Alex Federation @afederation

For the last months, we’ve been working at @MoleculeOne with @OpenAI on something that felt like a glimpse of the future of science. Together with @OpenAI , we achieved what we believe is the first near-autonomous discovery in organic chemistry. openai.com/index/AI-chemi… 🧵

Its hilarious the extent to which people dont understand that mass specs actually work amazingly well right now today for protein sequencing I think it is primarily the fault of our community in doing a poor job of applying the technology.

Steve Jurvetson @FutureJurvetson

a month ago

🐠 Everything we know about biology has been built on an incomplete picture. DNA tells us what a cell might do. Proteins tell us what it’s actually doing. Pumpkinseed announced their $20M Series A today (led by Future Ventures and NfX) to build the platform that reads proteins

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🐠 Everything we know about biology has been built on an incomplete picture. DNA tells us what a cell might do. Proteins tell us what it’s actually doing. Pumpkinseed announced their $20M Series A today (led by Future Ventures and NfX) to build the platform that reads proteins directly—for the first time. Proteomics has always faced a fundamental constraint: you can only measure what you already know to look for. The current workhorse, mass spectrometry, requires matching protein fragments against reference databases. If a protein isn't in the database, or doesn't ionize reliably, it's invisible. Other approaches rely on fluorescent labels or antibody-based affinity methods, which introduce their own biases and blind spots. The result is a field that has spent decades generating an increasingly detailed map of a small, well-lit corner of the proteome, while biology’s most important data layer remains hidden. This isn't a sensitivity problem. It's a category problem. Existing tools were never designed to read proteins directly de novo. They were designed to find what researchers already suspected was there. Pumpkinseed is built to find everything else. And proteomics is harder than most people outside the field appreciate. When we account for post-translational modifications, non-canonical amino acids, and glycan decorations, there are roughly a thousand distinct chemical monomers in the proteomic alphabet, compared to the four bases of DNA. deSIPHR (de novo Sequencing and Identification of Proteins with High-throughput Raman spectroscopy) is Pumpkinseed's proprietary nanophotonic chip platform, fabricated with semiconducting manufacturing. With over 100 million sensors per square centimeter, it reads proteins, known or unknown, letter by letter — amino acid by amino acid — without a reference catalog of proteins, and at high-throughput. The result is direct, high-resolution proteomic data, including post-translational modifications, non-canonical amino acids, and single-cell detail, that mass spectrometry-based approaches cannot match. What is Raman spectroscopy? Rather than tagging or fragmenting proteins, Raman spectroscopy reads the molecular vibrations of individual molecules. Each amino acid vibrates at a characteristic frequency, producing a unique physical signature that deSIPHR detects directly. This is physics reading biology in the most literal sense. With conventional Raman spectroscopy, only about one in ten million photons interacts with a molecule usefully, far too weak for single-molecule work. Pumpkinseed's answer is a silicon photonic chip patterned with a billion sensors per wafer. Those sensors concentrate light into volumes smaller than a single protein, amplifying Raman scattering efficiency by over 10 million-fold. And their future ventures? “The longer-term ambition is the virtual cell, a computational model that simulates not just how proteins fold but how they interact, respond to drugs, and behave under perturbation inside a living system. AlphaFold demonstrated what structural AI can do once a sequence is known. The gap that cannot be closed is determining the sequence itself from biological samples, particularly for proteins carrying modifications absent from existing databases. Pumpkinseed is designed to supply that input layer. "If the Human Genome Project was the data infrastructure that enabled genomic medicine, we believe the high-resolution proteomic dataset Pumpkinseed is building could be the analogous foundation for AI-driven biological discovery," co-founder Dr. Jen Dionne says. "In our vision, the molecular signatures driving disease, aging, and ecosystem health become fully legible. Medicine shifts from reactive to proactive. Optimal healthspan moves from aspiration to achievable reality." —synbiobeta.com/read/pumpkinse… • The biology mining company: Pumpkinseed.Bio • Today’s News: pumpkinseed.bio/news/pumpkinse…

Drug hunters everywhere, sear the lesson of Revmed and daraxonrasib in your brain: Life is too short for incrementally me-too and me-better. Drug the fucking undruggrable and you can double OS, even in pancreatic cancer. Absolutely inspiring. LFG.

Excellent post by Rachel Thomas in the ways AI hype is outrunning reality in life sciences linkedin.com/feed/update/ur…

Behold peasants, 10k synthetic enhancers with no wetlab verification

Garry Tan @garrytan

3 months ago

AI x Bio teams like Origin have coding agents, scaling laws, and a wave of big biotech deals all at their backs. This is barely touched territory. Crazy what this small team can do now.

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There’s an enormous gap in the longevity field that almost no one is talking about. No existing therapeutic modalities are capable of both systemic distribution and complex transformations. Until we solve this, we won’t solve aging. 🧵

Nice progress on drugging p53. It’s true that we need to find new targets, but it’s also important to keep expanding the druggable universe with better technologies. Lots of great targets we already know about that are just waiting for the right molecule

NEJM @NEJM

4 months ago

In a phase 1 study of the oral p53 reactivator rezatapopt in heavily pretreated patients with TP53 Y220C–mutated solid tumors, the most common adverse events were nausea and vomiting, and the overall response was 20%. Full PYNNACLE study results: https://nej.md/3OIQC5P Science

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Seeing several platform biotech companies from the '21 boom roaring back with compelling programs after staying heads down for a few years. Exciting times

Did anyone else notice that the PRV was renewed last week? If you develop a medicine for a rare pediatric disease in the next 3 years, you get a voucher worth $150M+ So why isn’t there an orphan drug gold rush underway?

Imagine not having to test transcription factors one-by-one to rejuvenate cells / avoid cancer We'd need an AI trained on real TF activity data that actually understands how this network of 1000s of proteins behaves That’s our bet at Talus Bio. Observe the regulome, learn the rules, then control it.

Bryan Johnson @bryan_johnson

5 months ago

New study: for years Yamanaka factors have driven longevity optimism. They factory reset cellular age, also erase the cellular identity. The drawback is potential cancer. Transcription Factor Perturbations is a new scalpel approach targeting specific rejuvenation levers while

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sequenceanddestroy.substack.com/p/issue-64-los…

proteomics is closer to biology than RNA-seq functional proteomics is closer to biology than 'regular' proteomics let's build virtual cells with better data

Target crowding is a real issue, but centralized subsidy seems like a gift to the subsidized, not a gift to patients. To encourage more novel-target discovery, I’d prefer a “cover song fee” — 5% mandatory royalty back to the first innovator that hits ph2 with a new target. Innovator shouldn’t block new entrants with improved pk/pd like they used to 30 years ago — but innovators deserve to get paid when they lay the groundwork for future cures… even if their specific attempt fails. It’s a risky business we’re in! And it shouldn’t matter how the drugs are made! AI, immunization, spontaneous discovery — however you get there, a drug in patients is a drug we can learn from.

Sam Rodriques @SGRodriques

5 months ago

The Humanity Project People always ask me: if we want to cure all diseases with AI, shouldn't we be building a huge robotic warehouse to run closed loop experiments on mice, human cells, and so on? From a naive outside perspective, this may seem like the obvious thing to do,

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Bio pretty much tops all of these.

Hubert Thieblot @hthieblot

5 months ago

Difficulty Levels of Startups: Insane: Robotics Hard: Hardware, consumer social Medium: Consumer Software, marketplaces Easy: B2B SaaS What did i miss?

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"AlphaFold3 fails to model receptor-ligand complexes for understudied targets" nature.com/articles/s4146…

Underrated for sure This is why we're building the biggest dataset ever of transcription factor function and how to change function with small molecules AlphaFold can't even predict Tf structure, we think this is the way

Niko McCarty. @NikoMcCarty

6 months ago

Underrated Ideas in Biotech (#7) AlphaFold predicts a protein's structure solely by looking at its sequence. But structure does not always suggest function. The next frontier is to train a model that can predict any protein's FUNCTION solely from its sequence. This is

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@iskander @OpenAI Same for me today, for vanilla transduction stuff

@MartinBJensen All the best undrugged targets*

All the best targets are inside the cell

Martin Borch Jensen @MartinBJensen

7 months ago

The recent breakthroughs from @nablabio & @chaidiscovery emphasize a split in early biotech strategy. For the specific range of problems that antibodies address, making the binder, is becoming trivial. This forces a choice between 'fast but competitive' and 'AI intractable'. 🧵

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