AI in drug discovery – what it is, where we stand and the path forward

Yep. Not everybody but enough to warrant vigilant monitoring and titration.

For everyone struggling with hair loss but concerned about side effects of Fin. Look into topical fin. It produces higher concentrations of the medication in the scalp and lower systemic concentration. Not perfect, but better.

About 2% of finasteride users experience these side effects, and they are reversible after discontinuation.

Absci is bullshit

I’m on 0.5 to 1mg oral minoxidil daily for a few years now and it’s working great. Blood pressure benefits too.

PP-405 too

I agree. I won't do heroes. Ever.

A National Treasure, on the other hand - they enrich life without being a vector for tribalism (eg:Michael Kramer/Kate Reading).

Make the feature a per user list of flagged sources.

I would have agreed in past, but the new random "blog" at whatever.etc being written by LLMs is making me skip clicking most links here now.

Yeah it's [x was never the hard part] all the way down. It's a very human thing and I bet we'd keep saying that even after we've solved the hardest mysteries including consciousness, the origin of life or the true nature of reality.

Psychedelics are several orders of magnitude stronger on the plasticity front.

In therapy as well.

Sure, but what kind of protocol are you going to use to actually reap the benefits of these transient neuroplasticity effects, especially for learning something new like math or piano as an adult?

Same with ketamine. It's not like you can take it a couple of times and open some magic window where everything suddenly becomes easier to learn. From my personal experience and surface-level understanding of the current research, psychedelics(including ketamine) seem to temporarily relax hardened beliefs/priors and rigid neural pathways, which can help you see things from a fresh perspective. But learning something substantial like math or a new language after you've passed the most plastic stages of development is still going to take much longer than what these drugs can realistically help with.

They might make you see something differently or even get you extremely interested in it, but sustaining that interest and consolidating the knowledge or skill is still slow compared with childhood/adolescence. Of course, it depends a lot on what you're learning. For example, crystallized intelligence and sufficient motivation can make some things much easier to learn as an adult, but many useful things are also just boring to learn when you no longer have a childlike plastic brain or an environment built around constant learning.

Edit: Theoretically, you could accelerate learning by taking psychedelics/ketamine at a set frequency but it's a huge gamble because of their risk profile. eg. HPPD/trauma risk with classic psychedelics and bladder/neurotoxicity risk with ketamine if you get addicted or take it too frequently.

Vs as an adult. Yes it's hard as a kid, but as an adult it's even harder.

Not that I'm a Derek Lowe fanboy or anything but the entirety of your comment is like "that guy needs to check himself" without, like, any specific context, any specific argument he's wrong on this specific question or like anything. It's like "deciphering questions around credibility" is hard ... all the way down. Where's yours? What are you saying?

The world needs more communicators like Derek Lowe who can give a clear-eyed, de-bullshittified perspective on the world, while also being great writers who are a pleasure to read. I follow his blog, and while a lot of it goes over my head (or seems very niche), a lot is great. The only other such writers I know about is Matt Levine at Bloomberg, and Atul Gawande (who's a bit less blunt, perhaps). And of course the late Carl Sagan. Any others?

NB: The post was updated twice. The first changed the link to the Nature Reviews Drug Discovery article; the second changed it back to Derek Lowe's blog post with the NRDD article in the body.

In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.

Though in some areas where I can sustain interest, AI is helping me go deeper. For instance, I've been putting myself to sleep at night by just asking it questions about expectation maximization and Bayesian statistics. This has seriously boosted my understanding of cryo-EM alignment algorithms in a way I couldn't do in grad school because there was no professor that understood enough to help me when I got stuck reading literature.

So it's a double edged sword for sure.

Yes, and it's relatively good for on-rails data collection and data processing pipelines that would have previously needed occasional human intervention. Especially now that I can run something like DeepSeek v4 Flash on a couple of RTX 6000s and just script an API to hammer away without having to worry about racking up a huge bill.

Yeah, for someone like me in a lot of ways it's a dream come true. I have domain expertise in my field, I have a ton of computer knowledge due to building HPC clusters and working in Linux every day for years, I have lots of problems computers can solve, but no formal programming expertise. Before, muddling through Python or fixing segfaults in academic software would have been days to weeks. Now it's minutes.

I think that (understandably) HN is full of professional programmers for whom code is the product, and so LLMs are often viewed through that lens. But for someone like me, a drug is the product, not code. One off vibe coded slop is both fine, and often an upgrade over the academic software I was using.

For instance, last week I took a piece of software that decompresses a TIFF file and multithreaded it for an almost 4x speedup. I'd always known it was single threaded and it irked me because I could see my pipelines waiting for it to catch up, but I never had the expertise in C++ to go fix it. Claude did it in 30 minutes and I didn't even have to go through the hassle of compiling it again - it did that too.

What I don't know is, if someone wasn't in the trenches for a decade learning how computers work, would the results be as good?

Hands down the best drug I have been on EVER; but its 11k a month with no insurance.

The 1st 2 injections where done by a nurse that came to my home, the others were done as self injections using their njection kits.

In chemicals and materials, 50 rows of good data is a really solid study. That’s e.g. a 3x4x4 experimental design (assuming replicates for each condition get averaged into a single row). If you managed to prep that many samples correctly and obtain consistent characterization data across all properties of interest, you’ve easily got a paper. It’s also kind of malpractice to jam this type of data (few samples, wide rows) into modern ML models. There are plenty of simpler statistical methods that will tell you what’s going on, and even then a well-made plot might be good enough. The difficulty is not in drawing insight from the final numbers, it’s almost always in how those numbers came to be in the first place.

Thus the reticence of science-oriented companies to invest heavily in these mass data-gathering exercises to feed ML. It’s damn expensive, and almost always leads you back to raw data issues, not breakthrough discovery. Doing it without a set purpose in mind is even more likely to yield garbage.

I worked on some of the very best funded plant research out there. When it comes down to it, there's enough variation caused by confounding factors, and it takes so long to capture more data, that almost everything anyone tries cannot be called a success or a failure for years, because the individual measurements for one small plot of land somewhere just don't mean anything. Once you do an entire experiment for a season, which takes months, and you grab the little noisy data you have, and turn it into real rows, we were down to very little.

You can do more tests on smaller things, like checking if some protein will kill some cells of a pest, but making sure a plant produces it enough that it actually does something significant to the real, live pests, that it's not toxic, and it doesn't harm the plant's yield massively (as it's now spending time producing your pesticide) is still going to take years. We might be able to fold proteins, but the kind of things we'd need to really simulate plant biology well enough to not need years of failures are still very far away.

And it's far worse in medicine, as with plants at least nobody has ethical concerns if they fail and die, and nobody needs to get consent from a corn seed. Getting to 50 actual data points from many medical studies is already a lot of effort. And imagine when it's a long term study, and you need to follow patients for 30 years, as theym move, or die, or decide to stop participating, or who knows what.

I completely agree with you. Data quality is the most important. One thing that's sad is how much academic data that's not reproducible. https://en.wikipedia.org/wiki/Replication_crisis