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On #2, as is well known, presumed mechanisms of action mostly don't predict correct clinical outcomes, because of the complexity of human biology. On this front, this FDA report looking at a bunch of drugs that failed in Phase II is illuminating: https://www.fda.gov/downloads/AboutFDA/ReportsManualsForms/Reports/UCM535780.pdf …pic.twitter.com/7FYV0qbckg
All of this is to say: I’m seeing a *ton* of VC funding of “AI to cure cancer / discover drugs” type companies. But if they’re not solving the two problems I identify above, it’s not clear what fundamental advance would make somebody bullish on them.
The goal is to make drugs that will be effective *in live humans*. If you don’t have a way of speeding up the feedback loop, or if you don’t have some fundamental advance in biology/knowledge, then how are you going to be 10x better than big pharma at this?
Moreover — getting results with AI requires both fast feedback loops (gaming!) and proprietary data. How (if at all) are these startups getting/generating data that is proprietary and that gives them some kind of competitive advantage?
I’m curious to hear from people who are more bullish on this than I am. Until we fix the above problems, it seems to me that drug discovery will not accelerate in the way that more internet-ish fields are, and I would be bearish on the average biotech startup.
I also glossed over explaining the 2 fundamental problems above properly, because this is Twitter, but would be happy to write an essay on this if there’s interest 
It wouldn’t surprise me if R&D productivity were still in stagnation in 20 years. One implication of this for people who want quicker results (which is most tech people) is that “prevention” is a more promising vector for health than “curing”.
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