alt Hacker News> “…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).
Replies
Here is an article by Pat Walters on the usefulness of ML in drug discovery. This article is a response to another one making the case that utility of ML models are very limited in drug discovery
https://patwalters.github.io/Response-to-Peter-Kenny/
> (4a) revert to traditional methods but keep the veneer of using ML to save face
I haven't worked in the industry side of things but in academia everyone kind of agrees that gradient boosting trees are some of the best models to do these things.
The obvious question is what limits getting more data? Astronomy (especially in Australia) has been quite good at designing surveys to answer multiple scientific questions with reasonable amounts of data (and then fed into ML systems like the cannon). Sadly one of the consequences of the LLM hype is the increasing cost of doing this, so "AI" is actually making things worse not better.
The real value right now is in figuring out how to generate robust data cheaply and quickly. I'd wager that the effect of a good model on marginal data is small, but the effect of a marginal model on great data is probably quite large.
(3) seems like a problem in its own right? Basing science, traditional or newfangled ML, on such small amounts of data looks pretty weak.
It was always easy to come up with new molecules/drugs/materials. The thing that changed is the scale that it can happen with the new ml-based approaches.
What hasn’t changed is finding ones that are manufacturable/synthesizable.
Even if you find 1 million new stable molecules, there no guarantee that even one of them is manufacturable.