Folding@home uses Rosetta, a physics-based approach that is outperformed by deep learning methods such as AlphaFold2/3.

Folding@home uses Rosetta only to generate initial conformations[1], but the actual simulation is based on Markov State Models. Note that there is another distributed computing project for Rosetta, Rosetta@home.

[1]: https://foldingathome.org/dig-deeper/#:~:text=employing%20Ro...

Folding@home and protein structure prediction methods such as AlphaFold address related but different questions. The former intends to describe the process of a protein undergoing folding over time, while the latter tries to determine the most stable conformation of a protein (the end result of folding).