Fedor Galkin

Impact

Fedor Galkin develops machine-learning tools for measuring and studying biological ageing. He first worked at Insilico Medicine and then led research at its spin-off Deep Longevity. There he was first author on deep-learning aging clocks built from gut-microbiome profiles (iScience, 2020) and DNA methylation data (DeepMAge, Aging and Disease, 2021). He also co-wrote a widely cited 2020 review of biomarkers of aging and later work linking psychological factors to faster biological ageing. Now back at Insilico, he applies aging-biology AI to drug discovery. This includes a toolset connecting idiopathic pulmonary fibrosis with accelerated ageing (Aging-US, 2025) and, as second author, a 2026 Nature Biotechnology study that used proteomic aging clocks in a phase 2a clinical trial to assess possible geroprotective effects alongside the primary endpoints. He is senior author of a 2026 Cell paper that released LongevityBench, an open benchmark for testing large language models on aging-biology tasks, together with a family of compact longevity-focused language models. His work matters to longevity research mainly for building and openly benchmarking computational biomarkers and models that could serve as ageing endpoints in research and trials.

Metrics

• h-index 15 · google scholar as of 2026-09-24 source ↗

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