[text70]

Dynamic modeling of proteins is an incredibly resource intensive process, and finding a ligand that binds to a model can also be difficult if the model is wrong.

What the folding model essentially does, is confirm that the modeled folded protein has the correctly modeled binding energies for each individual atom. Making modeling induced fit, and conformationally dynamic and difficult to drug proteins, more easy to model and find the correct ligands that bind to them.

[SAI_Peregrinus]

Right. It makes creating a candidate drug to hit your target vastly easier. It doesn't really make picking the right target in the first place any easier. The preclinical stage will be faster. The clinical stage is still necessary, and that's the stage where the big bottlenecks (toxicity & efficacy) happen. There's not yet a reliable toxicity model, and efficacy is even harder.

This will certainly help. But we shouldn't expect drugs to be discovered in weeks instead of years, since the bit that usually takes a year or more (the clinical trials) isn't changed here.

[ramraj07]

None of that matters because all preclinical methods of testing for drug efficacy are not the same. For most of the important diseases of today, preclinical models are bstshit poor in predicting actual clinical efficacy. Mainly cancer.