For a couple of years I've been expecting that ML models would be able to 'accelerate' bio-molecular simulations, using physics-based simulations as ground truth. But this seems to be a step beyond that.
When I competed in CASP 20 years ago (and lost terribly) I predicted that the next step to improve predictions would be to develop empirically fitted force fields to make MD produce accurate structure predictions (MD already uses empirically fitted force fields, but they are not great). This area was explored, there are now better force fields, but that didn't really push protein structure prediction forward.
Another approach is fully differentiable force fields- the idea that the force field function itself is a trainable structure (rather than just the parameters/weights/constants) that can be optimized directly towards a goal. Also explored, produced some interesting results, but nothing that woudl be considered transformative.
The field still generally believes that if you had a perfect force field and infinite computing time, you could directly recapitulate the trajectories of proteins folding (from fully unfolded to final state along with all the intermediates), but that doesn't address any practical problems, and is massively wasteful of resources compared to using ML models that exploit evolutionary information encoded in sequence and structures.
In retrospect I'm pretty relieved I was wrong, as the new methods are more effective with far fewer resources.
