Years after Daphne Koller left academia to pursue its reinvention with Coursera, circumstances have conspired to return her to a passion left by the wayside. Big data, machine learning and biology are suddenly in syzygy and Koller is intent on making the most of it with her new company, Insitro — and telling us all about it at Disrupt SF 2020.
Koller was working on applying machine learning techniques to biology as early as 2000 during her tenure at Stanford. But the concept was hardly the household word it is today. The ML techniques we’ve come to rely on in practically every domain were then quite primitive by comparison, yet to many a forward-thinking mind clearly a tool with immense promise.
Leaving in 2012 to join Andrew Ng in founding one of the original MOOC platforms, Coursera, Koller temporarily left behind the world of computational biology. Four years later, however, she left Coursera to head up computing efforts at Google’s health R&D arm, Calico. Her work here clearly inspired to go her own way in 2018 and — with the blessing of GV and others to the tune of $100 million — founded Insitro.
The cost of developing new drugs can reach into the billions, making it impractical for pharmaceutical companies to pursue them in cases where the population affected by the treated condition is not large enough. The cost of drug development must drop, and Koller joined others in believing that data was the key.
These pharmaceutical companies have amassed enormous databases from human testing of drug candidates and other processes, but have been unable to effectively leverage them. Insitro aims to change that.
“We’re now at a moment in history where a confluence of technologies emerged all at around the same time to allow really large and interesting and disease-relevant data sets to be produced in biology,” said Koller at a talk hosted by TechCrunch’s Connie Loizos last year.
“In parallel, we see on the machine learning side … technologies that are able to make sense of that data and come up with novel insights that can hopefully cure disease. We’re in the business of actually building data for the sole purpose of training machine learning models … what we think of as little crystal balls that would allow you to avoid doing experiments that are complex or even impossible.”
The resulting combination of “in vitro” (i.e., in the lab) and “in silico” (in computer) techniques they call “insitro” — not good Latin, but nevertheless perhaps the new reigning paradigm in this field.
With a number of partnerships and studies ongoing and a staggering $143 million Series B raised just this May, Insitro is going strong and Koller will no doubt have a lot to say about it at our all-online Disrupt SF 2020.
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