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Arrayfunction : Do you know what type of machine learning they happen to be using? This type of task isn't something a single artificial neural network model can handle all of. It would likely only be limited to taking guesses at potential candidates. Even then, likely only in so far as improving current drugs rather than novel ones.
A random forest model would be a bit more useful since you can at least compare how it connected all the variables within its tree. That still runs into the inherent limits of machine-learning models as they are today; they can't do anything truly novel since it has no imagination to draw from, just what was in the training data.
At at certain point, you would be much better off switching to a structural equation model or the like so you can use that a priori knowledge to offset the inevitable correlations an ANN will draw. Human biology is one of the fields where we can determine (for many)'which are false artifacts of coincidence or sampling bias in the training data.
I'm 1000% for using Bayesian based techniques to improve the process - especially with concepts like external control arms that limit the number of people being condemned to the necessary evil of a placebo group.
Stock_Drift OP Arrayfunction : No. I don’t. Sorry.
Dusters : Exactly. I don’t give financial advice. I just know a good stock when I see one.
Stock_Drift OP Dusters : I concur, you do!!!