Ex-Google CEO's early-stage fund adds $630M with new biotech bets at forefront
29 Jan 2024
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Source: FierceBiotech
Innovation Endeavours will help fund biotech platforms that are looking to scale up their use of machine learning, according to a partner at the firm.
Innovation Endeavors, a venture capital firm with bets oscillating between the tech and life science universes, has closed a new $630 million fund, with biotech atop the to-do list.
The early-stage venture firm, co-founded by former Google CEO Eric Schmidt, will use the new cash announced Thursday to continue investing in companies at the intersection of technology and science, according to a Medium post from co-founder Dror Berman. Innovation Endeavors has financed the likes of Uber, SoFi and Viz.ai but also Eikon Therapeutics and BigHat Biosciences.
“We are entering a golden age of scientific discovery and deployment,” Berman wrote. When asked to elaborate on the role biotech will play in the portfolio, Berman told Fierce Biotech it was a top priority.
“Investing in healthcare and biotech solutions is a core focus at Innovation Endeavors,” Berman said in an email. “As a firm, we have been investing in the life sciences since our inception.”
Helping guide the live science bets is partner Joel Dudley, Ph.D., former chief scientific officer of precision genomics company Tempus Labs and current genetics professor at the Icahn School of Medicine at Mount Sinai in New York. There’s an opportunity now to help scale up biotech platforms that wield machine learning and burgeoning tech, according to Dudley, although he hates the phrase “techbio.”
“Maybe Recursion was like a 1.0 kind of example of this, but I think we’re getting opportunities to have more intelligence in the loop to build these reiterative platforms,” Dudley told Fierce in an interview. The investor added that he wouldn't be surprised if a third of the new fund went toward life science investments.
Dudley also hypothesized that the initial clinical stumbles that have been seen from the first wave of AI-driven drug developers could be attributed to initial use cases, suggesting applying large language models to finding new antibodies or proteins isn’t a core issue plaguing drug discovery. Instead, he pointed to the potential of using AI to improve cell therapy and AAV manufacturing or gene therapy delivery.
He said there should be a shift toward thinking: “It’s not about a drug hitting a target, it’s getting a drug into the clinic and working in humans. And what data comes to bear on that?"
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