The desire to slow aging has been around for centuries, and despite modern advances in medicine, the search for meaningful anti-aging solutions remains elusive. Traditional approaches have often been slow and speculative, offering incremental progress rather than transformative breakthroughs. Could artificial intelligence accelerate the search for longevity solutions?
Scientists and researchers increasingly believe so. By leveraging AI-driven models, researchers can rapidly analyze massive datasets, identify promising compounds, and uncover potential age-reversal therapies faster than ever before.
Researchers from IIT-Delhi developed AgeXtend, an AI-powered platform designed to identify molecules that promote healthy aging. Since its introduction, the technology has been recognized as a key advancement in longevity science. It is helping researchers better understand aging mechanisms and potential interventions for age-related diseases.
Originally introduced as a multimodal geroprotector prediction platform, AgeXtend analyzes bioactivity data from known geroprotectors to pinpoint new molecules that may slow the aging process. Its AI modules predict geroprotective potential, assess toxicity, and identify the target proteins involved. This offers a structured approach to the discovery process.
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Geroprotectors are substances that help slow down the aging process and reduce the risk of age-related diseases. They can be drugs, natural compounds, or other therapeutic agents that promote longevity by protecting cells and tissues from damage. These molecules work by targeting biological pathways associated with aging, such as oxidative stress and inflammation.
While the AI-powered AgeXtend doesn’t produce ready-to-use medications, it identifies molecular candidates that can eventually be developed into oral or other forms of anti-aging therapies.
“Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear,” state the authors of the study published in the Nature Aging journal. “Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and aids in identifying additional geroprotectors.”
According to the IIT researchers, AgeXtend was used to screen 1.1 billion compounds, accurately identifying known geroprotectors like metformin and taurine, even when excluded from training data.
Leveraging the power of AI, the researchers validated the predicted compounds using multiple biological models. Yeast and Caenorhabditis elegans were used to assess lifespan extension effects, while human cell cultures were employed to evaluate their impact on cellular senescence.
Sakshi Arora, the lead researcher at IIT-Delhi, calls AgeXtend a “discovery engine” for anti-aging research. According to Arora, the AI tool “opens the door to understanding aging better and finding practical solutions to help people live healthier, longer lives.”
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Although initial findings show potential, the researchers emphasize that more in-depth testing and regulatory approval are essential before these compounds can be considered for medical use.
AgeXtend is just one of many AI-driven breakthroughs reshaping longevity research.
Insilico Medicine, a clinical-stage generative AI-driven drug discovery company, uses deep learning models to analyze large biological datasets to identify novel targets associated with aging. The AI-based platform helped researchers identify TNIK, a previously unknown protein linked to aging. This laid the foundation for the development of Rentosertib, a drug designed to inhibit TNIK’s effects.
The developers claim that Rentosertib is the first AI-designed drug to advance to human trials with potential anti-aging applications. The entire process, from identifying TNIK to reaching Phase IIa clinical trials, was completed in under three years – an unprecedented timeline in pharmaceutical R&D.
AI has also played a key role in advancing biological age clocks, which help estimate aging at a cellular level with greater accuracy. These AI-powered models analyze molecular markers like DNA methylation, blood composition, and gene activity, offering a clearer picture of a person’s physical health beyond their actual age.
Deep Longevity, a spin-off of Insilico Medicine, is considered a pioneer in this space. The company uses AI to refine biological age clocks, making them more adaptable to individual health tracking. These tools are now integrated into personalized wellness programs, allowing people to monitor how lifestyle changes, medical treatments, and other factors affect their aging process.
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NewLimit, a San Francisco-based startup co-founded by Coinbase CEO Brian Armstrong, is taking a different approach to using AI for longevity research. The company uses machine learning (ML) to identify gene programs that can reprogram aged cells to behave more “youthfully”, without losing their original identity. This approach, known as partial cellular reprogramming, is considered a breakthrough path in longevity science, as it aims to reverse cellular aging.
As AI continues to shape longevity research, its impact will only grow. But progress alone isn’t enough. Scientists and policymakers must ensure these advancements are used responsibly and with long-term consequences in mind.
