Insilico Medicine (HKEX: 3696) and Human Life Foundation Models, Inc. (HLFM), a newly established entity created by Human Longevity, Inc., announced a multi-million-dollar co-development collaboration to build large-scale AI foundation models for longevity science. The partnership pairs Insilico’s generative AI infrastructure with Human Longevity’s proprietary multi-omic and clinical dataset, compiled over more than a decade from thousands of individuals and spanning genomics, imaging, and longitudinal health records.
Under the arrangement, Insilico will contribute its MMAI Gym training and evaluation platform, providing model architecture design, benchmarking frameworks, training guidelines, and computational algorithms. HLFM will integrate those tools with Human Longevity’s de-identified dataset to train and validate multimodal AI foundation models focused on detecting, diagnosing, and managing age-related conditions. The companies said the jointly developed models are expected to be made commercially available, though no timeline was disclosed.
The collaboration is structured as a multi-million-dollar commitment, though no precise upfront figure, milestone framework, royalty terms, or territorial breakdown was disclosed.
Insilico has been active in platform-licensing transactions across the past 18 months. In January 2026, the company entered an USD 888 million multi-year collaboration with Servier to apply its AI drug discovery platforms to oncology, and in November 2025 it signed a research and licensing agreement with Eli Lilly covering novel oral drug candidates across multiple therapeutic areas, in a collaboration worth up to USD 2.75 billion in total commitments. The HLFM deal extends Insilico’s platform-out model into longevity research, with Human Longevity’s dataset serving a role analogous to the disease expertise and development capabilities that Servier and Lilly contributed in prior transactions.
This article was generated with AI assistance and reviewed and edited by the AllSci editorial team Explore more at AllSci News: https://allsci.com/news/