Skip to main content

10x Science

Category: AI in Biotech / Drug Discovery

AI-native platform for protein characterization that automates mass spectrometry data analysis to accelerate drug development. 10x Science was founded in 2025. The company is led by David Stephen Roberts. Based in San Francisco, California, United States. Team size: 2-10. Total funding raised: $4.8M. Latest round: Seed. Key investors include Initialized Capital, Y Combinator, Civilization Ventures, Founder Factor.

Founded
2025
Headquarters
San Francisco, California, United States
Team size
2-10
Total funding
$4.8M

Value proposition

Automates protein characterization from weeks to minutes using AI agents with deep memory architecture, enabling pharma teams to evaluate drug candidates at scale without manual expert analysis.

Products and solutions

AI-native peptide mapping and ultrafast de novo sequencing platform for protein characterization, automated mass spectrometry data interpretation, deep memory AI architecture that learns from each dataset over time

Unique value

The only AI-native platform combining frontier AI models with deep memory and deterministic chemistry/biology algorithms to interpret mass spectrometry data, reducing analysis from weeks to minutes with explainable, traceable results.

Target customer

Pharmaceutical companies, biotech firms, contract research organizations (CROs), and academic research institutions developing biologic therapies

Industries served

Drug Development, Biopharma, Biotechnology, Cancer Research, Infectious Disease, Agricultural Biotechnology

Technology advantage

Proprietary "deep memory" AI architecture that accumulates knowledge across datasets; combines AI agents with deterministic chemistry/biology algorithms; processes mass spectrometry data at native binary level; explainable and traceable outputs for regulatory compliance; founded by researchers from Nobel laureate Carolyn Bertozzi's Stanford lab with 45+ peer-reviewed publications

How they differentiate

Unlike traditional tools that reset with each analysis, 10x Science's deep memory system accumulates knowledge across organizations; founders are the same scientists who built the field of next-generation protein characterization; platform addresses the characterization bottleneck (not prediction) which is the actual gating step in drug development; SaaS model with recurring revenue independent of any single drug's approval

Main competitors

Protein Metrics (vendor-neutral mass spec software), Benchling (R&D cloud platform), traditional manual analysis workflows using legacy mass spectrometry software

Key partnerships

Y Combinator (W26 batch), Initialized Capital, Stanford University (Carolyn Bertozzi lab)

Notable customers

Rilas Technologies (CRO), multiple major pharmaceutical companies (undisclosed), academic research institutions

Major milestones

Founded December 2025, Accepted into Y Combinator Winter 2026 batch, Raised $4.8M oversubscribed seed round led by Initialized Capital (April 2026), First enterprise customer deployments with Rilas Technologies and major pharma companies

Market positioning

Early-stage AI infrastructure layer for the pharmaceutical industry, positioned as a must-have tool for protein characterization at every stage of the drug lifecycle

Geographic focus

Global (US-headquartered with enterprise pharma customers)

About David Stephen Roberts

Damon Runyon Cancer Research Fellow & Postdoc in Prof. Carolyn Bertozzi's lab (2022 Nobel Prize in Chemistry) at Stanford University; Ph.D. in Materials Chemistry & Analytical Chemistry, University of Wisconsin-Madison (2023); B.S. Chemistry & Mathematics, UC San Diego (2016); 1600+ citations, h-index 22, 38+ publications in Nature and ACS journals

Official website: