AI for Life Sciences at Anthropic. Former drug target hunter at Genentech and Bayer. Recovering marine biologist. Always an evolutionist. Views are my own.

San Francisco, CA
Joined June 2016
David Garfield (@[email protected] and post) retweeted
After a long wait, tangermeme is out @naturemethods! tangermeme implements the primitives of genomic sequence-to-function modeling with efficient, tested, and composable functions. I use it every day in my work, and I hope you find it useful too. nature.com/articles/s41592-0…
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David Garfield (@[email protected] and post) retweeted
I’m hiring a wet-lab Researcher/Senior Researcher at St. Jude. Hands-on training in cancer biology and opportunities to contribute to papers. Great if you’re considering grad/med school or building a career in science. Please share! talent.stjude.org/careers/Jo…
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Very nice work, and a really excellent build on previous methods -- science doing its thing!
Ok let me explain how cool this is from my perspective , please RT! I developed one of the first methods to probe RNA structure in high throughput inside living cells, and it takes tons of time and $$ to apply on thousands of viruses - it’s practically impossible. This ML approach is just as good and works in seconds for nearly free ! Not only that, it highlights the MOST IMPORTANT structures that track with function. It’s the future of RNA strucute prediction and discovery across all families 🔥 biorxiv.org/content/10.64898…
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David Garfield (@[email protected] and post) retweeted
Starting today, teams and institutions can apply to our Life Sciences Verification Program (LSVP), which gives verified researchers and organizations access to our most capable models for professional biology and drug development work. LSVP is built for teams of all kinds: academic labs, startups, non-profits, biotechs, pharma companies, and more. Learn more and apply here: claude.com/form/life-science… We know that it has been frustrating to encounter blocks on Claude Fable. LSVP is our mechanism to prevent biological misuse, like the documented cases we’ve disrupted, while enabling legitimate life sciences research. LSVP includes a new set of safeguards to provide a better UX for life sciences researchers. We believe that advancing biology research and drug discovery will realize some of the most important beneficial impacts of AI. Please DM me if you run into any issues with the application.
Today we’re opening applications for the Life Sciences Verification Program. Through the LSVP, life science professionals can use our models—including, for the first time, Mythos—with a new set of safeguards designed to enable the full range of biology-related work. We designed these new safeguards to provide a better experience for biologists and more protection from risk of misuse. The program is launching in beta for teams of all kinds—from academic labs to startups, pharma companies, and more. We will continue to improve the program and expand access to individual Pro and Max plans over time. Learn more about these access grants and apply: anthropic.com/news/life-scie…
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David Garfield (@[email protected] and post) retweeted
Last week, when Anthropic announced the binder design news, I said the exciting part isn't binder design, but the orchestration of models to produce a research assistant - like entity. This is quite cool and it seems Anthropic is continuing down this road of scientific automation. So what's happening? They propose the Model Hardware Standard (MHS), which is a common standard that lets AI agents safely connect to and control physical scientific and industrial equipment. This would include things such as microscopes, liquid handlers, lasers and manufacturing machinery. Today, integrating different pieces of hardware can take weeks or months because each device has its own interface and often requires custom software -- which is extremely annoying. MHS aims to reduce that setup to hours by giving devices a standardized software “driver” and a common way to communicate with AI agents. The blog post gives one example, which involves Claude learning to align a laser. Claude adjusted the laser, then observed the result through a camera, then repeated the process to understand the system and finally, it wrote a script that could perform the alignment automatically. I think this line of scientific automation is actually much more exciting and higher yield potentially than all the stuff that aims more at making specific scientific discoveries (eg using AI to improve binder design). I think it's underrated how much boring friction slows down biological research, and it's exciting to potentially reduce that. Of course, it also seems like being along the path to full scientific automation (including of human scientists) -- though still very early (and potentially it will never happen). I feel much more conflicted about that; I was just debating with some friends about this. In my mind, scientific discovery is an expression of something very core to the human spirit -- a sort of fundamental curiosity. And I'd be sad if that ceased to be an activity humans were involved in.
Today, we're kicking off the first phase of the research preview for Model Hardware Standard (MHS): a new standard for AI agents to safely operate physical equipment in scientific research and advanced manufacturing. Read more: anthropic.com/news/model-har…
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David Garfield (@[email protected] and post) retweeted
Our models are now capable of accelerating many aspects of scientific research. I’ve been having endless fun pointing Claude at problems I’ve always cared about, and in some cases watching it make progress for the first time. Today, we’re expanding our focus beyond bio to include support for every scientific discipline. We’re launching a new plan for scientists and are making an initial 10,000 seats available for free and at heavily discounted rates.
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…
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David Garfield (@[email protected] and post) retweeted
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…
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This is true. We are well enough paid and privileged to be part of what we all think is an important and exciting mission. We are a PBC and management is serious about that. Really.
Anthropic is a Public Benefit Corporation. this is not new.
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I use these too. Give it a try.
Claude Academy is now live. Whether you're figuring out what AI is or already using Claude every day, there's a path that meets you where you are. The courses and tutorials are free and open to anyone at academy.claude.com
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This is a fair and educational post!
Claude Science is an amazing tool. But, as @pdhsu has pointed out, these binders weren’t designed by Claude, but rather by domain-specific models, most of which are publicly available, like RFdiffusion. Maybe this is a good time to explain the whole science behind this a bit, as well as the history of these models. A “binder” in this case is simply a protein or a fragment of a protein engineered to stick tightly and selectively to another protein. One example is a molecule that can latch onto a receptor and prevent it from interacting with its normal partner, thus blocking its function. Optimising "binders" is just one small part of the drug discovery pipeline. We already have AI models that are very good at the task of binder design. These models have been extensively trained on relevant domain specific data: int his case, protein structure data. One example is RFdiffusion, a de novo protein design model which was published in 2023. Unlike AlphaFold, which predicts existing protein structure from sequence, RFDiffusion can generate new candidate protein structures, including structures shaped to bind a specified patch on another protein. RFDiffusion was built by taking RoseTTAFold, a protein-structure-prediction model, (so similar in nature to AlphaFold, which was released in 2021, and retraining it as a diffusion model. What researchers did in practice was take experimentally determined protein structures from the Protein Data Bank, added noise to their coordinates, and taught the model to reconstruct them. The original RFdiffusion paper was already demonstrating experimentally validated binders with nanomolar affinities and a 19% hit rate on several test targets. So the core of this technology was not created through Claude. The key models for protein design are now three to five years old. What Claude adds is still amazing useful and labour saving (I've written about how I'm excited about it before), but imo it acts more like a very useful interface and orchestrator.
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David Garfield (@[email protected] and post) retweeted
Cracks me up every time I see a major company like Anthropic or AWS use FreeBindCraft. It was literally a spite project launched because UW sent me a cease-and-desist. Everyone should be able to use OG Bindcraft. Rosetta Commons made themselves irrelevant chasing licenses. Sad.
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I work here and I'm still impressed
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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David Garfield (@[email protected] and post) retweeted
I think these are good points but the regulatory and data bottleneck are very related imo. The most useful data is in-human data. Ideally for cancer what you'd wanna do is cycles of in-human iteration and refinement based on the data you obtain. This isn't a fantasy: this is what we know works as a strategy and how so many of the best therapies for cancer have been discovered. This is also what China does and part of how they've become so successful particularly in oncology. For cancer in particular individualised medicine is becoming a possibility: targeting one's tumour using agents that are designed specifically to target one's tumour. Again, this is based and guided on high-dimensional data collection from the treated human and the strategy that @sytses has employed to lead to the relapse of his advanced osteosarcoma. But all of these loops of learning are slowed down by the speed of getting to so-called "first-in-human" data. In the US, that process is incredibly and needlessly slow, unlike in other countries, including China and Australia. It can take a year more to get first in human data in the US versus Australia. China has become a biotech powerhouse in a decade, starting from being of no importance in the industry, almost entirely on the basis of their speed of iteration. Today, we increasingly see American innovation get their first-in-human data in trials from China. This isn't fixed -- the HHS and FDA have announced a pilot for faster Phase I trials, modelled after Australian Phase I trials. But for this to happen, institutions (academic medical centres) have to enrol. Many are afraid of taking on additional risks. And legislation has to pass such that this doesn't get shelved in the next administration.
I disagree with Dario on most things, but this is spot on: “At this point, saying that AI will cure cancer is more a cliché than it is inspiring, and most people think it is deceptive. The thing that will work is actually curing cancer.” The thing that frustrates me is that some people often draw a straight line from smarter models → cures, without acknowledging how much infrastructure is missing in between. We’ve still got a lot of work to do, people. The regulatory bottleneck gets a lot of attention. But the bigger bottleneck may be that in many places, we’re lacking the right biological data. AI can’t reason its way to cures if we haven’t generated the data required to understand the disease in the first place. Ironically, that’s why I’m most bullish on AI making dramatic progress in cancer first: decades of investment have produced extraordinary datasets across genomics, pathology, imaging, clinical outcomes, and more. For complex chronic diseases, much of that infrastructure simply doesn’t exist yet. The good news is that many companies (including @ChronicleBioAI) are racing to build it. But model intelligence and biological infrastructure are going to have to scale together. I really do believe AI can cure all diseases but only if we build the infrastructure to translate intelligence into cures at the same pace that models improve (which is a high bar!). Otherwise, we risk having superintelligent AI with an incomplete picture of human biology, and delay that promise by years.
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David Garfield (@[email protected] and post) retweeted
As someone who went through MS public schools I still can’t believe this It’s incredible How badly is the rest of the country failing kids?
Mississippi fourth graders aren’t just one of the best in reading and math gains — they’re the best in the nation.
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Vey much worth a read
As promised, I put together my thoughts on the ZEUS failure of IL-6 inhibition in atherosclerotic cardiovascular disease and what it means for drug development efforts centered around human genetics. 🔗Link: thecodon.substack.com/p/why-…
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David Garfield (@[email protected] and post) retweeted
Learning biotech part 2: Once you understand process of finding drugs, you should study the incentives that drive decisions. Why does someone choose to launch the 200th EGFR program? Inevitably there's a set of incentives, and often data-driven risk analysis, motivating these seemingly silly decisions. While new biotechs often challenge existing assumptions, understanding Chesterton's fence is the smartest way to decide where and why you deviate. I would start by reading AstraZeneca's retrospective on why their programs won or lost: “Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework.” pubmed.ncbi.nlm.nih.gov/2483… The '5Rs' they identified are a good representation of the risks pharma prioritizes: right target, right tissue, right safety, right patient, and right commercial potential. YC tells their companies to talk to users. But who is the 'user' for biotech? - Ultimately, we make medicines for patients to enjoy healthy lives. - The FDA judges whether it works. - But doctors decide what to prescribe. - And insurance and medicare actually pay for the treatment. This makes the situation more confusing for new biotechs. But your product needs to work for all four of those user. (even if you plan to sell to pharma before approval, they have the same users, so you just have one more user to please) I recommend the book 📚 "Her-2" about Genentech's mega-successful drug Herceptin, which includes decisions about which diseases and trials they tried it for first. If those decisions had been wrong, the drug might have died. I'd also recommend (any and all) episodes of 🎧 "Hard Drugs" from @salonium and @JacobTref, especially for examples where the science incentives are stronger than financial ones. harddrugs.worksinprogress.co… After that, try deconstructing decisions you see in the news, like Genentech choosing to exit cancer immunology two years ago or everyone buying 'radiopharmaceutical' biotechs. What got derisked, or saw added risk? One source of 'irrational behavior' might be classic short-term performance goals among executives at public companies. At any given time, pharma's revenue come from a small number of patent-protected drugs. When patents expire on a top product revenue plummets, so you often see acquisitions of mature companies that might get a new big approval before the cliff. Basics of patent cliffs here: biopharmadive.com/news/pharm… and this episode of @alexkesin and Matthew Pech's 🎧 Approved goes deep on that drug, including at ~90m how Merck sought to protect its profits: podcasts.apple.com/us/podcas… Ultimately, incentives all come back to 1) a desire to improve humanity's conditions by scientists who go into biomedicine, and 2) free market competition. When you see something that seems cool (like PCSK9 gene therapy) but isn't super hot, check the incentive stack to understand why (in this case, strong competition with lower risk). And when you see an opportunity nobody is filling, at least find out why. More good content on this topic includes: @alexkesin’s ✍️ “Pharmacopoeia” broadly covers how biopharma behaves, for example in “Pharma Is Hollywood, Roughly”: alexkesin.com/p/pharma-is-ho… @ElliotHershberg’s ✍️ “The Century of Biology” has a series on platform companies, like BridgeBio, that dissects incentives: centuryofbio.com/p/bridgebio Finally, 📚 “The Great American Drug Deal” by @PeterKolchinsky is relevant as big picture context on how healthcare works and how the medicines it pays for flows back into the next generation of drugs. You can also get the short version on this website: nopatientleftbehind.org/fund…
Starting companies is hard, and it's a boon when you can learn from how others have done it. There's way more content on tech than biotech, but I thought I'd put together an overview of the good resources that do exist: For the big picture, start with @Atelfo blog post ✍️: atelfo.github.io/2023/12/23/… And if you want a fun read that tracks 'finding medicines' from prehistory to today, read The Drug Hunters 📚: amazon.com/Drug-Hunters-Impr… Now you know the system that exists. If you're a biologist, next step is to understand that finding a gene/protein target is just the beginning. 'What to do' is essential, but often things fail because you're able to do it in the right place for the right amount and duration (or even know what those are). Here I'd honestly ask your fave LLM to walk you through 'Medicinal Chemistry 101' 🤖, covering potency, selectivity, the main modalities ADME, PK/PD, formulation, tox studies, CMC. Make sure you understand the big differences between small molecules, antibodies, protein drugs, siRNAs, and cell/gene therapy. After that, I'd work through a couple of concrete examples to see things in (messy) practice: 📚 “The Billion-Dollar Molecule” (and the sequel “The Antidote”) on how Vertex was formed and struggled their way to a first success. 📚 For Blood and Money by @nathanvardi. Same: fun & teaches you a lot about the confluence of science, money, and people that go into biotech. After that, I'd go peruse @LifeSciVC's blog to understand how traditional biotech investors look at these endeavors - the main thing to pay attention to is what risks he keeps calling out. Note that Atlas and other 'Boston biotech VCs' are a dominant group, but not the only way companies get funded. The original biotechs (all the ones called 'Gen' something), and the 2010s 'techbio' companies have a different thesis inspired by full-time founders as the scarce resource, though most of the risks are the same. This post is a good place to start: lifescivc.com/2025/10/twenty… Finally, Derek Lowe’s “In the Pipeline” blog offers >20y of 1) commentary on biotech next, which you can now assess with the benefit of hindsight, and 2) more details on medicinal chemistry if you found that part exciting. Here's one starting place: science.org/content/blog-pos… Speaking of, someone should take his best posts and turn them into a book... 😉 I'll expand each of the topics here into more resources throughout the week, stay tuned.
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David Garfield (@[email protected] and post) retweeted
Oh my god. It's hard to express how big a positive impact this tip can have on your life if you talk to Claude a lot. > Tell Claude to write in ASD-STE100, or Simplified Technical English As a bonus, tell Claude to follow Zinsser's four principles of quality writing: 1. Simplicity 2. Brevity 3. Clarity 4. Humanity h/t @DanielLockyer
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