Data-driven startup Zephyr AI has raised $18.5 million in seed funding to advance precision medicine and drug discovery with machine learning.
The startup’s tech combines data from multiple sources, machine learning and analyses of structured biological networks and longitudinal outcomes to generate insights.
Those insights can then be used to develop tools to improve outcomes in patient care and research, according to the company.
Lerner Group Investments LLC and M-Cor Holdings led the seed round, with participation from Allen Y. Chao, AME Cloud Ventures, BoxGroup, MedStar Health, Roger W. Ferguson, Steve Oristaglio, Verily and other investors.
“The team at Zephyr AI impresses us with its unique and innovative approach to harnessing the predictive power of artificial intelligence and big data in healthcare settings,” said Michael Cohen, vice president of investments at Lerner. “Zephyr AI has the proven ability to generate and validate a remarkable breadth of extraordinary insights. We are very pleased to co-lead this funding round and help realize the profound potential for this technology in disease mitigation and treatment.”
The company was founded in 2021 by investment and incubation firm Red Cell Partners, led by Jeff Sherman, CTO, Grant Verstandig, executive chairman, and Yisroel Brumer, executive vice-chairman.
Verstandig served as UnitedHealth’s chief digital officer until returning to venture investing at the end of 2021.
“We could not be more pleased with investors’ enthusiastic response to this initial capital raise for Zephyr. Having set out to raise a smaller number and finding ourselves oversubscribed, we were elated to expand the offering to be able to continue to fuel our extraordinary progress in our first year of existence,” Verstandig said.
The startup plans to use the fresh capital to gather additional healthcare datasets and advance its machine-learning models to produce more actionable insights for improved patient care and drug discovery.
The company said it is prioritizing finding industry partnerships that allow it increased access to and use of data.
The startup currently has 24 employees.
Machine learning algorithms that draw on disparate health data sources to produce actionable insights are drawing significant interest from investors and healthcare organizations alike.
The push for precision medicine, accelerated as a result of the COVID-19 pandemic, underlies that excitement. Machine learning has a wide variety of potential applications in healthcare areas, too, including clinical decision support, drug discovery, diagnostics and predictive analytics.
Drug development, in particular, is an expensive process, and it can often take researchers years to identify molecules that respond to a given drug—if they’re able to trigger that response at all. AI tools can sift through genetic information and proteins to help identify molecular candidates, potentially shortening the discovery phase from years to months.