> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”.

This is the problem with AI for all of science - not just drug discovery. Applied ML has spread like wildfire through academia over the past decade - this started well before the LLM hype. It’s the perfect honey trap: research is painstaking and slow, ML offered a shortcut, and best of all, it just needs data. Research produces lots and lots of data! Surely this will be a match made in heaven.

I’ve watched the same pattern play out at least four or five times now in various roles.

(1) Propose an ML-guided approach to material/chemistry discovery/optimization.

(2) Gather existing data (real, experimental data).

(3) Realize there’s less than about 50 true rows of data on the outputs of interest.

At this point, you either: (4a) revert to traditional methods but keep the veneer of using ML to save face, or (4b) pivot to computational/simulation work or a high-throughput system that’s very far removed from your original problem, but allows you to keep playing with ML toys

It’s really bad. I left the industry. I don’t know how long it will take for people doing real science to take back the reins (and the funding).

I'm a structural biologist at a mid-sized biotech. I use AI tools daily. They make accomplishing the same things I was able to accomplish before quite a lot faster and easier. They don't help me magically accomplish new things that I couldn't previously.

For example, it helps me install academic software, debug things. It helps me take a large dataset and write scripts to ask questions. It helps me go through experiment drafts to see if I'm missing things. It helps me remember obscure formulas I use every 6 months. It has not, at least in my experience, come up with anything truly novel.

A concrete example: AlphaFold is great...to come up with a starting model for a chimeric fusion or something. What would have taken me 1-2 hours fumbling around in PDB or CIF files is now a quick prompt.

I think the real win here is for idiots like me:

A) no education

B) no resources

C) not smart enough to be a self-taught bio-hacker

Everyone hears "AI is going to cure disease" and pictures some cure-all pill from a bio lab which is what I feel this paper is hinting at is missingb but that's the top of the funnel; I'm at the bottom where patients live and that is where AI is already quietly working. Its just not being benchmarked.

I built https://crohns.ai. I set out to make an AI-native clinical-trial manager with a feedback loop (DDP) and ended up somewhere completely different: instead of chasing a new "drug" which is totally out of my grasp; financially, intellectually etc... I used it to codify a care protocol that helped me avoid a flare after I got laid off, lost my insurance, and lost access to Skyrizi.

Derek Lowe discusion of the paper https://www.science.org/content/blog-post/so-how-ai-drug-dis...

I think that was originally linked but got changed to the £30 to Elsevier version for some reason.

Need one for hair loss ASAP.

Obviously the missing part (which we already have for software and math) is that we need agents to be able to run automated loops in the real world. That basically requires robots. I think we'll be there in less than 5 years.

We're all about to come face to face with this reality. This dance can only last so long.

[AI drug discovery] was never the hard part.

How refreshing was this article vs. all the slop?

The lack of comparable data and testability really does seem to be a challenge. I wonder if people would be more willing to collect and share lots of health data if the collecting company was a non-profit dedicated to anonymizing it.

need one for brain plasicity. it would be nice to be able to easily learn a foreign language or musical instrument naturally.

> "The paper goes on to make recommendations for AI companies and investigators, and these are well worth reading. The common theme is that people need to think more about why they’re doing certain techniques or using certain technologies, rather than just using them because they’re newly available."

Please. Please let some people with power and influence understand this lesson sooner rather than later. I understand the reasons that's unlikely to occur, but usually the impact isn't quite so drastic and expensive as this is. Just because something is new and shiny doesn't mean that it'll produce the outcomes you need at the other end, and until it's shown that capability your approach to it should be MODERATE.

nerd-fanboi proposal: Articles by national treasures (like Derek Lowe, Raymond Chen) should be highlighted with specific identifiers on HN - like a distinctive title font or an ascii diamond ◊.

Has this yet produced a treatment for AI psychosis?

No? Well fancy that! :)

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