Owl Posting turned two this month. It’s difficult to ascribe any emotion to this whole endeavor other than love. You would think it’d get old at this point, but it hasn’t. Every single article still feels like I am twelve years old, staring up at a great big endless blue sky, and thinking that it’s going to swallow me up any second. Like a child, I believe in those moments that nobody has ever felt what I feel. Each time I hit ‘publish’ feels like love too, but the horrible kind, like I am watching someone die, their organs falling out of their chest as their eyes twinkle, telling me that they really enjoyed spending this time with me. I turn away, weeping, as their light fades. And then another creature walks through the door, with such an interesting energy to them, and I fall in love all over again, and the whole cycle repeats.

This all seems quite melodramatic. Perhaps some things are that serious, but certainly not running a blog. But unfortunately, you don’t get much say in what you end up loving. You can spend your whole life avoiding the subject, darting your eyes towards it as the days, months, years go by, never breaking down, never shamefully admitting what you actually desire most. Some people spend their whole lives in this pattern. Can you imagine? Of course you can! Because it is not just ‘some’ people, it is all of us. Nobody is free from the tragedy of self-denial. In exchange for this cruelty, life does sometimes throw us a bone: a chance to embrace the things that you love in a manner that is entirely inconsequential, utterly cost-free in every respect other than having the self-awareness necessary to reach out and grab it.

I have experienced this once, and it is writing.

This year, I wrote 24 essays, totaling ~108,000 words. I also filmed 8 podcasts, which I’ll discuss near the end.

Some of the written work, I admit, was not very good. For example, ‘Drugs currently in clinical trials will likely not be impacted by AI’ suffers from the sin of being boring in the worst possible way—obvious in the non-surprising parts, and likely wrong in the surprising parts. I suspect this is due to the fact that I wrote that article while unemployed, and thus exuberantly happy. Happiness is not strictly bad for writing, but it does not help things. What is strictly necessary for writing is pressure, an angry cloud hovering above your head wiggling its damp finger in your ear. Having a job with its own demands is a good way to create pressure, and barring that, it is up to you and you alone to supply that.

Luckily, I internalized this after a few weeks of unemployment, and shortly thereafter produced what remains, to this day, the most popular article I have ever written: ‘Endometriosis is an incredibly interesting disease’.

The endometriosis piece is interesting, because I did not at all expect it to go anywhere. In fact, I distinctly remember conferring with Claude—Opus 3 if I remember correctly—the night before its publication as to whether the title of the essay would be considered offensive. Yes, the model screamed, it is offensive, nobody wants their disease to be viewed as ‘interesting’. It begged me to change it, that I’d be hung from the rafters otherwise. In fact, the model mused, I may even kill you myself. In a decision that surprised even me, I stuck to my guns. And the essay ended up being a breakout success in a very classic sense, which is not something I ever expected to happen for the type of writing I do. It also indirectly led to the person who inspired the article in the first place to be recruited to work on endometriosis research at a great startup, which is probably the most positive side effect of anything I have ever done via writing.

Moving on: I only did two ‘Startup’ posts this year, which is 50% lower than I did in the previous year. The first was over EvE Bio (a non-profit) and the other was over Leash Bio—both of whom are wonderful. I’d really like to do more of these, but this type of writing is hard. You are basically acting like a comms employee, but with zero internal visibility into anything at all, which means you have to make a bunch of predictions that are almost certainly wildly incorrect. So why did I do these at all? EvE is just one of those ‘it’s boring, but so useful for humanity’ missions that felt irresponsible not to cover. And Leash has interesting science, yes, but the culture itself felt even more interesting, and how often do you find bio-ML companies with a culture worth talking about?

What is especially fun about these two is that they seem quite boring to a general audience—unlike the endometriosis one, which I consider genuinely interesting to laymen—and yet both were decently popular across social media. This is strange, and permanently updated my understanding of what could be considered ‘popular science’. I don’t think I talked down to anyone at all—the EvE essay has phrases like ‘Tango β-arrestin recruitment assays for 7TMs/GPCRs’—and yet non-biologists seemingly enjoyed it.

What else? Well, I joined a great bio-ML startup, Noetik, the reasons for which I detail here, and for the first time, I was constantly around a breed of individual I had never interacted with before: cancer biologists. I consider cancer experts a minor deity in the cosmic pantheon, all of whom are capable of providing an essentially infinite amount of information about one of the most complex diseases that afflict humanity. As a result, I am a big fan of them. And as I gobbled up information from these folks, I slowly became comfortable with the idea of putting together an essay over cancer.

Cancer is difficult to write about. There is a universe of popular essays and books that already exist on the subject, and I imagined a potential reader would roll their eyes if they observed that I am relying on some cliche. After many weeks of deep thought, I came across what I felt was a relatively unique angle: there will never be another Keytruda. At least, not in the sense of ‘a cancer drug that works extremely well across many cancer subtypes’—we have almost certainly discovered them all. What there will be are many cancer drugs that work for very specific patient populations. Thus, the job of the oncology field should be to discover these very specific patient populations, and what drug works best for specifically them. This led to ‘Cancer has a surprising amount of detail’.

It’s a very clean essay, and I am lucky to have it be part of the Works in Progress Issue 23 book (you should pick one up!). Not too many afterthoughts on it other than that it is one of the only pieces of mine where it required long stretches of deep thought to figure out the tempo. Writing is not always like research, but when it is, it is really like research.

Most of the other things I published this year were brought about via being nerd-sniped by a third party. The big upside of running a technical blog is that random folks will come up to you and patiently explain the insane intricacies of their field, and at that point, they’ve already kind of handed you the essay. It feels a bit lazy to not staple together the interesting things they told you—along with interviews with others to flesh the piece out—and put it out there. Four essays this year could be attributed to a particular person, whose passion for the subject was so infectious that it grabbed me too.

The first was, ‘RNA structure prediction is hard. How much does that matter?’ which was prompted by Connor Stephens, who told me about the difficulty of RNA modeling at an event I co-ran while visiting SF. The second was ‘Questions to ask when evaluating neurotech approaches’, which was prompted by Milan Cvitkovic, whom I met multiple times throughout 2024 and 2025, growing increasingly shocked at how much all-encompassing knowledge he had over the neurotech field. The third was ‘Heuristics for lab robotics, and where its future may go’, which was prompted by Michelle Lee, after visiting the company she founded and seeing, for the first time in my life, robotic arms performing wet-lab tasks. And finally, the fourth was ‘Reasons to be pessimistic (and optimistic) on the future of biosecurity’, which could be partially attributed to multiple people, but whose core orchestrator was Jacob Trefethen.

If I were forced to pick favorites amongst this year’s essays, I would point at these four, all of which required an order of magnitude more ‘worldview-expanding’ than any of the pieces I published during the first year of writing.

They were painful to stitch together. The RNA one required an immense amount of editing to deal with the fact that I was just completely incorrect in my first draft, the neurotech one languished in my drafts for months because I couldn’t figure out how to divide the sections, and both the lab robotics and biosecurity ones required so many interviews with domain experts (~12 and ~16 respectively!) before I felt confident enough in my perspective to put something out there. Maybe in an ideal world, I would only write pieces like these, since they are both very fun to create and probably the most counterfactually valuable thing I could produce, as most everyone else who could create something similar has better things to do. Unfortunately, making these requires an insane amount of time, is especially stressful, and is mostly impossible to consistently do without writing full-time. But fun to do in sprints!

However, favorites exist in many dimensions. While the aforementioned four were my favorite in the ‘jeez, I can’t believe I managed to do that” axis, there is one more that I’m a big fan of: the ‘saying something I’ve wanted to say for years’ axis. And the essay that scored best there was ‘Ask not why would you work in biology, but rather: why wouldn’t you?'.

This is, I think, the only thing I’ve ever published that is blatant emotional manipulation. I get why so many writers make that their whole shtick. It really is genuinely fun to cradle your reader’s head between your palms, and force them to stare at your worst fears and anxieties, trying to ignite those same fears and anxieties within their hearts. You feel powerful. You feel in control. I also get why so many of the same writers who constantly do this have deeply antisocial tendencies. There is something a little horrible, even soulless, about creating stuff like this. Some people may be able to do it forever, but I could not.

This all said: I believe every word I wrote in it. It echoes core beliefs I’ve had since I was twenty-three, and at no point in the piece do I veer off into territory that I don’t ponder at least once a week. I think about painful medical procedures often. I think about dementia often. I think about the fragility of my flesh often. When I was twenty-three, I was very upset at all this, and hated those around me who did not see what I saw: the writing on the wall for what awaits them, me, everyone. Our brains will turn to soup, our eyes will whiten, our fingers will tremble until they can barely hold a spoon. It felt so obvious to me that all wars, all petty conflicts, all of this useless bickering should be ceased until we figure out the solution here! The entirety of not only the US’s treasury, but every nation’s treasury, should be funneled into this effort. What else could possibly matter?

At twenty-eight, I still believe all this, but I have less anger about it all after funneling those anxieties into my own writing and work. Clean your own room first, you know? Still, it was a relief to get all this out of my skull and onto words on a page, and it is the only article of mine that I re-read every now and then.

This year has also involved some experimentation outside of purely technical writing. ‘A vibe check on the San Francisco biotech scene’ discusses the seeming disappearance of optimism amongst life-sciences founders and employees in the Bay Area; a location I have grown increasingly enamoured with. ‘Human art in a post-AI world should be strange’ is more for myself than for others, and was brought about by seeing how good the modern LLMs are at writing. Will this blog disappear soon after Opus 4.7? Maybe! It does increasingly feel as if the last remaining bit of alpha I have as a writer is being able to organize a piece rather than actually being able to write it. Luckily for me, all the LLMs seem to be quite bad at that. Unluckily for me, the LLMs seem to keep getting better at the things they are bad at.

On a similar note, this year had more fiction. ‘A compilation of eleven stories’ is exactly what it sounds like. There’s also an unemployment-era piece, ‘A body most amenable to experimentation’ and, no surprises, it was not very good and I kind of regret releasing it. The core theme is interesting (what if all in-vivo experimentation were done on a single creature?), but I think I really bungled the execution. Happily, I learned a few lessons and went on to have two fiction pieces in 2026 that I really liked: ‘The origin of rot’ and ‘The truth behind the 2026 J.P. Morgan Healthcare Conference’, both of which are long-form investigative journalism articles into something entirely fake. In fact, the J.P. Morgan one is the second-most-read thing I’ve published. I’d like to do more of these in the future—I have a lot of fiction in the drafts—but these pieces require a fair bit more courage than the technical pieces to send out, and so I end up spending a lot more time editing them than my usual articles.

That covers the writing. How about podcasts?

As mentioned, I filmed 8 this year, which is four times more than the prior year! And with view-counts that are somewhat respectable given that the TAM for this sort of content is almost certainly quite small.

Overall, the episodes were wonderful. I achieved my life dream of having Sergey Ovchinnikov and Ellen Zhong on, both of whose episodes were unsurprisingly the most-watched of this year. I kind of wanted to stop after that, but I kept stumbling across people with such interesting research directions that I kept going. I really enjoyed all of them, but given that Hunter’s episode is the least-watched despite being incredibly cool (did you know that organ transplant companies have a suite of private jets to ferry organs around?), I’m going to plug specifically his here and recommend you watch it:

This all said: I’m still unsure whether to keep doing these. On one hand, they are fun to make and are genuinely informative for the few people who watch them. In the case of interviewing founders, the podcasts also seem to be reasonably useful for making both potential employees and investors aware that they exist. On the other hand, they are a huge pain to edit, and my attempts to outsource have not really resulted in a smaller workload. It also costs a fair bit to rent a studio for these, and consistent sponsors have not yet emerged. As it stands, the podcast is in this weird middle-ground where they clearly have some audience of people worth advertising to, but most potential sponsors (e.g. CRO’s) either aren’t used to sponsoring podcasts and, if they are, would rather spend the money on, say, Luke Timmerman’s much larger audience base. To be clear: this is completely fair, I would do the same if I were in their position.

I’ve managed to stay in the black thanks to individual philanthropic gestures, which I’m deeply grateful for, but I’d ideally prefer not eating into people’s wallets without giving them something in return. I have ~2 more planned, but I may take a hiatus after that. Not the biggest loss in the world; my writing is niche, and podcast viewers are a sub-niche of that niche.

And that’s that for year two.

To end this off: while putting this together, I re-read my first anniversary post. It’s surprising how much things stay the same, but it is also surprising how much more I enjoy my writing now compared to articles from the first year. Some people really, really hate my style and I realize it isn’t for everyone, but I personally like it. I think I’m inching closer to whatever my own internal monologue is. And perhaps—though I’m unsure whether I actually believe this—having a polarizing writing style is all one can hope for, as the only alternative is a writing style that nobody reads at all.

What’s next? I have a few topics in the pipeline, but I’m taking a week off; the last few articles took up a lot of brainpower. But there’s a lot going on in the world right now that’s worth discussing: the bottlenecks to better cancer vaccines, protein models that can generate binders to intrinsically-disordered-proteins, in-vivo CAR-T getting closer to market, the differing strategies of liquid biopsy companies, strange therapeutic modalities, and a lot more. One thing that has changed in the last year is that I no longer feel worried about running out of topics. There’s just so, so much to cover.

Note: I am in San Francisco right now and, in an extraordinary coincidence, I stumbled across two of the people whose work I mention in this article! Very grateful to John Bradshaw for chatting about reaction prediction and Gina El Nesr for chatting about molecular simulation.

A second note: while here in SF, I will be co-hosting an event on Friday, Jan 16th, from 6-9pm, w/ Tamarind Bio! It will be at Southern Pacific Brewing, here is the link to the invite. You should come by!

1. Introduction

2. Generative ML in chemistry is bottlenecked by synthesis

3. Molecular dynamics data will be essential for the next generation of ML protein models

4. Wet-lab innovations will lead the AI revolution in biology

Introduction

There are two memories that I have to imagine are particularly heartwarming for any parent. One, seeing their child for the first time, and two, gleefully showing photographs of that child to an older version of that child, shouting, look how small you used to be! So small! Do you know how hard I worked to take care of you? You were so difficult! But it’s okay, because you were so, so tiny.

I will do something similar to this today. This blog has been operating for the exceptionally long period of 1.7~ years, which means I finally have blog posts that I wrote back in 2024 to resurface, dust off, and proudly present back to you, giving you an update on how things have shifted in the 1~ years since they were written.

I will do this for three articles, back when my cover images were stranger:

It’s fun looking back at these three in particular, because they all feel intellectually significant. All of them were, essentially, predictions of where the future in a specific subfield of bio-ML may go. The first was the first time I’d ever seriously engaged in the small-molecule design space, the second for the molecular dynamics space, and the third for what are durable startup plays. Each one required multiple conversations with multiple people, many of whom I’d talked to the first time ever, and some I continue talking to today. Nostalgic!

But why do this at all? It would be easy to write confidently about the future and then quietly memory-hole the predictions when they don’t pan out, which, to be clear, there’s nothing wrong with and I likely will do many times. This is a blog, nobody cares that much. Still, it is worth doing this purely because it forces me to wrap my head around what has changed since I last covered something, not merely everything that is new and exciting. This is a little boring, but it does feel an important muscle to flex for the same reason that it is important to do your A-B-C’s every few months; just making sure you’re still capable of accomplishing the fundamentals.

As for format: for each article, I’ll briefly recap the original thesis, look at what has actually happened since, and render some kind of verdict as to what went right/wrong. I’ll also attach a tl;dr at the top of each section.

Generative ML in chemistry is bottlenecked by synthesis

tl;dr: I was correct in a contrived sense. Arbitrary molecular synthesis is still hard and the models still aren’t perfect at telling you good synthesis routes for whatever they produce. But what has changed is a lot more money has flowed into making synthesis better outright, and, much more importantly, the space of ‘easily synthesizable molecules’ has slowly expanded from ~40B to ~80B, and will likely continue to climb. At a certain point, who cares about what is outside of that? Is it actually bottlenecking anyone?

Back in September 2024, I wrote an article arguing that generative ML in chemistry is bottlenecked by synthesis being slow, costly, or outright impossible. The thesis was not original in the slightest, and was clowned upon in the r/chemistry subreddit for being something that was so patently obvious that how could someone possibly have written 4,400~ words over it. This was very rude, but sadly, they were not wrong. It is pretty obvious.

My basic argument went something like this: creating proteins are easy. Every time I design a protein, I can just send the sequence to Twist Biosciences, have them create a plasmid, and cells will (almost) always pump out my protein. The same is not true for the rest of chemistry.

Of the 10^60 small molecules that theoretically exist, there is no ‘ribosome' for creating them, each must go undergo at least a somewhat custom synthesis process. Some chemicals are impossibly hard to create, some are easy to create, and lots lie in the spectrum between. A fun example of the former I used in the article was erythromycin A, a now-common antibiotic that was originally isolated from a bacterium. From beginning to end, this molecule took 9 years to figure out how to synthesize.

And some molecules are even harder! After 40 or so years, the total synthesis of Paclitaxel, a chemotherapy agent, is still an ongoing research effort. In the meantime, we just harvest the chemical from a very specific of tree.

This makes machine-learning in chemistry somewhat annoying, because it means your generative model can happily spit out thousands of candidate molecules to bind to some input protein, and 99% of them, perhaps 100% of them, are intractable to create given your non-infinite budget and time constraints.

When I first wrote the article, it seemed like there were two possible fixes on the horizon.

The first fix was that that small molecule models will become more ‘synthesis-aware’. What does that mean? Quoting from my article:

One definition could be low-step reaction pathways that require relatively few + commercially available reagents, have excellent PMI, and have good yield. Alongside this, we’d also like to know the full reaction pathway too, along with the ideal conditions of the reaction! It’s important to separate out this last point from the former; while reaction pathways are usually immediately obvious to a chemist, fine-tuning the conditions of the reactions can take weeks.

In this world, you may personally not know how to synthesize the bizarre stuff your model spits out, but, if your model is smart enough, perhaps it’d be able to helpfully provide all the steps needed to make it.

The second fix was that arbitrary synthesis of molecules would simply become a lot easier, and that something akin to ‘ribosome for chemical synthesis’ would miraculously be invented.

Now, there’s the obvious steelman to both of these: chemical screening libraries—or, chemical space that is known to be easily synthesizable—is quite large, and potentially accounts for all useful stuff. So, maybe you don’t need models that even generate molecules or better synthesis, you just need models that can filter from this pre-existing known of ‘reachable’ chemical space. From my article:

One example is Enamine REAL, which contains 40 billion compounds. And as a 2023 paper discusses, these ultra-large virtual libraries display a fairly high number of desirable properties. Specifically, dissimilarity to biolike compounds (implying a high level of diversity), high binding affinities to targets, and success in computational docking, all while still having plenty of room to expand.

With all this background context: how has this field changed in the 1.5 years since this article was published?

On the synthesis-aware modeling front: it may be somewhat interesting for you to learn that a singular MIT professor named Connor Cooley was—circa 2024 when I wrote that article—responsible for a rather significant chunk of the ML x synthesis literature I came across. As of 2026, this continues! And unfortunately, from a paper he published in mid-2025 that was attempting to evaluate possible failure modes of these synthesis-aware generative molecular models, he had this paragraph:

It is also natural to wonder if the task of reaction prediction has been “solved” to a meaningful degree. When using these models in practice, it quickly becomes apparent that the answer is a resounding no. In fact, when using reaction predictors in new domains, not only might a model make an incorrect prediction, it might hallucinate a product preposterous to a human chemist.

So, it does not immediately feel like there are major breakthroughs that have cropped up in the past year—which I further confirmed with John Bradshaw, the first author of the paper, who I coincidentally met up with the other day. Now of course, I’m certain there has been some material progress, but little that is immediately legible to me.

Let’s move on. How are we doing with the second problem: improving our ability to synthesize arbitrary chemicals?

Curiously, there has been a huge flurry of startup activity here over the last year. onepot raised a $15M Series A in November 2025 to synthesize arbitrary molecules, Chemify raised a $50M series B in October 2025 to synthesize arbitrary molecules, Excelsior Sciences raised a $95M series A in December 2025 to synthesize arbitrary molecules. A pattern is emerging here, and I’m not even naming everyone who has started something in this space!

The optimistic read is that we're witnessing the early stages of the long-prophesied chemical synthesis revolution, that the combination of better robotics, improved reaction prediction, and some clever engineering is finally paying off some fundamental fruit. Is that true? I don’t know! These things take time to play out.

Speaking of onepot—which is a synthesis-on-demand service startup—there’s a particularly illuminating text interview that the renowned Corin Wagen had with the founders of onepot (Daniil Boiko and Andrey Tyrin) back in December 2025 that may teach us something useful. A few interesting excerpts are as follows:

Andrei: I also think that synthesis is a very complicated problem, and I think we have unique insight on how to solve it from an interdisciplinary standpoint. So you can imagine the company would be trying to solve it just by improving the organic chemistry side of things, and that's a very reasonable approach, or there are companies that would really invest in developing very sophisticated models for synthesis, but in our perspective it's important to have all of the components, if that makes sense.

Both the organic chemistry side of things and the computer science side of things are intertwined. So that is very important here for the success of making synthesis automated basically.

This is, I think, a very fun take. Maybe there is no magic sauce that needs to be really invented, but rather, all the ML and chemical tools for (largely) solving synthesis are already out there, someone just needs to have a broad-enough knowledge base to glue it all together. Happily, Corin presses on this a bit further:

Corin: Where do you guys see that your big advances have happened so far? So are you inventing new reactions? New instruments? Is the magic in the integration? What are you doing that other folks haven’t figured out yet?

Daniil: Yeah, it’s a good question. So, automation is really straightforward. When you start doing something very complex, I always think that I’m going to hit a wall in what I’m doing—everybody says it’s very hard—and then we start doing it and it just never happens. We just do it and still it’s fine all the way down. It’s a lot of work but still totally fine. So we don’t see much of a problem on the automation side there. We have to customize existing hardware a little bit, but it’s fine.

We do see a lot of gains on this tool ML layer. So Andrei probably could tell more about this, but it’s the reason why we have success-based pricing. So if we fail an experiment, we’re the ones who have to pay for it, which is very unfortunate. So there is very clear value from these models. I mean, you can literally calculate the economic impact of increasing your accuracy of the model by another 5%. It’s very clearly translated.

And on the agentic side, it’s another thing: if you could make a thought experiment and let’s say replicate one of the largest companies that works in enumerated library space, you would need to get all the reactions, all the protocols, and develop them from scratch. Just imagine the amount of effort that will go on there. You would need chemists working on hardware, setting up the reactions, doing hundreds of experiments for every single reaction, then analyzing the data and making conclusions, then optimizing again. It’s absolutely ridiculous.

Andrei: I also think that with our approach, we have the pieces that improve one another. So you know: one direction is we have the agents that have this broad intelligence, that have this literature knowledge, that can design high-level conditions for experiments. This is an exploration phase, a creative phase.

And then we also have another direction with custom models that are highly specialized to capture these patterns hidden in reaction data. They just complement each other really well: we have this general-layer intelligence and this specialized intelligence that captures things at the atom level. You know, you place a nitrogen somewhere, reactivity changes suddenly, and you need to find another route, maybe even get more steps there. It’s a bit counterintuitive for humans and for LLMs as well, so having this specialized intelligence is very important.

Neat! There is a bet here on natural language LLMs being very essential to the whole process. And, for what it is worth, there is plenty of precedent that this is a directionally correct idea, here, and here, and here. Cool that someone started up a company with that explicitly part of the thesis.

Finally: how is the steelman I presented in the old article doing? Do we need arbitrary molecular synthesis? The definition of ‘easily accessible chemical space’ changes as reaction pathway design as a discipline gets better and better, and, when I first last looked into the subject, this space was hovering at ~40B molecules.

As of September 2025, this space has been expanded to 83B, which specifically refers to a chemical area that Enamine, a very well-established, chemical supplier company, considers easy-to-create within 2-3 weeks. And, in April 2025, Recursion and Enamine announced a partnership to use Recursion's MatchMaker tool (a model that assess whether a small molecule is compatible with a specific protein binding pocket), to intelligently filter Enamine’s 65B (at the time) compound library down to 10 enriched screening libraries from over 15,000 newly synthesized compounds designed to find binders to 100 drug targets.

This is, I think, exactly what you'd expect to happen if the steelman is correct. If easily-accessible chemical space is large enough to contain all the good stuff, then you don't need fancy synthesis, you simply need fancy filtering. As of today, I do not think there has been an update to this project, but excited to see where this goes! If anyone from Recursion is reading this and would allow me to break the scoop here, contact me!

So, to summarize, ML is still technically bottlenecked by synthesis, but there is increasingly aggressive effort to fix both the synthesis problems outright, and to figure out whether the ‘hard’ synthesis is even needed. Now, similar efforts almost certainly existed back when I wrote the article, but they seem much more well-capitalized and numerous today.

Molecular dynamics data will be essential for the next generation of ML protein models

tl;dr: My thesis was somewhat accurate. Some ML + molecular dynamics (MD models have been trained out since the article, and the synthetic data used for them was somewhat helpful. But the core dataset problem that comes alongside MD—timescales that are too short—have not been solved, and those prevent MD from being extremely useful as training data. There is good effort being put towards fixing this though! But my thesis was also too absolutist; non-MD models likely will continue to have their place.

Read more

Bubble Tanks is a Flash game originally released on Armor Games, a two-decade-old online game aggregator that somehow still exists. In the game, you pilot a small bubble through a procedurally generated foam universe, absorbing smaller bubbles to grow larger, evolving into increasingly complex configurations of spheres and cannons. Here is a reasonably accurate video of the gameplay, recreated in beautiful high-definition:

Bubble Tanks was first released in 2007, with a sequel out in 2009, and another sequel in 2010. Back when I first played it as a child, I was convinced, absolutely convinced, that there was someone in the world whose entire life was nothing but Bubble Tanks. This person—and I took it on faith that they were real—woke each morning and immediately, before coffee, before the basic animal functions of evacuation and sustenance, played Bubble Tanks. They posted on obscure forums, arguing bitterly over tank builds and bubble physics with three other people who had the same devotion. I knew that their room was disgusting, repulsive. This was essential to the vision, that their stained clothes lay across their floor, worms crawling over them. They were either skeletal or enormously bloated, monastic asceticism or excess gluttony, one or the other. Bubble Tanks was single-player, so they did not do all this for fame or glory, but for love, or for something even deeper than love. Everything had been sacrificed for this game, and excelling at it would be all that they had ever done, all that they would ever do.

And what if this person were just the start? What if this Flash game became the organizing principle of human civilization? The economy would shift to accommodate. Bubble Tanks coaching would become a viable career path. Parents would discuss their children’s talents at the game over dinner. Political candidates would be asked about their Bubble Tanks records during debates, and one would lose an election after it emerged that he never evolved past the third tank configuration.

Looking back on this fever dream I came up with a decade-and-a-half back, one thing that immediately strikes me is that being creative in such a world must be monstrously difficult. Not because all creativity must be ultimately tailored towards Bubble Tanks enthusiasts—that much is obvious, and is not especially different from creativity in the real world, which must tailor itself towards enthusiasts of human-understandable concepts—but rather because there would be astronomical amounts of Bubble Tanks content already in existence. In the latest stages of this civilization, billions of people have devoted their lives to Bubble Tanks. Millions of them are creative. Hundreds of thousands have genuine talent. Tens of thousands have produced work that is, by any reasonable measure, brilliant. The Bubble Tanks epic poem exists in fourteen languages. The Bubble Tanks symphony has been performed at concert halls on every continent. There are Bubble Tanks novels that have won Pulitzers, Bubble Tanks paintings that hang in the Louvre, Bubble Tanks films that win Oscars. All the obvious ideas have been executed. All the non-obvious ideas have also been executed.

I have wonderful news. You live in the earliest innings of this universe, at the start of it all, just as more and more of the population is beginning to wake up to how great this Flash game is. Even more fortunate for you, it is not only Bubble Tanks that is the object of human devotion. It is everything.

Humanity has been producing art for somewhere between 45,000 and 100,000 years, depending on how generously you define “art” and “humanity.” For most of this period, the constraint on creative output was not imagination, but production capacity. The printing press changed this, then radio, then television, then the internet, and at each stage the volume of creative work accessible to any given person increased by orders of magnitude. Today there are more novels published each year than a human being could read in a lifetime. There are more films, more paintings, more poems, more essays, more podcasts, more YouTube videos, more TikToks, more tweets, more everything than anyone could ever hope to consume.

And as more art is produced, the more we must learn to discriminate. Consider stories. They existed for millennia in the form of epics, religious hymns, folk tales. But with the rise of printing presses that allowed a wider variety of stories to circulate, we were forced to develop something very dangerous: filtering technologies. Genre is a filtering technology. It emerged because no one could read everything, and so readers needed a way to predict whether a given text was likely to satisfy their immediate demands. “Romance” is a promise: there will be a love story, probably with a happy ending. “Mystery” is a different promise: there will be a puzzle, and it will be solved. Both are not really descriptions, but more accurately a contract signed by the author about what the book will do for you. And like all technologies, genre has evolved to become more precise as the volume it must filter has grown. “Sci-fi” was once sufficient. Then it fractured into hard sci-fi and soft sci-fi, into space opera and cyberpunk. Brand awareness is a different filtering technology. Netflix originals have the flavor of something that will likely be decent, but also homogenous, whereas A24 movies have an art-house sensibility with a certain color palette. Each subdivision represents a refinement of the filtering mechanism, a narrower promise to a narrower audience.

Why are filtering technologies a problem? Aren’t they great? We’re getting increasingly good at giving people what they want!

Well, it wouldn’t be an issue if the creative process were limited by human scale, but we’re getting close to leaving that world. I feel pretty comfortable saying that, at this point, LLMs can handle nearly every sufficiently-chunked-up bit of music production, graphic design, video editing, background illustration, character concept art, voice-acting, essay writing, and a lot more. The list extends as far as creative production itself extends, which is to say: everywhere. Every domain that humans have developed aesthetic traditions within is a domain where AI can now perform the components of that tradition with reasonable competence.

One could imagine that in the near future, there will be a new button on your television, one with a sparkle animation. After clicking on it, it will offer you a QR code, politely asking to scan it with your phone. Upon doing so, the button will give you one of the ultimate promises of our new frontier-AI-lab-centric economy: a text box, that will generate a feature-length film using whatever prompt you enter into it. We have arrived. The long march is over. This is the ultimate final utopia that our filtering technologies have been building towards since the first monk started to distribute the Gutenberg Bible. What will we make? What wonders await us?

I suspect the answer is: mostly nothing. Or rather, mostly more of what we already have.

The problem with filtering technologies, one that becomes catastrophic precisely at the moment of their perfection, is that they assume you know what you want. The entire apparatus presupposes a subject who arrives at the interface with desires already formed, preferences already crystallized, a little homunculus sitting in their skull who knows exactly what kind of story it wants to hear tonight. And, to be clear, this actually works remarkably well in the case of a finite set of existing objects. When there are ten thousand, even a hundred thousand films in the Netflix library, the algorithm’s job is merely to surface the handful you’re most likely to enjoy from a pool that already exists. You don’t need to know what you want with any precision. You only need to recognize it when it appears before you, to say “yes” or “no.” Really, the algorithm is not an algorithm at all, but something even more basic: an ophthalmologist. It flips between lenses: better, or worse? This one, or that one? You do not need to understand the properties of curved glass or the anatomy of your own defective eyes. You simply must obediently respond to the question you are asked.

This all breaks down the second you are placed in the driver’s seat, because you do not actually know what you want. How could I make such a proclamation so confidently? I can’t, but I will anyway: what you want most, more than anything else in the world, is stuff that you never realized you wanted.

I realize that this is a tired sentiment, subtweeting the apocryphal Henry Ford line about faster horses. “If I had asked people what they wanted, they would have said faster horses.” The implication being: I, the visionary, know what you want better than you do. And I, despite the dullness of my audience, will give you the automobile. You would think, reading this essay, that I am making a case for the artist: the sacred figure who reaches into the void and pulls out something none of us knew we needed.

But I am saying something much worse, which is that nobody knows. Not you nor the visionary. The Ford line is wrong not because customers actually do know what they want, but because, if we’re being honest with one another, Ford didn’t know either. It was a happy accident that he later (again, apocryphally, because I don’t think he actually said it) narrativized into inevitability, because that is what popular culture does with fixations that turn out well.

You may guess where this is heading. It’s time to discuss Being John Malkovich.

Being John Malkovich is a nearly two-hour movie, filmed in 1999, directed by Spike Jonze, written by Charlie Kaufman. It stars John Cusack as a failed puppeteer named Craig who takes a job as a filing clerk on the seven-and-a-half floor of a Manhattan office building—a floor with ceilings so low that everyone must walk in a permanent stoop. This detail is never really explained, other than a vague mention of how the original building owner had a wife who was a dwarf, which raises far more questions than it answers, Did he build the entire floor for her? Did she work in this office? Was this an act of love or an insult? By the time these questions have been raised, the film has already moved on, and it is never mentioned again. One day, while filing, Cusack’s character discovers a small door behind a cabinet. He crawls through. In doing so, he finds himself inside the head of John Malkovich, the actor, experiencing fifteen minutes of Malkovich’s life from behind his eyes, before being ejected onto the muddy shoulder of the New Jersey Turnpike.

This is the basic premise, all introduced within the first half-hour of the film. And I have not yet mentioned the chimpanzee.

There is a chimpanzee, who has a reasonable amount of screen time. She belongs to Lotte, Craig’s wife, played by Cameron Diaz. The chimpanzee has intense psychological trauma as a result of being torn from her mother at an especially early age, a fact that was shown entirely through flashbacks. What is the purpose of the chimpanzee being traumatized? It is unclear, because it is never actually a relevant plot point. Why is Lotte taking care of this chimpanzee? Is she an animal therapist? She is not. She works at a pet store, and stores a wide variety of animals beyond just the chimpanzee in her (and John’s) small New York apartment for seemingly no reason at all. Why is the chimpanzee in the film? It seems to be for the sole purpose of a pivotal moment in the film which requires using the chimpanzee’s cage, but this moment does not actually need the prodigiously large cage for it to work, and one could imagine a thousand other more reasonable ways to accomplish the same narrative beat. Despite all this, the chimpanzee is there.

How did Charlie Kaufman, the relatively unknown screenwriter and driving force for the film, even come up with this plot line? In an interview, he says this:

I wrote Being John Malkovich while I was waiting for [the next sitcom] hiring season. My idea was that I would write a script and use it to get work. I had this idea that someone finds a portal into someone’s head, and I had another idea that somebody has a story about someone having an affair with a co-worker. And neither one was going anywhere, so I just decided to combine them.

Oh yes, there’s an affair too. But it gets even funnier. Why is John Malkovich the chosen victim of the portal? Kaufman also gave the answer in a different interview:

I don’t know... I thought it was funny. It’s hard to explain, but I thought it was funny, but not jokey. Because [John Malkovich] is a serious actor, he is a great actor, but there is something odd about him and there is something behind his eyes that you can’t see. And I thought that was a good person for this.

And then I think his name is perfect for the title...

Being John Malkovich is a worrying movie for a filtering technology maximalist, because it is both incredibly good, benefitting from both the insane premise and bizarre details, and is also something that nobody ever asked for. What is the film about really? What is the emotion that it is intended to evoke? It is about identity, I suppose. Also about desire, and the way desire makes puppets of us all. It is about the loneliness of being trapped behind your own eyes. It is also about John Malkovich, specifically, for no other reason other than it being an apparently funny choice. There are a lot of very strange, but ultimately invaluable, stylistic decisions made for this movie, all of which ostensibly made because Kaufman got a kick out of it.

To be clear, I am not saying something like ‘a sufficiently well-prompted AI could not come up with Being John Malkovich’. What I am saying—which actually feels like a pretty defensible viewpoint—is that very few people would ever think to assemble together a prompt to create Being John Malkovich. This opinion does not require any sort of humanist romanticism, or belief in some vague notion in ‘soul’. What it is grounded in, really, is a fairly basic observation about the structure of human desire; which is that desire is not a fixed quantity that exists prior to its satisfaction, but something that is frequently created retroactively by the very thing that satisfies it.

This would not be so bad if it were the only thing happening. The Kaufmans of the world would continue to write their chimpanzees, and the prompt boxes would continue to produce competent variations on existing themes. The end result would simply coexist alongside each other, one being deposited directly in the multiplex, the other in the art-house cinema, each serving its respective demographics.

But there is a second thing happening, and it is happening simultaneously.

Since AI is quite good at producing the art that isn’t too strange, I imagine nearly everyone will be, in due time, happy to hand over their consumption directions over to it. Soon, Suno will produce everyone’s music, Midjourney will make everyone’s phone backgrounds, and so on. Yes, it will be slop, not because it is bad, but because it repeats. Generative models are, by their nature, interested in modeling distributions—trained on everything, they converge toward the most likely areas of their distribution, which means that even when you prompt for something unusual, you are pulling against a gravitational force that wants to return you to the most common areas. The result is that the most common forms of AI output have a flavor, a kind of statistical residue that accumulates across pieces. But most people don’t mind this. They are happy to let the model play the same ophthalmology game with them, because they know they can play that game well, and the results will probably be roughly as good as the last algorithm they played the game with.

And herein lies the problem. Now, there is no longer any reason for the multiplex to exist, because the multiplex is not meant to be genuinely unique, and whatever is not unique can instead be entrusted to our own personal, finely tuned filtering technologies, combined with the infinitely patient AI. Is this bad? Not for the consumer! But it does put the artist in a pickle, because it now means their last remaining way of being seen at all, much less standing out, is to create something like Being John Malkovich. This cannot be easily made by the AI alone, because it does not submit to the ophthalmology game as easily. And creating something like Being John Malkovich is, I imagine, challenging.

Of course, strangeness has always been a useful strategy for art. Even beyond Charlie Kaufman, the greatest artists from the last century were all a bit off. Joan Didion had an unnerving flatness, describing a woman’s suicide from a sexless marriage in the same sentence as a shopping list. Hunter S. Thompson decided that the reporter should be more interesting than the thing being reported on, and shoved his demented, drug-addled brain into everything he wrote. David Lynch made movies in which the nightmarish mysteries refused to be made legible, just rather something you were forced to marinate in during the film. Importantly, nobody was strange in the same way. Didion’s strangeness is one of temperature. Thompson’s is one of proportion. Lynch’s is one of epistemology. What they share is not really a style, but a willingness to identify the thing that everyone else in their field was doing automatically, unconsciously, and to ask: what if I didn’t?

During their times, these people were made rich, famous, immortalized for doing something as brave as this. Things are different today. Now, to be above the crowd is the minimum required to be visible within it.

This is a very stressful situation, and one that all young artists born into the Bubble-Tanks-obsessed universe could likely sympathize with. They too live in a civilization that is utterly consumed with infinite creative production alongside the dimensions that matter—for them, Bubble Tanks—and are forced to produce something underneath a sky that has already seen it all. One can only imagine how strange their work is. Importantly, we occupy the antechamber of this world. What is coming next can be seen from where we stand, and our distance from it is decreasing at a rate that makes projection trivial; five years, ten, and the gap collapses into nothing. There is genuine cause for throat-closing anxiety at this prospect.

You can imagine a rather bleak future is the end result of this. One in which someone sits at their screen, asks their friend.com pendant to create an eleven-season series about a 45-year old Japanese woman and her tsundere relationship with a coworker at the glue factory she works at, and watch the end result with rapt attention. In this hyperatomized future, capital flows only to the frontier model companies and no one else, where nobody has common language to describe the media that they consume to anyone else, since every single piece of media has a singular person as part of both its creation and distribution.

But perhaps something better is possible. Consider an alternative future. This one is exactly same as the first one, with one minor difference: people have moments of intense boredom with what the machine spits out to them, and they decide to go out searching for something else that someone else has made, one that does not taste like something that they, in a million years, could’ve ever come up with themselves. Not because they do not have the technical talent to do so—technical talent is precisely the thing that has been commoditized—but because they lack the particular configuration of a life that would lead someone to write that, to make that choice, to include that detail that seems inexplicable, right up until they encounter it and realize it was obvious all along.

I am increasingly optimistic that the second version is the more likely one, only because it feels as if popular art is being increasingly dominated by the strange, the unmistakable, the ones that have an auteur-esque energy infused into it. To be clear, this is not new. But it used to be a privileged position, something you earned after decades of clawing your way through the studio system, or something you were granted by virtue of being from the correct lineage. Now the privilege has inverted. Now everyone must leave their own distinctive, strange smack on their work, or else disappear entirely. Just take a look around you. The auteur is increasingly colonizing forms of media that once operated on entirely different principles. Substacks, podcasts, technical news; many of the most promising ones today are largely held up by the specific and irreplaceable neuroses of the person producing them. This is strange and new and also very old. It is a return to something like the bardic tradition, in which the story and the storyteller were inseparable.

Of course, none of this is to say that the auteurs are rejecting AI. In fact, the best ones may use it more than anybody else does, as the speed at which production will be demanded in the new world will necessitate it. What makes auteurs so special? It is not the case that they, in their production of the strange, have any claim to particularly fine taste or even soul. In fact, their primary good fortune, often their only one, is that they want something, that they desire to tie great iron chains around some particular, ugly concept and drag it behind them wherever they go, clanking and scraping against the pavement, alerting everyone to their embarrassing presence. The machine has no such desire. It is capable of anything and interested in nothing. And the desire of what is uncommon, it turns out, is the only part of this whole system that struggles to be automated.

Nobody in New York City wants you to live forever. Really, they will typically find the proposition deeply problematic, their face curdling at the very suggestion of it. If you live forever, clucking in disapproval as their eyebrows furrow, you likely will not live at all. They will mention how their near-death experience catalyzed their desire to live, how the accident of a loved one taught them to be truer to themselves, and their family member succumbing to cancer finally made them make amends. Suffering is actually good for you, it ripens the spirit. You ask them if vaccines were a good thing. That, they insist, is different. Well, maybe. Either way, the narcissism needed to view tragic events as a part of your own personal character building exercise is probably really, really good for you, but it’s something that I find hard to stomach.

And, to be clear: I’m somewhat a fan of this. Since I pathologically consider any club that would have me to not be worth joining, the average mentality of the people in NYC — as much as I disagree with it — are helpful for keeping me on the right side of history. San Francisco would have me, and that is precisely why I could not live there, at least for the moment.

But in short doses, the city can be very refreshing. There is an ecclesiastical sect energy to the place, one that bleeds a sense of belonging. It is not only a vision of a better world, but a vision of a world in which you personally are one of the elect. You deserve to be frozen. You deserve to survive. You have thought the correct thoughts, read the right essays, and knew about all the latest foundation models before the normies did. You saw the signs. You believed. Perhaps this isn’t neccesarily good for you, but it is good for summoning up the energy to do something very, very hard.

Well, at least that’s what it was like at one point. Recently, I visited San Francisco, between June 16th to June 28th. But this was my second stint to San Francisco, the first time was in October 2024, just about 8 months ago. During that last visit, the intensity I saw in everyone I talked to there was overwhelming, and concretely cemented my view as San Francisco as a place I should visit much, much more often. Not so this past trip! While it was a lovely time, there was an gloom over a majority of the life-sciences people I spoke to. A background radiation of anxiety and fear so strong that, by the end of my third day, my mirror neurons had kicked in and I too had begun to feel uneasy.

One of three good writers on the internet, Sam Kriss, writes:

Does Vibe transform the world, or is it an index of the world’s transformations? Look: I’m a good Marxist. We’re all good materialists here, we all know what the right answer is. But you’ll still have noticed that there’s been a trend, lately, towards a totally vibe-based ontology. The vibe shift is also a shift towards vibes.

Yes, the essay written above is about politics, but vibes permeate everything. A hard science is not immune to it. Really, biology may be even more prone to dissolving into the vague, amorphous goo that is vibes given how much is it stake. Cheap decisions are made based on vibes, expensive ones based on a careful, deliberating analysis that hums and haws over the complex minutiae, and extremely expensive ones are based on vibes. And useful biology is very, very expensive.

So what is the vibe of biotech? Obviously, I’m the least qualified person to give their take on it, but, the few vibe-naming attempts I have seen have not offered up something interesting. Thus, the reason for this essay. Here is the pitch for what I think the vibe shift is after having talked to 30~ people during my time in the Bay: increasingly few people in biology believe that they will be rewarded for doing something ambitious.

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After 2.5 years at Dyno Therapeutics, I have left and joined Noetik, an SF-based ML-bio startup building foundation models of tumor microenvironments. The company is somewhat early stage, having raised a $40M Series A in August 2024, and hovering at just above 30 people. My role is a bit non-traditional, and will involve a mix of ML, writing, and perhaps other things as well. After all, the poster I most recently made had a mild Noetik theme! My first week just wrapped up, so this seems like a good time as any to write an essay about it all.

Here, I will explain some beliefs I’ve been developing over the past year about the ML-bio field, why Noetik is one of the only startups I’ve found who shares the same thoughts, and what the startup is creating. This is the first time I’ve picked a place to work based on an actually formalized economic + scientific worldview of the field I’m in, so I’m quite excited to share this!

If you’re curious about the work we’re doing or want to partner with us, please reach out to me at abhishaike.mahajan@noetik.ai or DM me on X!

But first, appreciation. Dyno was the first time I ever had a chance to really learn biology x ML, and I am eternally grateful to the company for very much taking a chance on me — someone who had never done that sort of work before. Some of the smartest people I’d ever met in my life currently work at or have previously worked at Dyno, and it’s a place I’d highly recommend to anybody else. Dyno is rare amongst ML-bio startups for having pretty clear product-market-fit and an actual product. Patients want their gene therapy delivered better, and Dyno has real capsids on the market that solve that problem.

But I’ve become somewhat restless. After some soul-searching, I have landed on Noetik for my personal bet for where the future of ML in biology will be the most interesting. But before explaining why, I should explain some of the beliefs I have about this field at large. It’s fine to disagree with these, but I don’t think any of them are, as of today, strictly disprovable.

First, scaling laws in biology clearly aren’t playing out the same way as they did in natural language.

Parameter counts and tokens-trained-on keep going up for protein language models, but there aren’t particularly interesting properties that emerge from such models. There is an even worse story being told for scRNA foundation models! In general, it seems like many biology foundation models made back in 2021-2022 are still decently competitive with models made today, which is a worrying place for the field to be in.

Now, I don’t think that the scale hypothesis is wrong, but rather that it is potentially being misapplied. Scale in biology may be irrelevant if you only do unimodality scaling. Maybe you only get benefits if you first scale on the modality axis! Perhaps collecting information from every layer of the biology data stack will be necessary for anything transformative; biology is not as leaky as we wish it was, and you get very little ‘for free’ if you focus on collecting, e.g. only genomic sequences or only proteins or so on.

I have no idea if this is actually true, but it feels true.

Second, too many bio-ML companies are focusing on a problem that has strong competition from wet-lab methods.

I think there are really two (viable) options when it comes to applying ML in biology. The first one is to use fully open-sourced models as part of your normal preclinical wet-lab process. As in, little-to-no ML development is done in-house, and you simply hope that the mild usage of commoditized ML will speed up your investigative work. The second one is to focus on a (useful) problem so obscenely difficult, so intractable through usual means, that hand-crafted ML may be the only real hope at making any progress. These are the only two modes of operation that make sense to me.

But a surprisingly high number of bio-ML startups fall into this weird third bucket where they are trying to do bespoke machine learning for a problem that could just as easily have been done by…..phage display or chemical library screening or running mouse model experiments. I can buy that this made much more sense in, like, 2022, less so now that so many great open source models are out there. Of course, if you truly believe in your heart of hearts that the open source model spigot will stop soon, sure, I can buy that running this type of company may eventually pay off. But I’m betting on the spigot continuing.

Finally, three, the space of interesting, clinically relevant biology operates on much larger scales than individual biomolecules.

I think this is the most disagreeable thing I’ll say here, because it’s roughly the same thesis as systems biology as a field (though I admit this is somewhat an abuse of terminology). And systems biology hasn’t done particularly well. Nobody in the life-sciences will deny that biology is quite complicated, but, in the search for better drugs, the systems biologists have demanded that this complexity must be grappled with. This is all while most others have ignored it and have instead focused on far simpler questions, like ‘do we observe binding between this small molecule and this receptor?’. Here, the systems biologists have been left with egg on their face; many papers published, but few practical contributions to the very hard problem of pushing a drug to market.

If we trust historical precedent, we should also trust the pure protein foundation model folks to deliver upon us better and better drugs, since they are a rejection of what seems like a failed paradigm. This may work! But some part of me is deeply skeptical of the whole approach, at least if we trust that therapies will get increasingly complex, requiring a far broader awareness of biology than a single biomolecule. Systems biology may remain too broad of a brush, but we may still need something higher level than individual biomolecules.

Noetik is the one of the very few AI-biotech I’ve found whose revealed preferences for R&D matches up with these three beliefs that I have.

For the first point, their data collection is heavy at the modality level.

Data is collected across four areas: exome sequencing, H&E, spatial transcriptomics1 (read the footnote!), and immunofluorescence across 16~ proteins, the last three of which are derived from human tumor biopsies. For context, most companies working in the same space as Noetik are primarily collecting a very different type of data: single-cell RNA readouts within in-vitro cell lines. That sort of data is a lot cheaper + faster to collect, but an order of magnitude less information-rich. This may be helpful for some therapeutic areas, but, on average, I’d bet on data quality over quantity.

Currently, Noetik has this data for over 40 million cells across 2,500 patients — which represents the largest multimodal, paired dataset of its kind in the world — and is growing every month. And, recently, we are collecting in-vivo tumor perturbational data in mice as well.

For the second, they have focused on what is one of the hardest drug development problems in the world: cancer therapeutics.

To take the example of immunotherapy: when immunotherapy works for tumors, it is miraculous. Basically every case of spontaneous cancer reversal has been the result of ‘natural’ immunotherapy; instances where the body's own immune system unexpectedly recognizes and eliminates cancer cells. It was under that observation that led to the first case of artificial immunotherapy, in hopes of replicating this natural success: Coley’s Toxins.

Nowadays, we have an expanding and increasingly sophisticated arsenal of tools to do exactly this: checkpoint inhibitors, chimeric cell therapies, bispecific engagers, and so on. But immunotherapy doesn’t work for the majority of patients, and it is unclear why. We know that some tumors are “cold”, devoid of meaningful immune infiltration from the outset, while others are simply "immune-excluded," where T cells circle the tumor periphery but fail to penetrate. In still others, even when T cells are present, they are exhausted, suppressed, or disarmed by tumor-intrinsic resistance mechanisms. But even this do not explain all the variation we see in immunotherapy responses.

Are there purely wet lab ways to study this? Not if you suspect that many immunotherapy failures/successes are subtle, spatially distributed, and heterogenous across thousands of tumors. As such, ML may be an excellent tool to throw at the problem, since there’s really nothing else we can throw at it. There’s also some reason to suspect that immunotherapy as a field will grow, given that CAR-T therapy may see massive cost drops in the near future (see: Umoja).

This all said: what Noetik is building transfers quite well to basically any cancer drug outside of immunotherapy as well, immunotherapy is just a good case study.

For the final third point, Noetik, as is the case with every phenomics company, is implicitly placing a bet on bigger-picture understandings of biology being important.

I can’t prove that this is important, or even necessary. It just seems like it should be if we go down the route of increasingly complex drugs that interact with many [things] instead of a singular receptor, but I think it’s tough to predict the future. The most successful drug of the 2020’s has been Ozempic and Ozempic-follow-ups, none of which worked in particularly crazy ways. So who knows? I think it will be the case that while perhaps Noetik’s approach will be necessary for some therapies, there is so much steam left in simpler therapeutics that this point doesn’t really matter. Interesting places to join are always going to be based on bets on the future, not certainties. I know what I’m betting on!

So, what has Noetik actually built?

Their first public release was a self-supervised model called Octo-VC, which was pretrained on the aforementioned 4 modalities of data. From the release:

OCTO incorporates spatially-aligned, multimodal data from different tumor regions into a unified representation. To achieve this, we encode different data types in ways that best preserve their biologically relevant features. For example, multiplex fluorescence images, which measure protein expression in cells, are first split into color channels that represent discrete proteins and then “patchified” into individual tokens. Spatial gene expression data is modeled with one token per gene at each location that contains a cell. Once processed, the tokens are combined into a single sequence so that the model can reason across modalities and learn latent relationships between the underlying biology they represent. OCTO was pre-trained on 20B tokens across 128 GPUs and is able to include up to 16,000 tokens in its “context window” at inference time.

Here’s a helpful graphic describing the input data:

And empirically, doing this paired learning has allowed the model to learn something that is generalizable across biology. It’s been established by Noetik that the model can distinguish cell type purely from nuclei morphology, infer that certain T-cells co-express certain proteins, and other basic sanity checks — all without being explicitly trained on any of those.

In many ways, you could view this model as a ‘virtual cell’. This concept is not something that Noetik came up with and has, very understandably, become somewhat of a meme — a very expensive way to create something that seems impressive, but seemingly has little practical clinical value. I empathize with this worldview! But I am increasingly coming around to the idea that virtual cells are a useful abstraction for this sort of work. But if the phrase sounds uncomfortable, you could simply view Noetik’s work as creating a ‘foundation model of tumor microenvironments’ as I have. Here’s a fun released demo of Octo-VC!

Still, however you define it, the elephant in the room remains: what is the clinical value of something like this?

On a very basic level, you could ignore the virtual cell aspect of it all and simply use the model as a way to embed information about a patients tumor state. There is good internal evidence to suggest that the latent embedding produced by Octo-VC, given the aforementioned modalities, is more discriminative of who will and will not respond to certain immunotherapies than the current gold-standard (checking if a single biomarker, PD-L1, is elevated in tumors). This laid the foundations for one of Noetik’s first partnerships, announced just two weeks back, which is working on patient stratification for a combo immunotherapy.

But an even more ambitious way to view the system is as a counterfactual engine, allowing one to artificially perturb the proteomic, transcriptomic, or genomic inputs that has been given to model the potential impact of it on every other modality. For example, Noetik studied how increasing the transcriptional value of one gene affects a separate protein. Specifically, they varied the input value of IFNg — a protein secreted by T cells — transcription to observe the associated effect on HLA protein — a protein that a cell uses to display its internal state to immune cells. It is known from the literature that increased levels of IFNg should force increased HLA levels in tumor cells. And indeed, this relationship was mirrored by the model:

They also found that the models inference of the HLA protein itself followed spatial patterns:

There are also hints that OCTO has learned more subtle biology. Small simulated increases in IFNg signaling raise HLA levels at the tumor border but decrease them in the interior – a spatial pattern that we began to notice in many patients after seeing these predictions. This shows how simulation with a world model can reveal structure in the data that would be hard to detect in aggregate analysis.

One hope is that this model is useful for understanding new rules of spatial tumor biology, ones that allow us to learn about new cancer targets, why certain drugs fail, and how to make better ones. The combination of having true, in-vivo data and spatial context of that data may lead to a model that can teach us genuinely novel biology.

Of course, despite this early evidence, it’d be definitely exaggerating to suggest that Octo-VC can perfectly capture distribution of all possible tumor responses. Foundation models are not magic! Having mostly observational data could very well lead the model to predict something is causal when it is only associative. There are ways to get models to better learn the ‘rules’ of the system rather than the ‘outcomes’ of the system — the former of which generalizes beyond the immediate training data — but even fields beyond biology are still figuring out the best ways to do that.

But the utility of models like Octo-VC will almost certainly not in being 100% correct, or even 75% correct, but in offering directional hypotheses where none previously existed. This is something that is very hard to benchmark! Unlike domains like protein folding, where you could assemble together a decent approximation of physical truth, the systems-level dynamics of real tumors in real patients are messy, multivariate, and context-dependent in a way that almost certainly resists clean formalisms. Models like Octo-VC will not generate these clean formalisms, at least not for a very long time.

But it will almost certainly help immunologists ideate far faster than they previously may have, allow clinical trial stratification decisions to be made with more supporting evidence, and strengthen conviction for whether a certain drug should be licensed. We are a long way off from curing cancer, but Noetik is quite close to the ‘metal’ of raw clinical utility than I could’ve appreciated prior to starting. This shows up both in terms of the culture (lots of ex-Genentech people!) and the priorities of ongoing projects.

Today, amongst other things, Noetik is thinking a lot about mechanistic interpretability, better masking strategies, how natural-language LLM’s can help with this work, curriculum learning, writing the largely unwritten rules for spatial tumor biology, understanding where our models hold the most promise for partnering with pharma companies, and deciding which licensable drugs we think we understand ideal patient cohorts for. Lots of things on the roadmap! And I’m very excited to be a part of it. If any of this sounds interesting, again, feel free to reach out to abhishaike.mahajan@noetik.ai or DM me on X!

Finally, I still plan to continue writing independently here at owlposting.com, and I have 4 articles already written + ready to release, along with a podcast I filmed during my recent stay in San Francisco. The future is bright!

1. Introduction

2. Comments

   1. Medical imaging is getting better for diagnosis

   2. Recurrence rate depends on the type of surgery

   3. Appearance of endometriosis doesn’t tie well to symptoms

   4. Does pregnancy cure endometriosis?

   5. Endometriosis is only mildly heritable

   6. A non-invasive, non-hormonal therapeutic that works as well as surgery

   7. Do 10% of women actually have endometriosis?

   8. People are working on better drugs

Introduction

My last article over endometriosis is by far the most popular thing I have ever written, with more likes, comments, and views than basically anything else I’ve published over the past year. This is great! But, as with all commentary over human disease that is shorter than a dozen page review paper written in 10-point font, it missed some things. I bathe myself in a river of compassion for this act, and, as an apology, offer a compilation of eight interesting perspectives/corrections given to me by the very patient readers who went through a 5,000 word article:

Comments

Medical imaging is getting better for diagnosis

I mentioned in the article that clinicians currently rely on laparoscopic surgery as the gold standard diagnosis for endometriosis. I have since learned that non-invasive imaging has improved an awful lot in the past few years, and that surgery is often not necessary for a conclusive diagnosis. A private DM and a HN comment alerted me to this. This said, relying on imaging doesn’t seem to have leaked into common practice, given how many other people seemed to bemoan how awful their diagnostic laparoscopic surgery was. From a 2022 review article:

The previous ESHRE guidelines suggested that laparoscopic histologic confirmation of endometriosis was the gold standard for an endometriosis diagnosis. In contrast, according to the new ESHRE guidelines, laparoscopy is no longer the diagnostic gold standard and is now only advised for patients with negative imaging results and/or when empirical treatment is ineffective or unsuitable. This change was based on a meta-analysis regarding endometriosis diagnostic tools. According to a Cochrane review, imaging modalities such as transvaginal ultrasonography and magnetic resonance imaging showed sensitivity and specificity for diagnosing endometrioma and deep endometriosis comparable to a surgical diagnosis

Recurrence rate depends on the type of surgery

I mentioned that post-surgical remission rates are quite high, with the upper-end being 40%~. But there is some nuance to add here. The typical surgical approach to endometriosis is ablative surgery, e.g. burning away the lesions. One HN comment points out that this is a really dumb idea, for the same reason that it is a dumb idea in cancer: the undesirable cells have likely leaked into the surrounding tissue, beyond the visible lesions. An increasing number of surgeons are now electing to do what is done in cancer, which is to excise the lesion directly with healthy margins around the seemingly healthy tissue. A 2021 retrospective study states that doing this vastly reduces recurrence rates compared to ablation. But, as the comment states, most surgeons still perform ablations because that’s what they’ve been taught.

One interesting note is that the story for excision being better isn’t as clear cut for actual symptoms. A 2017 review paper states that excision reduces symptoms compared to ablation, but a 2022 review paper shows no difference at all. More work to be done!

Appearance of endometriosis doesn’t tie well to symptoms

A comment by a clinician on the r/slatestarcodex subreddit notes that appearance of endometriosis doesn’t tie 1:1 to symptom burden. This is something I was aware of and pondered talking about in the article, but decided not to in the end, because it didn’t feel like there was much I could say about it. Lots of conditions have a mismatch between how bad they look and how bad the patient feels, and the best explanation I’ve heard for that is that pain is weird. Comment given here:

Something the article doesn't touch on, but which is equally intriguing is how severity of the lesions seen during laparoscopic surgery DON'T strictly correspond to symptom severity (though they're correlated of course). I remember seeing one surgery, for completely unrelated gallstones, where the inside of a woman's abdomen was incidentally found to be absolutely speckled with black spots... she was asymptomatic. Another woman we found only 2 possible lesions we could biopsy and no adhesions... she had severe pain.

Does pregnancy cure endometriosis?

A few people have commented that pregnancy seems to magically cure endometriosis, including this comment on Substack. This is something I too offhandedly mentioned in the article, but didn’t really follow up on it. But according to one paper that followed 15 women who had endometriosis and achieved pregnancy via IVF, the theory doesn’t seem to be true. Pregnancy does help with endometriosis symptoms initially, but they do return over time. A more recent, comprehensive review paper also supports this. Because of these results, I don’t think there is something very interesting going on, and chalk the ‘regression’ up to the dramatically increased levels of progesterone (which inhibit endometrial cell proliferation and angiogenesis) that come alongside pregnancy. But once those hormone levels disappear, the endometriosis eventually comes back.

Endometriosis is only mildly heritable

One person on LessWrong noted that I was a little fast-and-loose with saying that endometriosis is ‘strongly’ heritable. Quoting from them:

Lastly I have a very minor nitpick. The Nature paper you linked ostensibly showing very high heritability doesn't actually mention heritability in the abstract. The paper made a genetic predictor for endometriosis which explained 5% of the variance (not particularly high, especially given the sample size they were working with).

It does cite a paper about heritability, but that paper doesn't showing endometriosis as being unusually heritable; it shows 47% of the variance can be explained by additive genetic factors. That's pretty middle-of-the-pack as far as heritability goes. Conditions like Alzheimer's and Schizophrenia are significantly more heritable; roughly 70% and 80% respectively.

Actual heritability of Endometriosis is likely somewhat higher than that because most conditions have some non-additive genetic variance. This paper (somewhat questionably) attributes the entire remainder of the variance to "unique environmental factors".

This is helpful context! There is also at least one study in the literature where the heritability rate of the disease was far, far lower than what most others are reporting — only 2x higher chance to get endometriosis, as opposed to 7x-9x higher, if your mom had it. The same LessWrong commenter notes that the disease is ‘shockingly’ polygenic, which maybe helps explain the relative variance people see.

A non-invasive, non-hormonal therapeutic that works as well as surgery

I had one person on Substack mention that there are validated non-hormonal alternatives to endometriosis management. One that they mentioned is ethanol injection — also called sclerotherapy — is particularly interesting since it can preserve fertility in cases where surgery would threaten it and is minimally invasive. But certainly the downside is that it is less effective than surgery, right? Fascinatingly enough, it seems to work roughly as well as excising surgery according to one (albeit small) randomized control trial. Again, it doesn’t seem to have leaked into common practice as of yet.

Do 10% of women actually have endometriosis?

One person on Bluesky mentioned that 10% of reproductive age women (190 million) having endometriosis seems a bit extreme. I’ll admit, I didn’t look into this number too much and took the World Health Organization’s (WHO) number at face value. But where does this value come from? One 2019 survey of 27,000~ reproductive age women in the US came up with 6.4%, and another 2012 study of 62,000~ reproductive age women in Germany came up with a much lower .81%. There’s lots of other studies too, but the general theme is similar: vastly lower numbers than 10%. What’s going on? Did I and the WHO dramatically overestimate the prevalence of the condition?

Well, one study examined this strange variance and found that the true population count of the disease depends on what we count as ‘having endometriosis’. On the most strict spectrum, if we consider only ‘having an ICD-10 code for endometriosis’ as evidence for the condition, yes, the true rate is very low, just under 1%. But if we instead consider the likely prevalence of the condition given case studies of certain populations, the number expands to 7%. If we also accept likely endometriosis (the symptoms match or it is self-reported), it balloons to 13%.

To me, 10% feels like a decent short-hand number, but I could also buy that it is mildly inflated. In any case, if we go with 10%, it seems that the whole ‘the disease is under-diagnosed’ pitch is already slightly baked into that number, which reduces the impact of the DALYs:Dollars recalculation I made in the original article. Endometriosis still remains one of the most overlooked diseases, but it perhaps may still be far less overlooked than something like COPD. Here’s a helpful graph:

People are working on better drugs

This isn’t something I could’ve known in advance, but several biotech startup founders read the article and DM’d me to say that they are coincidentally currently doing preclinical work on non-hormonal drugs to aid in endometriosis management. Obviously, that work has years to go to wind itself through the FDA trial process, but still, exciting to hear about!

There’s a really interesting phenomenon in the immunotherapy field that has been going on for what seems to be several years now, but was raised to me — a non-oncologist — via a viral Twitter thread of some work at ASCO25:

Translating the jargon: amongst the patients who received their immunotherapy infusion before 3pm (as opposed to after 3pm), their cancer stayed under control for longer (11.3 months vs. 5.7 months) and on median lived longer (at least1 23.2 months versus 16.4 months). A near 2x~ improvement in the most important metrics doing something that is entirely risk-free and cost-free.

These two images shown in the comments of the post also demonstrate genuine changes in levels of circulating T-cells between the two groups:

Important context: the current standard of care for immunotherapy is not designed with timing in mind. You come in to get the injection when convenient for you or when there are free spots, there is no official recommendation to get it in the morning. But this study implies that we should potentially update our guidelines.

Weird, right? And if you have my relatively naive instincts, obviously wrong. Something must have been off in the study. After all, wasn’t there that one paper about how time-a-lab-test-is-taken is more predictive of patient survival than the test results themselves? The punchline? Sicker patients have strangely-timed emergency lab orders at 2AM, healthy patients have routine morning blood draws. Timing is hard to rely on!

But this paper was not a retrospective study of electronic health records, it was a randomized clinical trial, which is the gold standard. This means that we’ll be forced to immediately throw away our list of other obvious complaints against this paper. Yes, healthier patients may come in the morning more often, but randomization fixes that. Yes, patients with better support systems may come in the morning more often, but randomization fixes that. Yes, maybe morning nurses are fresher and more alert, but…well, randomization doesn’t fix evening nurse performance (which does dip during the night!), but I am inclined to believe the errors aren’t so high there as to cause this magnitude of a survival shift.

Okay. Well. Maybe there is something here. Caveats on this of course being a conference presentation without a corresponding, longer peer-reviewed paper, so we lack a lot of exact details on what exactly went on. Maybe the randomization used here is off for some reason, we’ll see once an official paper comes out.

But perhaps we should look beyond just this research. As it turns out, there is an astonishing amount of pre-existing literature on the immense benefits in giving patients immunotherapy earlier in the day, also known as ‘immunochronotherapy’. The exact time varies, but anytime before the evening seems to be good. Here’s one study that found, again, a massive improvement when giving immunotherapy before 11:30AM for advanced non-small cell lung cancer. And again for esophageal cancer, before 1pm. And again for melanoma, before 4:30.

All of this culminated in a really incredible review paper that is really worth reading. It walked through 18 retrospective studies covering 3,250 patients, each of which studied the impact of immunotherapy injection time on patient outcome. And, once you compile them all together, there is a very dependable story being told across multiple types of cancer.

TLDR: early-in-the-day immunotherapy administration consistently leads to massive improvements in survival time, matching up quite well with the 2x results from the original Twitter post.

Keep in mind, these results have not only been shown for short-lived small molecules, but also long-lived proteins with half-lives on order of weeks that shouldn’t be affected by 24-hour cycles: pembrolizumab (Keytruda), nivolumab (Opdivo), and ipilimumab (Yervoy). Now, skepticism here would be justified given that these are all retrospective studies, and it’d be very easy for these to be confounded. But this evidence combined with the extremely similar results from the randomized clinical trial done I showed at the start of this essay should lead us towards at least suspecting that this is an honest-to-god free lunch.

What’s going on? Where is this coming from?

First, it’s worth reminding ourselves that the human body — and perhaps most complex life on Earth — exists on a schedule: the circadian rhythm. There exist 15-or-so ‘clock’ genes, like BMAL1, CLOCK, PER, and CRY, that oscillate with a rhythm. Not in structure or conformation, but in expression; the amount of them present in cells rises and falls over the course of the day. BMAL1 and CLOCK form a complex that drives the expression of PER and CRY. Once PER and CRY accumulate to a certain threshold, they feed back to inhibit BMAL1 and CLOCK, suppressing their own production. Over time, PER and CRY degrade, releasing the inhibition, and the cycle begins again.

One full loop takes just about 24~ hours, though there is some degree of individual variation.

So our cells have evolved to take advantage of these genes as an internal timestamp, a marker of where we are in the circadian rhythm. Some things occur early in the cycle, some things occur later, purely as a matter of convenience.2

What’s the point of the cycle? One way to understand them is through an evolutionary lens, a way for the body to prepare for dependable environment cues.

For example, at the start of our circadian rhythm, we wake up. We crawl out of our safe cocoon — a private bed in modernity, or a predator-sheltered hole in ancient history — and start to engage in very risky behavior, immunologically speaking. Eating leftover food that may be contaminated, being scrapped by bacteria-covered rocks, holding dead animals to roast for dinner, and so on. But, as night comes, we retreat back to our private beds or holes, feasting on freshly cooked food, few interactions with unknown creatures, and little chance for injury as we wind down.

To anthropomorphize for a minute, millennia of evolution likely recognized this phenomena, and also noted that loading up an immune response is an unfortunately long process. A dendritic cell floating in the blood stream must first recognize + grab onto an antigen, then it needs to crawl into the lymphatic system, and then it hopes to bump into the few naive T-cell that recognizes that specific antigen. Then the adaptive immune response can kick off.

How could evolution optimize this process?

Well…if you didn’t have any priors on when new antigens would come through the door, you wouldn’t care when T cells decided to exit/enter the lymphatic system. When they exit, they are moving to new tissue. When they enter, they are actively looking for dendritic cells to bind to. Perfectly fine to do this randomly in the null case of uniform antigen exposure.

But! If you believe that antigen load is highest in the morning (which is something you can track via the clock genes), it would be smart to ensure that the lymphatic system is bloated with lymphocytes in the morning, removing their ability to migrate into the bloodstream. And according to one paper, that does empirically turn out to be the case in mouse models! Here’s a particularly useful graph:

The authors characterize the lymphocytes (T cells and B cells) of mice and find that it is highest during the resting period for mice (ZT 1-9), meaning that they are currently migrating throughout the body. Once the mice get closer to awakening (ZT10), circulating lymphocytes sharply drop, implying that they have moved themselves into the lymphatic system, awaiting for the morning antigens to arrive. Finally, the authors demonstrate that this entire process entirely depends on clock genes. If they are genetically edited out, bloodstream lymphocytes stay constant.

But this is just one immune-circadian tweak that evolution has made. Are there others?

How about prime T-cells such that they are more ‘willing’ to be activated by antigen-presenting dendritic cells at the start of the circadian rhythm? That exists. Perhaps improve the capacity for dendritic cells to migrate into the lymphatic system during points of low antigen exposure/during rest phases of the rhythm? That exists as well. Could we even allow the lymphatic system itself to become more permissible to entry? Technically, this was also a result from the prior paper, so this too exists. Maybe tilt the bodies hormonal signals such that such that immunosuppressive ones are minimized just before expected antigen exposure? Also exists!

Now, what is immunotherapy doing? In the common case of immune checkpoint blockades, it is simply allowing the immune system to more easily attack the cancer, since cancer typically chemically dampens their ability to do so. That’s all it does. It doesn’t provide new antigens, it doesn’t create new T-cell receptors, it doesn’t summon dendritic cells. Which means the effectiveness of that green light depends entirely on what the immune system is already doing at that moment.

Thus, we can propose a decent argument as to why immunotherapies seem to work best during the start of a circadian rhythm. The immune system, by evolutionary coincidence, is simply most prepared to begin their assault during that time.

But you may balk at this and say, “That would only make sense if immune checkpoint blockades had an extremely short half life that fit into this primed immune system period, but they don’t. To take an example, Pembrolizumab (Keytruda) has an extremely long half life of 27 days, is dosed every 3 weeks, and reaches steady-state blood levels at 19 weeks. How could it possibly be affected by initial infusion time?”.

Well, you’ve got me there! I do not have a clear, consistent answer to that question. And, as far as I can tell, neither does anybody else. But let’s take a stab at it.

Let’s pretend you have very early-stage cancer. The dendritic cells are in their normal cycle of desperately presenting tumor fragments to T cells, the T-cells rightfully getting upset, activating themselves, and going off to hunt the cancer. But cancer simply shuts them down by expressing an immune blocker protein: PD-L1. In response, the T-cell mostly shuts down, wanders back to the lymphatic system, and gets a little bit more ‘exhausted’. It believes that it activated itself for no reason, and thus will require a much higher bar for doing anything else in the future. The more times this occurs, the more exhausted the T-cell becomes, the more unwilling to ever activate again. In the limit, it will simply kill itself. Hence why you need immunotherapy to revitalize these cells!

Now let’s assume you received the immune checkpoint blocker Pembrolizumab at one of the best possible times: 7:30am in the morning, when the most T-cells are in your lymphatic system. Those get activated by the dendritic cells and are now finally able to attack the cancer, the checkpoint blocker preventing them from shutting down. Cancer is being killed! What advantages are you potentially privy to now as a result of the morning dose?

Of course all the ones we talked about earlier:

• A greater number of T-cells are in the lymphatic system, so more opportunity to prevent exhaustion.

• Dendritic cells are more aggressively collecting cancer antigens, so more opportunity for T cells to be activated.

• The lymphatic system is more permissible to dendritic cell entry, allowing more interactions between dendritic cells and T-cells.

And so on.

But, all of this would also eventually happen if you had an evening injection. If we squint, the only downside an evening injection would have is that the highest concentration of the drug (at the moment of injection) does not have access to all of these advantages. But given the clearance rate of Pembrolizumab, only 1.25% of it would have dropped in the 12 hours from the evening injection → following morning. So the morning injection upside entirely stands on this +1.25% drug concentration bump. Either we are missing something, or the sum total of the initial 12-hour-long immune advantages are so high that +1.25% is extremely significant.

Perhaps the second take is genuinely true and answers the story entirely. Lots of immunologically useful things are going on in the morning, each contributing a little bit. As is often the case in biology, there is no singular causal factor for why early-morning immunotherapy seems to help so much, just many small things.

But let’s veer off into speculation. Maybe we are missing something?

Perhaps we’re being overoptimistic on this idea of ‘steady state circulating antibodies’ being useful for T-cell activation. Maybe the first immediate dose of immunotherapy is the primary part that functionally matters for further T-cell activation. This idea was put forwards, albeit only theoretically, in a graphic from this paper.

Which is to say: each wave of activation of T-cells may set as a soft ‘ceiling’ of maximum immune response, even if the drug continues to circulate. So you’d ideally want the first ceiling to be as high as possible, which implies that a morning injection would be best! Is this true? Well, we do know that the clinical impact of the first immunotherapy injection is strongly tied to long term outcomes, and, accordingly, the timing of that first immunotherapy injection seems to matter the most. This same latter paper also says this:

…it appears that challenging the immune system with an antibody at a specific time of day not only changes the quantity but also the quality of the response so that the immune system, once stimulated at the “wrong” time, may not be able to respond anymore to the same level and quality as an immune system challenged at the “right” time—just 12 h apart.

Hence, why we should suspect that there is something fundamentally special about the first wave of activation of T-cells.

Of course, many questions follow from this. What is the temporal “window of imprintability” for T cells? Does that imply that early-activated T-cell clones dominate the final pool of T-cells? And what would mechanistically cause all of this? I don’t have the answer to any of these, and I suspect nobody does.

But again, maybe this is the wrong idea entirely, and there is no singular causal factor for these impressive time-of-day results. Maybe it is, once again, a bunch of small things — increased T-cell activation, but also stronger dendritic cell function and increased lymphatic vessel permissibility and many others — adding up to a strong signal.

For what it’s worth, we do know that this ‘early morning immunotherapy is useful’ phenomenon are also important for infectious disease vaccines, so it feels unlikely that this whole observation is entirely spurious. But vaccines mostly contain short-lived antigens and one-shot adjuvant signals, meaning they rely heavily on getting the initial priming window exactly right. That’s not the case for immunotherapy, so I suspect the benefits of morning injections in that context arise from a different mechanism—one that’s distinct from what makes morning timing valuable for vaccines.

We’ll see what the future holds. The phase 3 trial page that we talked about at the start is still ongoing and is currently the only randomized test of chronoimmunotherapy. But one more is getting kicking off for melanoma and there are calls for more to be run. Incredibly interesting subject, please reach out to me if you have any interesting light to shed here!

Edit on 08/06/2024

At least one person has mentioned that chronotherapy was also thought to matter for blood pressure medication, with rather convincing large retrospective studies, specifically the HYGIA trial. There was some mild mechanistic reason to suggest that circadian variation in sympathetic tone and cortisol levels could influence blood pressure regulation; going up when you sleep, potentially leading to more cardiac events. Thus, bedtime dosing of antihypertensives may prevent the ‘potentially harmful territory’ spike.

But multiple follow-up randomized studies, such as this and this largely disproved the whole concept. Given that, shouldn’t we be on guard for chronotherapy working in immunotherapy?

Well, yes! We should be on guard for everything, especially since our only major piece of evidence is a as-of-yet incomplete trial. But I’m personally erring on the side of the connection between the immune system and the circadian rhythm being much stronger than it is for other physiological functions, just given how large the lymphocyte concentrations in the bloodstream can shift from night to day. I’m also betting a little on the first wave of T-cell activation being particularly important, for reasons that are still not understood. Very open to being completely wrong though!

On a bigger note, someone else mentioned that the clinical trial methodology shifted midway across the 2 years of the study. I asked OpenAI’s Operator to create a table of the biggest changes made:

Which matches up with what the original poster says:

Which is concerning! And perhaps reason to discount the study entirely, mostly for the switch from interventional → observation → interventional. What’s up with that? The fact that it isn’t mentioned in the abstract either is also insane!

But I do consider the timing switches to be only mildly weird. Nobody has really figured out what is an optimal ‘early’ infusion, cut-off times can vary by 4-5 hours. Sure, they shouldn’t have amended it and stuck to one cut-off throughout, but the headline results seem strong enough that I’m not immediately worrying about them gradient-descending their way to a statistically significant result.

1. Introduction

2. The dismal state of snake antivenom production

3. A primer on snake venom heterogeneity

4. A primer on snake antivenom

5. Do computationally designed antivenoms actually solve anything?

6. An addendum: the NYT article over universal antivenoms

(Note: In case you were at ICLR and missed this, I did an interview with Sergey Ovchinnikov (of protein design fame) last week!)

Another note: Just yesterday, the same day this article was released, the New York Times put this out: Universal Antivenom May Grow Out of Man Who Let Snakes Bite Him 200 Times. I was scooped! Somewhat. I added an addendum section discussing this paper at the bottom.

Introduction

There has been a fair bit of discussion over this recent ‘creating binders against snake venom protein’ paper from the Baker Lab that came out earlier this year, including this article from Derek Lowe.

For a quick recap of the paper: the authors use RFDiffusion (a computational tool for generating proteins from scratch) to design proteins that bind to neurotoxic protein found in snake venom, preventing it from interacting with the body. They offer structural characterization results to show binding between their created protein binder and the protein in question (three-finger toxins), and in-vivo results in mice demonstrating that their created protein confers protection against the designed protein as proof.

It’s excellent work, and potentially one of the first times that RFDiffusion has been used in such a straightforward way for a clearly useful problem (though others disagree with me on this being the first time). But, there’s a few obvious questions here: why computationally design binders against venoms if we already have antivenoms available on the market? What further work remains to be done to create binders against any arbitrary venom? And when can we expect that?

I asked these questions, fell down a surprisingly rabbit hole, and decided to compile the results together to release as an article. This is the result.

Let’s get started!

The dismal state of antivenom production

Vaccines are the prototypical ‘clearly good for humanity, but terrible profit incentives’ drug. You spend billions on R&D and clinical trials, give someone a shot once, and then they’ll never need the drug again (or need it once every decade). As such, you end up with these insane cases of promising vaccines being left by the wayside for decades due to economic reasons (see the malaria vaccine debacle), with millions of lives being lost in the process.

In many ways, antivenom production mirrors the problem of vaccines, but on a smaller scale.

But you needn’t take my word for it! As it happens, the Works in Progress magazine has already published a lengthy essay on how horrifically bad the state of antivenom manufacturing is. As such, instead of taxing my own brain, I will rely on quotes from their very well written piece to cover the main points.

On how many people are affected by snake bites:

Venomous snakebites kill between 81,000 and 138,000 people each year, and leave another 400,000 with permanent disabilities. This ranks it among the deadliest of neglected tropical diseases, alongside better-known ailments such as typhus and cholera.

On the cost of antivenoms:

The average cost of antivenom alone in sub-Saharan Africa was $124 in 2010, a steep price to pay for the 40 percent of sub-Saharan Africans who live on less than $1.90 per day. And that doesn’t even include the cost of the care or facilities. A survey of 129 Bangladeshi snakebite victims found that over 40 percent had to take out loans to meet the costs.

On the state of the antivenom shortage:

Need for antivenom is increasing each year, yet the supply of quality antivenom fails to keep up. A 2011 study found six companies producing antivenom sold in sub-Saharan Africa. These companies produced 410,500 ampoules of antivenom, just enough to treat 96,000 cases. That’s only one third of the total estimated cases on the continent….The global antivenom market in 2016 was valued at $1.1 billion and was projected to reach $1.5 billion by 2021. It instead fell to $1.02 Billion.

Why there is a shortage at all:

The mess of poorly tested, mislabeled, and at times outright fraudulent products has resulted in the current predicament: a market for lemons. Confidence in antivenom treatment has fallen…..Even if antivenom production at high throughputs and low prices would be profitable over a longer time horizon, producers have been unwilling to make the large up-front investments necessary to achieve that scale, in no small part due to uncertainty over whether governments and clinics would follow through on promises to buy at those prices. Governments and clinics, on the other hand, have become wary of making large purchase orders after being burned one too many times on promises of cheap antivenom.

So, with the thesis of why we’d want better antivenom cleared out, we can continue. Before we start discussing how antivenom may be improved, we should first understand venom.

A primer on snake venom heterogeneity

Keep in mind, snake venom is not just one thing, but rather an umbrella term for a cocktail of proteins (and small molecules, though these are less important). The therapeutic issue here is that the composition of the toxin can vary at four increasing levels of taxonomic specificity:

• Between snake families (obvious)

• Between snake species (obvious)

• Between snakes of the same species (less obvious)

• Within the same snake over time (perplexing)

Let’s go in order.

At the familial level, venom composition heterogeneity is the strongest. There are around twenty snake families in total, but only four are known to be venomous: Viperidae (vipers), Elapidae (elapids), Atractaspididae, and Colubridae. Of these, the latter two are typically left out in discussions of snake bites, as they are either quite shy, have relatively innocuous venom, or physically are unable to bite humans due to the location of their fangs (e.g. rear-fanged snakes). Because of this, we’ll focus on vipers and elapids. These two possess the strongest differences in venom composition.

1. Elapids venoms contain three-finger toxins (3FTxs, which is the one discussed in the Baker Lab paper), Phospholipase A2 enzymes (PLA2), and Kunitz-type serine protease inhibitors. These toxins are primarily neurotoxic.

2. Viper venoms are predominantly composed of enzymatic proteins such as snake venom metalloproteinases (SVMPs), snake venom serine proteases (SVSPs), and L-amino acid oxidases (LAAOs). These enzymes primarily cause tissue destruction, hemorrhage, and coagulopathy.

So, obviously, entirely different classes of proteins, thus entirely different needed therapeutics. Simple, let’s keep two types of antivenom, one for each family.

But, while the broader strokes of proteins are different between snake families, there continue to be major deviations amongst species of the same family; the primary example being toxin compositions that are entirely unique to singular species. Naja nigricollis (Black-necked spitting cobra) venom uniquely contains cytotoxins in addition to neurotoxins, though, admittedly, the cytotoxins themselves aren’t especially dangerous unless injected near the eyes. On a more fatal note, Dendroaspis polylepis (Black mamba) venom has an especially high concentration of dendrotoxins, which are neurotoxic enough to cause death in an hour after a bite. But there also exist cases of extraordinary compositional heterogeneity amongst snake species venoms; no unique proteins, but unique clinical impacts. The prototypical case here is Daboia russelii (Russell’s viper), which uniquely produces a highly procoagulant venom despite lacking wholly unique proteins, leading to paradoxical blood clotting, kidney failure, and eventually death.

Okay. Two antivenoms for every snake bite was a pipe dream, perhaps we’ll need to scale things up. Perhaps an antivenom for every venomous snake species? There’s only 600~ of them. That’s not so bad.

But, once again, there’s another level of heterogeneity. Snake venom composition can vary not only amongst species, but within a species too. Famously, the Crotalus scutulatus (Mojave rattlesnake) has populations that produce completely different types of venom. Some populations produce venom with presynaptic neurotoxins (type A), while others lack it entirely and instead produce venom rich in hemorrhagic metalloproteinases and serine proteinases (type B), — both of which require completely different antivenoms. And, sometimes, there are snakes that possess both types of these venoms. Most worrying of all is that the geographic variation of all of these venom types is decently intermixed; sometimes separated by cities but sometimes separated by only a few miles. A nearly identical phenomenon is noted in Crotalus horridus (Timber rattlesnake).

Okay. Okay okay okay. We'll need different antivenoms for different populations of the same species. This does appear to be an edge case…so maybe it doesn’t bump up the number of antivenoms we need to keep in stock much. Is that it?

One last bit. Even species + region specificity isn't enough, because venom composition can vary within a single snake over its lifetime.

A dramatic example of this is in the family Pseudonaj (Australian brown snakes), which have non-coagulopathic venom as juveniles, but switch to coagulopathic venom as adults. Other than for one specific species, which continue to have non-coagulopathic venom as adults! Why does this occur? Often referred to as venom ontogenetic shift, it is likely driven diet-based selection pressures. And these venom shifts aren’t sudden snap changes in proteomic venom composition of their venom as they age, but rather a gradual shift over time. Because of this, there will be no ‘young snake’ antivenom and ‘old snake’ antivenom, since it’s all a spectrum.

Luckily, that’s the last layer of venom complexity.

Given all this, one may start to view venom as an almost...cancer-y phenomenon. It's all so heterogeneous, in the sense that what works for one case may be completely ineffective for another, even when they appear superficially similar. Even the theoretical framework for why such venom differences even exist is fuzzy. Like, we can point to diet-based selection pressures for ontogenetic shifts, but even that doesn't fully explain the geographic variation we see in species like the Mojave rattlesnake we mentioned earlier.

But unlike cancer we, at least theoretically, have a real solution for it: anti-venom. Antivenom that, admittedly, must account for the venom heterogeneity specific to the snake type, species, region, and age of the snake. But a solution nonetheless. Speaking of, that’s our next topic.

But, before we move on, it’s worth noting that this section has primarily dealt with the proteomic composition of venom. What of the small molecules in them? There is unfortunately relatively little information on them. We know they exist, one review paper specifically says ‘salts, amino acids, hormones, nucleosides, neurotransmitters, and polyamines’ have been found in venoms. But it is, from literature review alone, unclear what exactly these do. Given that off-the-shelf antivenoms seem to largely ignore the existence of these, I’m assuming the small molecules are largely there to amplify or stabilize the proteomic elements of the venom. As such, I too will ignore them in the following section.

A primer on snake antivenom

At a base level, antivenoms are relatively simple. As with nearly everything [bad] floating around in our body, the best way to deal with venoms is to simply prevent it from interacting it with anything. In the case of viruses, bacteria, and even cancer, one way the body goes about doing that is via antibodies (something I’ve written at length about). To create an antivenom involves doing the exact same thing. Simply produce en-masse antibodies that bind to the proteins that the snake bit you with, preventing it from interacting with anything else in your body, and you’re cured!

This may raise an immediate question: is the antivenom produced by one snake only applicable to that exact species? How much of an issue is the venom heterogeneity problem we’ve spent so much time talking about?

Well...it’s complicated. In the absolute ideal case, if you want to ensure that no case of snake envenomation goes untreated, you need to have snakes species that matches every single possible venom composition (the count of which is understudied) and are willing to consistently produce venom (a notorious problem with some snakes, such as coral snakes). Due to this, most antivenoms have, almost by necessity, become hyperspecialized. One review paper has this insane statement: For example, India has more than 60 venomous species of snake, and there is no specific antivenom against most of them. If you are bitten by a snake that is not one of the ‘big four’ (spectacled cobra, common krait, saw-scaled viper and Russell’s viper), the antivenom will be largely ineffective.

So how many venoms do we need to produce an effective antivenom against them? Well, while I did say there are 600 species of venomous snakes worldwide, only 200 produce venom that’d be dangerous to a human. But remember, we have the complexity of regional and ontogenetic differences to deal with. And, unfortunately, it is extremely unclear from the literature how many extra antivenoms that these two extra layers of complexity would demand.

But perhaps we’re going down the wrong route entirely. Instead of trying to come up with a universal list of all possible venom compositions, we could perhaps reframe the problem. Instead of caring about the universe of venoms, we should care about the universe of proteins contained within all possible venoms.

Clinically speaking, this means moving away from thinking about monovalent antivenoms — antivenoms derived from a single species — and instead relying on polyvalent antivenoms — multiple different antivenoms that have been combined together. This way, we needn’t need keep 200 or so different vials of antivenom together, but group them together into a few dozen combinations. If you’re bitten by a snake in Australia (and don’t die in the next 15 minutes), we needn’t try to identify the exact species that bit you and provide that specific antivenom.

Instead, you’ll simply be given the antivenom derived from all snakes that live in Australia. Or at least, at least some of the major snake species, with the assumption that there will be sufficient cross-neutralization (also called (paraspecific neutralization) for snake venom that we lack.

And, practically speaking, this is exactly what is done. At least in Australia, where there is a polyvalent antivenom licensed to treat envenomation by any of the major Australian snakes (taipans, brown snakes, tiger snakes, death adders, black snakes, many of whom share toxic proteins). Of course, there isn’t a free lunch here; polyvalent antivenoms must be administrated at relatively heavy doses, since they are diluted across multiple venom targets. This means more antibodies floating around, increasing the likelihood of immune reactions such as serum sickness or anaphylaxis. The more complex the mix, the greater the risk. Additionally, because you aren’t actually covering all snake species, neutralization efficiency for at least some snake species might be nonexistent.

But let’s ignore the nuances here and assume a few sets of polyvalent venom are all we need, which dramatically simplifies the extreme levels of toxicity heterogeneity we just discussed.

How exactly do we get those antibodies? Especially given the extreme heterogeneity we’ve seen in snake venom? Once again, I’ve been somewhat scooped here by another (very recent) Works in Progress essay, who has this to say about the traditional process of producing antivenoms:

In the case of molecular complexity, consider antivenoms. The method to make antivenom has not changed much over the last 100 years. Venom is first milked from snakes and then injected into horses or sheep, which then produce multiple different antibodies and antibody fragments that bind to the venom….Blood is collected from these animals, the antibodies are purified from the plasma, and they are then given to people bitten by venomous snakes.

Why horse and sheep for antibody production, and not E. coli or yeast or mammalian cells? Mostly size. Horses and sheep are big animals that can produce large volumes of blood (and thus antibodies) compared to microbial systems, which are often more expensive than they are worth given the terrible economics of antivenoms. Plus, their immune systems are reasonably similar to ours, which means the antibodies they produce are more likely to work in humans without causing severe immune reactions.

But could we produce antivenoms through microbial systems? This would be a necessity if we wanted arbitrarily designed antibodies, instead of whatever an animal naturally produces in response to an injected venom. We’ll discuss this a bit more in the next section.

Sure! Plenty of startups have spun up to do it. The basic pitch of it is to simply go through the usual horse/sheep production process, find the antibodies that are made in response to the venoms, and get an E. coli cell to produce the same thing via genetic engineering. And so we got companies like Venomyx, founded in 2015, and VenomAb, founded in 2016.

How are they doing? Well…both are out of business. It is unclear why Venomyx shut down, but for VenomAb the reason for the shuttering was, as could be guessed, that delivering a return to investors would be nearly impossible. No technological infeasibility, just hard to make money off of.

At this point, you should understand three things:

1. Why there is an antivenom shortage

2. The heterogeneity of venoms

3. The associated heterogeneity + production difficulties of antivenoms

Armed with this context, we can finally answer the question: will protein design models help with the antivenom shortage problem?

Do computationally designed antivenoms actually solve anything?

In the two articles discussing the original Baker lab paper, both touch on how this work alone doesn’t solve the problem of snake venom. Why?, you may ask, don’t the in-vivo results show that they do?. Well, importantly, the in-vivo results aren’t actually designed protein versus snake venom, it is designed protein versus three-finger toxin. Derek Lowe specifically says:

These are still not going to snakebite cures by themselves, though, it has to be noted. There are other classes of nasty ingredients in snake venoms, with phospholipase enzymes near the top of the list.

But, really, that’s a minor point. Honestly, we’re nearing a future of having binders-on-demand for any arbitrary target through computational methods. Some would say we are already there, so having to deal with a few extra enzymes shouldn’t be a big issue.

Moreover, for how much we’ve gabbed about venom heterogeneity, we’ve also discussed that it may very well be that the whole problem doesn’t matter, and cross-reactive/polyvalent antivenoms save the day. Australian antivenoms were one case of this, but it could be taken way, way further. Just in 2024, researchers screened fifty billion antibodies to find one that bound to all three-finger toxins found in elapids, many of which are structurally quite diverse. Will we be able to find similarly universal antibodies for the metalloproteinases in viper venom? For the phospholipases? And even if we do, can we get all of them to work together in a single cocktail that neutralizes all venom types without introducing new safety issues?

Honestly, probably. It feels like a decent bet that similar ‘universalities’ will be found across the toxin family — there is necessarily not a ton of evolutionary divergence amongst venoms.

In time, methods like this may lead to a singular, extremely cross-reactive antivenom. One that contains only one, two, or a similarly small number of unique antibodies, but still manage to simultaneously neutralize the venoms of differing classes, species, regions, and ages of snake. No need to identify a snake to administer the correct antivenom anymore — if you manage to survive the trip to the hospital, there will be a cure waiting for you. We may very well live in the universe where these sorts of ‘holy grail’ antivenoms never actually comes to fruition, but I feel like theres decent enough reason to believe it is possible, so I will presume their existence for the rest of this essay.

So, am I saying that computationally designed antivenoms will herald the end of the antivenom shortage? Well, let’s say we have this theoretical universal antivenom, granted to us by our protein design overlords. Or maybe through phage display. Or maybe phage display combined with protein design methods to further optimize them, whatever.

Here’s one pessimistic future: remember, antivenoms remain an awful market to work in, even independent of how hard they are to make. At least vaccines have the economic benefit of first-world children needing them, often supported by wealthy governments or global initiatives providing guaranteed demand and thus ensuring profitability. Antivenoms lack this advantage: they predominantly affect low-income populations in developing countries, regions with limited purchasing power, inconsistent healthcare infrastructure, and unpredictable demand. No wealthy parent or government is routinely lining up to buy antivenom ahead of time, leading to the economic catch-22 we've already outlined.

It…doesn’t feel like this changes dramatically even if we have a perfect universal antivenom. The market does expand a bit, people may be more willing to trust and seek hospital treatments, and governments may have a clearer incentive to maintain consistent supplies. But fundamentally, one is still serving populations with very limited purchasing power and unpredictable demand. A perfect computationally-designed antivenom doesn’t guarantee market incentives. It simply makes a previously terrible market slightly less terrible.

In this world, protein design models change little about the status quo. At best, the thermostable properties of our hallucinated antivenoms — a phenomenon seen in both the Baker lab paper and also a weirdly consistent characteristic of many AI-generated proteins — makes the cold supply chain less of a bottleneck. That’s not nothing! But it may not be enough.

Yet…there’s also a more optimistic side, one that the first WiP essay we discussed alluded to. Quoting from there:

If antivenom producers can be made confident that large investments will pay off, they are more than happy to make them. If governments are confident the antivenom they receive actually works, they are more inclined to buy it. When people start returning from the hospital healthy, happy, and with their wallets intact they are likely to go again and recommend their loved ones do too. In the Bangladeshian survey, most victims and their relatives expressed they would be willing to discontinue traditional healing practices if hospital treatments were easily available.

If we’re to trust the Bangladeshian survey, universal antivenoms, stemming from protein design models, may be a panacea to the whole economic problem faced by antivenoms. Even if antivenoms are a bad market, a sufficiently good product may be enough to conquer the major issues.

But, remember, universal antivenom could only be produced via cellular production methods. Why? Because there is, as far as I can tell, no known methods to easily elicit arbitrary antibodies (as in, the universal ones we want) from animals, you need to genetically modify cells to do it. And non-animal antivenom production startups haven’t been particularly successful!

But perhaps history won’t repeat. The improved market opportunity may very well push pharmaceutical companies to reconsider cell-based production methods, since a trustworthy antivenom would skyrocket demand. And without the burden of animal management, venom procurement, and purification processes, this would push production costs down even more, making investment here further attractive.

In such a world, the antivenom shortage problem would be solved outright, becoming a very tolerable expense for anyone unlucky enough to be bit by a deadly snake. No antivenom company will earn tens of billions working here, but will likely comfortably recoup their investments and achieve modest returns. A sustainable market attractive to both public and private investors. And likely also one that is trusted as an area for non-profits/governments to sponsor.

Which world do we live in?

I’m unsure.

It feels like protein design models really only solve the first bit, the creation of near-universal antivenoms. While intellectually interesting, the utility of these antivenoms would rely on cell-based production of those antivenoms, which is a tall order. Not because it’s a hard problem; in many ways, it’s likely solved, given that we have mass-produced antibody drugs available on the market produced in cell-based settings. But, rather, because it requires a huge amount of upfront capital and engineering work.

And antivenoms don’t seem to historically be a place where many people are willing to make that investment. But, given a sufficiently good protein design model that produces a good-enough universal antivenom, maybe that’ll change. Who knows?

An addendum: the NYT article over universal antivenoms

As I mentioned earlier, the same day I put out this essay, another antivenom-related article came out: Universal Antivenom May Grow Out of Man Who Let Snakes Bite Him 200 Times. Sad that I didn’t release this article far earlier! But there is a silver lining here: I can spend some time walking through the paper that came from this research, what it means, and answer some likely follow-up questions.

Quick background context: a man purposefully gets himself bitten by various snakes over 18 years. A scientist discovers this, realizes he may have acquired broadly neutralizing antibodies, and contacts him. He is right: he does indeed show astonishingly broadly protective effects against many venoms from the elapid class of snakes. From this set of ‘parent’ antibodies, they are mutated via phage display to come up with two ‘universal antibodies’. One named LNX-D09 and the other named SNX-B03. Importantly, neither of these are ‘natural’ antibodies, but are rather derived from the pre-existing set of antibodies found in the ‘hyperimmune’ man.

A curious note: the resulting Cell paper that came out of this cited the same antibody screening paper I cited in the last section (which went through 50 billion antibodies to find one that bound to all 3-finger toxins, or 3FTx, in elapid venom) and found remarkable sequence similarity between LNX-D09 and the one found via in-vitro screening, or 95Mat5:

“Comparing LNX-D09 to other anti-LNX antibodies revealed remarkable sequence convergence with 95Mat5, a broadly reactive antibody recently discovered from a synthetic yeast library. Both LNX-D09 and 95Mat5 share an identical and unusually long CDR-H3 length (95.4th percentile), three identical residues in a critical LNX-binding motif, and nearly identical light-chain sequences.”.

They claim that LNX-D09 shows ‘greater breadth in protection’ compared to 95Mat5, but there doesn’t seem to be proof of this, only that there is tighter binding between the designed antibody and a specific venom. That said, while 95Mat5 is hyperspecific to 3FTx, LNX-D09 seems to confer very modest protection against other toxic proteins, like conferring protection to 1 out of 5 animals injected with a PLA2-dominant toxin. Low, but still!

So what’s the purpose of SNX-B03? One curious note about 3FTx’s, they come in two forms: short-chain and long-chain, each of which bind to the same receptor (nAChR), but in different spots. And, remember, antibodies often work by replicating whatever its [target] usually binds to! SNX-B03 replicates the binding interface of where short-chain 3FTx’s bind, while LNX-D09 replicates that of the long-chain.

In the end, the combination of these two drugs (along with a PLA2 specific antivenom) offered protection to an insane number of snake species. Not all! But a shocking chunk.

Some of the failure cases are curious. Why is efficacy so low for Russian cobra’s, which predominantly have 3FTx’s in their venom? The successes are also curious! For example, how does the LNX antibody alone offer protection against black mamba venom? Which have 3FTx’s, but also, as we discussed, a completely structurally independent + highly lethal protein: dendrotoxin.

Of course, caveats that this is in mice and not larger animals, which can dramatically alter the pharmokinetics of an antibody-based drug. But still! Really incredible work, and a very fun paper to read.

But I should still iterate: achieving a universal antivenom is only one part of the challenge. The far bigger problem is the overwhelming majority of people who need this innovation are also people who cannot easily pay for it, which also compounds the problem of needing in-vitro production systems. It feels likely that universal antivenoms are at the early innings of the malaria vaccine: signs of efficacy, but still a decades-long slog to actually get it out there.

One last thing before we end this.

There’s a really, really obvious question that a few people have already asked. Why can’t we simply repeat this in animals? Fill a horse or sheep with a ton of different venoms and grab the broadly neutralizing antibodies? Why did we need this exceptionally strange ‘hyperimmune’ person who had been bitten 800 times over 18 years to create this paper?

As it turns out, we’ve done this exact thing in animals before! In fact, in 2020, there was a Scientific Reports paper that had arguably even better results than this human one. Horses were immunized using a "diverse toxin repertoire”, a carefully curated set of 12 neurotoxic venom fractions from multiple species. The result was a pan-specific antiserum that neutralized 36 different elapid venoms from 10 genera across 4 continents.

So, what’s the point of this hyperimmune Cell paper?

The important distinction here is that the antivenom derived from the horses was a heterogenous slurry of many different antibodies, all of which were natural. This isn’t the end of the world and is in fact how polyvalent antivenoms are made. But if we want to move to microbial production methods, which will be necessary to reduce costs, we can’t go down that route. There can often be hundreds of thousands of unique antibodies that can be found in blood, which would be intractable to produce.

What the Cell paper offered were two distinct non-natural antibodies derived from a pre-existing set of good, natural antibodies, the combination of which was all that was necessary to serve as an extremely effective antivenom.

The authors of the horse paper could’ve done the exact same thing: isolate the most broadly neutralizing antibodies from their horses, sequence them, express them recombinantly, and find the best ones! But they didn’t. It may have just been the case that the authors were quite set on finding ways to make the existing antivenom production process, which relies on animals, better. Remember, if we have ‘few antibodies of importance’, that presents the opposite problem of the horse antivenom: cannot be produced in animals, since eliciting arbitrary antibodies from animals is really, really hard. But it does make microbial production far easier!

It was a very interesting frame of reference required to arrive to the Cell paper, very ‘if you ask people what they want, they’d say a faster horse’. I wonder how many other research concepts that are out there where this ‘obvious follow-on’ isn’t being done, because the problem is being framed too narrowly; within the constraints of existing workflows, tools, or assumptions.

Anyway, that’s the end of this addendum. If you’re curious for more, I had a whole podcast with someone trying to make broadly-protective vaccines. Lots of similar thoughts echoed there!

Between July 2023 and January 2024, I read between twenty and thirty novels. This is a sizable number in its own right, but especially meaningful in light of it being the first time since childhood that reading felt urgent. Something of the utmost priority. None of the books related in any meaningful way to what I write about on this blog. They were fiction, unrelated to biology, and I imagine the primary reason their authors wrote them was to impart one singular emotion to the reader: pain.

Prior to my season of reading, I had felt off. I had told myself it was melancholy or disquiet or malaise, but, on retrospect, it wasn’t anything as grand as that. It was something more subtle, fleeting. A background emotional sensitivity, the kind that leaves one glassy-eyed during something as normal as a movie trailer. Does that make sense? An ambient eeriness you expect to carry during your teenage years, something you naturally assume you'll outgrow as your brain matures. But one painful realization of growing older is that while your expectations for personal dignity evolve, the emotions threatening that dignity often stubbornly remain the same.

In time, these feelings demanded emotional texture. Something wiry to grip onto, contours, edges. I visited a bookstore with the firm intention of satisfying this desire, and left with a copy of My Year of Rest and Relaxation, by Ottessa Moshfegh. I had wanted to read this book since it came out in 2018, but it never felt like the right time. I think some works shouldn’t be consumed if you don’t feel like you’re ready to grasp what the creator of it meant for you to feel. This is not out of respect to the writer or director or artist, this consideration is for you and really you alone. There are only so many good things in the world, and only so many chances to be cracked open by them. Being cracked is a wonderful feeling, so you should wait for the right moment to achieve it.

My Year of Rest and Relaxation is about a woman who feels so exhausted with life that she desires to sleep for a year straight via chemical means. Through a cadre of seedy psychiatrists and drug dealers, she assembles together a cocktail of sedatives and opiates and tranquilizers to achieve this, waking only for an hour each day to have a small meal, some water, and an exercise routine to prevent muscle wasting. But this is the ‘climax’ of the book. Much of the narrative is first spent circling that comatose desire, spiraling through the protagonist’s apathy, passive aggression, and hatred for the life she has curated for herself.

Sleep, she believes, will purify her. If she can just stay unconscious long enough, the parts of her life that feel intolerable might slough off on their own. Her body, infinitely kind and patient, simply needs the uninterrupted time to do that. The protagonist is never given a name, because she really isn’t the point of the book.

I’ve come to suspect that this type of untethered misery, the kind without a cause, has more to do with narcissism than anything else. I’m not the first one to make this connection. The Last Psychiatrist probably did it first:

Narcissism says: my situation is different. I am not like other people, who are merely automatons, shuffling towards oblivion.

But of course, nobody wants to be narcissistic. Most are simply unaware of it, how much we desire to be the main character, the one whose pain is deepest, most complex, most worthy of attention. We don’t necessarily want sympathy; we want significance. We want our sadness to mean something.

But the hedonic treadmill awaits. To those who are in genuine pain, who are performing experiments upon themselves to relieve the agony, do they seem happy? Do they feel significant? Do they? Do they? Well, maybe at the start. Maybe in the brief, narcotic haze of a new theory, a new identity, a new chemical. But misery rarely sustains meaning. It dulls. It becomes bureaucratic.

When I read Ottessa’s novel, the sadness felt by the main character of the novel felt like it belonged to me. All of her numbness, her passive disdain, her impulse to vanish beneath a weighted blanket of pharmaceuticals. My brain gorged on the grotesque theatricality of it all, her fictional suffering mine to borrow, to imbibe momentarily. I hated the beautiful things she hated, I loved the awful things she loved. But the more I read, the more her sadness began to feel stylized. Of course, it was stylized from the start, but it takes pages upon pages to realize it! Her pain had clarity, direction, aesthetic coherence. Too much of it! It was a supernormal stimulus, so overt in its legible bleakness that it became almost garish. A spectacle.

As a result, I began to find my own malaise embarrassing, just as I found her malaise embarrassing. Not because either was false, but because both had started to feel indulgent, mine in its shapelessness, hers in its choreography. In this sense, painful books serve as a reflection, a moment to reconsider is this what I’ve been doing? Is this what I’ve been feeling?. Sometimes the answer is an enthusiastic yes and ‘thats okay’, things have been quite awful after all. But sometimes it’s a quieter yes and ‘perhaps it’s time to stop’, an admittance that the emotions you've been nursing are based in a previously unconscious grandiosity.

But not all painful books are meant for reflection. Sometimes they are meant as a warning.

When I was 15 and in a similar mood, I read Stoner, by John Edward Williams. The book is a fictional biography of a man named ‘William Stoner’, walking through his early childhood, adult life, and senior years. William Stoner’s life is quiet, uneventful, and profoundly depressing. Not in a stylized way, but in the way a table rots, or wallpaper yellows, or a man forgets why he married his wife. Nothing explodes. No revelations arrive. He simply endures. It’s not a tragic story in the conventional sense, there’s only time, pressing down.

Stoner was published in 1965, which came to some surprise to me after finishing it, because the tinge of desperation in the main character has a very modern feeling to it. Unlike Ottessa’s protagonist, Stoner doesn't hate the world theatrically. He barely reacts at all to the slow erosion of his hopes, his work, his relationships. In many ways, the novel is an allegory of stoicism, but a brand of which that I found nauseating. Stoner doesn’t transcend his pain, not really, he simply absorbs it until it becomes indistinguishable from his character.

At fifteen, this was horrifying to me. I had no vocabulary for that kind of resignation! I thought life was supposed to be dramatic, shaped by decisive moments and meaningful choices. It jolted me out of complacency because, unlike him, I could still choose differently.

I think I have. Or, at least, I have tried in decade-plus since.

Either way, I felt fixed. Over six months, Ottessa-esque books had helped me realize how self-indulgent my emotions were, whereas Stoner-esque books had reminded me how passive awareness of negativity is not the same thing as resistance to it. Recognizing despair is easy; doing something with it is harder. And simply naming your unhappiness, no matter how eloquently, doesn’t absolve you from the responsibility of moving through it.

With my new-found happiness, I settled on more sustainable forms of self-care, like eating a few eggs every day and going on long, undirected walks.

So I largely stopped reading.

But if painful books are so useful, why stop at all? Why not endlessly pursue perspective through emotional discomfort? There is an archetype of individual who does exactly this, constantly simmering themselves in the newest and greatest in media-based torture. They read only the bleakest literature, watch only the most devastating films, and write long-winded essays—like this one!—about the moral and psychological rewards of feeling very, very bad. They believe that pain is not only clarifying but ennobling, that suffering polishes the soul like sandpaper against raw wood.

But, eventually, external emotional pain ceases to offer meaningful perspective and instead settles into your psyche, becoming indistinguishable from emotion that uniquely arose from within you. You begin to inhabit borrowed sorrows rather than confront personal reality, effectively replacing authentic self-awareness with a carefully curated imitation of suffering. Given enough time, melancholy goes from an unfortunate outgrowth of narcissism to outright comfortable. No longer a catalyst for change, instead morphing into a habitual form of indulgence disguised as introspection or emotional depth.

Of course, despite being aware of this, I still felt an urge to continue. While the last painful book I read was Life Ceremony by Sayaka Murata, it wasn’t the last one I bought. That honor belongs to A Certain Hunger, by Chelsea G. Summers. It had a beautiful cover, though the reviews aren’t fantastic.

But I never started it. I immediately felt regret after having bought it, loaned it to a friend, and never asked for it back.

On April 15th 2024, I started this Substack. But I technically started writing on WordPress on March 8th 2024. Either way, it’s been just about a year.

Some statistics: in total, I have written 37 articles — 45 if we’re using link compilations — over the last 12 months. This amounts to ~128,000 words when summed up, with an average of ~3,600 words per post. Each one had at least a dozen or so hours of reading + writing put into them, some more. There were ~33,000 visitors to the site during the last 30 days, and about 300,000 visitors over the last year. There are also 4,103 official subscribers and 4,755 ‘followers’, which translates to a subscriber that I do not have the email of because Substack wants to keep me distributing via this website. Which is fine! Everyone needs to find their dark pattern alpha somewhere.

I’ve also made two podcast episodes. Hopefully I’ll make more soon, but each one costs so much money and requires a huge amount of prep time, so it’s slow going. I listened to the two episodes months later and am surprised at how information dense they are. In retrospect, people telling me that the podcasts go completely over their heads make more sense to me. At this point, they go over my head too. But I like them that way. Hopefully will release another one this month if all goes well.

At some point, I started to sell poster and shirt designs I made. I haven’t made a new one in two months, but I’ll get back to it soon.

There were also some fun accolades along the journey! I got to the front page of Hacker News a few times, published on Asimov Press twice, shouted out on Marginal Revolution twice, and featured in The Diff and Astral Star Codex once. These were fun things to wake up to, it’s nice to feel recognized by a cultural side of the internet that you align with.

Whenever I meet people in real life, they ask me why I started this blog. My answer is really boring: I wanted to know more about machine learning in antibody engineering, so I wrote about it as I learned about it. People seemed to like that article. It turned out to be a really interesting subject too, so I made another one about how antibodies as a modality are evolving. This one was less interesting to people, but that was okay. I’m not sure I liked that one much either.
Making good things is hard, I get lucky once every 4-5 articles.

And so I just kept churning things out, ping-ponging around, writing about whatever seemed interesting in the moment. I wrote something about why toxicity is hard to predict (a topic my favorite biology writers always alluded to but never explained why), an introductory text to molecular dynamics (a field I long considered completely impenetrable to me), and so on. At some point I got bored of writing tutorials, and started to branch out into covering companies I really liked and scientific arguments I wanted to make. And so on.

My highest effort article, in terms of ‘people talked to’, was ‘Things I learned talking to the new breed of scientific institution’, which required discussions with 7 different people and was published on August 2024.

The article I liked the most was ‘Why Recursion Pharmaceuticals abandoned cell painting for brightfield imaging’, which was published on November 2024 and the only one that got cited in a draft New York Times article (unfortunately, the article in question seems to have never gone through).

My longest article to date is 7.9k words, titled ‘A primer on machine learning in cryo-electron microscopy (cryo-EM)’, which was published on December 2024. It took me two months to finish.

Needless to say, I like writing a lot. I’m usually working on somewhere between 1 to 4 articles at a time and they are rarely ever abandoned outright. That used to happen more early on, when I lacked experience on how I should satisfactorily wrap things up. But now I’m pretty good at finishing things once I start them. I’ve put some writing advice in a different article in case you’d like to know more about that. This all said, sometimes I’m not working on anything at all, because the whole idea of writing feels so deeply stressful that I prefer to not think about it. This has happened twice so far. The first time was in November 2024, the second time was in March 2025. I think I’ve exited that lull.

Some unconnected thoughts from the past year:

Putting essays out on a schedule can be really exhausting. Mid last year, I tried to do an article every week, and I was not sleeping a healthy amount. Right now, I’ve settled into a pace of an article every 2 weeks (1 week only if I have a backlog of unpublished things), and while I have a healthy lifestyle at the moment, writing has still managed to swallow up basically all my other hobbies. It’s nice in some ways to have a single, all-consuming thing to pour myself into outside of work, but I sometimes crave more heterogeneity.

You’d think the bottleneck is ideas, but that’s not it at all. Those are actually really easy to come up with. If an LLM came out today that could perfectly recreate my style of writing, I think I could rattle off a dozen titles of essays I’d love to read (and, absent such an LLM, plan to write myself). There are so many interesting things in this field that nobody has bothered to ever write down. Why? The answer I’ve settled on is that the best people to write about a given topic usually have far better things to do.

The far bigger hurdle is creating a story around the topic. You can’t just explain a concept outright. Your task as a technical writer is to build up mental scaffolding, and that necessarily implies a winding, circuitous route of teaching. LLM’s are really bad at coming up with these sorts of cohesive narratives (though they can serve as inspiration given enough prompting), so the onus is on me — the writer — to do that. This takes time! The whole task because closer to creative writing, so I usually can’t just brute force an article. I often have to step away from it, let my brain marinate on the topic for a bit, come back, and repeat to figure out the best way to explain things.

This can be fun, but it is often a lengthy slog in the earliest stages, especially if the [thing] I’m covering doesn’t have easily accessible online resources. Which, more often than not, is the case.

Writing has helped me understand my field a lot more. Not perfectly! But my conceptual underpinnings of this area, the zeitgeist of it, where it currently stands, where it will go, and so on, are all so much clearer. This isn’t just because of the writing itself! That does help in refining my own thoughts, but the far more important part is that much more knowledgable people read my work, reach out, and then I get to learn their takes on where the future is going. It’s a flywheel.

I’m sure I’m wrong in many of the predictions I have for the future, but feeling at all confident enough in my knowledge base to even offer an opinion in the first place is an improvement over where I was a year ago. Of course, many people who are subscribed to this blog could trounce me in the skill of prescience, so I have aways to go.

Even scientific writing can be emotionally taxing. Sometimes people get annoyed by me, a nobody, explaining or claiming things about a field that I haven’t spent decades working in. And they verbalize that annoyance. I luckily seem to reside in a cultural spot of the internet where people are a big fan of the ‘you can just do things’ mentality, so I luckily don’t face that form of vitriol much.

That said, while I do get nauseous whenever some anonymous person on the internet says my writing sucks or is inaccurate, there is also a thrill that goes alongside that. I usually have a vague worry that I’m completely wrong about whatever I write about, and there’s almost a reassurance that can be found in someone fervently supporting my self doubt. You’re right! You’re right! My fears confirmed, now I can rest easy. And hopefully improve for the next essay.

But on the bright side, some people really enjoy the writing and reach out to meet me. I’ve tried various AI email tools to help me find the exact number of people I’ve had coffee chats with as a result of this blog, but none of them quite work. So I’m going to guess 100? And that feels like a lower bound. Because I’m writing about the field I love, that generally selects for people who also love that same thing, so the meetings are always very nice. And now at least a few of them are people I’d consider friends or mentors.

I’m reminded of Henrik Karlsson’s piece: A blog post is a very long and complex search query to find fascinating people and make them route interesting stuff to your inbox. I don’t meet as many new people these days though, it’s a very specific type of article that makes people want to talk to me instead of passively consume the writing, and I haven’t written one of those in awhile. Or maybe I’ve exhausted most of the extroverted bio-curious people. Who knows?

An unintended side effect of writing is that you learn to enjoy other writing a lot more. Before I started running this blog, I mentally treated a lot of other writing as TikTok videos; throwaway material that is interesting in the moment, but something I’d soon forget. There was, of course, some writing that’d shock me out of these: Eugene Wei’s work, certain Ribbonfarm essays, early Scott Alexander work, but rare.

Nowadays, I have a much deeper appreciation for writing in all forms. I suspect this is the case for many people who have a blog.

One of the shifts over the last year has been how my mental model of an “audience” has evolved.

In the early days, I wrote as if nobody would read it (which was largely true) and that gave me a sort of freedom. Anything was on the table for coverage, whatever felt most interesting to me in the moment. But as the year has gone on, with my list of subscribers reaching into the thousands, I sometimes find myself anticipating their reactions mid-sentence, mid-title, and so on. Will they like this paragraph? Will they think the title is dumb? Will someone get angry at me? And on and on and on.

But now, I have come to believe that my true audience is much smaller than the numbers would imply. Not in a depressing way, more in a clarifying one. Out of the (sometimes, tens of) thousands of people who click, skim, or skim-and-archive my articles, there’s probably maybe like… a few dozen (maybe less!) who are actually reading. I feel like that’s just the nature of technical writing; people are busy, and will only stick with a dense piece if it’s something they are personally deeply passionate about it.

Take me, for instance. I’m a huge fan of Construction Physics. It has around 57,000 subscribers, and I’d call it one of my favorite blogs. But I think, to a pretty large extent, I like the vibe of the blog more than any of its articles, of which I’ve deeply read maybe like…4? Maybe 5? If we ever met in person, I’d tell Brian how much I love his writing and how much I admire his output. It wouldn’t even occur to me in the moment how surface-level my appreciation of him is.

So, if he quizzed me about any of the details of his writing beyond those, I’d look quite dumb! So who are Brian’s true fans? Probably people who work in policy or industrial manufacturing or the like. Almost certainly less than several hundred people worldwide. Of course, some articles will appeal to many more people than that, such as Brian’s piece ‘How to design a house to last 1000 year’s’, which once got to the top of Hacker News and was so good that I remember it 4 years later. But pieces like that don’t describe most of his work.

Mountains are mountains, until they aren’t anymore, and then they are again. You know?

Overall, it’s been a great year. It’s hard to imagine a time before writing. Looking forwards to another year of this, and thank you for continuing to read.

Note: this essay could not have been written without the help of Aishwarya Khanduja and a few other anonymous people who have worked in the pathology startup scene. Also, another reminder about the April 16th NYC bio-ML meetup!

1. Introduction

2. The problems

   1. The death of traditional pathology was greatly exaggerated

   2. The right business model is…unclear

   3. The value of the AI is somewhat questionable

3. Where does the industry stand today?

Introduction

Back when I was in college (2015-2019), I wasn’t super tuned into how AI was altering biology research. In many ways, it still wasn’t. Alphafold2 wasn’t released, the earliest ‘let’s create drugs with AI’ companies were still either starting or failing, and the scale hypothesis had yet to fully be realized. A dark era!

But what I was tuned in to was how AI was altering the ‘pre-existing medical data’ research field. It felt like the obvious place ML could be easily applied. After all, terabytes of data here were collected month-after-month, just as an incidental result of patients going through the medical system. Data here came ‘free’; no need to spend millions of dollars to acquire new datasets, no need to hire annotators. In the absolute earliest form, this was X-rays, MRI scans, ultrasound, electronic health records, and the like.

But, for a long time, pathology was left out of this. This was because it initially lacked the same level of digitization as radiology or electronic health records. Unlike those modalities, which had been stored in digital formats for decades, pathology slides were still predominantly physical, analyzed underneath a microscope and then filed away in physical storage. This was for a decent reason: pathology slides are huge if you want to account for the massive resolution that microscopes are capable of. On average, each slide could be on the order of gigabytes of size, and hospitals, lab centers, and universities likely would collect hundreds, if not thousands, of these slides per day. Moreover, they required expensive whole-slide imaging (WSI) systems to digitize and store them, which lacked FDA clearance for years.

But by the mid 2010’s, unbeknownst to me, things began to shift. Advances in whole-slide imaging technology (alongside FDA approval of them!), declining storage costs, and the rise of digital pathology—a term used to differentiate the analysis of scanned pathology slides from traditional microscopy—suddenly made the prospect of mass-collecting this data feasible. At first, it was primarily for communications sake; it’d be a nice convenience if a pathologist could easily ask a friend a continent over what they thought of a slide, or to digitally annotate a slide with their thoughts.

But more computationally trained people saw the potential value in doing something even more with this data. And so, startups in this space were founded around the same time as the rise of digital pathology, often spun out of collaborations with companies or hospitals who had their own gargantuan datasets of digitized pathology slides.

The two biggest names from back then are still around. There’s PathAI, which started in 2016 and had a network of 400~ pathologists to crowdsource pathology slide data. And then there’s Paige, which started in 2017 and had exclusive access to Memorial Sloan Kettering Cancer Center’s archive of 25 million digitized pathology slides.

Their thesis of these companies and companies like them was pretty simple: if deep learning could already outperform humans in tasks like object detection and classification, why couldn’t it do the same for pathology? Histopathology was, at its core, an image recognition problem—pathologists were trained to identify morphological patterns in tissue slides to diagnose diseases like cancer. AI models, given enough high-quality data, could potentially match or even exceed human accuracy in detecting tumors, grading disease severity, and even predicting patient outcomes.

Early research validated this idea. In 2017, a JAMA study demonstrated that deep learning models could identify breast cancer metastases in lymph nodes with accuracy comparable to trained pathologists. Google Health followed in 2020 with a model that could grade prostate cancer aggressiveness at sensitivity levels exceeding human experts. The stage was set for something very interesting to happen!

And yet…each of the companies I named earlier seem to have all the outward signs of doing badly. For one proxy, all have dramatically slashed their workforce.

Here is PathAI, dropping 34% in two years:

Here is Paige, dropping an even worse 44%:

Past that, their recent papers aren’t anything particularly impressive and I rarely hear about them at all anymore. Of course, workforce count, paper prestige, and being publicly visible aren’t necessarily tied to success at all. Downsizing, not caring about papers, and being private about internal affairs are all characteristics found in successful companies, but it does lead one to wonder.

This should lead us to an obvious question: what happened to pathology AI companies?

Did they succeed beyond their wildest dreams, quietly thriving in the background? Did they outright fail for reasons that are under publicized? Or, much like other biotech-ML companies, are they still stuck in an awkward middle ground, where progress is being made, but far slower than expected? And whatever the answer is, why did that happen, and not something else?

This essay will attempt to answer that question.

The problems

The death of traditional pathology was greatly exaggerated

This is, fairly, somewhat of a bait-and-switch on my end. Earlier, I mentioned that by the mid-2010’s, the digitization of pathology slides had really begun. And it had! By 2017, the first whole-slide-imaging system had been approved by the FDA, and by 2019, the second.

But approval doesn't equal adoption. According to one interview conducted in 2021, 56% of surveyed pathologists reported not having digital pathology infrastructure in their NHS hospitals. On a more anecdotal level, this particular Reddit thread from just a few months back was illuminating. Here are some quotes:

“I’m at a large state hospital and they use slides on a microscope”

“While digital sign out is being more widely adopted, the vast majority of practices are still looking at glass slides with a microscope.”

“In the US, 99% of pathologists are using traditional microscopes for diagnostic work.”

Why has been such little adoption of a seemingly useful tool?

First, the economics are challenging. I mentioned that WSI scanners are expensive, but didn’t give exact numbers; a single whole-slide scanner can cost between $150,000 to $250,000 at the top end, with additional expenses for storage infrastructure, image management software, and workflow integration. For large academic medical centers processing thousands of slides daily, the initial investment can easily run into millions. This explains why pathologists employed by private companies spend far more of their time looking at digital slides than those working in the NHS (75% vs 25%). And, unfortunately for startups hoping to sell to people with digital slide workflows, there is a roughly equal number of pathologists in both groups.

Second, even for the groups that are able to afford a WSI scanner, pathologists still often don’t rely on them. This isn’t for luddite reasons, the software to navigate digital slides just…isn’t very good. At least if the bar for ‘good’ is ‘as easy as doing it by hand’. Remember, at least for now, humans must still be part of the pathology scanning process, so their opinions matter!

What are examples of software issues? Simple, common tasks on a microscope—quickly switching magnification, scanning across a slide, or comparing multiple slides side-by-side—became multi-click operations. Moving a glass slide under a microscope has zero latency, whereas digital systems introduce perceptible lag when navigating high-resolution images. Color artifacts can be introduced by digital displays. Logging in and out. And so on. For a pathologist reviewing dozens of slides daily, these small delays mean that they will be quick to abandon an otherwise theoretically useful system.

But let’s say you’re able to find pathologists willing to do digitization and actively rely on those digital slides. How do you make a business model with that? Well…

The right business model is…unclear

If you started a pathology AI company, you had roughly two choices on how to sell things to people.

The first option is to hire your own team of pathologists, armed with your in-house set of AI tooling, and tell hospitals and research groups to send their slides to you (no digitization needed), assuring them that you can get them results faster and cheaper than anyone else. In other words, services.

The second option is to simply pre-package the AI tooling itself and try to license it to whichever external group is already doing digital pathology analysis. In other words, products.

A lot of people chose the first category.

On paper, this made some sense. Vertical integration! Full control! You get access to your own stream of pathology data, you avoid slow enterprise sales cycles, and you can spin up a business that looks more like a tech-enabled lab than a pure software vendor. Given all we’ve discussed about how pathologists are loath to work with software tools, this wasn’t an insane path to go down. If the legacy businesses do not wish to adapt, simply compete with them on where they stand. If you believe in your ability to make efficient a difficult process, why wouldn’t you win? After all, it’s worked before in a related industry. It should work here!

Well, it’s not that cut and dry. Though there are some auxiliary benefits to trying to spin up your own AI-enabled pathology lab, there is a one small hurdle: in going down this path, you will, at some point, start competing with Labcorp and Quest. And it is really, really hard to win a battle with Labcorp or Quest.

To give some sense of the scale at which these two companies operate: Labcorp processes over 500,000 specimens per day. Quest clocks in around 400,000. They have nationwide logistics, massive economies of scale, and negotiated contracts with every major insurer. Their labs run like clockwork. Their margins may be thin, but they make it up in volume, efficiency, and market entrenchment. They have been optimizing clinical workflows for decades. They have fleets of drivers, entire warehouse-style labs optimized to process slides and bloodwork at scale, and enough regulatory experience to make new test approvals far less of an issue than it’d be for any upstart. They are not the slow-moving incumbent that one may mistake them as. They are a logistical marvel akin to Amazon that just happens to do pathology, alongside hundreds of other diagnostic tests.

And at least one pathology AI startup tried to compete with them: PathAI, who acquired Poplar, a clinical diagnostics pathology lab, in 2021. It feels deeply likely to me that PathAI believed that their AI capabilities would allow them to run a pathology lab more efficiently than the incumbents—that their models could streamline workflows, reduce error rates, maybe even lower costs per diagnosis. And maybe, in narrow ways, they were right. But at least from the outside looking in, the gains weren’t enough. Quest, in turn, bought Poplar from PathAI in 2024. Perhaps there was a multiple earned on this deal, but if clinical diagnostics wasn’t such a pain to deal with, why sell it off? This said, I’ve attached an alternative take on the situation by someone I talked to in the footnotes.1

Yet, a skeptical reader may have one response: for however bad a services play is, a products play feels worse.

Even in the best-case scenario where your product works perfectly and delivers some level of value, you are still at the mercy of institutions that adopt technology at glacial speed, have labyrinthine procurement processes, and often have no clear incentives to change anything at all. You're selling into healthcare with a contract, not just a one-and-done thing as with the services play. The end result would likely be that you might sell into a pilot. A small departmental deployment. But wide-scale enterprise adoption? That almost never arrives. Why would it happen? How much money is a pathology tool actually saving a hospital? Given that a fraction of the universe of pathologists have access to slide digitization, and that AI-based tools cannot by themselves sign off on cases given current regulatory guidelines…what’s the value pitch? You make things slightly faster? How much could that be worth? Empirically, the answer is not that much according to studies in the UK NHS system.

Well…unless your customer really does care about radical change, and where even minor workflow improvements are worth it. Unfortunately, that isn’t in most clinical systems. But it is in the private sector.

One pathology AI founder told me that it wasn’t hospitals or diagnostic labs that showed the most promise. It was R&D groups within Big Pharma. Those scientists and executives wanted new tooling. They were often sitting on massive internal datasets, had real budget allocated to experimental tech, and — critically — had a clear ROI if your model helped shave months off a study or more precisely target the right patient cohort. Most importantly, pharma didn’t care as much about the regulatory headaches, as they weren’t using your model to diagnose patients. In many ways, this was the path that Paige went down with their Omniscreen platform — helping pharma groups do better patient stratification, hopefully leading to better results in clinical trials.

But the markets there are small and growth is slow — there are only so many such R&D groups in the world that would want a product like this. So how could the increased margins be worth the squeeze? Past that, if ones pathology tool was so fundamentally useful, wouldn’t your customers just try to develop the tool internally? At least if you have a services play, you’d appeal to R&D groups that lack pathology digitization. But if they do have access to that, then isn’t it only a matter of time before they use their internal data to replicate your model?

So, between services and products, neither seems to be a particularly good category. Yet, empirically, some pathology startups have been able to make each individual category work. Consider Proscia, who just raised a $50M round and is a pure products play, and Artera, who is actively growing and is a pure services play. What’s going on there? We’ll discuss that at the end.

The value of the AI is somewhat questionable

Let’s assume you’ve solved digitization. You’ve figured out a workflow. Through an ungodly amount of effort, you’ve got a buyer or a partner or a lab. The slides are flowing in. You have clean, well-labeled data, and you can run your models without friction. And let’s even assume your model is well-calibrated and accurate, because, as of 2025, there are indeed off-the-shelf models that can rival expert pathologists in certain narrow tasks. Surely now is when the AI finally starts to deliver?

I think it’s worth considering that the average job of a pathologist is a far bit more complicated than simply ‘look at the slide and think’. A slide read is rarely an isolated act. It’s informed by clinical context, by knowledge of prior patient history, by awareness of what’s even being looked for in the first place.

Consider the following situation: a pathologist is reviewing a lung biopsy. The H&E stain suggests a possible adenocarcinoma, but there are ambiguous features—maybe some squamous differentiation, maybe not. The patient has a history of multiple primary tumors, and the oncologist has specifically asked whether this lesion is new or a metastasis. The pathologist pulls prior slides, requests IHC staining, and spends time correlating the imaging report. Only after that broader synthesis can a diagnosis be confidently rendered.

What exactly could the the AI contribute here?

Identifying areas of abnormality is helpful, yes, but it’s only a fraction of the interpretive task. The AI doesn’t have a way to reason within the workflow. It doesn’t correlate. It doesn’t ask follow-up questions. Which makes it easy to overstate how much of the workflow a model can actually own. Now, to be clear, could it? Could a model someday integrate clinical data, query prior records, suggest stains, and reason across multi-modal evidence? Absolutely! If given all of that context, I have relatively little doubt that such a model would be extraordinarily useful.

But, as far as I can tell, none of the models can do that, they poke at a very small part of the overall workflow: simply identifying areas of concern. A skeptical reader may shrug and say ‘sure, but isn’t replacing that small part still helpful?’. Maybe! And I’m sure there are some very specific niches in which replacing that slice of a pathologists workflow can indeed be useful. But given my earlier points about how much of a slowdown digital pathology tools often can be, it still feels like a hard sell.

One interesting note: unrelatedly to this article, I mentioned to a biotech AI founder — one who has worked with pathology slides before — that I was working on this piece, and what their take on pathology AI companies were. They barked with laughter and said ‘Have you seen how fast a pathologist works? A tool would need to outright replace them for it be a good value proposition to their employer.’. Maybe a useful datapoint to have as to difficulty of this problem.

Where does the industry stand today?

For all that I’ve mentioned about PathAI and Paige, the pathology AI industry as a whole doesn’t seem to be outright dead. At least, venture capitalists are still active in the area. A recent LinkedIn post highlighted a series of 10M+ funding rounds of a set of recently started pathology AI companies. And even if you look at Paige and PathAI, they haven’t outright disappeared. Struggling, but still alive!

So, what happened to pathology AI companies? Given the relative lack of literature on this area, I am forced to rely mostly on anecdotes from people I’ve spoken with, but I’ll call out two things.

One, the hopes and dreams of the prior generation of pathology companies can still be found in the upstarts — there hasn’t been some sort of reckoning that dampened anyones ambitions. But this may not be naiveté; whole slide imaging is accelerating year after year, and there will be some tipping point where the possible market for a computational pathology company really is quite sizable. Time will tell if we have yet reached that tipping point, but at least some people are optimistic.

And two, the business model problem and the value-of-AI problem has turned out to be somewhat related to each other in a pretty interesting way. As we’ve discussed, both services and products seem to be bad businesses, but yet some of the newer startups still seem to make it work. How? The answer may lie in the specialization of the AI they are developing.

• For an adequate services play, you cannot merely have a ‘pathology foundation model’ that can do breast cancer screening or whatever. Instead, you must be able to offer a useful clinical service that no human pathologist can accurately do. Because otherwise, one of the bigger players will just eat your lunch through pure logistical power. Artera’s pathology tools for prostate cancer biopsies fits this. The results from this test include not only the typical clinical markets (e.g. Gleason score), but also how likely an intervention — specifically ST-ADT, or Short-Term Androgen Deprivation Therapy — would help the patient, judged entirely via AI applied to the pathology slide. A human pathologist cannot offer such an insight! And, importantly, the utility of such a metric was proved out in retrospective analyses of multiple Phase 3 trials.

• For an adequate products play, your software must not only handle the application of AI to a digitized slide, but also handle…basically every single other part of a pathologist’s workflow. Proscia’s platform, Concentriq, is akin to this. It is closer to a full-stack digital pathology operating system rather than a ‘throw in digital slide and AI will be ran over it’ API. The product integrates slide management, collaboration tools, workflow automation, and ML tools into a single interface. For institutions already committed to digitization, this holistic approach can justify the cost, even if the AI itself isn’t actually all that helpful.

Of course, the problems for both sides will persist. For services, being limited by lab space can be annoying and your margins will be thin. For products, proving ROI can be a challenge, and growth is slow given that you have relatively few customers. And one issue will be faced by both groups: your competitors (services) or customers (products) are extremely eager to find some way to copy/replace you.

So, answering the question of what happened to pathology AI companies, a more succinct answer seems to be: they are still being proved out. There hasn’t been a clear winner as of yet, but the losers didn’t arise from some fundamental flaw with the underlying technology. This is a boring answer, given that the same thing could be said about nearly every AI biotech, but it does seem to be the correct one. ML applied to pathology is undoubtedly the future — as is ML being applied to everything — but it feels like we’re still in early innings for the whole process, even a decade after the first companies in the space popped up. I do get the sense of a ‘we’ve learned what doesn’t work’ from people in the field, which I consider to be a hopeful sign.

Of course, this essay is very much an oversimplification. Digital pathology is an insanely large space, and while I think my coverage of the area does cover a sizable fraction of it, I am undoubtedly missing some. If you’re working in this space and think I’ve missed something, I’d love to hear from you!

Well, one last thing that I couldn’t quite fit in anywhere else. Mostly because it’s somewhat unrelated to the point of this essay, but it’s an interesting tidbit.

A phenomenon I’ve long been a fan of in the biotech space is the existence of startups that exist to solve some incredibly hyper-specific bit of a workflow, one that you’d never even think about. Plasmidsaurus is my canonical example of this, plasmid Q&A wasn’t something I ever assumed was a big deal. But I didn’t consider that there was such a company in the digital pathology world — everyone here seemingly wants to solve everything, or at least a very broad category of problems.

But I did stumble across one startup that does fit the bill: Pictor Labs. The thesis of this company is virtual staining — using AI to computationally generate histological stains (like H&E, trichrome, or IHC markers) from label-free or minimally stained tissue images. Pictor’s premise is simple: physical staining is slow, expensive, and destroys tissue. Traditional staining requires slicing tissue into multiple sections, applying chemical reagents (some toxic or proprietary), and waiting hours or days for results. For rare biopsies, this wastes precious material. For labs, it’s a logistical headache. Pictor’s AI models instead take a single unstained slide and predict what other stains would look like if applied.

Simple! Saves time! And money! This is ostensibly a software product, but it has the ‘we’re doing something nobody else can’ services vibe to it. Very cool and something I feel like will make their founders quite rich via an eventual buyout. But it’s not really worth mentioning in the grander scheme of this essay, as Pictor just launched in 2019 and don’t seem to have any (public) partnerships, so perhaps there is some flaw here I’m not seeing. But I found the pitch endearing enough to mention here. Hoping for more startups in this vein!

1. Introduction

2. Applications

3. The roadblocks

   1. Gene therapy is hard

   2. The light source problem

   3. Maintenance

4. How do we fix it?

5. Is it worth it?

Introduction

Optogenetics is a technique in bioengineering invented in the early 2000s where light can be used to have direct control over neuron activity. The central mechanism revolves around channelrhodopsins, ion channels and pumps found primarily in species of bacteria and algae, that open when triggered by light. Similar to human eyes where rhodopsin acts as a photoreceptor that converts light into chemical signals that communicate vision to the brain, channelrhodopsins used in optogenetics change conformation when exposed to light such that ions can enter the neuron and trigger neuronal excitation or inhibition.

But if we’ve implied that rhodopsin (naturally expressed in retinal rod cells) does the same functional thing as channelrhodopsins (only made in bacteria), why use the latter? We’d need to do genetic engineering regardless, since rhodopsin only exists in one type of human cell, but it is a fair question. The answer lies in how these proteins respond to light and what they do downstream.

Rhodopsin, while light-sensitive, doesn’t directly alter ion flow. Instead, it activates a G-protein signaling cascade that eventually modulates ion channels indirectly. Helpful for complex mammalian vision applications, but too slow and complex for precise, moment-to-moment control of neuron firing.

Channelrhodopsins, on the other hand, are light-gated ion channels themselves. They cut out the middleman: when exposed to light, they change conformation and instantly open to allow ions to pass through, directly shifting the membrane potential of the neuron, forcing it to fire.

Thus, if we can force a neuron to produce channelrhodopsins, it gives scientists a button by which they can precisely turn it off and on, at the scale of femtoseconds. That is, as long as light can reach it — more on that later. But first: why do this at all?

Applications

This has a multitude of applications in basic neuroscience research such as loss and gain of function studies, as brain regions can be “shut off” for necessity studies without needing to cause brain lesions that cause permanent damage to the model. Likewise, optogenetics can be used for sufficiency studies by triggering excitation of neurons on demand and observing the effect without having to use electric stimulation. Invasiveness and risk of harm to model organisms is a big setback in many basic neuroscience experiments, so being able to minimize this with optogenetics is highly applicable. Additionally, with the ability to choose which neuronal cell type channelrhodopsins are expressed in, we can more accurately examine the role of different groups of neurons in various brain and body functions.

Optogenetics also has impressive potential for application in healthcare. One of the most widely discussed applications is in blindness caused by retinal neurodegeneration. A group led by José-Alain Sahel was actually able to partially restore vision in a patient with retinitis pigmentosa by injecting channelrhodopsin proteins into their affected eye and having the patient wear light-stimulating goggles. Before stimulation from the goggles the patient had no ability to perceive objects, but during stimulation could perceive, locate, and grab objects in front of them. This is incredibly exciting, and hopefully will become an approved treatment for blindness caused by neurodegenerative eye diseases.

Blindness is a great place to start with optogenetic treatments as there is already a system in place for light to reach neurons that express channelrhodopsins (via the eye). Things get slightly more complicated for other areas of the body, but nonetheless have their own exciting interventions being developed. One of my personal favorites is the application of optogenetics as a treatment for depression and depressive-state symptoms. For example, a group led by Allyson Friedman found that by hyper-stimulating midbrain dopamine neurons depression phenotype could be reversed. Moreover, optogenetic research is helping neuropharmacologists discern what reward pathways are impacted in depression for better development of antidepressive agents for treatment-resistant patients.

This list of applications is nowhere near exhaustive, and the potential of optogenetics as a technique is really exciting. You may be wondering, if this technique was invented in the early 2000s, why haven’t we heard more about it? Why haven’t we cured blindness, or depression, or any of the number of ailments that optogenetics could treat?

As is often the case with world-changing scientific technologies, it is not without limitations. In vitro, optogenetics is not that difficult. Cells can be transfected to take up your channelrhodopsin-encoding DNA (typically with electroporation, but you could use a viral vector or CRISPR if you were feeling fancy), expression levels can be checked easily since channelrhodopsins are fluorescent, and light can be shone on them without barriers that block electromagnetic waves or alter the wavelengths of waves that pass through. Easy peasy lemon squeezy.

The roadblocks

All of this changes when we go in vivo. Crucial to conducting in vivo optogenetic manipulation are two particularly foreboding hurdles: gene therapy and getting a light source inside the body. Gene therapy and the light source don’t only cause issues upon initial insertion, but require long term maintenance that can be costly and invasive. Any method that relies on gene therapy is bound to encounter some pretty tricky issues. Gene therapy was first successfully used in 1990, but there were no FDA approved treatments until 2017, nearly 30 years later. The primary issues keeping gene therapy from widespread use are less about how difficult the method is, and more about how difficult it is to mass-produce. Gene therapy is expensive and individualized by nature, so it is largely not worth it for pharmaceutical companies to put in the effort to develop treatments. Additionally, gene therapy isn’t without risk.

Gene therapy is hard

The primary method used in gene therapy for introducing new genetic information is with viral vectors. Lentiviruses are commonly used as these vectors, but can be genotoxic and potentially cause cancer. Additionally, lentiviral gene insertion is essentially random, and if a promoter were to be inserted near an oncogene it could also cause cancer. Site-specific gene editing like CRISPR comes with the risk of off-site deletions that, you guessed it, can cause cancer (and lots of other bad things that lead to death and disability). Lentiviruses are starting to fall out of style in favor of adeno-associated virus (AAV) vectors (Dyno Therapeutics plug), a generally safer alternative to lentiviruses. The major setback of AAVs is combatting the immune system so genetic material can be inserted. Major immune reactions aren’t incredibly common, but when they happen they can be deadly.

The light source problem

Say the complications of gene therapy are all solved. We are still left with an incredibly daunting engineering challenge of getting light inside the body. With the earlier case of blindness, goggles were designed that allowed light to reach retinal cells with minimal invasiveness. This is not possible anywhere else in the body, and much more invasive methods must be used. In mouse models, you can pretty easily insert optical fibers attached to an LED over a hole in their skull, as shown.

One can imagine it's pretty unlikely people are going to be walking around wearing giant LED hats with holes in their skulls. This is incredibly invasive, and less than fashionable. Different intervention locations come with different complications. For example, if your target neurons are in a sensitive tissue like neurons in the heart, optical fibers can irritate tissues and cause cardiac irregularities. In addition to the issue of invasiveness, it is difficult to keep these devices powered. Similar to cardiac pacemakers, light-emitting devices are typically charged with a battery. When the battery dies, the whole device has to be replaced, requiring yet another invasive surgery. The LED devices also heat up when they are continuously activated, which can be harmful to brain tissue.

There are also a number of difficult physics kinks yet to be worked out. When light passes through any non-air medium there is scattering that changes its path and lowers its wavelength. This optical scattering causes some serious distortion when light passes through brain tissue. This is a big issue when channelrhodopsins require precise neuron targeting and a specific wavelength of light in order to be activated. In order to compensate for this effect you’d have to send more intense light into the brain, at a higher wavelength than what you would eventually need to stimulate your channelrhodopsin. This, in turn, changes for each new brain area due to heterogeneity in tissue depth and density. Additionally, the more intense light you send into the brain, the more you risk brain tissue heating and causing permanent damage.

Maintenance

Compounding all of this, optogenetic therapy is a long term therapy with a long term patient commitment. All of the components of the therapy need pretty frequent maintenance, as channelrhodopsin expression can decrease over time and light-producing devices can have mechanical issues, in addition to the aforementioned issue of needing to be charged. Maintaining expression of a genetic intervention is a common problem for gene therapy. If the gene is recognized as foreign after being integrated into the DNA the whole cell could be killed by the immune system. If the immune system is successfully suppressed and the DNA is integrated without any problem, genes introduced via gene therapy and their promoters are often silenced by post-translational modifications like DNA methylation. It might be worth exploring inserting an extra copy of a gene that is frequently expressed in the neuron along with our target gene to see if this silencing can be reduced. For example, a neurotransmitter release-associated gene. If we could link the expression of the neurotransmitter release gene to our channelrhodopsin such that both genes are undergoing transcription often, it is less likely that our channelrhodopsin would be silenced by post-translational modification (this is of course just speculative, but I would love to see someone try it).

As of now though, gene therapy requires re-dosing to ensure channelrhodopsins stay at therapeutic levels. This means more risk of life-threatening immune reactions and painful injections into sensitive areas. To repair or charge light-producing devices they must be surgically removed, raising the risk of infections and the many other complications associated with surgery.

If a patient decides all of these issues aren’t worth the benefit or can’t afford the maintenance, it’s not an easy thing to stop. All gene therapies are meant to be permanent, and channelrhodopsins will continue to be expressed in your neurons whether you like it or not (though they may be present below therapeutic levels). If your brain had adapted to the stimulation of its neurons, conditions that were being treated may revert back to even worse symptoms than patients started with. For example, if you were treated for depression with an optogenetic intervention where neurons were stimulated to release more serotonin, if those neurons are suddenly no longer stimulated there would be an extreme drop in serotonin levels. These neurons may have previously been releasing low yet present levels of serotonin that caused your depression phenotype, but now may release none at all because their threshold for sending the serotonin release signal has changed. This is incredibly dangerous, and it is important we have systems in place to support long-term patient care before widely implementing these therapies.

How do we fix it?

Though difficult to overcome, none of these issues are total dealbreakers in my opinion. Luckily, there’s lots of research in progress to fill in the gaps to making optogenetics truly great. In the gene therapy arena, we’re figuring out how to bypass the immune response triggered by AAV vectors by using similar techniques to organ transplants. In addition to using standard immunosuppressants, researchers are using plasmapheresis, where plasma is removed from the body and all anti-AAV antibodies are lost with it. This eliminates basically all immune response, allowing AAV vector DNA to successfully enter the host genome while minimizing harm. Additionally, AAV vectors engineered to have fewer CpG motifs (oligonucleotides found primarily in pathogens) have been found to further minimize immune response. All of these methods combined could make using AAV vectors nearly harmless, and could revolutionize optogenetics and the larger field of gene therapy.

On the light-emitting device side of things, devices are being designed that keep batteries and other mechanical components that may need repair outside of the body while optic fibers transmit light to the target neurons. MicroLED probes have also been created that can be implemented with minimal invasion, reducing the risks associated with surgery to implant larger light sources. These flexible, silicon-based arrays can be implemented just under the skull, greatly reducing the risk of damage to brain tissues, making them a preferable intervention compared to optic fibers for cortex stimulation. MicroLED probes also allow for reduced optical scattering due to their spatial specificity and small distance from the light source to the target tissue. These probes still cause brain tissue to heat up, but at a slower rate than a larger LED or directed laser.

There are also many developments happening in the engineering of channelrhodopsins themselves. As more channelrhodopsins are discovered, researchers are beginning to figure out which are maximally effective for different purposes. For example, channelrhodopsins that absorb red light can be used in deeper areas of the brain, as red and near-infrared light can travel deeper into the brain with less optical scattering. Studies using machine learning are being conducted to design channelrhodopsins that require less light to produce neuronal excitation, so signals can be sent while minimizing the heating of brain tissue. Additionally, structural studies are being done that can elucidate what parts of channelrhodopsin structure are most important for ion conduction, and how we can improve the channels that we have to be faster and more efficient.

The only roadblock to optogenetics being used widely that could really delay progress is the economic problem of gene therapy. Hopefully with the development of more AI tools for biology, we will be able to optimize gene therapy to a point that its production cost is low enough for us to see a major surge in commercially available therapies.

Is it worth it?

It is worth considering that all of these setbacks might make optogenetics not worth it. After all, there is more research right now in how to fix optogenetics than how to apply it clinically.

That said, I think optogenetics has some key advantages over other neuromodulation techniques that make it worth it to work the kinks out. These other techniques, chemogenetics and magnetogenetics, open ion channels via small molecule binding to designer receptors and magnetic field-generated torques mechanically opening ferritin-bound ion channels, respectively. Compared to optogenetics both techniques lack temporal and spatial resolution, and could have unknown off-target effects that light does not.

Ligands used in chemogenetics could potentially have unknown impacts on other cell receptors and tissues despite being designed to only bind with designer receptors. Additionally, chemical binding takes much more time to turn on or off compared to the femtosecond scale optogenetics operates on. Magnetogenetics is much faster than chemogenetics, but still doesn’t quite reach the same temporal resolution as optogenetics. It can also disrupt electronic devices in the body like pacemakers if the magnetic field is strong enough, as well as potentially inducing unwanted voltages in cells other than the target, although the extent at which these unwanted voltages cause any harmful effect is debatable.

Finally, timing is of the essence in many basic neuroscience studies and clinical applications. If you wanted to use any of these techniques to treat something like cardiac arrhythmia, you would want to inhibit the abnormal heart rhythm as quickly as possible. However, both of these techniques come with the incredible benefit of being almost entirely surgically noninvasive.

But, the beauty of all of these methods is that none of them exist alone, and the three could be used in different situations where their unique benefits are best suited. For example, optogenetics could be used to reverse neurodegenerative blindness, as light can easily enter the eye so invasive devices wouldn’t be used and a quick temporal response is necessary. Chemogenetics or magnetogenetics could be used in depression treatment, where quick temporal response isn’t as important. I hope to see further development of chemo- and magneto- genetics in addition to optogenetics to fill out our bioengineering toolbox, in order to fully explore the possibilities of neuromodulation.

Overall, I think optogenetics is an important technique that will see widespread clinical applications, especially in the areas where it can be applied least invasively. In combination with other bioengineering techniques, I believe that optogenetics could certainly change the world. It’s just going to take some time.

1. Announcements

2. Links

3. Jobs (Contact me to be added here!)

Link to the last roundup.

Announcements

Over the last two weeks, I released two articles:

1. A socratic dialogue over the utility of DNA language models (Part 2 of 2)

   1. Part 1 is here!

2. Opinionated advice for writing about science

Also, I’ll be hosting another NYC biotech meetup, this time on Wednesday, April 16th, at 7:30PM at Radegast Hall in Williamsburg. This will be the fourth meetup I’ve done in NYC, and there usually triple-digit attendees of extremely interesting people. You should come!

Finally, I had hoped a podcast would come out this month, but some delays have pushed it off. There should be one in April! If we’re lucky, maybe even two…

Links

Colossal Biosciences genetically edited a bunch of mice! Specifically to give them wholly-mammoth-esque phenotypes in their fur, which also made them very cute. Kenny from Latchbio had a great criticism of it (given below), but I still find this work cool; no huge biological advancement, but still strong technical execution on something hard + sets them up for more advanced work.

Corin Wagen wrote a wonderful post about the state of binding-affinity prediction using a Gödel, Escher, Bach-esque setup. It took me awhile to come back to this to read it, but it was surprisingly funny + informative, highly recommend!

All-atom diffusion transformers for periodic and non-periodic materials and molecules. Cool work! Currently only unconditional generation is available, but the sampled materials seem to meet in-silico benchmarks for validity quite well. This is an aside, but I’m not super familiar with material designs, so my mind immediately leaps to the controversy over the material design stuff that came from Deepmind in late 2023. What was the issue there? Kinda…nothing? At least no criticism that would be alien to someone familiar with how these types of models work. So likely little of it transfers to this new work.

Reticular dropped a web portal to do mechanistic interpretability on any given protein (using ESMFold). Here is a (AI-generated) thread over it.

Learning the language of protein-protein interactions. From the abstract: Here, we introduce MINT, a PLM specifically designed to model sets of interacting proteins in a contextual and scalable manner. Using unsupervised training on a large curated PPI dataset derived from the STRING database, MINT outperforms existing PLMs in diverse tasks relating to protein–protein interactions, including binding affinity prediction and estimation of mutational effects. Instinctively, I find the results here really surprising; I’ve always mentally modeled STRING as more of a protein-association thing. All it tells you is that a set of proteins are roughly found in the same place, not that they directly interact with each other. But, empirically, I suppose that if you have a large enough dataset of this sort of ‘probably interacting’ proteins, you can derive some very useful binding signal from it. Perhaps an example of unintentional generation 3 data?

NewLimit dropped their monthly update! Lots of good results on transcription factor discovery, but I think the most interesting bit is that their in-silico protein models: ‘These models take in a representation of an old cell state and a set of TFs and predict the effect of partial reprogramming on cell age and type….Our models can now explain more than half of the variation in cell age effects for unseen TF sets..’. I think it’s an open question how much models here can be pushed for transcription factor discovery (given how much of an issue OOD predictions are for protein models), looking forwards to an ML paper someday!

This isn’t scientifically interesting, but this animation by Profluent is really nice. There is an even better version on the homepage of their website.

Benchmarking AlphaFold3-like Methods for Protein-Peptide Complex Prediction. Success rate here is given by 'ratio of the number of native interface contact residue pairs retained in the predicted model to the total number of native complex interface contact residues in the experimental crystal structure'. Crazy if true!

Jobs

Lada Nuzha, a very well-known figure in the longevity space, just launched a startup called Epi Labs! They are hiring for wet-lab scientists in the Bay Area.

• Also feel free to reach out to her at lada@epilabs.co

Another longevity company, NewLimit, is aggressively hiring senior ML people in the Bay Area.

Asimov Press — a fantastic online journal — is hiring a part-time researcher to help write articles!

1. Introduction

2. Some long advice

   1. Write for people who actually exist

   2. History is often fluff

   3. Expressing confusion is interesting

3. Some short advice:

   1. Trust (and develop) your own sense of taste

   2. Stress is unavoidable at the start

   3. Make yourself easy to discover

   4. No matter what, someone will be annoyed with you

Introduction

I’ve been actively writing on this Substack for about 11 months now, and am poking at 4,000 subscribers. It’s a small number in absolute terms, but many of them are people I have looked up to for years and never imagined would even be aware of my existence.

It’s been great! I highly recommend starting a blog over your personal interests. Chances are high you make some great friendships out of it too.

Over this period, at least a few people have reached out to ask for advice on writing technical pieces, how to do better research for articles, how to be more disciplined, and the like. I don’t like advising people, much less people who I barely know. Mostly because my advice is considered bad by almost everyone who doesn’t know me from the internet. I think I’ve lived a life that’s given me a very ‘spiky’ view of the world, and a fair bit of what I genuinely consider to be true/correct is viewed by most as obviously wrong.

Yet, I do usually give advice to people who ask, because being asked for help does feel good and it’s probably annoying to be told ‘I don’t think you should ask me for advice’ when you specifically requested that advice in the first place. There’s this interesting essay I once read about how denying someone’s positive value assessment of you is doing both them and you a disservice; insulting them for having bad judgment and disparaging your own sense of self-worth.

So, in that spirit, I’ll give some advice.

I would also consider SSC’s advice on writing one of the best collations of useful writing wisdom out there. In many ways, the primary advice I have mirrors his advice, so this article will try to deviate from it a little.

Some long advice

Write for people who actually exist

I started my first blog ever in college around 2018. In the blog, I wrote about certain independent ML research projects I was working on, hoping that forcing myself to consistently write would encourage me to make progress on the projects. It didn’t help, and I gave up writing altogether after the third post.

I usually posted my posts to r/MachineLearning right after I published each one, since that was the only method I had for getting eyes on my work. To my surprise, Ferenc Huszár — a now-professor at Cambridge who has an excellent ML blog with an excellent name (inference.vc) — commented on one of them.

In an effort to semi-anonymize the comment and, consequently, my Reddit account, I asked Claude to rewrite it for me:

In other words, write for people who actually exist.

We can round the number of readers who are deeply interested in your thoughts about the scaling laws of protein language models and have never heard of MSA’s to roughly zero. So, if your article diving into the physics of mitochondrial uncouplers stops to explain the concept of organelles, you’ve lost the plot a little on who you actually want to target with your essay. Of course, every post you write needn’t purely target experts/beginners, a mix between posts is much more interesting, but each post should be appealing to one demographic at a time.

You could try to target everyone at once, but it’s hard and usually leads to extremely bloated posts that satisfy no one. To echo Ferenc’s comment, this is hard to do and I fail to do it very often!

One of my earliest posts about scRNA-seq foundation models is my least favorite article for this reason. It’s very unclear who exactly I’m targeting with the way I’m writing. I constantly dip in between very simple and very complicated topics, which feels super distracting to read. Maybe it was unavoidable, but I feel like I could have done a better job had I tried writing it today.

Conversely, my microbiome article is one of my favorites, largely because it stays at a medium level of complexity throughout. I assume a fair bit of background knowledge on the reader’s end, but I stay consistent with it throughout.

History is usually fluff

Read more

1. Introduction

2. The dialogue

Part 1 is focused on variant pathogenicity prediction using these models.

Part 2 is focused on genome generation using these models.

I am aware that DNA language models are useful for things other than those two (like protein fitness), but variant prediction and genome generation are the two bits that I find most interesting.

Twitter link for this article.

Introduction

(The Introduction is a repeat from Part 1. Skip it if you’ve already read it!)

I think I, alongside many other people in this field, live in this seemingly parallel universe where we don’t really understand why anyone is working on DNA language models. I say ‘parallel’, because there is obviously a world in which some very smart people are very much bullish about them: specifically the Arc Institute. Who, just yesterday, released a paper that many people are quite excited about: Evo 2, a successor to the original Evo model. From the news article:

Arc Institute researchers have developed a machine learning model called Evo 2 that is trained on the DNA of over 100,000 species across the entire tree of life. Its deep understanding of biological code means that Evo 2 can identify patterns in gene sequences across disparate organisms that experimental researchers would need years to uncover. The model can accurately identify disease-causing mutations in human genes and is capable of designing new genomes that are as long as the genomes of simple bacteria….Building on its predecessor Evo 1, which was trained entirely on single-cell genomes, Evo 2 is the largest artificial intelligence model in biology to date, trained on over 9.3 trillion nucleotides—the building blocks that make up DNA or RNA—from over 128,000 whole genomes as well as metagenomic data. In addition to an expanded collection of bacterial, archaeal, and phage genomes, Evo 2 includes information from humans, plants, and other single-celled and multi-cellular species in the eukaryotic domain of life.

Which on face value seems cool and obviously impressive. Right? You read this and instinctively go, ‘wow, neat!’. But if you then try to ask ‘why would anyone want this?’, there aren’t many resources to go to (though I did enjoy Asimov’s recent piece about it). At least outside of the paper, which is a bit hard to immediately understand. For protein folding, the use case of something like Alphafold2 was obvious: if you know the shape of a protein, you can design things that bind to them. And even use the model to generate binders! If you have a model that has a really good latent understanding of DNA sequences, you can…do what exactly? Maybe for some people, the answer is obvious. But it’s not for me, someone who isn’t a genomics person. So this post will be discussing that.

I learn best via Socratic dialogue, which is just argumentative dialogue between individuals based on asking and answering question. This post will have that same setup, but for DNA language models. Notably, this is not specifically a post about Evo 2 (though I will often refer to it). It is more about what utility such a model would even have. So, no data or architectural details here, outside of where necessary to discuss use cases.

The dialogue

Okay, so, previously we talked about how DNA language models are useful for variant pathogenicity. It’s an open question how useful it’ll end up being, but I can see the argument there. But there’s a second part to the Evo 2 paper: the utility of it for genome generation. I don’t understand this at all. Why would you want to generate genomes?

Remember how, when we discussed variant pathogenicity, we drew this line connecting conservation scores and Evo 2 log likelihoods? It may be helpful to do something similar here for genome generation.

Do people ever actually do genome generation?

Somewhat. To start off with, let’s focus on partial genome generation. We’ll come back to full genome generation later on.

The most basic case of genome generation can occur when people need to fill in gaps in sequencing data. Next-Generation-Sequencing (NGS) data involves chopping up DNA into small fragments, reading those fragments, and then trying to stitch them back together. Sometimes, there are regions that just don't get sequenced well—maybe they're too repetitive, or too GC-rich, or [something else]. That’s why some parts of the human genome remained technically incomplete until just a few years ago.

A model like Evo 2 could potentially generate biologically plausible sequence for those gaps, based on the surrounding context. Definitely not something that’d be considered ‘completing a genome’, but, just as is the case with variant pathogenicity, it helps researchers start from somewhere as opposed to nowhere.

That sounds like a terrible use-case. Imputing the rest of a genome given fragments of it feel like the sort of thing where nearly perfect accuracy really matters, and I’m not sure I’d trust an autoregressive model like Evo 2 to get there.

You’re right! And that’s why the authors don’t even bother to test Evo 2 on that, it’s not a particularly compelling problem, especially since there are plenty of other methods, like long reads with nanopore sequencing, to side-step the whole issue. But it’s a good mental stepping block!

Now, let’s go slightly further. A more complex place people do genome generation is by trying to get the cell to produce a molecule of interest, say, insulin. They wrap the INS gene into a plasmid, force it into the yeast cell, and it starts to produce insulin.

Okay, but that also doesn't really count. That's just inserting a gene we already know into a cell, which doesn’t even integrate with the yeast’s genome. There's no generation happening, just copying and pasting. And before you say, ‘you need to do codon optimization also’, that also doesn’t count! That’s just a lookup table,"CAG becomes CAA" and so on. It's still hardly "genome generation" in any meaningful sense.

You’re wrong! In practice, the way yeast cells are told to produce insulin does involve a fair bit of engineering beyond pure codon optimization.

Remember, in all cells, insulin isn’t produced as insulin right away. It starts as preproinsulin, which then gets processed into proinsulin, and finally into active insulin. In human pancreatic beta cells, preproinsulin has a built-in signal peptide at the very beginning of the protein sequence to cause this chain of events. This signal peptide tells the cell to send the protein into the endoplasmic reticulum (ER), where it gets processed into proinsulin and eventually into mature insulin. Once preproinsulin enters the ER, the signal peptide gets cleaved off, and the remaining protein continues along the processing pathway.

But yeast doesn’t have the same protein processing machinery as humans do. So if you want yeast to secrete insulin, you can’t just rely on the human version of the signal peptide. Instead, you need to swap it out for a yeast-specific signal peptide—one that yeast actually recognizes and responds to. And, unlike codon optimization, there wasn’t a lookup table for this sort of work, since yeast doesn’t naturally produce insulin! It took years of work to nail down the most optimal signal peptide to use.

But even then, that’s still simple! It’s just a peptide you need to modify on a pre-existing INS gene, which, while annoying to tune, still has a reasonably small search space.

Fair, so let’s now consider a genome generation task that is actually complicated.

Consider antibodies. Antibodies are really, really hard to make. This is partially because you typically need mammalian cells to produce them, which are notoriously easy to kill, hard to genetically modify, and grow slowly. Why can’t we use yeast, like we do for insulin? I’ve discussed this before in an antibody article, quoting from here:

Yeast is challenging to kill, replicates easily, grows fast, and is amenable to genetic manipulation. So why don’t we use them [to create antibodies]? There’s a really wonderful review paper that discusses all this. Antibodies require specific post-translational modifications, particularly glycosylation, at a specific residue (residue 297 of each heavy chain), to function ideally in the human body. While yeast cells do have their own glycosylation machinery, it differs substantially from that of mammalian cells. Yeast tends to add high-mannose type glycans (as in, sugar molecules that contain a lot of mannose) to its produced proteins, which are not typically found on human proteins and can potentially make the antibody more immunogenic, which is obviously undesirable. Looking beyond yeast has similar issues; many types of bacteria lack any glycosylation system at all.

So are we doomed? Well, no. You could do something called ‘glycosylation engineering’ on the yeast cell, which would hopefully push our yeast cell into producing more human-like sugars.

The obvious question is…how exactly do you do this?

Simple: knock out yeast-specific glycosylation enzymes that add high-mannose sugars, introduce human glycosylation enzymes that add the correct sugars in the correct locations, modify regulatory elements so the new pathway is expressed at the right levels, and, of course, ensure that all the above changes don’t affect the capacity for the yeast cell to continue surviving, reproducing, and secreting the antibody.

That doesn’t seem simple at all.

It’s sarcasm, which understandably doesn’t come across well through text. But yes, it’s hard. It shouldn’t be surprising that glycosylation engineering as a field is very much in its infancy, precisely because the genetic engineering you need to perform involves replacements much of a yeast cells genome. And there is no simple way to do that! There are tons of genome generation tasks like this that people have to do almost entirely manually or are just left completely unexplored because it’s so hard to explore the search space.

Okay, that’s a complex-enough task that I’d be comfortable identifying it as ‘genome generation’. But what you’ve just described is a conditional form of genome generation, where you’re trying to push a yeast’s genome into a specific direction, that direction being ‘use human-like sugars’. Returning back to Evo 2, the model seems like it’s only capable of conditional generation in terms of surrounding nucleotides, but it cannot be steered towards a specific function. All it can do is make ‘natural’ looking DNA. Am I wrong?

You’re right, but as with all generation processes, guiding them isn’t too hard if you have access to a good-enough oracle to sift through the generations. And in fact, the Evo 2 authors did exactly that.

Some quick background: some parts of a genome are open chromatin, meaning they’re accessible to transcription factors, polymerases, and other regulatory proteins. These are typically active promoters, enhancers, and regulatory regions that influence how genes are expressed. Other parts are closed chromatin, meaning they’re tightly packed and essentially invisible to the transcription machinery. These tend to be silenced genes or non-functional regions. Why isn’t everything open? Because not all cell types need access to all genes; a liver cell does not need the same proteomic machinery as a cardiac cell.

This pattern of open/closed are also often referred to as the ‘chromatin accessibility profile’ of a genome. While one would be correct in referring to this as an epigenetic feature of a genome — as in, a chemical marker on top of the nucleotides, and not the nucleotides themselves — it’s also true that the nucleotide sequence can predispose certain regions to accessibility or inaccessibility. Thus, if you have access to the sequence of the genome, you have access to a fuzzy view into its chromatic accessibility profile.

Returning back to the subject at hand, Evo 2 was used to redesign a genome’s chromatin accessibility profile. The following strategy was employed:

1. Start with a small prompt sequence (in this case, a few thousand base pairs from a mouse genome).

2. Generate multiple sequence continuations (128 bp chunks at a time) using Evo 2.

3. Evaluate each generated chunk using external chromatin accessibility prediction models (Enformer and Borzoi).

4. Keep only the best-scoring chunks—i.e., the ones whose predicted chromatin accessibility matches the desired pattern. Regenerating the nucleotide continuations from 2 over and over again (up to 60 times) until these models gave the green light led to increased performance.

5. Repeat the process until a full sequence (~20 kb) is generated.

This is a complex task! It isn’t perfectly nailed down exactly how to modify chromatin accessibility, but we do know that certain motifs and sequence patterns contribute to it. Transcription factors (TFs) play some role here; if a sequence has a strong TF binding site, it’s more likely to have an open chromatin profile. Conversely, certain DNA structures — like tightly packed nucleosomes — can make a region inaccessible, making it have a closed chromatin profile. But there’s no lookup table for this sort of work.

What was the actual problem that Evo 2 was tasked with?

The specific task assigned to Evo 2 was to generate DNA sequences that encode predefined chromatin accessibility patterns, including “EVO2” (. ...---- ..---) and “ARC” (.- .-.-.-.), along with a few related designs. In this encoding scheme, open chromatin represents dots and dashes, while closed chromatin represents spaces. Each character corresponds to 384 base pairs, equivalent to three sequential Evo 2 generations.

If you’re confused by the referring to this as ‘morse code’, so am I. Practically speaking, something like EVO2 translates to open-closed-open-closed, and ARC translates to open-closed-open. Which feels somewhat clumsy, but it’s a minor point.

And how did they assess performance?

This is where at least a few people have been somewhat bewildered. The authors rely entirely on the chromatin accessibility method models to evaluate the success of their designed sequences—specifically, whether the predicted accessibility pattern aligns with the intended Morse code signal. In other words, they generated the sequences, fed them back into Enformer and Borzoi, and checked if the models agreed that the correct regions were open or closed.

So, no experimental validation? Evo 2 is optimizing its outputs based on Enformer and Borzoi, and then its success is measured by Enformer and Borzoi? Isn’t that a clear case of adversarial optimization?

Well, maybe. The authors do touch on this a little. Here’s a plot:

So, figures E and F come from nucleotides generated via Evo 2. Figure E says that that Borzoi and Enformer are confident and accurate about their prediction of open/closed chromatin regions — at least, if you trust the models predictions. And Figure F says that the distribution of generated dinucleotides (so, pairs of nucleotides, of which there are 16) match up with the pattern of typical mouse genomes.

To study this the possibility of adversarial optimization, the authors compare the Evo 2 generated nucleotides to nucleotides that were generated through an entirely random process (and still filtered via Enformer and Borzoi, and sampled at high rates). Here is that plot:

Figures B and C are analogues to figures E and F. Here are the two fundamental claims that the authors make from these plots:

1. If Evo 2 produced adversarial sequences, you’d expect strong deviation between Enformer and Borzoi. You do see this deviation with uniform generation, but not with Evo 2.

2. If Evo 2 produced adversarial sequences, you’d see deviation from the naturalness of the produced nucleotide patterns to the reference mouse genome. You do see this deviation with uniform generation (at least for some patterns), but not with Evo 2.

Thus, Evo 2 is (probably) not falling prey to adversarial generation.

I think, fairly, this wouldn’t be considered a satisfying enough explanation to most. But I do think there is an inkling of some assurance here, especially with 1. After all, RFDiffusion also relies on this same sort of ‘orthogonal selection’ by assessing generated proteins partially on the structural agreement between RFDiffusion and Alphafold2. And that seems to work well!

So why wouldn’t it work well for the nucleotide parallel?

Who knows? I’m not deep enough in the field to say whether Enformer and Borzoi are independent enough to serve as a valid cross-check. I also do not know whether ‘unnatural-looking’ dinucleotides are evidence of…anything really. I agree with basically everyone else that experimental validation will be necessary to trust these results further.

I think a particularly worrying bit is Figure A in that second figure you attached. Uniform seems to work…surprisingly well. It’s certainly worse than Evo 2, but not that much worse. So, all the fuss you made about the complexity of glycosylation engineering doesn’t transfer to this genome generation task. Empirically, remodeling chromatin accessibility doesn’t seem to be that hard!

Yeah…I don’t know. I think the reliance on trained oracles to use for filtering Evo 2 outputs means that, for now, it is a bit limited in the complexity of what it can do. I do wish the authors tried it out on a few, super low-shot genome engineering tasks, where uniform nucleotide generation completely fails. Something like generating a minimal enhancer that can activate a reporter gene in a specific cell type or designing a simple promoter that works across multiple species. I have no idea what that would look like…maybe fine-tuning Evo 2 on a few cases of enhancers + promoters, and seeing how well it can make new versions?

I do think there will be a slew of follow-up papers that try to attack all the potential use cases here. Maybe something even equal to the complexity of glycosylation engineering! We’ll see, I can very much understand wanting to not make a 40 page paper even longer.

Okay, moving on. Thus far, we’ve only discussed partial genome generation, but there is also this take I keep seeing on Twitter about how DNA language models are useful for generating whole genomes outright. I don’t really grasp this at like…an object level. What does it even mean to generate an entirely new genome?

Well, it’s not an entirely new concept. People have made synthetic genomes for a while now, the most well-known and oldest one being the Mycoplasma minimal genome project, where researchers at the J. Craig Venter Institute synthesized an artificial bacterial genome back in 2008. From the Wiki page:

To identify the minimal genes required for life, each of the 482 genes of M. genitalium was individually deleted and the viability of the resulting mutants was tested. This resulted in the identification of a minimal set of 382 genes that theoretically should represent a minimal genome.^{[a 3]} In 2008 the full set of M. genitalium genes was constructed in the laboratory with watermarks added to identify the genes as synthetic.

One of the newest works in this space was published just a few weeks ago in Jan 2025— part of the ‘Synthetic Yeast Genome Project’ and similarly hosted at the J. Craig Venter Institute —and is a degree more advanced, designing an existing yeast chromosome from scratch. The resulting chromosome, 902,994-bp long, reshuffles, redesigns, and removes existing genes.

As one may expect, genomic generations of this scale are painful to do in practice. Each chromosome was broken into chunks, then synthesized, tested, and debugged piece by piece. Genomic error correction had to be used to fix problems that arose after synthesis. The design phase itself took years of manual effort to decide which genes should stay, move, or be deleted. It should be no surprise that the Synthetic Yeast Genome Project has required over a decade of meticulous work by an international consortium of hundreds of scientist and still remains incomplete. While this 2025 paper wrapped up the final of 16 synthetic chromosomes to be designed, there still is work left to be done to actually integrate them into a single cell strain.

So…that’s the pitch for Evo 2. To do exactly this, but in a purely in-silico fashion and with a hopefully much higher hit-rate than other methods.

I still don’t get what the synthetic genome is doing that’s so different. Are they just adding in new, mostly innocuous nucleotides everywhere? Are there brand new biochemical processes being made? It feels like there’s a spectrum here between ‘new genomes that lead to functionally identical outcomes for the organism’ and ‘genuinely new life that could’ve never naturally evolved’.

I think it’s a mix.

Closer to the former side, the Synthetic Yeast Genome Project made it a point to drop transposons from the engineered yeast genome. Transposons are genetic elements that can move around and disrupt important coding or regulatory regions. In turn, they contribute genomic instability and make it harder to maintain a consistent genetic background for experiments or industrial applications. So removing them is genuinely novel and helpful for researchers! But you’re also not fundamentally changing the genome’s functionality. While transposons are present in nearly all life on Earth, they are more of an evolutionary byproduct that provides genetic variation over long timescales rather than something absolutely essential to the life of the host organism.

One far more interesting addition, closer to ‘never would’ve evolved naturally’ is the SCRaMbLE system (Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution) that was directly encoded into the synthetic yeast genome. This system introduces recombination sites throughout the synthetic yeast genome, allowing researchers to induce large-scale genome rearrangements on demand using Cre recombinase, effectively giving the yeast a built-in, programmable method of rapid evolution. SCRaMbLE enables controlled deletions, duplications, inversions, and rearrangements of genes in real-time, creating thousands of genome variants in a single experiment.

Of course, most created yeast strains will be completely dysfunctional or worse at doing [X] than the wildtype strain, but you only need to be lucky once! This just dramatically ramps up your ability to take shots on goal.

Did the Evo 2 authors actually do something like the aforementioned yeast genome redesign in the paper?

Well, no, the paper is very much focused on proof-of-concepts for now. What they did are three things:

One, valid mitochondria genome generation. Mitochondria have reasonably simple genomes—circular DNA molecules with a well-characterized set of protein-coding genes, tRNAs, and rRNAs, so they are a good first step if your goal is whole genome synthesis. Given a prompt of human mitchondrial DNA, Evo 2 infilled in the remaining, 16 kB worth of nucleotides. The generated genomes (of which they made 250) passed basic sanity checks, such as containing the expected number of genes and maintaining synteny (the physical location of the gene sequence) with natural mitochondrial genomes, like the below plot shows:

Two, prokaryotic genome generation. Specifically M. genitalium, the same organism redesigned back in 2008 that we previously discussed, and specifically known for its relatively small genome length (580 kB). Using the first 10.5 kb segment from the host genome, Evo 2 generated the remainder. The primary way that the resulting whole genome was assessed was by checking whether the predicted protein-coding regions contained Pfam ‘hits’, meaning that the proteins encoded by the Evo 2-generated genome matched known functional domains from real biological proteins. In turn, this serves as a proxy for biological plausibility.

In total, 70% of the Evo-2-generated genome had a Pfam hit. Predicted protein structures from the synthetic genome also had — according to ESMFold — structural analogs.

Three and finally, eukaryotic chromosome-scale genome generation. Specifically, Saccharomyces cerevisiae chromosome III, a 316 kb long chromosome from yeast. Using a 10.5 kb segment as a starting prompt, Evo 2 generated the remaining sequence, creating 20 fully synthetic yeast chromosomes. Just as with before, Pfam hits and structural assessments of genes with high Pfam hits were done, both of which looked good. That said, the authors note that it is unlikely that the generated yeast chromosome actually works, given that the density of tRNA (predicted via yeast-genome-specific tools unconnected to Evo 2) and absolute number of genes falls a fair bit below those of natural yeast genomes.

These feel like really strange ways to assess correctness of a generated genome. Protein domain matching? Structural analogs of predicted proteins from the genome? The genome doesn’t exist to produce proteins alone, it’s a huge, self-regulating conditional logic system! Pfam hits and structural analogs don’t capture any of that. They are, at best, heuristics for determining whether Evo 2 has generated something that looks like a genome, rather than one that actually functions as one. Why didn’t they just test this in a real cell?

Listen, we’re on the same page. But I think it’s worth considering how insanely complicated whole genome synthesis is.

Let’s say you wanted to personally test these DNA designs. You would’ve needed to assemble overlapping DNA fragments 10kB~ long, assemble them together inside of a cell, and then painstakingly screen for correct assemblies, because even with modern synthesis methods, errors creep in all the time. DNA synthesis isn’t perfect, and when you’re working with hundreds of thousands to millions of base pairs, the odds of introducing mutations or assembly failures are practically guaranteed. You’d then have to transfer the assembled genome into a viable host, which is an entire challenge on its own. You need to remove the original genome, prevent the cell from dying in the process, and hope that the synthetic genome actually starts functioning correctly.

It would’ve cost thousands of scientist hours and potentially millions of dollars. And what would’ve been the point? It’s almost certainly likely that the DNA generated by Evo 2 isn’t actually fully functional, I’m sure any of the authors would agree to that. But how nonfunctional? Is it entirely missing a whole set of extremely important transcription factors? Or, through sheer miracle, is it maybe just 10~ nucleotide mutations away from genuinely functioning? In either case, the only reaction from your cell would just be death, with you having learned no new information from the extremely expensive, multi-year endeavor.

I think one should look at the whole-DNA generation task that Evo 2 is doing as something that has like…no real functional way of being meaningfully evaluated beyond "does this look like somewhat like a genome?”. At least for now. You could get upset at this and the authors for not willing to really put skin in the game to test this out (for no reason, because they’d almost inevitably fail). But I think it’d be better to view this as an early poke into what really is a brand new scientific field, and brand new fields often lack easily accessible benchmarks for success.

Then what’s the point of genome generation? Why do any of this until we have those benchmarks?

I don’t control the levers of how R&D money is allocated, so my opinion is obviously meaningless. But I do think it is a very good idea for machine learning (ML) in biology to focus on problems where there is literally no alternative way to solve it. The current zeitgeist is people ignoring this advice and using ML to do CDR engineering with antibodies, which is questionable given that CDR’s are loopy, and you can assess protein loops at decently high-scales without needing any machine learning whatsoever (e.g. phage display). And what’s the end result of this? Maybe marginally better antibodies, that’s it. Nothing crazy happens as a result of someone really solving this.

On the other hand, if you can do reliable genome generation, you can create plants that sequester carbon at 1000x the typical rate, bacterial cells that can pump out antibodies fast enough to drive the cost of Humira down to pennies, and so on. But there is no other scalable way to do it at all without ML. If you wanted to rationally design a whole genome, you’d have to manually select every gene, every regulatory element, every replication origin, every transcription factor binding site, and then somehow figure out how all of them interact across a multi-megabase sequence. We’re clearly able to poke at very basic versions of this, but doing anything genuinely complicated is simply not feasible with human intuition alone, or at least requires so many decades to do as to not be scalable.

Now, fairly, it clearly seems to be infeasible for machines right now too, but at least ML is capable of trying—of exploring the combinatorial space of possible genomes in a way that humans fundamentally cannot. The alternative isn’t “do it manually”; it’s not doing it at all! And it’s clearly something worth doing.

So, in case you’re wondering if I’m a DNA language model supporter or hater, I’m officially counting myself in the former group, despite having starting these series starting at the latter. I just hope someone invents better ways to validate the results of them. This area just feels very much like something I’d want a blue sky, biology x computational research institution like Arc to focus on.

And that’s it! Thank you for reading, here is Part 1 again in case you’d like to see that again. And sorry for making this a 2-parter, the full thing would’ve been nearly 9,000 words long and that’s a bit much.

Is that it? There’s so much more covered in the paper! What about the whole mechanistic interpretability section?

I think it’s all very interesting, but it doesn’t feel worth talking about in depth, at least not yet. Not because it’s not cool, more because I cannot see the direct value of it. Similarly, while I find the mech interp stuff in the protein world cool, I’m still a bit unclear about the point. I’m sure I’ll be proven wrong about this very, very soon. Maybe that’ll be another essay in the future!

1. Announcements

2. Links

3. Jobs (Contact me if you’d like to be added here!)

Announcements

Since the last roundup, two articles have been published. In order:

• AI-Designed Enzymes

  • A walkthrough of a Baker-lab paper that designed a complex class of enzymes

  • But this was actually published on Asimov Press, that link is here.

• A socratic dialogue over the utility of DNA language models (Part 1 of 2)

  • An essay that discusses the question that everyone has been asking for awhile: what’s the point of DNA language models? At least with regards to variant pathogenicity, I answer that question. Next part will come out soon and cover these models role in genome generation!

Thank you to everyone who reached out to me to be a guest writer! Got tons of really great pitches, hoping to publish some of them over the next few months. Also thank you to everyone who offered recommendations on people to use for podcast editing!

Links

STAT piece about how two AI biotechs (Generate and Absci) aren’t actually creating drugs from scratch with AI.

• I say this with a deep appreciation for how hard it is to take a strong stance online and deal with the vitriol sent your way: this article has an excellent hook but fails to deliver. I think there is a very interesting argument to make here about how, for example, CDR-only redesigns of existing targets fall short of the possibility of full de novo engineering (e.g. Fc engineering, which some AI biotechs do!) or the beginnings of full de novo engineering in the nanobody space, but neither is touched upon. Instead, headlines and soundbites by these companies (which, fairly, may overpromise!) are focused on as the opponent, instead of the very real research that is being done. My beef here really may have little to do with the author of this piece — they are reporting on the news, and the cultural sentiment being echoed here is indeed news. And it is not the job of news to search for capital-T truth, but the essay would’ve been so much stronger had it at least tried to do so.

RFdiffusion Exhibits Low Success Rate in De Novo Design of Functional Protein Binders for Biochemical Detection.

• Again, great title with a potentially amazing point (RFDiffusion doesn’t work that well for binder generation? Tell me more..), but the methods used are a bit lackluster. They only test 5 RFDiffusion-generated binders against 6 targets and find that most fail. Which is fully expected! RFDiffusion does not have a 20%< success rate for binder creation, it’s more on the order of 1%~ (potentially an order of magnitude lower, which is still far better than wet-lab tools for binder generation), which is explicitly detailed in the paper and known by users of the tool. The comments on the original thread may be interesting to read as well.

Interesting essay on reasoning model approaches from Andrew White (a founder of FutureHouse)

Essay on practical guide to biotech partnerships by Lucas Harrington (a founder of Mammoth Biosciences)

Jobs:

Contact me if you’d like to be posted here! If I’ve posted you here before, feel free to ask again. Moving forwards, I’ll just throw up a posting here if I see a job description that my reader-base might be interested in.

Dyno Therapeutics (where I work!) is hiring a machine learning scientist.

• Excellent company, excellent culture, and excellent research. You should apply!

Plasmidsaurus is hiring across the board, but especially for wet-lab and software positions.

• I think it’d be difficult for me to overstate how valuable of a company I think Plasmidsaurus already is and will become, both financially and in terms of net good for the world. Amongst every company I’ve written about, I strongly believe Plasmidsaurus will be the one that will have a book written about them in a decade. For a primer on what they do, here is an article I’ve written about them.

Pratyush Tiwary’s lab at U Maryland is hiring a postdoc in physics-driven AI for RNA and proteins.

• Ideal for someone who wants to transition to industry!

Adaptyv Bio is hiring across the board, but especially for senior lab automation engineers, high-throughput assay development, and bioengineering leads.

• Must be based in Switzerland!

1. Introduction

2. The dialogue

Part 1 is focused on variant pathogenicity prediction using these models.

Part 2 is focused on genome generation using these models.

I am aware that DNA language models are useful for things other than those two (like protein fitness), but variant prediction and genome generation are the two bits that I find most interesting.

Introduction

I think I, alongside many other people in this field, live in this seemingly parallel universe where we don’t really understand why anyone is working on DNA language models. I say ‘parallel’, because there is obviously a world in which some very smart people are very much bullish about them: specifically the Arc Institute. Who, just yesterday, released a paper that many people are quite excited about: Evo 2, a successor to the original Evo model. From the news article:

Arc Institute researchers have developed a machine learning model called Evo 2 that is trained on the DNA of over 100,000 species across the entire tree of life. Its deep understanding of biological code means that Evo 2 can identify patterns in gene sequences across disparate organisms that experimental researchers would need years to uncover. The model can accurately identify disease-causing mutations in human genes and is capable of designing new genomes that are as long as the genomes of simple bacteria….Building on its predecessor Evo 1, which was trained entirely on single-cell genomes, Evo 2 is the largest artificial intelligence model in biology to date, trained on over 9.3 trillion nucleotides—the building blocks that make up DNA or RNA—from over 128,000 whole genomes as well as metagenomic data. In addition to an expanded collection of bacterial, archaeal, and phage genomes, Evo 2 includes information from humans, plants, and other single-celled and multi-cellular species in the eukaryotic domain of life.

Which on face value seems cool and obviously impressive. Right? You read this and instinctively go, ‘wow, neat!’. But if you then try to ask ‘why would anyone want this?’, there aren’t many resources to go to (though I did enjoy Asimov’s recent piece about it). At least outside of the paper, which is a bit hard to immediately understand. For protein folding, the use case of something like Alphafold2 was obvious: if you know the shape of a protein, you can design things that bind to them. And even use the model to generate binders! If you have a model that has a really good latent understanding of DNA sequences, you can…do what exactly? Maybe for some people, the answer is obvious. But it’s not for me, someone who isn’t a genomics person. So this post will be discussing that.

I learn best via Socratic dialogue, which is just argumentative dialogue between individuals based on asking and answering question. This post will have that same setup, but for DNA language models. Notably, this is not specifically a post about Evo 2 (though I will often refer to it). It is more about what utility such a model would even have. So, no data or architectural details here, outside of where necessary to discuss use cases.

The dialogue

Obvious first question: why make a DNA language model at all? All you’ll really get in the end is something that will tell you if a stretch of DNA looks ‘natural’ or how to infill in a section of missing DNA. What’s the point of that?

An obvious question deserves an obvious answer: the former bit about naturalness implicitly allows one to predicts what genetic mutations do — as in, whether or not it is deleterious to the host. For the moment, let’s just focus on that.

Right now, if you sequence someone's genome and find a mutation, you have no idea if it’s important unless it’s already been studied. If it’s a mutation we know causes disease, great, problem solved. But what if it’s a random nucleotide switch in some weird noncoding region? Does it break a regulatory element? Does it subtly mess with splicing? Does it do nothing? There are millions of genetic variants in humans, and we only know what a fraction of them do. Evo 2 can potentially help us uncover the harmful ones.

How? All Evo 2 can say about a stretch of DNA is how unusual it looks. Is the model equating a variant being harmful with it having a low log likelihood (LL)? If so, it feels deeply strange to equate non-naturalness with something dangerous.

It is doing that!

And I get the hesitation about equating non-naturalness to danger, but it’s genuinely not a big leap. Evolution is a probability engine. If a particular genetic sequence has been around in thousands of individuals, across thousands (or millions) of years, that suggests it’s been evolutionarily stable. It works. It doesn’t kill you. If a mutation pops up that has never been seen before, that doesn’t necessarily mean it’s bad, but it does mean evolution hasn’t had the chance to vet it yet. Which also (probably) implies that it is, indeed, bad.

And, truthfully, it doesn’t really matter if you personally buy this line of reasoning, because it’s a mental model that normal geneticists have used for decades with a pretty decent success rate. Traditional pathogenic variant annotation rely pretty heavily on conservation scores, like PhyloP, which all work on the assumption that mutations in highly conserved regions are more likely to be harmful, or at least worth taking a closer look at. Evo 2 can be seen as doing the same thing, except at a much, much larger scale.

So…just like Alphafold2 is technically ‘just’ doing homology matching for proteins, Evo 2 is technically ‘just’ doing conservation scores for DNA?

You can think of that way, but...we’ll discuss a more nuanced view on that later on in this essay. The real hope is that it’s actually doing something deeper than that. It’s useful mental scaffolding though!

Also, it may be worth mentioning that the authors did actually test out how well this ‘Evo 2 may be good at finding bad variants’ claim is by assessing how it did on variants of BRCA1. If someone says they have BRCA1 mutation, they usually mean one of the well-known pathogenic variants, like 185delAG or 5382insC, which both cause a frameshift and wreck the protein from being created at all. But what about all the other mutations in BRCA1? Most of them don’t cause frameshifts. They might be single-nucleotide swaps, insertions or deletions that don’t obviously kill the whole protein, or even mutations in the noncoding regulatory regions around BRCA1. And for a lot of these, understanding their impact is a bit less clear.

So, can Evo 2 help? To answer this, the authors gave the model 4,000 mutation variants of the BRCA1 gene (sourced from a mutagenesis study), across both coding and non-coding regions, and asked it to pick out which ones led to complete loss of function of the gene. No labels, just the raw nucleotides. It works quite well:

How do they actually do that? Like, given a stretch of a given BRCA1 mutation, Evo 2 will spit out a likelihood of it existing. How do you convert that to a ‘loss of function’ or ‘no loss of function’?

Yeah, so you do the forwards pass on Evo 2 twice. Once on the reference sequence (the normal BRCA1 gene) and the mutated version. You take the LL of each one and compare the two. If the mutated DNA likelihood is much lower (unexpected) than the normal one, they interpret that as a loss of function of BRCA1. They don’t binarize the differences, they just rank them and calculate the AUROC on that.

But...implicitly, doesn’t that mean that Evo 2, and DNA language models as a class, cannot do absolute categorization of pathogenicity? Like, with BRCA1, we have a nice, cleaned-up, large dataset for us to use to potentially find a good cutoff point for when the LL differences become high enough for us to define a variant as pathogenic. I’m doubting we can do that for most other genes. Doesn’t that dramatically reduce the use case of this whole system?

I think it’s a bit pessimistic to say ‘dramatically’, but the thesis of your point is right. Evo 2 cannot currently do a ‘this is pathogenic’ or ‘this is benign’ call, you still need some kind of external validation—either clinical data, functional assays, or a huge pre-labeled dataset like the BRCA1 mutagenesis study — to actually interpret the resulting LL differences.

So, in practice, Evo 2 will almost certainly look like something closer to a heuristic, just as is the case with conversation scores, a way to triage/prioritize variants for further studies. It’s just better at that task than traditional conservation scores.

That vaguely makes sense. I think the last question I have on pathogenic variant detection is…why do this at all? What’s the point? I get that there is some latent benefit in consumers being able to go to a genetic counselor and be told something other than “Variant of Uncertain Significance” for most of their mutations. That’s fine. But…I dunno, I’m just kinda bearish on consumer genomics as a category. Doesn’t really feel like it fixes anything, just points out what the problem is.

The pre-cached answer is that this ‘consumer being aware of a disease earlier than expected’ thing is more impactful than you’d expect. For a ton of conditions, early intervention matters a lot! And if we can discover more of those variants earlier — via models like Evo 2 — it’s all the faster that people can get that necessary intervention.

But you’re hitting across something important here in the overall utility of DNA language models. Their utility in actually helping cure things, instead of just helping intervene earlier, does strongly hinge on assuming some level of simplicity of DNA.

Like, we know the schizophrenia is highly heritable, but we haven’t been able to nail down the genetic basis for it. We know that heart disease has a strong genetic component, but outside of a few well-known risk alleles (like in PCSK9 or APOE), most of the heritability is tied up in thousands of tiny effect variants that we don’t fully understand. And for neurodevelopmental disorders like autism, we’re pretty sure genetics is a huge driver, but no one has a good map of how it all works.

And maybe Evo 2 would help us find those variants and thus, help us generate better drug/genetic engineering targets. On the other hand, maybe we live in the universe where all of it is too hard to disentangle, and Evo 2 saying "this mutation is unusual" doesn’t actually get us any closer to a real understanding of disease. Maybe complex disorders like schizophrenia aren’t a matter of finding the right mutations. Maybe it’s all thousands of weakly acting variants, all context-dependent, all interacting with each other in ways that a simple likelihood score can’t capture. And maybe there’s dozens environmental factor that confound it all. In that world — which is the one that we empirically live in —, Evo 2 will just tell us that most genetic risk is blurry, probabilistic, and fundamentally hard to act on. Which is something that researchers already knew pretty well.

Or maybe DNA language models change this by taking into account a huge number of datapoints and being able to consider mutations hundreds of kilobases apart and being entirely unsupervised. Who knows?

Wow. Bleak. So this model has a chance of being useless?

No. But it does have a chance of being far less radically transformative than the initial pitch may make it seem. At least if we’re limiting our purview of ‘impact' to be equivalent to ‘variant discovery that is directly impactful for human health’. Even if we end up in that world of ‘DNA is still really, really complicated‘, DNA language models still have use for the same reasons that conservation scores have use. Variant discovery may still be frustrating and probabilistic, but variants are still useful things to be aware of. After all, PCSK9 inhibitors were discovered as a result of an unusual genetic variant someone found. So having a better heuristic for prioritizing which variants to study during an R&D project is still potentially really nice.

I think it’s also worth noting that, since models can Evo 2 can be applied to non-human genomes just as well as it can be applied to human genomes, there’s potentially a lot of impact to be had there in the same general realm of variant discovery. Instinctively, I’m more bullish on this area than human genetics. It takes a huge amount of work to be able to study gene region functionality, and humans are a category where we’ve been (no surprise) willing to do that work.

But what about for esoteric cell lines and rare insects and hard-to-gather birds? Maybe there’s something deeply useful about being able to go 0-1 for those groups in terms of gene annotation. Not 0-100, like we’ve done for humans through decades of experimental work, but a kickstarter is always useful in research.

My instinctive assumption is…we’ve naturally stumbled across most of the genetic variants of clinical importance. I can see there being a lot left for non-model organisms, but what about humans? How much is there left on the table?

It’s an unknown unknowns thing, but instinctively the answer is ‘a lot’, even if you factor in how much time the scientific community has spend studying human genetics.

Like, noncoding DNA often have really long range dependencies. Say you have a transcription factor binding site that’s 100,000 bases away from the gene it regulates. A normal conservation score might catch this if the binding site itself is highly conserved across species, but also maybe not. Conservation scores often only look at individual positions or short windows of bases. Evo 2, on the other hand, sees the entire 100,000-base sequence in one go (and is in fact capable of seeing a million bases at once). It can pick up on statistical dependencies between distant elements, even if they’re not obviously related at a single-nucleotide level. Which, again, is probably useful.

Do ‘genomic areas that affect each other from 100,000 bases away’ actually exist or are you making it up?

Yeah, like enhancer-promoter interactions.

For example, in mammals, the SHH (Sonic Hedgehog) gene is controlled by an enhancer called ZRS, which is one million base pairs away from the gene itself. If you delete or mutate ZRS, you can get serious developmental disorders like polydactyly (extra fingers and toes), even though the SHH gene itself remains perfectly intact. Evo 2 would likely pick up on this.

But preempting the obvious question: lets say that Evo 2 has told you that ZRS is important for SHH expression, but what does that actually get you? The model may recognize ZRS mutations are “low-likelihood” and flag them as potentially impactful, but we already know ZRS is important. It’s not like this is some deep mystery in genetics that we’ve been struggling to figure out.

But let’s be charitable for a second. Evo 2, in theory, could do this without prior experimental evidence. No need for decades of painstaking enhancer screens or chromatin interaction assays—it just looks at a sequence and goes, “this bit of DNA looks weirdly important for something else really far away.” That would be nice, because while we know a handful of these long-range regulatory elements, we have absolutely no clue how many others exist across the genome. A lot of noncoding genetic disorders could be caused by things like this—faraway enhancers breaking genes that look perfectly fine in standard sequencing analyses. And, if Evo 2 is capable of identifying those relationships from sequence alone, that helps triage further research! We still probably need to do those enhancer screens and chromatin assays, but at least we can start off with a prioritized list of genomic regions to target.

It feels a little bit like a hammer that was invented for a nail that has precedent for existing, but we haven’t actually found the nail yet. You’re using the word ‘probably’ a lot.

That’s not a question, but yeah, I wouldn’t disagree with that characterization.

I feel in my bones that models like Evo 2 accelerate some aspects of the current variant discovery pipeline and we empirically know that variant discovery is useful. What we don’t yet know is how much Evo 2 will accelerate it all and whether anyone will actually make use of it. It may be the case that models like Evo 2 help us annotate the genome of some tiny, ultra-rare bacterial species discovered off the coast of California, and, just like the mountains of currently known pathogenic variants, scientists largely ignore it.

Or it might be the case that it’ll be extraordinarily useful right off the bat even for human genomes and people gain a dozen new target ideas after using it. I think the ultimate utility of it will be an exercise in creativity rather than there being a crank you need to turn. I think institutes like Arcadia Science, who study non-model organisms, may be extraordinarily well suited to do something really interesting here using models like these.

Can we move onto the genome design bits of Evo 2?

Unfortunately, we’ll have to cut this short before we get to that, because I’m sleepy and that’ll add another 3,000~ words to this essay.

But, prior to ending things, I think it’s worth sitting for a bit and re-assessing this conservation score comparison you made awhile back. DNA language models are like that, but I feel like it’s important to consider that something fundamentally beyond conservation may be being learned here.

Traditional conservation scores work by looking at multiple sequence alignments (MSAs). You take a bunch of genomes from different species, align them, and then see how often each nucleotide changes. Like we’ve mentioned, if a position is very conserved across millions of years, that’s a strong sign that messing with it might cause problems.

Evo 2 does not use MSAs at all. Instead it, as with every language model, is attempting to learn the underlying distribution that causes valid DNA. So instead of explicitly measuring conservation by comparing sequences across species, Evo 2 implicitly learns conservation from first principles. But the hope is that it’s learned something much deeper than that — since it’s never been provided MSA’s — resulting in it understanding ‘grammar of DNA’ in some capacity. Grounding this, this means that we’d also expect that Evo 2 likely wouldn’t suffer from the same problems with traditional conservation scores and may in fact be capable of doing a lot more than them.

It’s a cool idea, but I am instinctively unsure of it. Like, did you see that Sergey Ovchinnikov paper about how protein language models are simply implicitly learning evolutionary statistics of proteins? And not biophysics, even though people had hoped a model that lacked MSA as input (just like Evo 2), would in fact have a better physical understanding of proteins? How do you know that these DNA models aren’t doing the exact same thing and will suffer from the exact same limitations that conservation scores have had, outside of just being able to cover more DNA in its ‘conservation score’ than traditional methods?

Maybe! Given how closely PhyloP got to Evo 2 in the above BRCA1 variant prediction benchmark, it wouldn’t be shocking. I think to genuinely test this, we’d need to do something similar to how this sort of ‘evolutionary dependence’ was found for protein structure models: test it out on things with low evolutionary relation.

What are such things for DNA?

One easy case are so-called human accelerated regions, or HAR’s. They are a set of 49 segments of the human genome that are conserved throughout vertebrates but are highly mutated in humans. They are fascinating constructs, here’s a blurb from the wiki page over HAR2:

HAR2 includes HACNS1, a gene enhancer "that may have contributed to the evolution of the uniquely opposable human thumb, and possibly also modifications in the ankle or foot that allow humans to walk on two legs". Evidence to date shows that of the 110,000 gene enhancer sequences identified in the human genome, HACNS1 has undergone the most change during the evolution of humans following the split with the ancestors of chimpanzees.

If Evo 2 could tell the difference between pathogenic and non-pathogenic versions of these sorts of DNA elements, I think that’d be a strong signal of there being a universal genomic grammar being learned. If not...the model is still obviously useful, but it is, as are most of these sorts of models, trapped by the well of evolution. Which, maybe a bad thing, but also maybe not a thing that ends up mattering that much.

Given that Evo 2 is fully open-sourced (an amazing move by Arc!), it feels likely that benchmarks like these — alongside many of the other ‘what if you used the model in such-and-such way?’ statements raised in this essay — will be answered by curious researchers over the next few months. Prior to Evo 2, DNA language models were in a hazy place where it was unclear whether pretraining even helps them understand DNA better. Evo 2 does feel like a strong step in the direction of these sorts of models being more useful, given the data ablation results they present in the appendix, but I think it will be the papers that come in the wake of this model that more concretely flesh out its role in the variant prediction pipeline. Currently, I’m cautiously optimistic.

But what of genome generation? A task for which there isn’t a traditional method? What’s the role of DNA language models there? It’s a very interesting question, and one that shall be deferred to part 2.

Luckily, you needn’t read it yourself, at least if you aren’t an enzyme designer who needs undiluted information! Because, just yesterday as well, I, alongside my co-writer Eryney Marrogi, published a short article covering the paper in Asimov Press. Here’s the article. We go through why enzymes are hard to redesign, prior attempts to redesign enzymes, and why this paper is a step-level change in our ability to do so.

If you’re starved for more material, I published this speculative fiction biology piece with Asimov in September 2024, titled ‘Models of Life’.

I also wrote an essay titled ‘There aren’t enough smart people in biology doing something boring’ alongside Eryney in October 2024.

1. Announcements

2. Links

3. Jobs

Announcements

Since the last roundup, one podcast and one story have been released:

1. Podcast:

2. Story:

On a related note, you should subscribe!

Subscribe now

Also, one more poster has been put up:

Also, I am actively looking for two things:

1. A podcast editor.

   1. Currently, I handle most of the editing myself and outsource the clip-generation process to freelancers. These freelancers often aren’t great, and I’d love to be able to push the editing process outside of my to-do’s. This blog generates some amount of money which I am happy to use to pay a good freelance editor above market rate. But I don’t know of any! Would highly appreciate a referral to anybody!

2. Guest writers.

   1. I think this field (biology x computation) has an awfully high number of curious scientific ideas that would benefit from more eyes — some of which I try to cover, but my backlog list is ever growing. There is also a rather high number of students/scientists who are looking for their next role or desire more eyes on them. Writing is a good way to do that, and I’d love to host that! I don’t know what this looks like: maybe full articles, maybe a compilation of 3-4 small ‘thesis statements of interesting problems/ideas. Reach out to me if this feels interesting!

Links

Mammoth Biosciences releases a version of Cas9 that is three times smaller, termed ‘NanoCas’, which, for the first time, allows for genetic engineering tooling to fit inside an AAV vector. In-vivo genetic engineering on the horizon?

Sarah Constantin’s post about why prior synbio companies have failed. Open request for a deeper dive into this stuff!

Entirely Alphafold2-based model of a membrane protein complex with 17 chains! Fairly, no wet lab experiments done to confirm the model, so one may be fully in their rights to toss this into the trash. But it’s still quite interesting to see the reasoning here, very ‘lets try this out in Alphafold, okay it doesnt look good, lets try this out instead…’.

Very similar performance amongst all recent protein-ligand folding models (AF3, Chai-1, Boltz-1, etc) w.r.t unseen protein-ligand. I’ve long been pessimistic on the idea that building better foundation models for these problems is a (computational) skill issue instead of a data issue (something that Demis at Deepmind takes the other side on). Now, fairly, all of these models are trained in nearly identical ways with reasonably similar architectures, as they were all fast followers to AF3, so this is perhaps an unfair conclusion. But even if we expand to the world of all ligand-docking models in general, as this paper did, we still don’t see much difference on PoseBusters between ligand-only models like DiffDock-L and AF3-esque models (attached below). I get that PoseBusters is also not a great benchmark, but I don’t know of a leaderboard that uses something better. Anyway, as always, I could be wrong about all this and I hold this opinion loosely.

Jobs

(Contact me if you’d like to be posted here!)

Machine Learning/Computational Biology Scientist at Kerna Labs

• From the JD: Kerna Labs is building foundation models of RNA biology to discover and develop better genetic medicines. Our mission is to leverage advanced computational techniques and high-throughput biology to fundamentally change the way we do drug discovery and development.

• Alongside the institutional investors, there are a great set of angels: Patrick Hsu (cofounder of the Arc Institute), Melissa J Moore (CSO of Moderna) and Jacob Kimmel (cofounder of NewLimit).

Senior Researcher and Senior Research Engineer at Microsoft

• From the JD: for our lab in Cambridge, UK, or Amsterdam, NL we are seeking Machine Learning Researcher candidates to work at the intersection of machine learning, chemistry, and drug discovery. If you are passionate about this research area and believe you can make a lasting impact, we would love to receive your application.

1. Announcements

2. Links

3. Jobs

Announcements

I’m officially deprecating my ‘Interesting Links’ posting. I feel like I prefer something richer and more information dense, and ‘roundup’ fits that need better. I’d also like these sorts of posts to function as ways to announce stuff about this blog, alongside some interesting links + jobs (more on that later). Also removing the paywall, because it wasn’t particularly useful as a funnel anyway.

So, announcements!

One, I’m ‘hosting’ (reserved 4 tables for free) a bio-ML event at a bar in Williamsburg on Wednesday, Feb 5th from 7:30pm-10:30pm. Come by and hang out! Here’s the Partiful link w/ more information.

Two, I spent the winter holidays learning a bunch of graphic design, and got really into poster-making. Specifically, biology/biopunk posters, which is a niche that surprisingly few people are creating for. Early iterations of them had pretty good reception, so I’ve decided to sell them, you can find the shop here. Here’s one of my favorites:

I’d like to make tons more of these, so reach out if you have a fun idea for something! I’ve collaborated with two companies to turn their research into a poster (the one above, and another on my shop here) and would like to do it with others.

Three, and finally, these roundups will now include a ‘Job’ section! Lots of non-bio SWE’s/ML/product people read this blog and ask me where they should be applying to if they want to get into biology. I have no idea usually (other than, of course, Dyno Therapeutics). If you’re a founder or very happy employee at a biotech (stealth or otherwise), feel free to DM me with the role and I’ll add it here! Ideally will be focused on engineering/computational/product roles.

Links

OpenAI has collaborated with Retro Bio (a longevity startup which has Sam Altman as an investor) to create a protein language model specifically meant for transcription factor proteins (TF’s).

• The branch of TF’s they focused on were redesigning two Yamanaka Factors (SOX2 and KLF), which are a total set of 4 TF’s meant for converting normal cells to stem cells. You can do a partial ‘conversion’ of cells for the purposes of life extension, which I’ve written about before.

• TF’s have a pretty high rate of intrinsically disordered regions, hence why they went with pure sequence over structure.

• Very few details on what exactly they did.

  • Quote from the article: OpenAI’s new model, called GPT-4b micro, was trained to suggest ways to re-engineer the protein factors to increase their function. According to OpenAI, researchers used the model’s suggestions to change two of the Yamanaka factors to be more than 50 times as effective—at least according to some preliminary measures.

• What does ‘50 times more effective’ mean? Conversion rate? Compared to what baseline? Vague! The below picture is the only bit that somewhat pokes at what happened, potentially implying that the improved two TF’s are better compared to the standard set of 4 Yamanaka Factors. But there’s still a lot of factors to call out. Did these cells develop cancer at a higher rate? What were they reprogrammed to? Did their epigenetic age still decrease? Riffs on the Yamanaka Factors are quite common, but it seems like relatively few of those riffs have stuck around.

  

• Hopefully more details come out soon. Cool that OpenAI is getting in on biology though!

ESM3 published in Science.

• No real changes to the original preprint, which was released back in June 2024. I’ve written about the paper before here, specifically about the protein they redesigned (GFP).

Colossal Biosciences (the ‘bring wooly mammoths back’ startup) raises $200M at $10.2B valuation.

• There seem to be two camps here.

  • One side is very upset with this, and thinks that it’s a sign of extreme overvaluation for an idea that is clearly far off (ex-vivo wombs). Like here.

  • Another side is quite happy with this, and thinks these sorts of dreamy, hyper-ambitious biotech startups should be encouraged. Like here and here.

• I think both sides are right. I hope Colossal succeeds and I want more crazy companies! But I also think $10.2B is a bit much for a company that hasn’t shown positive results…ever. If you’re curious, Asimov Press wrote an excellent overview of ex-vivo technology back in October 2024, and specifically touched on Colossal’s ambitions (which are indeed lofty).

Derek Lowe has a great piece on the recent ‘using AI to create binders for snake venom’ paper from the Baker Lab.

• Spoiler alert: he is very optimistic! We’re definitively entering the realm of ‘on demand protein binders’ for at least some targets. There are concerns with the developability of these binders (not aggregating, etc), but that’s a far easier problem than coming up with the binders in the first place.

• If curious, they use RFDiffusion for generation. Binding results held in both in-vitro and in-vivo settings, successfully protecting a mouse from snake venom.

• Niko McCarty wrote a post about it here also.

Jobs

As mentioned at the start of this, I will now start advertising jobs! Will try to keep it minimal and only for places I think are working on cool things. Please reach out to me if you are hiring and would like to be a part of this! Will only include you here once per roundup, unless you reach back out to me again.

Senior/Staff Software Engineer - Full Stack (Dyno Therapeutics)

• I work here, helping out on protein engineering efforts to solve the ‘delivery problem’ in gene therapy. Obviously I have a biased opinion, but Dyno is one of the very few AI-biotech companies with clear product-market-fit. So betting on us isn’t a bad idea!

Founding Software Engineer (Tamarind Bio)

• From the founder: Thousands of scientists from top biotechs and top 20 global pharma companies use our software to discover and optimize protein drugs. We are profitable, well-funded from investors including Y Combinator, and hiring to keep up with 20+% monthly growth. Our founding engineer position involves scaling inference to thousands of GPUs, deploying the state of the art AI models for customer use in practical drug discovery, and automated fine-tuning pipelines.

Product Engineer, ML Infrastructure Engineer, Software Engineer, and Life Science Specialist (Convoke Bio)

• From the founder: Convoke is building the autonomy software stack for drug development. Our biotech and pharma customers use our infrastructure to automatically source and integrate information that feeds their most critical decisions. We're hiring ML and full-stack engineers to quickly build out our product suite, working closely with our customers. Our founding team has deep life science experience and has previously built several large vertical businesses and high growth technology startups. We are well capitalized, with the backing of top tier investors. Our team is based in South San Francisco.

• My personal take: Alex Telford, the CEO of the company, has one of the most informative and deeply researched biology blogs I’ve ever read. I feel like it’d be quite challenging to read some of his articles and not come away deeply optimistic on whatever the writer of such material works on. You should apply!

Senior Data Engineer (Pattern Bio)

• From the JD: At Pattern Bio, we are creating next-generation cancer therapies at the intersection of synthetic biology and machine learning, with data as the cornerstone of our platform. Our mission is to transform disease treatment, starting with cancer, by leveraging our innovative biomolecular computing technology. By utilizing multi-input, molecular-level computation within individual cells, we aim to deliver curative therapies where the traditional one drug-one target approach has fallen short. Pattern Bio is assembling a world-class team to bring two decades of advancements in DNA computing and machine learning into the clinic