Series B



insitro is an exciting startup company that aims to take a new approach to drug development: one with big data and machine learning at its core. We plan to build on the ground-breaking innovations that have occurred in life sciences to develop large data sets that are designed from the start to allow machine learning to address fundamental bottlenecks in the drug development process. Our goal is to cure more people, sooner, and at a much lower cost. We are fortunate to have the strong support from the top investors in both biotech and tech: ARCH Ventures, Foresite Capital, A16Z, GV, and Third Rock Ventures. We are building a remarkable team that embodies a new type of culture, one based on a true partnership between scientists, engineers, and data scientists. Together we are working to define the problems, design experiments, analyze the data, and derive the insights that will lead us to new therapeutics. Join us, and help make a difference to patients!

Daphne KollerCEO and Board Member1

Vijay PandeBoard Member

RoundDate AnnouncedInvestors
Insitro raises $143 million series BMay 27, 20208
Insitro raises $100 million series AMay 31, 201810

Investor NameInvestor TypeAUM ($)
Alexandria Venture InvestmentsCorporate Venture Capital737,100,000
Andreessen HorowitzVenture Capital10,000,000,000
Arch Venture PartnersVenture Capital0
Bezos Expeditions0
Canada Pension Plan Investment BoardGovernment, Pension420,400,000,000
Foresite CapitalVenture Capital3,000,000,000
GVCorporate Venture Capital4,500,000,000
Mubadala Investment Company0
Third Rock VenturesVenture Capital2,714,000,000
Two Sigma VenturesVenture Capital348,000,000


Powerful machine learning requires powerful data. Our data pipelines and automation infrastructure allow us to go beyond artisanal chemistry and biology, and rapidly generate massive amounts of high-quality data. This scale allows us to span much more of the diversity of human disease and potential therapies. With massive amounts of high quality data, we then develop and deploy a variety of leading-edge machine learning methods. As witnessed in other industries, machine learning can make sense of vast amounts of high-dimensional data that are beyond human ability to interpret. Our machine learning models allow us to differentiate between cell states at much finer granularity and predict disease-relevant clinical traits.

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