Comment by smj-edison

Right now techniques that exist and used now are mostly around target discovery (identifying proteins in humans that can be targeted by a drug), protein structure prediction and function prediction. Identifying sites on the protein that can be bound by a drug is also pretty common. I worked on a project recently where our goal was to identify useful mutations to make to an engineered antibody so that it bound to a specific protein in the body that is linked to cancer.

If your goal is to bring a drug to market, the most useful thing is predicting the outcome of the FDA drug approval process before you run all the clinical trials. Nobody has a foolproof method to do this, so failure rates at the clinical stage remain high (and it's unlikely you could create a useful predictive model for this).

Getting even more out there, you could in principle imagine an extremely high fidelity simulation model of humans that gave you detailed explanations of why a drug works but has side effects, and which patients would respond positively to the drug due to their genome or other factors. In principle, if you had that technology, you could iterate over large drug-like molecule libraries and just pick successful drugs (effective, few side effects, works for a large portion of the population). I would describe this as an insurmountable engineering issue because the space and time complexity is very high and we don't really know what level of fidelity is required to make useful predictions.

"Solving the protein folding problem" is really more of an academic exercise to answer a fundamental question; personally, I believe you could create successful drugs without knowing the structure of the target at all.