Insilico Medicine's AI-designed drug candidate, rentosertib, originally developed to treat a rare chronic lung disease, now shows signs of slowing biological aging markers. The company published results in Nature Biotechnology showing the drug reduced aging indicators across six different "aging clocks"—AI systems trained to measure biological age. This comes from the same clinical trial that initially tested rentosertib for lung disease, so the anti-aging findings emerged as an unexpected secondary observation rather than from a dedicated aging study. The company used neural networks to build two separate AI systems for drug discovery. The first analyzed massive datasets of patient health records, blood tests measuring microscopic proteins, and academic literature to understand disease mechanisms. The second system studied protein structures and how molecules bind to targets, then generated entirely new molecular candidates that could hit specific drug targets. This approach accelerated what normally takes years of traditional chemistry and testing. The results come with serious caveats. The clinical trial involved a small sample size, and the aging clocks themselves remain scientifically controversial—experts debate how reliably they actually measure aging. More critically, Insilico only tested the drug in patients with lung disease, not in healthy people. The drug is still years away from regulatory approval even for its original lung disease indication, let alone for anti-aging use in otherwise healthy patients. Alex Zhavoronkov, the company's founder, acknowledged these limitations while still presenting the findings as promising evidence that AI can accelerate drug discovery.