Insilico Medicine Releases Open Longevity AI Toolkit in Cell Study

Summary

AI Models & Platforms Insilico Medicine Releases Open Longevity AI Toolkit in Cell Study Add Unite.AI to your preferred sources on Google Insilico Medicine on September 17, 2026 announced the publication of a study in Cell introducing an openly released AI toolkit for aging biology: the LongevityBench benchmark, a family of five compact Longevity-LLMs, and the Longevity Claw agentic research platform. Publication in Cell The paper, “An open benchmark and language models for AI in aging biology,” appears in Cell volume 189, issue 19, at pages 5980–5994.e8, dated September 17, 2026. It was published open access under a Creative Commons Attribution 4.0 license with the DOI 10.1016/j.cell.2026.08.026, and it lists 13 authors, including Alex Zhavoronkov, Vladimir Naumov, Denis Sidorenko, Alex Aliper, Ramin Hasani, Alexander Amini, Vadim N. Gladyshev and Fedor Galkin. Insilico said the study was selected as the cover feature of the journal’s September 17, 2026 issue and was conducted with researchers from Liquid AI, the Buck Institute for Research on Aging, and Harvard Medical School and Brigham and Women’s Hospital. According to the announcement, the Cell publication follows Insilico’s September 7, 2026 study in Nature Biotechnology, which reported that rentosertib, the company’s AI-discovered and AI-designed drug candidate for idiopathic pulmonary fibrosis, reduced biological age across six independent proteomic aging clocks in a Phase IIa clinical trial. An Open Benchmark for Aging Biology The study introduces LongevityBench as an open suite of 17 tasks spanning five biodata domains: clinical data, genetics, epigenetics, transcriptomics and proteomics. In the paper’s summary, the authors write that no existing benchmark evaluated whether AI systems can interpret these heterogeneous data types in the context of aging biology. According to the announcement, the benchmark was designed to reduce the likelihood that models could succeed through recall of information encountered during training, testing instead the ability to analyze biological data, recognize meaningful patterns and solve problems relevant to aging research. The authors used LongevityBench to assess 18 frontier AI systems from six developer teams, which the announcement identifies as OpenAI, Google, Anthropic, xAI, DeepSeek and Moonshot AI. The paper reports that no single model dominated all tasks…