| Mostly it's hard to get from "here's a protein" to "here's a drug for that protein" for a variety of reasons. 1.) It's hard to know whether the candidate drug will bind. This is because while the physical principles are somewhat understood, they're way hard to compute and there are a lot of things that still need to be empirically determined. For example, if you have a protein structure, that's a single snapshot in a highly unusual environment for that protein (either a solid crystal or some form of NMR solution). It is often accurate, but a protein in a cell is always in thermal flux, getting knocked around by other proteins, and "breathes" so your structure may not reflect important biology. This also assumes you have a structure to go off of, but you don't always. Models and computation are making progress in this particular area, but it's still nowhere near plug and play. 2.) Even if you have a drug that binds the protein of interest, it might be toxic, might bind to other things as well, might not last very long in the bloodstream, might not even get to the bloodstream in the first place if you take it orally. Tons of things can go wrong. This area is still quite difficult for computation because the datasets aren't great and it's hard to get enough data to make progress. 3.) Assuming you have 1 and 2 covered, it's possible your protein is bad. Maybe it's not actually as important to the disease in practice and you don't actually alter patient outcomes. For these reasons, in-silico drug discovery is still unreliable. I'm working on a project that would try to address these shortcomings by taking a hybrid experimental-computational approach. Hopefully it works out :) |