Opentrons Labworks, Inc., headquartered in Brooklyn, New York, has introduced its latest innovation aimed at transforming lab automation and streamlining scientific research. The company has launched a new protocol library along with generative AI tools designed to empower scientists with automated, user-friendly workflows across various fields including genomics, proteomics, cell biology, and synthetic biology.
The protocol library, compatible with all Opentrons robots including the Opentrons Flex™, offers plug-and-play protocols to simplify and scale lab automation. This initiative is part of Opentrons' broader ecosystem, which integrates generative AI tools for protocol development alongside rigorous wet- and dry-lab verification processes. The goal is to establish the largest open-access collection of robust liquid-handling automation protocols, facilitating research across multiple scientific disciplines.
“Our objective is to eliminate the hands-on, laborious tasks in the lab with easy-to-use software so that researchers can place more focus on scientific breakthroughs, rather than spending time at the bench with manual, time-consuming activities,” said Jonathan Brennan-Badal, CEO of Opentrons. “With hundreds of protocols already developed by our community of users and internal scientific team, the easily accessible protocol library and our latest innovation in generative-AI protocol development tools both mark a major milestone for scientists. Our approach will help scientists unravel the complexities of the genome and accurately capture large-scale genetic variations in disease, especially in areas where there is a growing need for accessible automation solutions, such as sample prep for long-read and single-cell sequencing.”
In addition to the protocol library, Opentrons has introduced an AI-powered protocol generation tool that utilizes large language models to create novel workflows for its robots. This effort aligns with broader initiatives led by research institutions such as Carnegie Mellon University and MIT, which seek to develop AI-driven lab assistants capable of designing and executing automated experiments.