. Big congrats to @AnthropicAI @amirshanehsaz and @adaptyvbio
Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per target, sifting through a large number of candidates to identify the few that work. We wanted to test if Claude could successfully design novel protein binders from scratch (also called de novo design). With a protein design prompt written by a human expert, Claude autonomously designed protein binders against 14 out of 15 targets. We then worked with Adaptyv Bio and Twist Bioscience, who independently built and tested the proteins Claude designed.
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Exciting to see agentic workflows on top of Proteina-Complexa having impact in the real world!
Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs. @adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity. The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘muni.bio/research/closing-th…
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Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs. @adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity. The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘muni.bio/research/closing-th…
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On their first shot teams in an agentic med-chem hackathon by @RowanSci & @muni_bio submitted compounds to the TBXT Challenge that included 6 new TBXT binders. A 50% hit rate. In 7 hrs. Pretty impressive. Grateful for this effort, and that they put the results in the open!
We've gotten results back from our first agentic med-chem hackathon, and the teams found real wet-lab hits! In this post co-written w/ @muni_bio, we explore how the teams did, which strategies worked, and what we've learned from this first experience: rowansci.com/blog/hits-from-…
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