May 2025 — La Jolla, CA / Singapore — A new study published in Aging Cell demonstrates that artificial intelligence can be used not just to accelerate drug discovery, but to fundamentally transform how it's done—by targeting the full complexity of biological aging.
In a collaboration between Scripps Research and Gero, a biotechnology company focused on aging, scientists developed a machine learning model trained to identify compounds that act across multiple biological pathways—a process known as polypharmacology. Instead of seeking a single "magic bullet," the system embraces aging as a complex, multifactorial process—and finds drugs to match.
When tested in Caenorhabditis elegans, a widely used model organism in aging research, the compounds extended lifespan in over 75% of cases. One increased lifespan by 74%, placing it among the most effective life-extending compounds ever recorded in this model.
"Traditional drug discovery obsesses over precision, aiming to modulate a single pathway with laser-like focus," said Dr. Peter Fedichev, CEO of Gero. "But aging doesn't work that way. It's systemic, intertwined, and defies one-dimensional solutions. That's what our approach embraces."
Until recently, intentionally designing multi-target drugs was considered impractical across most areas of medical research due to the complexity involved and increased risk of side effects. Such compounds were often discarded rather than developed. The research by Fedichev and Dr. Michael Petrascheck, professor at Scripps Research, demonstrates that AI can now navigate this complexity, making their research the first known example of AI successfully designing polypharmacological interventions for aging—by intention, not chance.
"It's not just an incremental step. This is a genuine step change," said Petrascheck. "It shows that AI can help researchers tackle exponentially more complex biological questions than they could have unassisted."
A Broader Discovery Model
From a translational perspective, the findings lay the foundation for a new generation of therapeutics that act systemically, not in isolation.
"The main impact is on the future development of drugs that can extend lifespan and treat chronic, age-related diseases," said Petrascheck. "Intentional polypharmacology increases the likelihood of efficacy because aging isn't the failure of one system—it's the gradual breakdown of many systems simultaneously."
This research was conducted by Petrascheck's laboratory at Scripps Research, with support from the National Institutes of Health. Fedichev and Gero contributed the AI algorithm, which identified and selected compounds for the study.
Paper citation:
Konstantin Avchaciov 1 , Khalyd J Clay 2 , Kirill A Denisov 1 , Olga Burmistrova 1 , Michael Petrascheck 2 , Peter O Fedichev 1 (2025). AI-Driven Identification of Exceptionally Efficacious Polypharmacological Compounds That Extend the Lifespan of Caenorhabditis elegans. Aging Cell, e70060. https://doi.org/10.1111/acel.70060
