Starting to get a little stir crazy so it’s time for the unpopular opinions game. Gimme a topic, I’ll give you an opinion.
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Replying to @s_r_constantin
one legitimately exciting use for maching learning, and one overhyped stupid use please
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Replying to @gravity_levity
exciting: vaccine optimization. overhyped: QSAR.
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Replying to @s_r_constantin @gravity_levity
what's a good resource on the former?
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Replying to @jsdenain @gravity_levity
what little I know I learned from Alex Rubinsteyn http://www.rubinsteyn.com/ and keeping up with new biotech startup news
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also rough intuition: the number and distribution of cell surface proteins & antibodies is in the range where ML seems like a big improvement (huge diversity between individuals, combinatorial explosion making traditional stats impractical)
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and you can have a tight in-silico/in-vitro feedback loop for designing/optimizing this stuff
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computing binding affinities of proteins and small molecules has a.) a 50-year history of abject failure; b.) is easily tested experimentally; c.) is sparse as hell so maybe you don't need ML as much; d.) mOsT aPpRoVeD dRuGs dOn'T hAvE tHeIr pUtAtIvE mEcHaNiSmS oF aCtIon
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This is very interesting. Why do you think computational prediction of binding affinities is so hard? Afaik the enthalpic part is getting pretty good, but there's much to be done about the entropic contribution.
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you probably know more than me given your comments. I just see the prediction accuracies and they're b a d.
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Well I'm still very much interested in your opinion, your understanding of the whole field is undeniably deeper than mine!
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