This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…
No. It really is just heuristic building. A core problem with using ML in this sort of use case is that it is often brittle. Once it gets outside of the context it was trained in it may or may not be able to generalize it's training to new contexts. We may have difficulty knowing when it is very wrong.
I think ML in research science could be viewed as a very good intuitive oracle. Even if they are right 95% of the time, you have to do this work prove the long way every time because that 5% matters. The real utility is in "scanning the field" to better focus research on things likely to bear fruit.