Health AI Central
Peer-Reviewed Publication
Cell2026;189(19):5980-5994.e8.September 17, 2026Journal Article

An open benchmark and language models for AI in aging biology.

Alex Zhavoronkov1, Vladimir Naumov2, Denis Sidorenko2, Alex Aliper2, Vladimir Aladinskiy2, Ramin Hasani3, Alexander Amini3, Katerina Nasto3, Mathieu Reymond2, Rim Shayakhmetov2, Zulfat Miftakhutdinov2, Vadim N Gladyshev4, Fedor Galkin5
1Insilico Medicine AI, Masdar City, Abu Dhabi, UAE; Buck Institute for Research on Aging, Novato, CA 94945, USA. Electronic address: alex@insilico.com.
2Insilico Medicine AI, Masdar City, Abu Dhabi, UAE.
3Liquid AI, Cambridge, MA 02142, USA.
4Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
5Insilico Medicine AI, Masdar City, Abu Dhabi, UAE. Electronic address: f.galkin@insilico.com.
PubMed DOI

Abstract

Over the past two decades, human aging has been characterized across DNA methylation, transcriptomic, proteomic, and clinical modalities, yet no benchmark evaluates whether AI systems can interpret these heterogeneous data types in the context of aging biology. We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six…

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