The eternal quest for an "elixir of youth" is one of humanitys oldest dreams. Aging is an incredibly complex, multifactorial process affecting virtually all body systems, and traditional drug development approaches targeting a single target often prove insufficiently effective against it. But what if we could teach artificial intelligence to think like nature itself, attacking aging on multiple fronts simultaneously? Such a revolutionary approach has been demonstrated by scientists from Scripps Research and Gero, a biotechnology company specializing in aging research. Their collaborative study, the results of which were published in the prestigious scientific journal Aging Cell around May 15, 2025, marks a true breakthrough: their created Gero and Scripps Research AI platform for anti-aging drug discovery was able to "by intention, not chance" design polypharmacological drugs that significantly extend the lifespan of model organisms.
What is the "essence" of this discovery? The scientists moved away from the "magic bullet" concept – one drug for one target. Instead, they used the power of machine learning (specifically, graph neural networks) to identify chemical compounds capable of simultaneously affecting multiple different biological pathways involved in the aging process. This approach is called polypharmacology. Artificial intelligence analyzed vast arrays of data on known geroprotectors (substances that slow aging) and their interactions with biological targets to learn to predict which combinations of molecular properties could exert the most pronounced anti-aging effect.
The results of the experimental verification were astounding. Compounds selected using AI were tested on Caenorhabditis elegans, roundworms widely used in aging research. Over 70% of the computer-predicted substances actually extended the lifespan of these organisms! Moreover, the effectiveness of some of them ranked in the top 5% compared to all known geroprotectors registered in the DrugAge database. This is not just a lucky hit – its a demonstration that AI can purposefully design complex therapeutic interventions for a process as intricate as aging. Peter Fedichevs team at Gero was responsible for developing and fine-tuning the AI algorithms, while Michael Petraschecks lab at Scripps Research conducted the experimental validation, including lifespan assays and mechanistic studies in nematodes.
This success opens entirely new horizons in gerontology and drug development. Instead of combating individual age-related diseases, it becomes possible to create drugs that target the fundamental mechanisms of aging, thereby potentially preventing or delaying a whole spectrum of ailments. The Gero and Scripps Research AI platform for anti-aging drug discovery is not just a scientific success; its a new standard for aging research methodologies and a vivid example of how the synergy of advanced computational technologies and deep biological knowledge can lead to revolutionary discoveries capable of one day radically changing the quality and duration of human life.