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a16z Podcast: Move Fast But Don't Break Things (When It Comes to Computational Biology)

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June 14, 2016 02:37

00:00 / 29:02

The mindset of "move fast and break things", while great for code, isn't
exactly great for the human body. So adding computation to biology --
especially in the slow-moving pharmaceutical industry, where drug approval can
take years -- brings with it both opportunities (like drastically faster
discovery and assessment) and challenges (the need for hard evidence, not just
soft-ware). But there's more: We don't want just better outcomes for
healthcare. We want better outcomes at a cheaper price. And that's where
machine learning comes in. The benefits of such computation -- i.e., software
-- can provide a powerful, frictionless, and far more cost-effective tool for
biopharmaceutical research ... but it requires data. So who provides that
data? Is it the pharmaceutical companies, or the payers (insurance)? How are
organizations incented to overcome intellectual property silos in sharing
their data? Especially since it was only relatively recently, Jeff Kindler
(the former CEO of the world's largest pharmaceutical company, Pfizer) reminds
us in this episode of the a16z Podcast, that the FDA even allowed data to be
put in computers vs. on paper. But there's a reason the self-driving car was
pushed out of the software and not the auto industry, argues TwoXAR co-founder
and CEO Andrew Radin -- and it has to do with the unique nature of the
developer's mindset applied to novel problems. The deterministic nature of
Moore's Law -- it's not a matter of if, but when -- plays a role too, observes
a16z bio fund general partner Vijay Pande. There are things that big data and
simulations will be able to accomplish that a hundred lab experiments on
animals can't. Still, the two mindsets will have to merge, so we can move fast
... but without compromising quality, safety, and reliability. That's the big
difference between computer science and biology after all. image: mattza/
Flickr Read more
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