Causaly nabs $60M Series B
Causaly, a maker of an AI platform for biopharmaceutical drug discovery, raised a $60 million Series B that gives the company "several years" of runway, CEO Yiannis Kiachopoulos tells Axios.
Driving the news: Causaly is this week's second major investment in AI for drug discovery after Nvdia invested $50 million in tech-enabled drug discovery company Recursion on Wednesday.
Be smart: Drug discovery and development is a notoriously sluggish process and has been highlighted as a major application for generative AI.
How it works: Causaly's platform uses include machine learning, large language and symbolic models for the drug discovery process, Kiachopoulos says.
- The technology is used for everything from target identification to biomarker discovery.
- Customers include Gilead, Novo Nordisk, Regeneron, the FDA and the National Institute of Environmental Health Sciences.
- "It is using machine learning models, but also symbolic models for constructing a really large, high precision 'knowledge graph,' as we call it, that represents all the human biology," Kiachopoulos says.
Details: ICONIQ Growth led the round, with participation from returning investors Index Ventures, Marathon Ventures, EBRD and Pentech Ventures.
- Former Johnson & Johnson CEO Alex Gorsky and former Datadog CEO Olivier Pomel also participated.
- Proceeds will go toward product development, investing in new talent, and expanding commercial relationships. Fresh funds will also finance a new office in New York.
- The funding brings Causaly's total capital raised to $93 million. The business is not yet profitable, and Kiachopoulos declined to disclose valuation or other financial details.
What's next: Causaly plans to continue its organic growth strategy, and is focusing on expanding its 120-person team via hiring key talent, instead of acquiring other businesses, Kiachopoulos says.
What they're saying: "We have been extremely impressed by the Causaly team’s deep curiosity and commitment to empower scientists to accelerate life sciences research," says ICONIQ Growth general partner Caroline Xie.
- "The platform allows researchers to conduct deep, unbiased scientific exploration in a highly trusted and verifiable manner," Xie says.
- There's a good deal of ambiguity in the market regarding proper clinical use cases for AI — an issue that can be solved by increased transparency, Kiachopoulos says.
- "A lot of it is related to how transparent your systems are for making those choices," he says. "There's always the question of bias, of course, and one way to address bias of a system is to create transparency first."