Insilico Medicine Opens AI Longevity Discovery Toolkit to Researchers Worldwide in Cell Cover Study

The paper introduces three interconnected resources for the global scientific community:

• LongevityBench, the first open benchmark designed to rigorously evaluate AI reasoning across multiple domains of aging biology;
• Longevity-LLMs, a family of compact, open-source language models specifically trained on clinical and multi-omics aging data; and
• Longevity Claw, an open-source agentic research platform that autonomously integrates specialized longevity tools to identify and prioritize potential therapeutic targets.

CAMBRIDGE, Mass., Sept. 17, 2026 /PRNewswire/ -- Insilico Medicine ("Insilico"; HKEX: 3696), a clinical-stage biotechnology company powered by generative artificial intelligence (AI), today announced the publication of a landmark study in Cell introducing an open AI toolkit, including LongevityBench, Longevity-LLMs and LongevityClaw designed to accelerate research and therapeutic development in aging and longevity.

The study, selected as the cover feature of the journal's September 17, 2026 issue, was conducted in collaboration with researchers from Liquid AI, the Buck Institute for Research on Aging, and Harvard Medical School and Brigham and Women's Hospital.

The publication follows Insilico's September 7, 2026 study in Nature Biotechnology, which reported that rentosertib — Insilico'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.

Together, the two studies connect clinical evidence from an AI-discovered therapeutic with an open research infrastructure intended to help scientists identify and develop the next generation of longevity interventions.

"Insilico is at the forefront of longevity research. Our recent work on rentosertib, published in Nature Biotechnology, demonstrated that an AI-designed drug can modulate biological aging signatures. In our Cell cover paper, we build on this work by empowering the global scientific community with tools such as LongevityBench, Longevity LLM and Longevity Claw, helping transform AI into an autonomous engine for longevity discovery.

The longevity community is moving beyond static aging clocks toward foundation models capable of generating measurable, actionable insights. We are developing benchmarked, agentic systems that can evolve into personalized longevity assistants and longevity companions, ultimately helping people monitor and improve their healthspan. The system has the potential to transform longevity medicine, drug discovery, insurance, and public health by accelerating target identification, enabling more personalized interventions, and improving population-level health outcomes." said Alex Zhavoronkov, Ph.D., Founder and Co-CEO of Insilico Medicine.

A New Standard for Evaluating AI in Aging Biology

Aging is driven by complex and interconnected biological processes that span multiple levels of human biology. Understanding these processes requires researchers to integrate clinical records with genetics, epigenetics, transcriptomics, proteomics and other large-scale biological datasets.

While general-purpose large language models have demonstrated strong performance across many scientific tasks, their ability to reason reliably over real-world aging data has remained largely untested. Existing evaluations also frequently reward the memorization of scientific facts rather than the ability to interpret new biological measurements or generate evidence-based conclusions.

To address this gap, the research team developed LongevityBench, an open benchmark specifically designed to determine whether AI systems can reason across the diverse data modalities that define human aging.

LongevityBench evaluates AI performance across five major biological domains:

• Clinical data
• Genetics
• Epigenetics
• Transcriptomics
• Proteomics

The benchmark was designed to reduce the likelihood that models could succeed through simple recall of information encountered during training. Instead, it tests the ability of an AI system to analyze biological data, recognize meaningful patterns and solve problems relevant to aging research.

The researchers evaluated 18 leading frontier AI systems, including models developed by OpenAI, Google, Anthropic, xAI, DeepSeek and Moonshot AI. The analysis revealed substantial performance gaps across biological domains.No single frontier model achieved the strongest results across all five data types. Model performance also changed significantly depending on how questions were phrased, highlighting a lack of robustness that could limit the reliability of general-purpose AI systems in scientific research.

The most difficult challenge was predicting biological age directly from omics measurements. Even the largest frontier models struggled with this task, suggesting that model scale alone is insufficient to produce consistent biological reasoning.

Compact Longevity Models Outperform Frontier AI Systems

To determine whether these limitations could be overcome without frontier-scale computing resources, the researchers developed a family of five compact, open-source Longevity Large Language Models, or L-LLMs.

The models ranged from 0.6 billion to 9 billion parameters and were fine-tuned on aging-specific clinical and multi-omics data using Insilico's MMAI Gym for Science, a training and evaluation framework designed to develop specialized AI systems for scientific applications.

The family was built on open model architectures from Liquid AI and Alibaba, including Liquid AI's LFM2 architecture and Alibaba's Qwen3 and Qwen3.5 model families.

Despite their comparatively small size, the specialized Longevity-LLMs matched or exceeded all 16 frontier systems evaluated on LongevityBench.

The best-performing model, L-Qwen3.5-9B, achieved the highest overall score among all 26 AI systems included in the study, outperforming every tested frontier model, including Google's Gemini 3.1-Pro, while using only a fraction of the parameters.

Even the smallest specialized model, with approximately 0.6 billion parameters, outperformed most of the frontier systems tested.

The results demonstrate that carefully curated scientific training data and domain-specific optimization can be more important than model size for specialized biological applications. Compact models may also offer practical advantages for research institutions by lowering computational requirements and enabling more secure, cost-efficient deployment on local infrastructure.

From Benchmarking to Autonomous Target Discovery

To demonstrate that the models could support practical research beyond benchmark performance, the team embedded L-Qwen3.5-9B into Longevity Claw, a newly released open-source agentic platform for aging research.

Longevity Claw combines the specialized language model with scientific tools for:

• Gene-set enrichment analysis
• Biological aging-clock calculation
• Population-level profiling
• Evidence retrieval and synthesis
• Candidate target evaluation and prioritization

The platform was designed to move beyond a conventional chatbot interface. Rather than responding only to individual questions, Longevity Claw can formulate and execute multi-step research workflows, use specialized analytical tools, evaluate intermediate findings and assemble evidence for candidate biological targets.

The researchers deployed the platform across 14 recognized hallmarks of aging, allowing it to investigate multiple interconnected mechanisms implicated in age-related decline. Through this autonomous workflow, Longevity Claw nominated 328 genes as potential targets for aging intervention.

When compared with an independently published reference set of experimentally supported aging-related targets, the candidates nominated by Longevity Claw demonstrated statistically significant enrichment of up to 5.6-fold, supporting the biological relevance of the platform's results.

One of the nominated genes, KDM1A, was independently validated in a separate published study as a dual-purpose aging and cancer target. In that study, modulation of KDM1A extended lifespan in C. elegans. The independent finding provides an early indication that the platform can surface biologically credible targets that may not previously have been prioritized for longevity drug discovery.

Opening AI Longevity Research to the Scientific Community

The publication further demonstrates Insilico's broader commitment to advancing pharmaceutical superintelligence. Insilico is releasing the benchmark, specialized models, training resources, evaluation code and Longevity Claw platform to enable independent testing, validation and further development by researchers worldwide.

By making these resources openly available, Insilico and its collaborators aim to provide a common foundation for measuring progress in AI-enabled aging research. The open framework may also help scientists distinguish systems that demonstrate genuine biological reasoning from those that primarily reproduce information contained in their training data.

Access the Open-Source Resources:

• LB Dataset & L-LLMs Collection (Hugging Face):
  https://huggingface.co/collections/insilicomedicine/longevity-llm
• Longevity Claw Repository (GitHub):
  https://github.com/Insilico-org/longeclaw
• Public Leaderboard:
  https://longevitybenchmarks.org

Recently, Insilico reported total revenue of approximately $106 million in the first half of 2026, a 287% year-over-year increase, and achieved its first profitable half-year since listing, with an adjusted net profit exceeding $51 million. This milestone was driven by a series of out-licensing, co-development, and R&D collaborations with global partners, including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, Qilu Pharmaceutical, Hygtia Therapeutics, CMS, and Tenacia. As of the latest practicable date, the total contract value of transactions announced by Insilico in 2026 reached approximately $7.3 billion, pushing the cumulative contract value of its major collaborations since 2021 approximately $11 billion.

On the AI-driven R&D front, Insilico nominated nine development candidates within nine months of 2026 as of late August, setting a new company record for annual pipeline productivity and achieving eight clinical milestones across its proprietary and co-developed programs. Leading this progress is rentosertib (ISM001-055), the world's first drug candidate discovered and developed using generative AI, which has advanced to a Phase III trial evaluating for idiopathic pulmonary fibrosis (IPF).

In addition, Insilico Medicine launched a comprehensive set of benchmarks that allow foundation models to be evaluated in all tasks needed for drug discovery and launched state of the art (SOTA) foundation models outperforming other models and even internal tools in benchmarks. Using this new capability Insilico hopes to expand and accelerate longevity drug discovery and development both in terms of scale, therapeutic modalities, and indications.

About Insilico Medicine

Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend healthy longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, under the stock code 03696.HK.

By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma. AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine.

For more information, please visit www.insilico.com

SOURCE Insilico Medicine