timr

Not even close to a relevant metaphor. Compared to drug development, coding is trivial. It was always obvious that coding would be the first to fall: there’s a corpus of all the code ever written, the system is fixed in scope and relatively small, the data is text, and it’s all easily validated by machine for correctness.

There is no objective function you can optimize your way to a drug discovery machine, let alone a fundamental understanding of how the underlying system works.

Well first off: molecular screening (which is basically what you're describing) is inadequate for drug discovery. There are too many other factors that matter: water positions, molecular flexibility, short and long-range electrostatics, entropy loss, coordinating ions, and more. You need not only the "pocket", but an accurate model of the system's conformational change over time, to have any hope of understanding binding energetics. It's a bigger problem for small molecule drug discovery than biologics, but it still matters.

That said, we have extremely well-refined force fields that attempt to model the underlying physics, and still fail to do this well. There's absolutely no reason to believe that a generic LLM will bring anything to the table that improves the situation. Even with the hand-wavy assertion that we (somehow) get "lots and lots of data", and somehow train a purpose-built model to do the same, it's kind of like saying that we could turn a minivan into a formula one car with enough effort. Different tools for different jobs.

I actually do believe that informatics approaches will be helpful given lots of system-specific data, but when I say that, I mean "a bespoke neural energetic model that is trained specifically for some molecular system down to customized force-field parameters", not "Claude".

So sure, maybe Anthropic will invest lots of time and money and energy into making these kinds of molecular simulation models, which they can then use for high-throughput screening experiments. But even if they do all of that, there's still no guarantee that they'll bring anything to the party that hasn't been done before...because this is the lowest-value step of drug discovery, and a model like that won't tell you if your drug is going to be toxic, or have off-target effects, or have metabolic problems, or...a million other things, which are what kill drug programs in phase 1 and beyond.

That's all true, but they don't mention the relative costs of those different tasks, or how AI helps them.

Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).

Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.

Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.

It doesn’t. That’s what I mean when I tell you the bottleneck is somewhere else. Obviously everyone claims that they do this with new technology X, but empirically, it isn’t helpful.

Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.

Sort of. There’s also a big helping of intellectual arrogance, wherein the tech people think they can apply their l33t skills to biology, and get the same results (I say this as a person who has spent a non-trivial portion of my life as one of those people.)

That said, the LLM version of this is perhaps more solution-seeking-problem than past iterations. LLMs are obviously useful [1]. Scientists already use them. The labs are just desperate to find a multi-billion-dollar application outside of coding, and they’ve all decided that “drug development” is lucrative, so they're gonna apply their magic to that problem and collect profits!

[1] In the way that photocopiers are useful in drug development: as a tool. A powerful tool, but still a tool.

> Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.

This is the fundamental problem with nearly all tech-meets-drugs startups. They almost always focus on the part of the problem where there isn't a bottleneck, because the parts where there is a bottleneck are the parts we don't understand at all.

Yeah, I mentioned this in another comment. There's clearly a trend toward equalization over longer distances, but even here, you see differences in performance out to 100 miles. But there are also no error bars, so we have no idea how significant these differences are.

No, it doesn't. Read the links. I'm giving you papers, and you're citing anecdotes from CNN and Women's Health.