by chermi | 2025-05-20 15:48:22+00:00
Protein structure prediction is a pretty useful tool. There are various 'foundation' models in biology now that are quite useful. I don't know if you want to count those as AI or ML.

If you're looking for breakthroughs due to AI, they're not going to be obviously attributable to AI I think. Focusing on the biology related foundation models ... The ability to more quickly search through the space of sequences->structure, drugs, and predicted cell states (1) like with the biology based foundation models will certainly lead to some things being discovered/rejected/validated faster.

I heard about this vevo company recently so it's on my mind. Biology experiments are hard and time consuming, and often hard to exactly replicate across labs. A lot of the data is of the form

a) start in cell state X (usual 'healthy' normal state) under conditions Y (environment variables like temperature, concentrations, etc.)

b) introduce some environmental pertubation P like some drug/chemical at some concentration, or maybe multiple pertubations at once

c) record trajectory or steady/final state of cell.

This data is largely hidden within published papers in non-standardized format. Vevo is attempting to collate all of those results with considerations for reproducibility into a standard easy-to-use format. The idea being that you can gradually build up a virtual sort of input-output (causal!) model that you can throw ideas for interventions against and see what it thinks would happen. Cells and biology are obviously enormously complicated so it's certainly not going to be 100% accurate/predictive, but my experience with network models in quantitative biology plus their proclaimed results make me pretty confident it's a sound approach.

Thus approach is clearly "AI" driven (maybe I would call this ML) and if their claims are anything close to reality, this is an incredibly powerful tool for all of academia and industry. You can reduce the search space enormously and target your experiments to cover the areas the virtual model doesn't seem so good, continuously improving it in a sort of crowd-sourced "active learning" manner. A continuously improving experimentally backed causal (w.r.t. perturbations) model of cells has so many applications. Again, i don't think this will directly lead to a breakthrough, but it can certainly make the breakthrough more likely and come faster.

There are many other examples like this that are some combination of 1) collating+filtering+refining existing data into an accessible easily query-able format

2) combining data + some physically motivated modeling to yield predictions where there is no data

3) targeted, informed feedback loop via experiments, simulations, or modeling to improve the whole system where it's known to be weak or where more accuracy is desired.

Assuming it all stays relatively open, that's undeniably a very powerful model for more effective science.

And that's just one approach. In physics ML can be use for finding and characterizing phase transitions, as one example. In the world of soft matter/biophysics simulation here's a few ways ML is used:

a) more efficient generation of configurations (Noe generative models). This is a big one, albeit still kind of early stages. Historically(simplifying), to generate independent samples in the right regions of phase means you need to integrate the system for long enough to hit that region multiple times. So regions of the space separated by rare transitions will take a loooong time to hit multiple times. The solution was simply longer simulations. Now, under some restrictions, you can leverage and augment existing data (including simulation data) to directly generate independent samples in the regions of interest. This is a really big deal.

b) more efficient, complex and accurate NN force-fields. Better incorporation of many-body and even quantum effects.

c) more complex simulation approaches via improved pipelines like automated parameterization and discovery of collective variables to more efficiently explore relevant configuration space.

Again, this is tooling that improves the process of discovery & investigation and thus directly contributes to science. Maybe not in the way you're picturing, but it is happening right now.

1) vevo https://www.tahoebio.ai/