One of AI's most significant contributions to science has been Google DeepMind's application of a deep learning model to predict the intricate structures of proteins—the molecules that power nearly every function within living cells. However, as AI models generate an ever-growing list of potential treatment candidates, a new bottleneck is emerging: the practical challenge of characterizing all these candidates for testing and mass production. This is the precise problem that startup 10x Science, founded in December 2025, aims to solve. The company announced a $4.8 million seed funding round today, led by Initialized Capital with participation from Y Combinator, Civilization Ventures, and Founder Factor.

The founding team consists of experienced biochemists David Roberts and Andrew Reiter, alongside serial founder Vishnu Tejas, who brings expertise in computer science and AI models. As Roberts explains, "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."

For researchers developing biologic drugs—complex treatments produced in living cells and engineered to precisely target diseases—understanding protein structure is fundamental. A prominent example is Keytruda, a widely used Merck drug that assists the immune system in recognizing and attacking cancer cells. The three founders previously collaborated in the Stanford laboratory of Nobel laureate Dr. Carolyn Bertozzi, where they studied interactions between cancer cells and the immune system. Their work was frequently hampered by an inability to decipher exactly what was occurring at a molecular level.

The gold standard for assessing molecules is a sophisticated technique known as mass spectrometry, which determines atomic structure by measuring molecules within an electric field. This relatively new method produces highly complex data that demands considerable expertise to interpret, a process that is notoriously time-consuming. The 10x platform addresses this by integrating deterministic algorithms grounded in chemistry and biology with AI agents capable of interpreting the spectrometry data. Considerable effort was required to train the models on this specialized data and to ensure their analyses are traceable—a critical feature for any tool intended to help companies meet regulatory standards.

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Matthew Crawford, a scientist at Rilas Technologies—a firm that provides chemical analysis services, saving biotech startups from multi-million-dollar investments in their own spectrometry equipment and specialists—has been using the 10x Science platform for several weeks. He reports that it significantly accelerates his work. Crawford was particularly impressed by the model's ability to explain its conclusions, independently locate the correct data for analyses, and adapt to evaluating various molecule types. Unlike some previous AI tools that over-promised or faced accuracy problems, he notes this platform makes reasonable assumptions, a quality he attributes to the deep domain knowledge 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."

Company executives report that 10x is also collaborating with several major pharmaceutical firms and academic researchers. The seed funding will be used to expand the engineering team, further refine the model, and onboard new customers. If the company succeeds in streamlining protein characterization, Roberts envisions expanding its scope to offer a novel, integrated understanding of biology by combining protein structure data with other cellular information.

"The deeper thing behind what we’re building is actually a new way to define molecular intelligence," Roberts said.

For investors, 10x represents an appealing entry into the biotech sector that isn't reliant on the success or regulatory approval of any single drug. If the founders' vision is realized, the platform will become an essential tool for drug development, regardless of whether the final products achieve commercial success.

"This is a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates," said Zoe Perret, a partner at Initialized. She is banking on the founders' extensive experience to create a competitive moat, given the scarcity of experts who truly understand these methods and their resulting data.

Crawford believes the platform's greatest impact may be in democratizing advanced techniques for researchers who could benefit from them but lack the time or resources to implement the methods directly.

"They just want to get a quick, simple answer out of mass spec, and then it opens up a whole can of worms," Crawford said. "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."