The day in conversation for 2026-10-22

The discussion: Ines: So the takeaway for today is that explicitly modeling periodicity through those learned harmonic potentials is actually a surprisingly simple shortcut for boosting flexible docking performance. Marcus: Simple perhaps, but I wonder if that simplicity hides some massive assumptions about the system's conformational space. We're essentially forcing a circle constraint onto something inherently messy like a protein pocket, and hoping the learned potential captures the necessary physics instead of just overfitting to the training set geometry. Yuki: I think it’s more than just a shortcut, though. When you look at how Harmony aligns the score parameterization directly with the diffusion geometry—that's not just fitting parameters; that’s making sure we respect the underlying rotational symmetry during sampling, which is exactly what generic methods struggle with when they encounter high-frequency torsional movements. Ines: Right, and that ties back to what we saw last week with the variance-exploding diffusion process. We theorized that when those processes hit high torsional motion, they essentially break down into an incoherent Fourier basis, making accurate reconstruction nearly impossible. Harmony seems to be solving that incoherence issue by baking the periodicity directly into the energy landscape definition before it even starts sampling. Marcus: But we need to be careful about scaling. The paper focuses on EBNA1 and KRAS G12D—those are relatively contained systems. If we apply this harmonic bias to a much larger, more complex protein system, where do you think that learned potential will break down first? Does it become too rigid, or does