GoKawiilAI’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.
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