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Root Bio

Root Bio (formerly Agteria) is transforming biological discovery from a serendipitous process into an engineering discipline by combining AI-driven molecular design with physical laboratory validation. The company's closed-loop platform generates novel protein candidates in silico, verifies them experimentally, and feeds results back into its models, compressing discovery cycles from years to weeks for applications in therapeutics and industrial biotechnology.

Traction 10/10
Stockholm, Sweden · HQ
Founded 2023183K+ followers
  • Biotechnology
  • Artificial Intelligence
  • Drug Discovery & Therapeutics
Updated 6 days ago

Funding

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Biological discovery has historically been a serendipitous process, relying on chance events and time-intensive experimentation rather than systematic engineering. This approach makes the development of new molecules slow and unpredictable, delaying progress in fields like therapeutics and industrial biotechnology.

Solution

Root Bio transforms biological discovery into an engineering discipline by combining AI-driven molecular design with physical laboratory validation. The company uses computational models to propose new proteins and other biomolecules, then verifies these candidates experimentally, creating a closed-loop process that compresses discovery cycles from years to weeks. Each validated result feeds back into the AI system, continuously expanding what is possible and improving the accuracy of future designs.

Target Audience

Primary customers are pharmaceutical, biotechnology, and industrial companies seeking faster development of novel proteins and biomolecules for therapeutic, chemical, or materials applications.

Features

  • AI-powered molecular design platform that generates novel protein candidates and other biomolecules in silico
  • Integrated physical lab verification workflow that tests AI-designed molecules for real-world functionality
  • Iterative learning loop where every experimental result refines the underlying models, expanding the design space
  • Application across multiple sectors including therapeutics, industrial enzymes, and materials
  • Focus on making biological design reproducible and systematic rather than reliant on chance discovery
This profile is AI-generated and may contain inaccuracies.