
Show HN: Artificial Intelligence Based Drug Discovery Toolkit - rmaiti
https://www.producthunt.com/r/2886430edfe4f7
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dalke
As someone who writes a lot of software for "old-school pharma", I'll point
out a few things.

1) "Old-school pharma" includes AI - we were developing AI-based software
products for drug discovery back in the 1990s - so what makes this anything
new?

2) Clustering chemical compounds dates from the 1980s. Interactive 2D/3D
clustering are of course newer, but still ... what makes this new?

3) "tons of molecular data" in this case is appears to be
[https://www.drugbank.ca/](https://www.drugbank.ca/) . That is, "the license
of our data-provider" links to that page. Quoting from that page:

> The latest release of DrugBank (version 5.1.3, released 2019-04-02) contains
> 13,339 drug entries including 2,594 approved small molecule drugs, 1,289
> approved biotech (protein/peptide) drugs, 130 nutraceuticals and over 6,304
> experimental drugs.

That's tiny. ChEMBL contains 1.8 million entries, most pharmas have much more
than that.

So, what justifies the use of "tons of molecular data"?

4) "If a drug candidate is in the neighborhood of another molecule, there is
an extremely high chance that they are related!"

How was that quantified? It's trivial to have a high true positive rate. For
example, graph edit measures like in SmallWorld can make that guarantee.

But as the cluster boundary decreases, it's less and less true.

