If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands.

If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://norvig.com/chomsky.html), then you'll be quite happy because this is yet another validation of the value of statistical approaches in moving our ability to predict the universe forward.

If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models?

If you're the sort of person who likes simple models with as few parameters as possible, you're probably excited because developing more comprehensible or interpretable models that have equivalent predictive ability is a very attractive research subject.

(FWIW, I'm in the camp of "we should simultaneously seek simpler, more interpretable models, while also seeking to improve native human intelligence using computational augmentation")