AI가 생성하는 신약 후보 물질이 폭증하면서, 이를 실험적으로 검증하고 분석하는 '특성 분석(Characterization)' 단계가 새로운 병목 현상으로 떠올랐습니다. 이에 스타트업 10x Science는 AI와 질량 분석법(Mass spectrometry)을 결합한 플랫폼을 구축하고 480만 달러의 시드 투자를 유치했습니다. 이 회사의 솔루션은 바이오 제약사의 분석 시간을 단축하고 규제 기관의 승인을 받을 수 있는 정확한 데이터 추적 기능을 제공하여 주목받고 있습니다.
번역된 본문
과학 분야에서 AI가 미친 가장 큰 영향은 구글 딥마인드(Google DeepMind)가 딥러닝 모델을 사용해 생세포 내 거의 모든 과정을 주도하는 분자인 '단백질의 복잡한 구조'를 예측한 것입니다. 하지만 AI 모델이 잠재적 치료제 후보를 계속해서 쏟아내면서, 이 후보들을 테스트하고 대량 생산하기 위해 실제로 특성을 분석하고 규명하는 과정에서 새로운 병목 현상이 나타나고 있습니다. 이것이 바로 오늘 Initialized Capital의 주도로 Y Combinator, Civilization Ventures, Founder Factor로부터 480만 달러의 시드 투자를 유치한다고 발표한 스타트업 10x Science의 목표입니다. 이 회사는 2025년 12월에 설립되었으며, 경험이 풍부한 생화학자인 David Roberts와 Andrew Reiter, 컴퓨터 과학 및 AI 모델 전문 지식을 갖춘 연쇄 창업자 Vishnu Tejas 등 세 명의 공동 창업자가 이끌고 있습니다.
Roberts는 TechCrunch와의 인터뷰에서 "바이오 제약사가 신약 후보를 개발하려고 할 때, 정말 훌륭한 예측 도구들이 많이 있습니다"라고 말했습니다. "깔때기(퍼널) 최상단에 원하는 만큼 많은 후보를 넣을 수 있지만, 이들은 모두 이 특성 분석(Characterization) 과정을 통과해야 합니다. 모든 것을 정확하게 측정해야 하죠." 단백질의 구조를 이해하는 것은 생세포에서 생산되며 특정 질병이나 상태를 표적으로 삼기 위해 정교하게 설계된 '바이오 의약품(Biologic drugs)'을 개발하는 연구자들에게 핵심적입니다. 예를 들어, 이들은 면역 체계가 암을 식별하고 공격하도록 돕는 머크(Merck)의 인기 치료제인 키트루다(Keytruda)처럼 특정 세포를 표적으로 삼도록 설계될 수 있습니다.
10x의 세 창업자는 노벨상 수상자인 Carolyn Bertozzi 박사의 스탠퍼드 연구실에서 함께 일했습니다. 그곳에서 그들은 암세포와 면역 체계 간의 상호작용을 연구했지만, 분자 수준에서 정확히 무슨 일이 일어나고 있는지 제대로 파악하지 못하는 것에 좌절감을 느꼈습니다. 분자를 평가하는 가장 정확한 방법은 '질량 분석법(Mass spectrometry)'이라는 복잡한 기술을 통하는 것으로, 전기장에서 분자를 측정하여 원자 구조를 결정하는 방식입니다. 비교적 새로운 이 기술은 복잡한 데이터를 생성하므로 이를 해석하기 위해서는 상당한 전문 지식이 필요하며, 분석에 많은 시간이 소요됩니다.
[참고: TechCrunch Disrupt 2026 행사 홍보 문단 - 번역 생략]
10x의 플랫폼은 화학과 생물학에 뿌리를 둔 결정론적 알고리즘(Deterministic algorithms)과 해당 데이터를 해석할 수 있는 AI 에이전트를 결합합니다. 팀은 질량 분석 데이터로 모델을 학습시키고 분석 결과를 추적 가능하게 만들기 위해 상당한 작업을 수행해야 했습니다. 이는 기업이 규제 준수를 달성하도록 돕는 데 사용될 도구의 핵심 요구 사항입니다.
Matthew Crawford은 다른 기업을 위해 화학 분석을 수행하는 회사인 Rilas Technologies의 과학자입니다. 그는 바이오 기술 스타트업과 같은 고객이 자체 질량 분석 장비와 이를 운영할 전문가에 수백만 달러를 투자하는 대신 아웃소싱을 맡기는 역할을 합니다. Crawford은 몇 주 동안 10x Science 플랫폼을 사용해 왔으며 이것이 자신의 작업 속도를 높여주고 있다고 말합니다. 그는 이 모델이 자체적으로 분석에 필요한 올바른 데이터를 찾고, 결론을 도출하는 설명 능력, 그리고 다양한 종류의 분자를 평가하도록 적응하는 능력에 놀랐다고 밝혔습니다. 과거에 실험했던 일부 AI 도구들이 과대 광고를 하거나 정확도에 문제가 있었던 반면, 이번 플랫폼은 합리적인 가정을 내린다는 점에서 그는 이를 창립자들의 깊은 도메인 전문 지식 덕분이라고 평가했습니다. "저는 특정 단백질을 이 시스템에 입력했는데, 시스템이 제가 무엇을 원하는지 파악하고 스스로 알아서 처리했습니다."
AI's biggest impact in science is Google DeepMind's use of a deep learning model to predict the complex structures of proteins — the molecules that drive virtually every process in living cells. But as AI models continue to spit out more candidates for potential treatments, there's an emerging bottleneck: actually characterizing all those candidates in practice, for testing and mass production. That's the goal of 10x Science, a startup founded in December 2025 that announced a $4.8 million seed round today, led by Initialized Capital and with backing from Y Combinator, Civilization Ventures, and Founder Factor. Its three founders are David Roberts and Andrew Reiter, experienced biochemists, and Vishnu Tejas, a serial founder with expertise in computer science and AI models. "When biopharma tries to create a drug candidate, they have all of these really nice prediction tools," Roberts told TechCrunch. "You can add as many candidates as you want to the top of the funnel, but they all have to pass through this characterization process. Everything needs to be measured." Understanding the structure of proteins is key for researchers developing biologic drugs, which are produced in living cells and use sophisticated design to specifically target diseases and conditions. For example, they can be designed to target specific cells, like Keytruda, a popular drug sold by Merck that helps the immune system identify and attack cancers. 10x's three founders worked together in the Stanford lab of Nobel laureate Dr. Carolyn Bertozzi, where they studied the interactions between cancer cells and the immune system, and were frustrated by their inability to understand precisely what was happening on a molecular level. The most accurate way to assess molecules is through a complex technique called mass spectrometry, a way of determining their atomic structure by measuring them in an electric field. The relatively new technique generates complex data that requires significant expertise to interpret, and analyzing it takes up a lot of time. Techcrunch event Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. San Francisco, CA | October 13-15, 2026 REGISTER NOW 10x's platform combines deterministic algorithms rooted in chemistry and biology with AI agents that can interpret that data. The team had to do significant work to train the models on spectrometry data and make its analyses traceable, a key requirement for a tool that will be used to help companies achieve regulatory compliance. Matthew Crawford is a scientist at Rilas Technologies, a firm that runs chemical analyses for other companies — saving clients like biotech startups from having to invest several million dollars in their own spectrometry equipment and the experts to operate it. Crawford has been using the 10x Science platform for several weeks and says it is speeding up his work. Crawford said the model surprised him with its ability to explain its conclusions, find the right data for analyses on its own, and adapt to evaluating different kinds of molecules. While some AI tools he has experimented with in the past over-promised or suffered accuracy issues, he says this one makes reasonable assumptions, something he attributes to the deep domain expertise of its creators. "I ran a particular protein through it, and it just kind of figured out, from what I named the file, what the protein probably was," Crawford said. "It then searched databases online for the sequence for that protein, so I didn't have to program in the sequence." 10x executives say they're also working with multiple major pharmaceutical companies, as well as academic researchers. The plan is to use this seed funding to hire more engineers and continue to refine the model and offer it to new customers. If they are able to gain traction characterizing proteins, Roberts hopes the company will expand to offer a new kind of understanding of biology, combining protein structure with other data about cells. "The deeper thing behind what we're building is actually a new way to define molecular intelligence," Roberts said. For its investors, 10x offers a useful way into the biotech space that isn't dependent on a specific drug succeeding and winning regulatory approval. If the company works out the way its founders hope, it will become an important tool for drug development, whether or not the eventual products succeed in the marketplace. "This is a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates," Zoe Perret, a partner at Initialized, said. She's counting on the deep experience of the founders to protect the company from competitors; there simply aren't that many people who understand these methods and the data they produce. What the platform could do, Crawford says, is help unlock the techniques for researchers who could benefit from these methods but lack the time or resources to deploy them. "Groups here are trying to make a new drug," he told TechCrunch. "They just want to get a quick, simple answer out of mass spec, and then it opens up a whole can of worms. This software is going to help keep that can of worms closed and just get them the answer they actually need to then do the next thing in their research." Topics 10x Science , AI , Biotech & Health , Exclusive When you purchase through links in our articles, we may earn a small commission . This doesn’t affect our editorial independence. Tim Fernholz Senior Reporter Tim Fernholz is a journalist who writes about technology, finance and public policy. He has closely covered the rise of the private space industry and is the author of Rocket Billionaires: Elon Musk, Jeff Bezos and the New Space Race. Formerly, he was a senior reporter at Quartz, the global business news site, for more than a decade, and began his career as a political reporter in Washington, D.C. You can contact or verify outreach from Tim by emailing tim.fernholz@techcrunch.com or via an encrypted message to tim_fernholz.21 on Signal. View Bio April 30 San Francisco, CA StrictlyVC kicks off the year in SF. Get in the room for unfiltered fireside chats with industry leaders, insider VC insights, and high-value connections that actually move the needle. Tickets are limited. 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