Comment by turkeygizzard
2 years ago
My understanding is that protein folding is a major cost bottleneck in drug design.
Researchers can come up with candidate molecule formulas that might work as good drugs, but the problem is that these proteins organize/fold themselves physically in a hard-to-predict way. And how they fold directly affects their properties as drugs.
If AlphaFold can accurately predict folding, it’ll allow researchers to prioritize drug candidates more accurately which will reduce research time and costs. Supposedly the major pharmaceutical companies can spend up to billions when designing a single drug. Optimistically, predicting protein folding better will allow for much more rapid and cheaper drug development
I love AlphaFold, but this is a big misconception. The biggest cost bottle neck in drug development and design, by orders of magnitude, is associated with assaying (and potentially reducing) off-target binding or toxicity and assaying (and potentially increasing) efficacy. Determining a protein structure empirically with cryoEM, NMR, or crystallography will generally cost less than $1M (sometimes far less), which is tiny compared to the many millions or billions of dollars that get poured into clinical trials for a single drug. AF2 is useful in some basic research cases but isn't really that useful for traditional drug design and development.
A machine learning approach for predicting toxicity would have a far greater impact on public health than AF2 does.
My understanding is that protein folding is not a bottleneck in drug design.
Yes, once you identified a target protein, its structure is useful to selectively target it. But the main bottleneck is identifying such targets. In other words, the main difficulty is to figure out what to hit, not how to hit it, and protein folding mostly helps with how at the moment.