AlphaFold-Powered Drug Discovery of a Novel CDK20 Inhibitor

I know CDK20 is a difficult target, otherwise presenting results where your best hit is at 9μM would be pretty disappointing.

It would be interesting to see the Kd values for the other CDK family members, there's no mention at all of specificity in their paper.

Russians in particular?

Regarding the biology:

- One of the strategies in drug development is to find a protein that is more present in people who have a disease versus those who don't (called "overexpressed" proteins), and attempt to stop this protein from work correctly (called "inhibition") by having a small molecule drugs that binds to a specific site of the protein (called the "allosteric site") in order to make it mechanically unable to execute its function.

- Cyclin-dependent protein kinases (CDKs) are a family of proteins that seem to play important roles in controlling cell division. CDK20 is overexpressed in a number of cancers.

Regarding the novelty:

- Discovering new inhibitors of proteins based on AI is definitely less novel than it was 5 years ago - while it's definitely still not the norm, AI is making big waves in the pharmaceutical industry. However, I think this might be the first publication validating the use of Alphafold for small molecule drug development, which is a major step forward.

- While it's interesting to see that it's possible to design a small-molecule CDK20 inhibitor, it's currently still very uncertain whether this is a promising drug: i. the compound could be insufficiently specific to CDK20 and could bind to other important proteins and cause unwanted and potentially serious side-effects, ii. the compound could have bad "drug-like" properties (e.g. bioaccumulate in the liver) or be toxic in some way, iii. the compound could interact badly with other drugs that cancer patients receive, iv. the compound could induce resistance (a common problem in small molecule drugs in oncology), and finally, and most importantly, v. the drug might just not be effective at treating cancer or any other diseases - it's not because a protein is over-expressed that it's the cause of a cancer, but rather a symptom of another biological dysregulation.

Still, it's definitely an achievement, and I applaud the efforts of the team and hope they'll find successful treatments.

I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all).

The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identity. This target family has a very similar structure, and conserved active site residues. It's relevant that this target has approved cross-CDK family inhibitors (and thousands of data points of CDK family binders on ChEMBL). The conventional way to enable structure-based design is to build a homology model using a similar structure (see here: https://swissmodel.expasy.org/repository/uniprot/Q8IZL9?temp...), and in this case, there is very low deviation from the AF2 model and this "old fashioned" approach.

To recap, this target had a decent model that would have likely sufficed for drug discovery. The community already knows that "homology models" can be used for structure-based drug design, so any methodological hypotheses of this paper are not supported by evidence.

Have they ever?