Medra Raises $52 Million Series A to Build Physical AI Scientists

Medra筹集5200万美元A轮融资，用于打造物理AI科学家

Pioneering the next era of discovery by integrating AI algorithms with robotics execution to reinvent how new medicines are designed.

通过将人工智能算法与机器人执行相结合，开创发现的新时代，重新构想新药设计的方式。

SAN FRANCISCO, December 11, 2025-- Medra, the company developing the first platform for Physical AI Scientists, today announced a $52 million Series A financing led by Human Capital, with participation from existing investors Lux Capital, Neo, and NFDG, alongside new investors Catalio Capital Management, Menlo Ventures, 776, Fusion Fund, and others..

旧金山，2025年12月11日——Medra公司正在开发首个面向物理人工智能科学家的平台，今天宣布完成了5200万美元的A轮融资。本轮融资由Human Capital领投，现有投资者Lux Capital、Neo和NFDG参与，新投资者包括Catalio Capital Management、Menlo Ventures、776、Fusion Fund等。

Medra’s Physical AI autonomously runs experiments end-to-end, interfacing with standard laboratory tools and instruments and allowing scientists to adapt workflows through natural-language instructions. Its companion system, Medra’s Scientific AI, interprets results and co-pilots protocol improvements to enhance experimental outcomes and create a continuous learning engine..

Medra的物理人工智能自主运行端到端的实验，与标准实验室工具和仪器交互，并允许科学家通过自然语言指令调整工作流程。其配套系统Medra的科学人工智能解读结果并协同优化协议，以改进实验结果并创建一个持续学习的引擎。

“Pharma runs millions of experiments, but most of that data can’t be reused or fed back into AI. We’re closing that loop by tying predictions to outcomes in a continuous, self-improving cycle,” said Michelle Lee, Ph.D., CEO & Founder, Medra. “To accelerate drug development, we need to link predictions directly to automated execution and feed the results back into the model.

“制药行业进行了数百万次实验，但其中大部分数据无法被重复利用或反馈到人工智能中。我们通过将预测与结果联系起来，形成一个持续的、自我改进的循环，从而闭合了这一环节。”Medra首席执行官兼创始人米歇尔·李博士说道，“为了加速药物开发，我们需要将预测直接与自动执行相连接，并将结果反馈到模型中。”

This continuous loop enables drug discovery companies to run far more experiments, iterate faster, and advance therapies with a higher probability of success.”.

这种连续的循环使药物发现公司能够运行更多的实验，更快地迭代，并以更高的成功概率推进疗法。”

Current AI lab alternatives tend to fall at one end of the spectrum, offering either traditional industrial automation without meaningful machine learning or AI-driven software without any robotic execution. Bringing a new medicine to market still takes 10-15 years and over $2B because discovery and preclinical work are slow, manual, and fragmented..

当前的AI实验室替代方案往往处于频谱的一端，要么提供没有有意义机器学习的传统工业自动化，要么提供没有任何机器人执行的AI驱动软件。将一种新药推向市场仍然需要10到15年的时间，并且花费超过20亿美元，因为药物发现和临床前工作缓慢、手动且支离破碎。

Pharma has tried to fix this with partial lab automation that remains brittle, inflexible, and dependent on scientist intervention, while separately building ML programs that still require manual, time-consuming experiments to generate data. None of these efforts operate in a closed feedback loop, leaving experimentation, data generation, and model improvement disconnected.

制药行业试图通过部分实验室自动化来解决这一问题，但这些自动化系统仍然脆弱、不灵活，并且依赖于科学家的干预，同时单独构建的机器学习程序仍需要手动进行耗时的实验来生成数据。所有这些努力都没有形成闭环反馈，导致实验、数据生成和模型改进相互脱节。

Medra solves this by unifying robotics, AI, and data generation into a continuous system..

Medra通过将机器人技术、人工智能和数据生成统一到一个连续的系统中来解决这个问题。

“Medra is creating an entirely new category in biopharma R&D, one where we believe science can continuously learn and scale to create groundbreaking therapeutics with a higher chance of clinical success,” said Armaan Ali, Co-founder, CEO & Managing Partner, Human Capital.

“Medra正在生物制药研发领域创造一个全新的类别，我们相信科学可以不断学习和发展，从而创造出具有更高临床成功率的突破性疗法，”Human Capital的联合创始人、首席执行官兼管理合伙人阿尔曼·阿里表示。

Patrick Hsu, Assistant Professor at UC Berkeley and co-founder of the Arc Institute, added: “AI models are generating predictions far faster than we can validate them experimentally. Integrating these tools with traditional lab automation is often too rigid to scale effectively. Medra’s Physical AI Scientist bridges this gap using autonomous, general-purpose robotics.

加州大学伯克利分校助理教授、Arc研究所联合创始人帕特里克·徐补充道：“人工智能模型生成预测的速度远远快于我们通过实验验证的速度。将这些工具与传统的实验室自动化相结合往往过于僵化，无法有效扩展。Medra的物理人工智能科学家通过使用自主的通用机器人技术弥合了这一差距。”

The system learns from every experiment, creating the continuous feedback loop needed to scale data generation and drive breakthroughs in frontier science.”.

该系统从每次实验中学习，创建了扩大数据生成规模并推动前沿科学突破所需的连续反馈循环。

During JPM Week (January 12–16, 2026), Medra will host private tours of its San Francisco facility. For more information, contact tours@medra.ai.

在摩根大通医疗健康周期间（2026年1月12日至16日），Medra 将在其旧金山工厂举办私人参观活动。欲了解更多信息，请联系 tours@medra.ai。

About Medra

关于Medra

Medra is building the world’s first end-to-end Physical AI Scientist platform for continuous experimentation. By combining AI-driven scientific reasoning, robotic execution, and real-time optimization, Medra enables a new paradigm for drug discovery, one that continuously learns, scales, and predicts therapeutic success across every stage of development.

Medra 正在构建世界上首个端到端的物理人工智能科学家平台，用于持续实验。通过结合人工智能驱动的科学推理、机器人执行和实时优化，Medra 实现了一种药物发现的新范式，这种范式能够在开发的每个阶段不断学习、扩展并预测治疗成功。

The company is headquartered in San Francisco, CA. For more information, visit www.medra.ai..

公司总部位于加利福尼亚州旧金山。欲了解更多信息，请访问 www.medra.ai。

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Kimberly Ha

金伯利·哈

KKH Advisors

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917-291-5744

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kimberly.ha@kkhadvisors.com

kimberly.ha@kkhadvisors.com