The profound impact of artificial intelligence in scientific discovery is perhaps best exemplified by Google DeepMind's pioneering use of deep learning models to accurately predict the intricate structures of proteins — the fundamental molecules orchestrating virtually every biological process within living cells.
However, as AI models increasingly generate a multitude of promising candidates for novel treatments, a critical bottleneck has emerged: the practical characterization of these candidates for subsequent testing and eventual mass production.
Addressing this challenge is the core mission of 10x Science, a startup established in December 2025. The company recently announced the successful closure of a $4.8 million seed funding round, spearheaded by Initialized Capital, with significant contributions from Y Combinator, Civilization Ventures, and Founder Factor. Its founding team comprises David Roberts and Andrew Reiter, both accomplished biochemists, alongside Vishnu Tejas, a serial entrepreneur recognized for his expertise in computer science and AI modeling.
David Roberts highlighted this industry challenge to TechCrunch, stating, "When biopharma tries to create a drug candidate, they have all of these really nice prediction tools. 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."
A comprehensive understanding of protein structure is indispensable for researchers engaged in developing biologic drugs. These advanced therapeutics are cultivated within living cells and employ sophisticated designs to precisely target specific diseases and conditions. For instance, they can be engineered to zero in on particular cell types, much like Keytruda, a widely used Merck drug that empowers the immune system to detect and combat cancers.
The three co-founders of 10x Science collaborated in the Stanford laboratory of Nobel laureate Dr. Carolyn Bertozzi. Their research focused on the complex interactions between cancer cells and the immune system, during which they encountered significant frustration due to the inability to precisely comprehend molecular-level events.
The most accurate method for assessing molecules involves a sophisticated technique known as mass spectrometry. This process determines atomic structure by measuring molecules within an electric field. As a relatively new technique, it produces complex data that demands considerable expertise for interpretation, making the analysis a time-intensive endeavor.
10x Science's innovative platform integrates deterministic algorithms grounded in chemistry and biology with AI agents designed to interpret this complex data. The team dedicated substantial effort to training these models on spectrometry data and ensuring the traceability of its analyses—a crucial prerequisite for a tool intended to assist companies in achieving regulatory compliance.
Matthew Crawford, a scientist at Rilas Technologies, a firm specializing in chemical analyses for other companies, has been utilizing the 10x Science platform for several weeks. Rilas Technologies helps clients, such as biotech startups, avoid multi-million dollar investments in their own spectrometry equipment and specialized operators. Crawford reports that the platform significantly accelerates his work.
Crawford expressed his surprise at the model's capacity to articulate its conclusions, independently identify relevant data for analyses, and adapt to evaluating diverse molecular types. While previous AI tools he experimented with often over-promised or suffered from accuracy issues, he noted that 10x's solution makes reasonable assumptions, a quality he attributes to the creators' profound domain expertise.
“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 recounted. “It then searched databases online for the sequence for that protein, so I didn’t have to program in the sequence.”
10x executives have disclosed ongoing collaborations with numerous major pharmaceutical companies and academic researchers. The seed funding will be strategically deployed to expand the engineering team, further refine the model, and introduce it to a broader customer base. Should the company successfully gain traction in protein characterization, Roberts envisions an expansion that would offer a new paradigm for understanding biology, integrating protein structure with other cellular data.
“The deeper thing behind what we’re building is actually a new way to define molecular intelligence,” Roberts affirmed.
For its investors, 10x Science presents a compelling entry point into the biotech sector that is not contingent on the success or regulatory approval of a single drug. If the company fulfills its founders' aspirations, it will become an indispensable tool for drug development, irrespective of the ultimate market success of the resulting products.
Zoe Perret, a partner at Initialized, described the offering as "a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates." She places confidence in the founders' extensive experience to safeguard the company against competitors, noting the scarcity of individuals who truly comprehend these intricate methods and the data they generate.
Crawford suggests that the platform's potential lies in democratizing these advanced analytical techniques for researchers who could greatly benefit but currently lack the necessary time or resources to implement 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.”
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