A biotech startup founded by three scientists from a Nobel laureate’s Stanford laboratory has raised $4.8 million in seed funding to apply artificial intelligence to one of drug development’s most persistent bottlenecks: the characterization of molecular candidates before they can be tested and manufactured at scale.
10x Science, founded in December 2025, announced the round led by Initialized Capital, with backing from Y Combinator, Civilization Ventures and Founder Factor. The company was founded by biochemists David Roberts and Andrew Reiter, along with serial founder and computer scientist Vishnu Tejas. All three worked together in the Stanford lab of Nobel laureate Dr. Carolyn Bertozzi, where they studied interactions between cancer cells and the immune system.
The company’s platform targets a gap that has emerged as AI tools generate increasingly large numbers of drug candidates. “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.”
The most accurate method for assessing molecular structure is mass spectrometry, which determines atomic structure by measuring molecules in an electric field. The technique generates complex data that requires significant expertise to interpret and consumes substantial time. 10x’s platform combines deterministic algorithms rooted in chemistry and biology with AI agents that can interpret that data, with analyses designed to be traceable — a key requirement for regulatory compliance in pharmaceutical development.
Matthew Crawford, a scientist at Rilas Technologies, a firm that runs chemical analyses for other companies, said the platform has accelerated his work since he began using it several weeks ago. He said the system demonstrated an unexpected ability to identify proteins from file names, search online databases for the relevant molecular sequence and adapt to evaluating different types of molecules. “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.
The company said it is working with multiple major pharmaceutical companies as well as academic researchers. Funding will be used to hire engineers and refine the model for new customers.
Zoe Perret, a partner at Initialized, said the platform’s value lies in the deep domain expertise of its founders and its position as a recurring-revenue tool that pharmaceutical companies must use regardless of whether any individual drug candidate ultimately succeeds in the market.
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