SAN FRANCISCO BAY AREA, Calif.

加利福尼亚州旧金山湾区

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April 22, 2026

2026年4月22日

/PRNewswire/ -- 10x Science, which builds frontier AI for molecular-level protein characterization across the life sciences, today announced the closing of its $4.8M seed round led by Initialized Capital. The oversubscribed round includes investments from Y Combinator, Civilization Ventures, Founder Factor, and a group of strategic angel investors.

/PRNewswire/ -- 10x Science致力于为生命科学领域的分子级蛋白质表征构建前沿人工智能，今天宣布完成了由Initialized Capital领投的480万美元种子轮融资。此超额认购轮融资还包括来自Y Combinator、Civilization Ventures、Founder Factor以及一群战略天使投资者的投资。

Starting with drug development, the company's platform delivers automated, explainable molecular insights in minutes where current tools and manual workflows require months. With tens of thousands of biologic drugs in active development worldwide and regulatory demands for molecular characterization intensifying, 10x Science is unlocking a new category at the intersection of AI and the life sciences..

从药物开发开始，该公司的平台在几分钟内提供自动化、可解释的分子洞察，而当前工具和手动工作流程需要数月时间。随着全球范围内数以万计的生物药物正在积极开发中，并且对分子表征的监管要求日益严格，10x Science正在开启AI与生命科学交汇处的一个全新领域。

Protein characterization is foundational to drug development. Every biologic therapeutic, from cancer immunotherapies to gene therapies, must be characterized at the molecular level to determine whether it is safe, effective, and manufacturable. Today, this work depends on specialized scientists spending weeks or months manually interpreting complex mass spectrometry data using tools that have not fundamentally changed in decades..

蛋白质特性分析是药物开发的基础。从癌症免疫疗法到基因疗法，每一种生物治疗药物都必须在分子水平上进行特性分析，以确定其是否安全、有效且可生产。如今，这项工作依赖于专业科学家花费数周或数月时间，使用几十年来基本未变的工具手动解读复杂的质谱数据。

The pharmaceutical industry is developing more complex protein therapeutics than ever before, and the demand for characterization is growing far faster than the supply of experts who are trained for it. The 10x Science platform addresses this bottleneck with a purpose-built AI architecture that reasons across hundreds of thousands of spectra, identifies molecular forms and chemical modifications, and delivers comprehensive, explainable results..

制药行业正在开发比以往更复杂的蛋白质治疗药物，而对其表征的需求增长速度远远超过了受过相关训练的专家的供给。10x Science平台通过专门构建的人工智能架构解决了这一瓶颈，该架构能够跨数十万条光谱进行推理，识别分子形式和化学修饰，并提供全面且可解释的结果。

10x Science was founded by David Stephen Roberts, Ph.D., Andrew Reiter, and Vishnu Tejus, out of Professor Carolyn Bertozzi's Nobel laureate laboratory at Stanford University. The three founders shared a common frustration: they were studying what happens molecularly when an immune cell meets a cancer cell, one of the most critical problems in cancer research, and the tools they needed to characterize the proteins involved did not exist..

10x Science 由大卫·斯蒂芬·罗伯茨博士、安德鲁·雷特和维什努·特贾斯在斯坦福大学卡罗琳·贝尔托齐教授的诺贝尔奖得主实验室创立。三位创始人有一个共同的困扰：他们正在研究免疫细胞遇到癌细胞时分子层面发生的情况，这是癌症研究中最关键的问题之一，而他们用来表征相关蛋白质所需的工具并不存在。

Roberts, a Damon Runyon Cancer Research Fellow with over 38 publications in the Nature and ACS families of journals, spent his career developing the foundational science of next-generation protein characterization.

罗伯茨是一位 Damon Runyon 癌症研究学者，他在《自然》和美国化学学会系列期刊上发表了 38 篇论文，职业生涯致力于开发下一代蛋白质表征的基础科学。

Reiter trained at the Broad Institute of MIT and Harvard, where he built the analytical tools pharma uses to understand how drugs bind their targets, before joining the Bertozzi lab at Stanford as a Ph.D. student.

雷特曾在麻省理工学院和哈佛大学的布罗德研究所接受培训，在那里他构建了制药行业用来理解药物如何与其靶标结合的分析工具，之后作为博士生加入了斯坦福大学的贝尔托齐实验室。

Tejus is a two-time Y Combinator founder who went to college at age 11. The three founders share a conviction that the life sciences and the technology world have been working on deeply complementary problems without ever connecting, and 10x Science is the bridge between them.

特杰什是一位两次入选Y Combinator的创始人，他11岁就上了大学。三位创始人坚信，生命科学和技术领域一直在研究深度互补的问题，却从未连接起来，而10x Science正是它们之间的桥梁。

'The people building AI have historically not been life scientists, and the life scientists have not been building AI; we come from both worlds,' says co-founder and CEO David Stephen Roberts. 'We realized we could build something that had never existed: an AI system with the scientific depth to reason about proteins the way the best experts do, but at a speed and scale no human team can match.

“历史上，构建人工智能的人不是生命科学家，而生命科学家也没有在构建人工智能；我们同时来自这两个领域，”联合创始人兼首席执行官大卫·斯蒂芬·罗伯茨说。“我们意识到我们可以构建一个前所未有的东西：一个拥有与最优秀的专家一样关于蛋白质推理的科学深度的人工智能系统，但其速度和规模是任何人类团队都无法匹敌的。”

For the first time, we can begin to ask the question that the entire pharmaceutical industry has never been able to answer: across thousands of characterized therapeutics, what molecular patterns distinguish the drugs that work from the ones that do not.'.

我们第一次可以开始提出一个问题，这个问题是整个制药行业都无法回答的：在数千种已知的治疗药物中，是什么分子模式区分了有效的药物和无效的药物。

The platform's core capability is deep memory: it learns from every dataset, processing and developing an increasingly deep understanding of each customer's molecular portfolio over time. Every result is explainable and traceable, which is essential in a regulated industry where characterization results appear in filings to the FDA.

该平台的核心能力是深度记忆：它从每个数据集中学习，随着时间的推移，不断处理并加深对每位客户分子组合的理解。每一个结果都是可解释和可追溯的，这在需要向FDA提交表征结果的受监管行业中至关重要。

Legacy tools start from zero with every analysis. Combined with the founding team's unique expertise at the intersection of chemistry, biology, mass spectrometry, and modern AI architecture, the company is positioned to define a new category in the life sciences..

传统工具每次分析都从零开始。结合创始团队在化学、生物学、质谱学和现代人工智能架构交叉领域的独特专业知识，该公司有望在生命科学领域定义一个全新的类别。

'AI has already made meaningful contributions to biology at the prediction layer, asking what a protein might look like based on its sequence,' says co-founder and COO Andrew Reiter. 'What no one has built is AI for the characterization layer, where you interpret real experimental data from real therapeutic molecules: that is the layer where drug development decisions are actually made, and it has remained painfully manual.'.

“人工智能已经在预测层面为生物学做出了有意义的贡献，例如根据蛋白质的序列预测其可能的结构，”联合创始人兼首席运营官安德鲁·雷特表示。“但尚未有人构建用于表征层面的人工智能，即对来自真实治疗分子的实际实验数据进行解读：这才是真正做出药物开发决策的层面，且这一过程一直痛苦地依赖人工。”

The company's vision extends well beyond faster protein characterization. As the platform processes more molecules across more organizations, 10x Science is building toward a shared layer of molecular intelligence for the life sciences: a deep, evolving understanding of protein therapeutics grounded in real experimental data at a depth and scale that has never existed..

公司的愿景远远超出了更快的蛋白质表征。随着平台在更多组织中处理更多分子，10x Science 正在为生命科学构建一个共享的分子智能层：一种基于真实实验数据的深度且不断发展的蛋白质治疗理解，其深度和规模前所未有。

'This is a critical moment in pharma; the industry is looking for AI that actually works, and protein characterization is needed at every stage of the drug lifecycle regardless of whether any single drug succeeds or fails. We're talking about the infrastructure layer of drug development,' says Zoe Perret, partner at Initialized Capital.

“这是制药行业的一个关键时刻；行业正在寻找真正有效的AI，而且在药物生命周期的每个阶段都需要蛋白质特性分析，无论任何单一药物成功与否。我们谈论的是药物开发的基础设施层，”Initialized Capital合伙人佐伊·佩雷特说道。

'The 10x Science founders helped build this field, and they're now showing up with a product that solves an expensive, critically important problem. There is no more credible team to do this.'.

“10x Science 的创始人帮助构建了这个领域，而现在他们推出了一款解决昂贵且至关重要的问题的产品。没有比这支团队更可信的了。”

'Biologics are the fastest-growing segment of the pharmaceutical industry and are the most complex to develop. Every antibody, every cell therapy, every engineered protein requires characterization at a level of detail that existing tools simply weren't designed to handle. The field has outgrown its infrastructure.

“生物制品是制药行业中增长最快的领域，也是开发最为复杂的。每一种抗体、每一种细胞疗法、每一种工程蛋白都需要在现有工具根本无法应对的细节层面进行特性分析。该领域已经超出了其基础设施的承载能力。

That's not sustainable,' says Carolyn Bertozzi, 2022 Nobel Laureate and Stanford University Professor. 'I've spent my career at the intersection of chemistry and biology, trying to understand how molecules behave in living systems. The biggest constraint I see across the field, whether in academic labs or industry, is the gap between the data we can generate and the insights we can extract.

“这是不可持续的，”2022年诺贝尔奖得主、斯坦福大学教授卡罗琳·贝尔托齐说。“我整个职业生涯都在化学与生物学的交叉领域度过，试图理解分子在生命系统中的行为。无论是在学术实验室还是工业界，我看到整个领域面临的最大限制是，我们能够生成的数据与我们能够提取的见解之间的差距。”

10x Science closes that gap.'.

10x Science 弥补了这一差距。

Right now, the pharmaceutical industry is sitting on an enormous amount of molecular knowledge that has never been aggregated or learned from at scale. 10x Science's AI can characterize any protein, with implications spanning cancer biology, neurodegeneration, infectious disease, agriculture, and fundamental research into how living systems work.

目前，制药行业坐拥大量从未被大规模整合或学习过的分子知识。10x Science 的人工智能可以表征任何蛋白质，其影响涵盖癌症生物学、神经退行性疾病、传染病、农业以及对生命系统运作方式的基础研究。

With this funding, 10x Science is hiring Founding Engineers and expanding its work with pharmaceutical and biotech partners to open the doors for these applications..

通过这笔资金，10x Science正在招聘创始工程师，并扩大与制药和生物技术合作伙伴的工作，为这些应用打开大门。

'If we build this right, we give people across every discipline access to a new paradigm of molecular understanding that has never been possible before,' says Roberts. '10x Science can be the foundational layer of molecular intelligence for the life sciences. If we are, the world gets a deeper understanding of the molecules that govern health, disease, and life itself.

“如果我们构建得当，我们将为各个学科的人们提供一个前所未有的分子理解新范式，”罗伯茨说。“10x科学可以成为生命科学领域分子智能的基础层。如果我们做到了，世界将对支配健康、疾病和生命本身的分子有更深入的理解。”

That understanding belongs to everyone.'.

这种理解属于每个人。

For more information, visit:

欲了解更多信息，请访问：

https://www.10xscience.com/

https://www.10xscience.com/

About 10x Science:

关于10x科学：

10x Science was founded in December 2025 by David Stephen Roberts, Ph.D., Andrew Reiter, and Vishnu Tejus out of Professor Carolyn Bertozzi's Nobel laureate laboratory at Stanford University to build the first AI-native protein characterization platform for the life sciences. Roberts is a Damon Runyon Cancer Research Fellow with over 38 publications in the Nature and ACS families of journals.

10x科学公司于2025年12月由大卫·斯蒂芬·罗伯茨博士、安德鲁·雷特和维什努·特朱斯在斯坦福大学卡罗琳·贝尔托齐教授的诺贝尔奖得主实验室创立，旨在构建生命科学领域的首个AI原生蛋白质表征平台。罗伯茨是一名达蒙·鲁尼恩癌症研究研究员，在《自然》和《美国化学学会》系列期刊上发表了38篇论文。

Reiter trained at the Broad Institute of MIT and Harvard building new tools to decipher drug interactions. Tejus is a two-time Y Combinator founder who went to college at age 11. The company builds frontier AI models with deep memory that deliver automated, explainable molecular characterization of protein therapeutics, serving pharmaceutical companies, biotechs, and research institutions.

雷特曾在麻省理工学院和哈佛大学的布罗德研究所接受培训，开发新工具以解析药物相互作用。提贾斯是一位两次参与Y Combinator的创始人，他11岁就上了大学。这家公司构建具有深度记忆的前沿人工智能模型，为蛋白质治疗药物提供自动化且可解释的分子特性分析，服务于制药公司、生物技术公司和研究机构。

10x Science is a Y Combinator W26 company headquartered in the San Francisco Bay Area and has received $4.8M in seed funding led by Initialized Capital. 10x Science's frontier AI models are building a new paradigm for how scientists understand biology at the molecular level, starting with drug development..

10x Science是一家总部位于旧金山湾区的Y Combinator W26公司，已获得由Initialized Capital领投的480万美元种子轮融资。10x Science的前沿人工智能模型正在构建一个新范式，帮助科学家从分子层面理解生物学，首先应用于药物开发。

SOURCE 10x Science

来源 10x 科学

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