Matt Soellner (@mbsoellner) header
Matt Soellner (@mbsoellner) profile picture

Associate Professor, Department of Chemistry @ U. Michigan.

Ann Arbor, MI
Joined December 2017
Photo by Matt Soellner (@mbsoellner)Photo by Matt Soellner (@mbsoellner)
At some point the glacial rate of LSVP approvals starts to look less like ignorance and more like strategy I know things go more slowly at tech companies like Anthropic than in academia, but a month and a half (and counting) for a simple check is pretty lethargic
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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I don’t know if it’s related but I started filling in the feedback form when I got biology denials. Several of them. A week later I got invited to apply. And a week after that I got approved. And I had applied the first day LSVP was announced (and a couple other times).
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A cancer drug designer just told me AI makes his work ~100x more efficient and ~100x more effective. Not some AI hype guy. Matt Soellner is a University of Michigan chemistry professor who's spent almost 20 years designing the kind of drugs that keep people like me alive. A year ago he thought these AI models were silly. Now he calls the last 3 months the highlight of his career. When a guy like that says 100x, buckle up. Cures are about to start coming at a pace nobody's ready for!
Yesterday I posted about working with an AI scientist to find new ways to fight my cancer. A chemistry professor at the University of Michigan saw it and emailed me. Matt Soellner @mbsoellner works on the exact kind of lung cancer I have. He offered to synthesize compounds and test them in cells and animals. A lightbulb 💡 goes off above my head. Obviously...I MUST introduce Matt to Jan. Andddd by this morning, they're talking about how to take an AI-designed molecule off a computer screen, make it in a lab, and find out whether it can fight my cancer. EXCUSE ME!? Less than 24 hours after my post. We haven't made a drug. We don't know whether any of this will work. Making and testing compounds will take months. But Matt saw a patient trying to find a way forward and chose to help. He brought the ability to make and test things that might otherwise never leave a computer screen. I'm sharing this as it happens because this is what gets me excited about AI. I can put my story out there, connect people with different skills, and help move an idea toward an actual experiment against cancer. I deal with the every day bullshit from cancer as a patient but have realized there's an army of incredible people out there working hard behind the scenes on treatments and cures for people like me and millions of others. It's an honor and a privilege to bring their efforts out for the world to see. AI is accelerating what these scientists can do. I'm just the human meat proxy making introductions and sharing the story. But if that helps get a promising idea off a screen and into a lab faster, I'll take it! 🥩
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This is true, however, if AI helps us come up with better drug candidates, the overall “time to breakthrough” will shrink. If we see 5 failures instead of 10 before we find a cure, that is still good.
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For sure. Better designs will mean better PK, better target engagement. And as the prediction tools for things like ADMET get better, so too will the designs. Overall better and faster shots on goal.
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nature.com/articles/s41586-0… Aside from helping ALK-driven NSCLC turn into a chronic disease, I'm intrigued by many brushed over aspects of this story: 1. Screening for glues via CRBN-directed 4k library - would love to know more and what other targets have been tested... 1/2
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2. No direct measureable affinity between lead compound and ALK - many companies would have killed the project then but thankfully they kept pushing! 3. Also initial hit didn't degrade the target in cells - could have also killed the project! #medchem 2/2
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I would love to see a medchem focused paper on TRI-611. I'd love to see the SAR and yes, I'd love to know what else they have found within their library.
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In an unexpected intersection of my two main interests... ...major shots have been fired by His Majesty's Government, which now has an official position that AlphaFold 3 and ESMFold is more complex to implement than RFdiffusion3 I'm glad this issue can now be put to rest
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“Complex to implement” seems no longer relevant. I ask Claude Science to setup ESMFold2 last week and it had it running with test cases relevant to my work in less than an hour.
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Astonishing how bad @AnthropicAI's biosafety filters remain, even after this long. "Sometimes flag[s] biology-research-adjacent work" for obviously benign requests is pure marketing nonsense. Absolutely amateurish for a company that claims to sell intelligence
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I like when something gets flagged (like working on co-folding structures of irreversible kinase inhibitors), and Opus 5 objects and says everything we are doing is fine and has no idea why we got flagged. Good times.
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Where do you go next after reporting from the Strait of Hormuz? For the past three months, Citrini Research, an independent research firm, has been working with our team and an internal research model to see if they could use AI to create a new drug. What follows is an entertaining, candid, and humbling account of a group of motivated nonexperts (or “filthy casuals”, as Citrini would say) trying to tackle a real problem in human health with Claude. We landed on scorpion neurotoxins, which kill over 3,000 people a year. Of the 211 designs we produced and tested, 17 met our criteria for a binder. None of the designs bound more than one toxin and none have been tested for neutralizing a toxin. One of my favorite quotes from the piece is: “Our virtual biologist may have compressed a year's worth of work into a couple days, but biology works at the speed of life…Over two days, we watched Claude work through problems at a speed that was difficult to comprehend. For the next two months, we spent much of the time becoming familiar with laboratory turnaround times (This mostly involved sending memes in Slack).”
What is AI really capable of in bio? To separate hype from reality, we spent 3 months working firsthand with @AnthropicAI's internal research model to see if it could create an experimental scorpion antivenom. Introducing Citrini Therapeutics. citriniresearch.com/p/citrin…
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Interesting story, but imagine if domain experts in science could get access to even the best public models (Claude 5.5) for life sciences. (LSVP does not seem to work).
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Very excited about Claude Teams for Science! Claude is doing amazing things to help scientists move faster and explore new ways of doing scientific work. It has been totally transformative for my own work! If you’re a PI in academia or a non-profit org, check out this program!
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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Claude Science and this Claude Team plan is great. But is there a way to couple this (which I have done) to the LSVP? I cannot use Opus 5.5 for kinase inhibitor (chemistry) work targeted at cancer - I applied for LSVP but have not heard back.
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Today we launched Opus 5.5, which matches and even beats Mythos 5.1 on many bio-related tasks. We launched Opus 5.5 with the same safeguards as Fable 5.1, which means that most bio-related queries fall back to Opus 5. To use Opus 5.5 for life sciences work, enroll in our Life Science Verification Program (LSVP). I imagine that many in the bio community will be frustrated by this decision and I want to share more context on how I think about these things. I also want to say upfront—bio is one of our most significant areas of focus, and it’s why I’m here. When we set about designing the new LSVP, it was with the goal of providing broad access for life sciences professionals with far fewer annoying classifier misfires, while also protecting against many of the more sophisticated risks. Naively, it would seem that we have to choose between enabling broad access and protecting against misuse—I view this as a false choice. The key is to think of developing these safeguards as an engineering problem that requires the same level of investment, ingenuity, and ops execution as developing our frontier models, or any other large scale technical project. The risks we’re defending against—which, as we shared in our threat report a few weeks ago, are no longer hypothetical—were certainly not obvious to me until recently, and I imagine are also not top of mind for most biologists. When most people picture bio risk, they imagine a small group trying to develop a bioweapon in a single session, from their own account. The threats that we need to defend against are much more sophisticated than that. They include malicious work split over hundreds of distinct benign-looking sessions run from different accounts, and legitimate organizations’ accounts being hacked by a well-resourced actor or misused by a rogue employee. To protect against these risks and provide a better experience for our users who are doing legitimate life sciences work, in the LSVP we’ve introduced the concept of use-case grants. While it isn’t always possible to distinguish benign from harmful usage in isolation, it becomes much easier when comparing activity against a team’s own description of intended usage. You describe what your team works on at a high level, without revealing anything sensitive. Data are retained for 30 days, and automated offline review compares usage against that description and flags notable mismatches for a strictly compartmentalized review team. My team, the life sciences org, can never access this data. Because of these additional safeguards, for users in the LSVP we're able to tune down the classifiers so that they misfire far less often, without sacrificing on safety. I’ve described why I believe that the LSVP provides a better way for people to use our most capable models in bio, but one can still wonder: why are we implementing this now? There is no unambiguous threshold for when model capabilities reach a point that they start posing grave risks. But capabilities in bio, as in most other domains, progress very quickly and we should not be in the business of trying to time it perfectly. When in doubt, we’ll always choose to proceed with caution, and I’d much rather implement this new system too early than too late. The LSVP is currently in beta, which means that there are lots of improvements that we’re working on. We’re working to expand it so that individuals can get access—for example, for people developing or identifying treatments for a family member’s cancer or rare disease. Personally, I feel both the urgency to get our models into the hands of the broader life sciences community and a visceral sense of the stakes in getting it right. I think this is the right balance, and I look forward to seeing what people do with our most capable models. anthropic.com/news/life-scie…
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Any chance you can whitelist academics who are already verified as part of the 10k seats plan? Unusable for bio atm as it drops immediately to Opus 5 and the LSVP needs org level registration which isn't happening in the UK...
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Agreed. I filled out the LSVP when it first came out and heard nothing. Opus 5.5 is unwilling to do any kinase / cancer work of any sort...
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Thanks for posting! The client protein (here c-Src) conformation aspect is intriguing (to me, at least)... Not in this paper, but the DFG-out analog of these PROTACs doesn't degrade c-Src at all (despite being a very good cellular binder/inhibitor). More on that coming soon!
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Thanks for "tweeting" our publication. This was our first foray into PROTACs (quite late to the game), but we have more kinase PROTACs on the way!
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I was sorry to learn Nanosyn closed their SC site. We're looking for a new CRO to conduct in-vitro kinase activity assays. Any recommendations?
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Nanosyn was good (and reasonably priced); bad news. Reaction Biology is great for in vitro. I like Luceome for profiling in lysate and... And Promega (nanoBRET) for cell-based profiling w/ full length constructs.
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Surprisingly, whether a ring system is aromatic or not is not always a black and white thing but we can probably all agree with Dave Weininger, that "aromatic is something that smells nicely".
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So is this one aromatic or “aromatic”? @felix_s_k might have opinions…
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My answer (as influenced by Brian Coppola) would be that resonance contributor has one aromatic ring and one ring that is not aromatic. But one can draw a resonance contributor where both rings are aromatic. That is what we teach at UMich…
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Just started reading a paper that uses its tool compound at 50-100 uM concentrations from the very first figure. I stopped reading after that 🚩🚩🚩
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If you put a gun to my head though, I’ll say that using a potent compound at too high of a concentration is an incrementally lesser sin, because at least that’s a fixable problem by using the right concentration. Nothing will save the weak compound from polypharmacology.
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Good point. At 50-100 µM you will be well past most small molecule CMCs. However, many off-target assays can reasonably be run at 10 µM to assess the polypharmacology. And I'm mostly thinking about this from a 'use inhibitor for biology' approach vs validating a lead compound.
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Once again input needed from #ScienceTwitter: I need published examples for high quality COVALENT chemical probes. Balanced reversible affinity & reactivity, high selectivity proven in focused & unbiased experiments, labeled amino acid proven. Matched controls. Examples shown.
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EGFR-mut lung cancer patients get osimertinib, resistance can be in cis (C797S) or in trans (Met amp, KRAS mut, BRAF mut, etc). Met/KRAS/BRAF alterations are relatively rare (<10%). Are there any targeted therapies for which a single trans resistance mechanism is common (>50%)?
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PLCG2 mutation (gain of function) is a common resistance pathway for BTK inhibitors. But often alongside BTK mutation (which might exclude this example from your criteria).
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I need your help tweeps. I'm trying to lay out the very most basic fundamental principles of how the NIH grant funded science game works. Do you have a favorite thing? obvious ones welcome (won't be obvious to me perhaps) as are anything that shocked you the first time
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That every R01 isn’t at least $250K a year direct. Some ICs can / do cut up to 24% of the budget (even when modular budget) without allowing changes to the aims. (25% cut and you can renegotiate). That was a shock to me. Until it happened on a modular R01 budget.
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One more. The R00 at least used to be a total cost grant. I had no idea the implications of that until I had destroyed my year 1 budget. Then I ended up trying to buy as much equipment on it as possible. Because equipment = no overhead. Hopefully they have fixed that with R00s.
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