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Comment by dekhn | Hacker News Reader

That's the aspirational goal. And I would say that it's a bit of an inflexible one- for example, if we had an ML that could generate molecules that cure diseases that would pass FDA approval, I wouldn't really care if scientists couldn't explain the underlying principles. But I'm an ex-scientist who is now an engineer, because I care more about tools that produce useful predictions than understanding underlying principles. I used to think that in principle we could identify all the laws of the universe, and in theory, simulate that would enough accuracy, and inspect the results, and gain enlightenment, but over time, I've concluded that's a really bad way to waste lots of time, money, and resources.

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It's not either-or, it's yes-and. We don't have to abandon one for the other.

AlphaFold 3 can rapidly reduce a vast search space in a way physically-based methods alone cannot. This narrowly focused search space allows scientists to apply their rigorous, explainable, physical methods, which are slow and expensive, to a small set of promising alternatives. This accelerates drug discovery and uncovers insights that would otherwise be too costly or time-consuming.

The future of science isn't about AI versus traditional methods, but about their intelligent integration.

Or you can treat AlphaFold as a black box / oracle and work at systems biology level, i.e. at pathway and cellular level. Protein structures and interactions are always going to be hard to predict with interpretable models, which I also prefer.

My only worry is that AlphaFold and others, e.g. ESM, seem to be bit fragile for out-of-distribution sequences. They are not doing a great job with unusual sequences, at least in my experience. But hopefully they will improve and provide better uncertainty measures.

> if we had an ML that could generate molecules that cure diseases that would pass FDA approval, I wouldn't really care if scientists couldn't explain the underlying principles

It’s actually required as part of the submission for FDA approval that you posit a specific Mechanism of Action for why your drug works the way it does. You can’t get approval without it

A substantial proportion of FDA-approved drugs have an unknown mechanism of action - we can handwave about protein interactions, but we have no useful insight into how they actually work. Drug discovery is bureaucratically rigid, but scientifically haphazard.

How much do you believe that the MoA actually matches what is happening in the underlying reality of a disease and its treatment?

Vioxx is a nice example of a molecule that got all the way to large-scale deployment before being taken off the market for side effects that were known. Only a decade before that, I saw a very proud pharma scientist explaining their "mechanism of action" for vioxx, which was completely wrong.

Underlying principles are nice for science, whatever works is nice for engineering. There is plenty of historical precedent where we build stuff that works without knowing exactly why it works.

Me like thee career path. Interesting.

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