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Recursion OS: The AI product engine to industrialize drug discovery

Inside the loop: Learn how we map disease biology and design potential medicines, and carry them into the clinic — at scale

Take a look inside the automated labs that form our AI-native data factory to see our end-to-end drug discovery and development engine work as one connected system. Driven by bilingual teams of scientists and technologists, our wet and dry "lab-in-the-loop," forms a continuous learning system of physical automation and machine learning models. See how we run up to 2 million weekly experiments to build a 50+ petabyte proprietary dataset, tightly connecting biology, generative de novo chemistry, and translational clinical tools to systematically de-risk drug discovery.

Chapters:

An end-to-end platform for AI drug discovery and development

Data is the foundation of the Recursion OS drug discovery and development system, underpinned by algorithms that can make predictions that will then be tested in our labs. Every physical experiment conducted is converted into digital representations of biology and chemistry, forming loops of physical data and digital analysis.

This approach allows us to iterate across the discovery process, leveraging machine learning and large language models (LLMs) built-for-purpose to uncover novel insights across biology and chemistry, fueling our clinical-stage pipeline.

Our data & models
Flowchart of the Recursion OS 2.0 platform showing three iterative phases: AI-powered Biological Insights (Nomination Workflow), AI-enabled Precision Design (Design Workflow), and AI-informed Clinical Development (ClinTech Workflow).
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