10x Science raises $4.8M to automate drug candidate characterization

10x Science, a startup founded in December 2025, announced a $4.8 million seed funding round today. The investment was led by Initialized Capital, with participation from Y Combinator, Civilization Ventures, and Founder Factor. The company plans to use the seed funding to hire more engineers, continue to refine its model, and offer its platform to new customers.

The startup aims to solve the “characterization bottleneck” in drug discovery. While artificial intelligence models can now generate vast numbers of potential drug candidates, researchers face a slow, expert-heavy process to actually measure and validate those candidates for testing and mass production. This is especially critical for biologic drugs—which are drugs produced in living cells—where understanding molecular structure is essential.

To accelerate this process, 10x Science developed a platform that combines deterministic algorithms with AI agents to interpret mass spectrometry data. Mass spectrometry is a technique that measures the mass and charge of molecules to determine their composition and structure. Because this technique generates highly complex data that typically requires significant expertise and time to analyze, the startup’s platform is designed to automate the interpretation. According to 10x executives, the company is working with multiple major pharmaceutical companies and academic researchers.

For investors, the startup represents a scalable software-as-a-service (SaaS) business model within the biotechnology sector. Zoe Perret, a partner at Initialized, noted that this is a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates. Investors are counting on the deep domain expertise of the founders—who include chemical biologist David Roberts, biologist Andrew Reiter, and computer science and AI specialist Vishnu Tejus—to serve as a competitive moat.

Why it matters

10x Science is shifting the focus of AI in biotech from merely generating drug candidates to the operational reality of validating them, addressing a critical time-and-cost bottleneck for pharmaceutical companies.