Stephen Malina retweeted
Surprise 1: Anthropic acquired a one-person biotech
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
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Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Stephen Malina retweeted
This was very fun to write! Thanks to @salonium and the whole @WorksInProgMag team for the incredible editing and art. Very exciting times in RNA land! worksinprogress.co/issue/the…
Stephen Malina retweeted
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact.
In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code.
Read more: anthropic.com/research/claud…
I haven't posted much lately but wanted to share my thoughts on this (all my personal opinions).
Though I don't talk about it much online, I have been aware of and concerned about eventual AI x-risk for over a decade now (like many, first sparked by reading LessWrong). Similar to many, for a time, those risks seemed more theoretical, but I never dismissed that they'd arrive eventually and my worry has steadily grown since it became clear scaling was working (which was around the time of GPT-3 for me).
That said, ever since I started mainlining sci-fi and discovered the extropy list archives as a teenager, I have been a "partisan for the future". A major reason I abandoned my career in tech and chose to pivot to the (at the time, nascent and uncertain) world of AI for bio was because I was motivated to bring about faster progress towards the unimaginably better world I'd dreamed of, including but not limited to developments like those discussed in Machines of Loving Grace. I also think that the upsides of AI have meaningfully outweighed the downsides so far.
I'm including all this to establish that, temperamentally, I'm a technological and market optimist. People who know me know that I think free markets are one of humanity's greatest inventions. I also am very concerned about over-regulation and stagnation due to vetocracy. For example, I think we would be much better off with less strict and bureaucratic nuclear regulation and think it's a tragedy how much value was lost through this. I also support proposals from people like @RuxandraTeslo to accelerate clinical trials and reduce certain clinical regulations (e.g. loosening GMP requirements for Ph1 trials). To date, I have also been opposed to the idea of an "AI pause" and even didn't sign the original pacing the frontier petition because I felt unsure whether the implementation would be good on net.
With all that context, hopefully it's clear that I don't take supporting the proposals described in this essay lightly. But I do support them and think they represent the best attempt I've seen to find a narrow path to a positive AI future. Even as someone who worries about the regulatory ratchet, given the pace of progress and magnitude of the risks, I believe using thorough safety audits to pace deployment is the right approach. On the whole, I think, if implemented, these proposals will not only help prevent potential catastrophes but also avoid situations where a "warning shot" and society's understandable reaction leads to a worse societal/regulatory situation.
Getting back off my soapbox, I want to be clear that I personally remain laser focused on trying to accelerate biotechnological progress and help the benefits of such progress spread as quickly and widely as possible.
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so.
Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training.
You can read the full post here: darioamodei.com/post/we-must…
Stephen Malina retweeted
I personally believe there is a much greater than 10% chance that AI relieves the burden of disease and ushers in a post-scarcity Star Trek future of abundance and unimaginable human flourishing and scientific discovery.
I'm very grateful to have colleagues who have been thinking deeply about alignment, and working on it for a very long time, to prevent bad futures and make sure we get to good outcomes for humanity.
Stephen Malina retweeted
I’m excited to share that Intercept just published a Request for Candidates for broad-spectrum preventatives. We’re looking for the best ideas that could meet our target product concept, the spirit of which is: safe, easy to administer, long-acting preventatives that will prevent ≥75% of symptomatic respiratory infections in as few doses as possible.
We’ll prioritize proposals received by October 12 (though this call will be open until at least the end of the year).
Please share with anyone who might be interested:
interceptfund.com/apply/broa…
Stephen Malina retweeted
On behalf of the Intercept team, I’m excited to share that we’re launching our first “Request for Candidates”! We’re looking for the best science (and teams!) in the world that could plausibly meet our target product concept. Our ultimate goal is to radically reduce the burden of respiratory viruses, and an easy-to-use, safe, and effective preventative is a critical part of that puzzle.
This request for candidates will be open until at least December 31st, but proposals received by October 12 will be prioritized.
interceptfund.com/apply/broa…
Stephen Malina retweeted
Great talking with @ashleevance on Core Memory about why delivery has been genetic medicine's hardest problem.
We talk about delivering DNA using protein shells and creating a world with genetic agency, where each of us can act at the genetic level to live a healthier life.
We have been promised glorious gene therapies for decades. This is supposed to be the stuff of reprogramming the body to blunt or cure diseases with something approaching a permanent fix. While gene therapy progress has been made, treatments remain rare and expensive, and it’s very tough to get the therapies to go into the desired parts of the body.
Our guest this week is here to help. He’s Adrian Veres, the chief scientific officer and co-founder of @Dyno_Tx Therapeutics. Founded in 2018, Dyno has been pursuing new and better ways to deliver gene therapies via modified viruses.
Dyno was well ahead of the AI meets biotech curve, using AI models to design new viral shells that let genetic instructions get into the right places of the body. For the moment, Dyno has focused on creating delivery mechanisms for companies working on therapies aimed at the brain, eye and muscles.
In this episode, we talk with Veres about the promise and perils of gene therapy technology, some of the most recent cases where gene therapies worked and how Dyno’s technology came to be and functions. And we get Veres’s take on whether or not we’re entering a golden age of biotech on the back of AI.
Due to some poor camera work on my part, Veres, who is a tall man, looks particularly giant. Fear not, he did not harm me and was actually quite nice.
youtube.com/watch?v=JXRBTugG…
Stephen Malina retweeted
Come learn how gene therapies work and why they may - may - finally be coming to cure all sorts of disease. corememory.com/p/gene-therap…
Stephen Malina retweeted
We are on the cusp of a wave of new therapies for some of the worst diseases. But the world won’t benefit unless the US fixes its drug regulatory system. My new essay for @nytimes, on how slow clinical trials are now the biggest obstacle to curing cancer.
nytimes.com/2026/09/04/opini…
- I interviewed dozens of researchers, especially oncologists at leading U.S. centers. A striking consensus emerged: science is no longer the main bottleneck to new cancer drugs. It is our ability to test discoveries in patients through clinical trials.
- The cost of starting a Phase 1 trial in America has roughly doubled over the past decade. As a result, companies increasingly take early trials abroad: Australia’s Phase 1 trial volume has nearly doubled in a decade, driven mainly by U.S. companies.
- Unfortunately, the underlying incentives are badly asymmetric: Institutions can be blamed for harms caused by moving too fast, but almost no one is blamed when patients deteriorate during avoidable delays. One doctor called the emerging system “ritualized safety over actual risk assessment.” Or as, @DavidHongMD put it: "We often forget that the biggest risk is the cancer itself."
- This problem is becoming more urgent because medicine itself is changing. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations.
- @sytse, the co-founder of GitLab, shows what personalized medicine can achieve: after relapsed osteosarcoma and being told there were no options left, he pursued a highly individualized approach and has now been cancer-free for a year. But doing so required extraordinary resources and regulatory expertise.
- Pierce Ogden’s father was less lucky. After molecular analysis identified a drug that might target his glioblastoma, the manufacturer agreed to provide it. But administrative barriers delayed access until it was too late. “My dad was ready to try anything,” Pierce told me. “But the system is paternalistic.”
- The A.I. revolution is making this bottleneck more important, not less. A.I. relies on relevant data. Information from early-stage trials could compound with A.I. tools to achieve truly revolutionary medicines. Without the data, this is far less likely to happen.
- Another important shift is that innovation increasingly comes from academic labs and small biotech companies rather than Big Pharma. These small companies find it far harder to unable to absorb delays and regulatory barriers.
- Apart from cancer, China is the biggest winner from America's outdated medical regulations. China has a much faster trial system, with testing often starting a full year earlier. This allows Chinese pharmaceutical companies to experiment and improve medicines while American companies play with mice. Today, half of all drugs licensed by major pharmaceutical companies originate there, up from less than 5 percent only a decade ago.
- But we don't need to copy China. The best model is Australia: lots of on-site scientific and ethics reviews, and requirements that are proportionate to small, early-stage trials. Phase 1 studies there begin roughly 6–12 months faster, without any notable increases in adverse safety events.
- Operation TrialBlazer, a 2026 HHS initiative is a good start in this direction, but we need legislative action by Congress to truly make Phase I trials faster and more efficient!
I want to thank everyone who helped me with this article: everyone I interviewed and the amazing editors at the Times. This is the result of a months long journey of extensive interviews and research.
Special thanks go to those who came on the record. One of the features of the system is an atmosphere of fear, where practitioners are afraid to publicly come out and explain these issues. So anyone who does is a hero in my book!
Stephen Malina retweeted
We're launching a program to get free or heavily discounted Claude usage to scientists, through Claude Teams for Science.
If you're a student or researcher in academia or a non profit org, ask your PI to apply. We're starting with 10,000 seats and will expand from there.
Starting today, 10,000 scientists across every field, from math to chemistry to physics and more, can get Claude through our new Claude Team plan for scientists. Standard seats are free, and premium seats with 5x usage limits are $15 per month, an 80% discount, for one year.
Claude is becoming increasingly capable of scientific work, with recent progress on problems from advanced physics calculations to protein design. Alongside that progress, we've been investing in the research community: Claude Science launched in June, and our AI for Science program funds high-impact projects with free credits. Today's expansion builds on both.
Principal investigators (or equivalent) at academic and nonprofit research institutions can sign up, then add the researchers in their group. Over the coming months, we plan to extend the program well beyond the initial 10,000 seats.
Learn more: claude.com/programs/team-pla…
Stephen Malina retweeted
We're hiring.
Think: Sr partner at a venture incubator, but the outputs are Focused Research Organizations
Form the thesis, find the founders, build the boards, launch the org
AI resilience: bioresilience, cyber-physical systems, epistemic infrastructure, compute governance
Stephen Malina retweeted
Today we’re sharing an update on Claude’s protein design capabilities.
Working with Adaptyv Bio and Twist Bioscience, Claude designed de novo protein binders against 14 of 15 targets. With a 30k token prompt written by a human expert, Claude achieved hit rates up to 28.2% when designing against all targets simultaneously over 48 hours, with no human intervention or steering. When designing against a single target in a 24 hour campaign, Claude achieved an overall hit rate of 35%.
We included all targets in Adaptyv’s BenchBB, and novel targets 15-PGDH and GDF-8. Claude performs at or beyond the level of the top competition participants in both hit rate and binding affinity.
Stephen Malina retweeted
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.
Stephen Malina retweeted
If your response to the current AI x bio discourse about accelerating drug development is "I want to help make that happen", we're hiring @AnthropicAI in NYC and SF.
As part of our growing investment in building capabilities for drug discovery and development, we're looking for exceptional biotech operators to help accelerate research in these areas. We're looking for sharp scientific judgment and operational expertise.
An ideal candidate has grown up professionally in an organization that prioritizes scientific and executional rigor -- for example, at a high-growth life sciences company or a leading science-focused investment or venture firm.
Link to apply below 👇
Stephen Malina retweeted
It would be great if we could monitor anything in the body continuously. The continuous glucose monitor (CGM) was a great start, but there are so many interesting molecules to human health beyond glucose, but no one has successfully made monitors that work for other things. This is largely because display-based screening techniques for binders are detached from the ground truth physiologic data that matters. It would great to have a screening method that is coupled to a readout, like a GFP or nanopore, so that readout of a designed binder produces a number that is more meaningful.
In a preprint with @mu_anand and other folks at Caltech, we ask whether existing architectures around protein binders tied to a GFP reporter can serve as this screening vehicle to speed up ML-design of new monitoring proteins.
We took 18 OpuBC-cpGFP biosensors that our lab previously evolved toward nicotine, SSRIs, and other neural drugs, and screened them against 63 human health-relevant compounds, creating function data for 1134 sensor-ligand pairs.
124 pairs came back at a signal level we deemed suitable to begin directed evolution campaigns, with a little over half of those having readouts at a dynamic range that could make them immediately usable in application today. In total about a third of our compounds in our limited screen found a paired sensor worth evolving on.
We ultimately think that PBP-cpGFPs could be useful to increase the hit rate of initial screens of possible continuous monitors.
You can read it below.
Stephen Malina retweeted
Looking to meet cool people in bio and AI?
Come to the NYC high quality hangout, hosted by myself, @an1lam and @owl_posting this July! We ran this a long while ago and it was amazing, so we figured it’s worth doing again.
The only qualification you need to have a good bio take to share.
partiful.com/e/atBaN6K8RO1kx…
Stephen Malina retweeted
Introducing Claude Opus 4.7, our most capable Opus model yet.
It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back.
You can hand off your hardest work with less supervision.
ALT Claude Opus 4.7 Benchmarks
Stephen Malina retweeted
pipeline-in-a-person (h/t @Prof_Oak_ for the concept) is one skilled biotech operator + a bunch of Claudes running a virtual biotech company. reach out if you want to help build this or pilot it.
Stephen Malina retweeted
New blog post.
There's been a lot of discussion about peptides recently. This reminded me of another type of treatment that got tons of press a few years ago: stem cell injections. I think there are lessons we can learn about peptide therapies, specifically BPC157 but also others, and how to appropriately consider their effects.
As of right now, I'm not convinced by any of the anecdotal data for reasons discussed in this article.
Give it a read. Feel free to call me a Big Pharma Shill, I don't mind.
eryney.substack.com/p/stem-c…
And thank you to @an1lam for the help editing this one.
