Insilico Medicine launches open-source AI longevity research toolkit

Clinical-stage biotechnology company Insilico Medicine has unveiled an open-source artificial intelligence toolkit designed to accelerate research into ageing and longevity.

The study, published in Cell , was selected as the journal’s cover feature and was conducted with scientists from Liquid AI, the Buck Institute for Research on Aging, Harvard Medical School and Brigham and Women’s Hospital.

The work introduces LongevityBench, an open benchmark designed to assess whether AI systems can reason across different types of biological data linked to human ageing. It evaluates performance across clinical data, genetics, epigenetics, transcriptomics and proteomics.

The researchers said conventional AI evaluations can reward the recall of information rather than the ability to interpret new biological measurements. They therefore designed LongevityBench to test evidence-based reasoning using data across multiple biological domains.

Smaller models challenge frontier systems

The team assessed 18 leading frontier AI systems from companies including OpenAI, Google, Anthropic, xAI, DeepSeek and Moonshot AI.

No single frontier model produced the strongest results across all five biological data types and performance varied substantially depending on how questions were phrased.

The researchers then developed five compact open-source Longevity Large Language Models, ranging from 0.6 billion to nine billion parameters. The models were trained on ageing-specific clinical and multi-omics data using Insilico’s MMAI Gym for Science framework.

According to the study, the specialised models matched or exceeded the 16 frontier systems evaluated on LongevityBench. The L-Qwen3.5-9B model recorded the highest overall score among all 26 systems tested, including outperforming Google’s Gemini 3.1-Pro.

Autonomous target discovery

Insilico also used the L-Qwen3.5-9B model to power Longevity Claw, an open-source agentic platform designed to carry out multi-step research workflows.

The platform combines a specialised language model with tools for gene-set enrichment analysis, biological ageing-clock calculations, population-level profiling, evidence retrieval and synthesis and candidate target evaluation.

In its first large-scale autonomous discovery campaign, Longevity Claw analysed 14 recognised hallmarks of ageing and nominated 328 genes as potential therapeutic targets.

The researchers said the candidates showed statistically significant enrichment of up to 5.6-fold against an independently published set of experimentally supported ageing targets.

One nominated gene, KDM1A, was independently identified in a separate published study as a potential ageing and cancer target. That research found that modulation of KDM1A extended lifespan in C. elegans.

Resources released publicly

Insilico is releasing the benchmark, specialised models, training resources, evaluation code and Longevity Claw platform for researchers to test and develop independently.

“Our work with rentosertib demonstrated that an AI-discovered and AI-designed drug can influence biological ageing signatures in patients. Now, we are opening the tools that can help the global scientific community discover the next generation of longevity therapeutics. LongevityBench establishes a rigorous standard for measuring whether AI can truly reason about ageing biology, while our specialised models and Longevity Claw turn that intelligence into an autonomous discovery engine. Our goal is to make longevity research faster, more rigorous and accessible to scientists worldwide.”

Dr Alex Zhavoronkov, Founder and Co-CEO of Insilico Medicine

 

The publication follows Insilico’s recent 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 ageing clocks in a Phase IIa clinical trial.