I'm interested in how they measure accuracy of binding site identification and binding pose prediction. This was missing for the hitherto widely-used binding pose prediction tool Autodock Vina (and in silico binding pose tools in general). Despite the time I invested in learning & exercising that tool, I avoided using it for published research because I could not credibly cite its general-use accuracy. Is / will Alphafold 3 be citeable in the sense of "I have run Alphafold on this particular target of interest and this array of ligands, and have found these poses of X kJ/mol binding energy, and this is known to an accuracy of Y% because of Alphafold 3's training set results cited below'
I've never trusted those predicted binding energies. If you have predicted a ligand/protein complex and have high confidence in it and want to study the binding energy I really think you should do a full MD simulation, you can pull the ligand-protein complex apart and measure the change in free energy explicitly.
Also, and this is an unfounded guess only, the problem of protein / ligand docking is quite a bit more complex than protein folding - there seems to be a finite set of overall folds used in nature, while docking a small ligand to a big protein with flexible sidechains and even flexible large-scale structures can have induced fits that are really important to know and estimate, and I'm just very sceptical that it's going to be possible to in a general fashion ever predict these accurately by the AI model with the limited training data.
Though you just need some hints, then you can run MD sims on them to see what happens for real.
