Gero announced it has raised $17 million in new financing, bringing its total equity funding to $34 million. The company is developing medicines designed to slow aging and treat age-related diseases by combining longitudinal human data, AI, and a physics-based framework for therapeutic target identification and drug design.
Gero previously secured a collaboration with Chugai Pharmaceutical, a member of the Roche Group, that includes an upfront payment and up to $250 million in milestones, in addition to royalties.
Gero’s platform is based on the idea that aging and age-related disease onset follow quantifiable physical laws that can be identified in human data and translated into medicines. The company said it trained AI world models of human health on approximately 10 million curated longitudinal medical records selected from more than 100 million records and integrated with molecular, omics, and genetic data.
The company’s approach is inspired in part by long-lived mammals that appear to defy typical aging patterns. For example, while the risk of death in humans roughly doubles every eight years after early adulthood, naked mole-rats can live far longer than expected for their size while showing little increase in mortality risk with age.
Gero said its models distinguish reversible disease biology from deeper, long-timescale aging processes to identify “hub” targets that may be shared across multiple chronic diseases. Under the Chugai collaboration, Chugai is developing drug candidates against targets identified by Gero. Gero also previously conducted a research collaboration with Pfizer in 2023.
The new funding will support preclinical development of Gero’s pipeline and continued expansion of pharmaceutical partnerships. The financing included participation from Melnichek Investments, an AI-focused investment firm, along with an early backer of a company acquired by Facebook, the co-founder of EPAM Systems, and senior operators from the pharmaceutical and technology sectors.
Gero’s platform also includes ProtoBind-Diff, a generative AI model for small-molecule design directly from protein sequence. The company is headquartered in Singapore and has a U.S. subsidiary in San Francisco.
KEY QUOTES:
“Target selection is the critical bottleneck in translating aging biology into medicines, and the field has not lacked ideas, it has lacked human-evidence-grounded targets that pharma is ready to develop. Gero is one of the few groups bringing published aging theory, longitudinal human data, and pharmaceutical-partner validation into a single discovery engine.”
Brian K. Kennedy, Ph.D., Distinguished Professor at the National University of Singapore, former President and CEO of the Buck Institute for Research on Aging, and Independent Director of Gero
“Gero stands out for grounding discovery in a published, physics-based framework and real human data, and for turning that science into a working platform, which is why I invested.”
Anita Cosgrove, former Senior Vice President, Strategic Business Development at Human Longevity, and investor in Gero
“Countless molecular events happen every second, both in a naked mole-rat and in us — yet over decades our risk of death doubles every eight years while theirs stays almost flat. That gap is a physics problem. We’ve spent a decade building the theory. What’s changed is that we now have the data — tens of millions of longitudinal health records — and generative AI that can extract the mathematics of aging directly from that data, the way physics has always worked but at a scale and complexity no analytical theory could reach. In aerospace, that level of modelling transformed engineering outcomes. We expect the same in medicine. And aging is the place to start: it’s the shared engine behind virtually every chronic disease, which means slowing it is the highest-leverage intervention in human health.”
Peter Fedichev, Ph.D., co-founder and CEO of Gero
