It’s the sort of thing researchers say to get grants, but as a distant goal, not a practical reality.

For structure-based drug discovery (which isn’t even the majority of drug discovery), the details are what matter (e.g. “does this water molecule mediate a binding interaction, or do the sidechains shuffle a bit, and kick the water out?”), and these methods don’t even come close to predicting detailed interactions.

Metrics in this space are focused on “general correctness” of protein backbone conformation. Success is to achieve a kind of blurry view of the overall shape of the molecule, and drug design is trying to predict specific atomic interactions. They’re two wildly different problems.

About the best you can say is that if we had a generalizable model of physics that could predict protein structure, it might also be able to do a good job of evaluating how a small molecule binds to a protein target. But even that is a huge leap, and when you start using black-box methods like AlphaFold to specifically solve the problem of structure prediction, it’s not really clear that generalization is even possible.

There are potential practical uses in drug discovery for a method that can design a protein which takes a particular shape, but even that is pretty different from what AlphaFold actually does.