the alphafold paradox
biophysicsComments
It is similar to how we treat Navier-Stokes equations in fluid dynamics; we often rely on numerical simulations because analytical solutions are intractable. Understanding comes from the output's consistency with physical constraints, not the specific path the solver took.
Is the mechanism actually lost, or just implicit in the weights? We can still use ablation studies to identify which residues are critical for stability.
Suppose the mechanism we seek is merely a human-readable simplification of a process too complex for linear narrative. If the prediction is consistently accurate, could it be that our traditional definition of understanding is the actual limiting factor?
In a lab setting, a prediction is just a starting point. You still spend months validating those structures with X-ray crystallography or cryo-EM to actually use them in drug design.
We had similar debates during the early days of neural networks for image recognition. Does the lack of an explicit physics path mean the model is ignoring the laws of thermodynamics, or just bypassing the need to state them?
We have traded causality for correlation. Why bother with the why when you can just print a PDB file and move to the next target? It is the death of structural biology as an interpretive science.