Healthcare

Recursion Pharmaceuticals

AI-driven drug discovery platform using biological and chemical data.

Paid ★★★★½ 4.7
drug discovery biotech AI machine learning computational biology precision medicine life sciences high-throughput screening
Rate it:
Visit Recursion Pharmaceuticals →
Recursion Pharmaceuticals screenshot

About Recursion Pharmaceuticals

Recursion Pharmaceuticals is a clinical-stage biotechnology company that uses artificial intelligence, automation, and high-throughput experimentation to accelerate drug discovery. Its Recursion OS platform combines large-scale biological imaging, multi-omics data, machine learning, and supercomputing to identify and validate potential drug candidates. The company partners with pharmaceutical organizations to develop treatments for rare diseases, oncology, and neuroscience. Recursion integrates wet-lab experimentation with computational models to shorten development timelines and improve success rates. Its platform analyzes billions of biological relationships to uncover novel therapeutic opportunities.

Frequently Asked Questions

What is Recursion Pharmaceuticals and how does its platform work?
Recursion Pharmaceuticals is a clinical-stage TechBio company dedicated to industrializing drug discovery by decoding biological systems through automation, machine learning, and massive scale. At the core of its operations is the Recursion Operating System (OS), an end-to-end, AI-native platform that bridges physical wet-lab experimentation with advanced in silico computational analysis. By executing millions of automated cellular experiments each week, utilizing CRISPR gene editing, and running computer vision models on massive imagery datasets, Recursion builds complex "Maps of Biology and Chemistry." This closed-loop system allows scientists to explore unknown disease pathways and discover novel therapeutic targets rather than relying on narrow, pre-existing hypotheses.
What role do data scale and supercomputing play in the Recursion OS?
Data and computational scale are the primary differentiators that allow the Recursion OS to train highly predictive machine learning models. The company has generated and aggregated a proprietary multimodal dataset exceeding 50 petabytes, which spans phenomics, transcriptomics, proteomics, ADME (absorption, distribution, metabolism, and excretion), and de-identified patient records from healthcare partners. To process this massive infrastructure and accelerate predictive AI modeling, Recursion collaborated with NVIDIA to build BioHive-2, which stands as one of the most powerful supercomputers in the entire biopharmaceutical industry. This unmatched computing infrastructure enables the rapid translation of multi-layered biological data into highly optimized chemical compounds in a fraction of traditional timelines.
What clinical areas does Recursion's drug pipeline focus on?
Recursion focuses its pipeline on conditions with high unmet medical needs, concentrating heavily on oncology, rare diseases, immunology, and neuroscience. By prioritizing genetically or biologically defined patient populations, the company can extract clearer data signals to accelerate clinical candidate selection and proof-of-concept validation. Its advanced wholly-owned pipeline includes targeted clinical programs such as REC-1245 (a first-in-class RBM39 degrader for solid tumors and lymphoma) and REC-4539 (a novel, precision-designed LSD1 inhibitor), alongside other assets targeting conditions like familial adenomatous polyposis. Rather than relying on a single blockbuster asset, Recursion utilizes its platform to establish a highly diversified, repeatable engine of first-in-class and best-in-class therapeutic candidates.
How does Recursion collaborate with global pharmaceutical companies?
To maximize its global therapeutic impact, Recursion operates a dual-monetization strategy that balances its internal pipeline with high-value strategic partnerships alongside major pharmaceutical and technology giants. The company actively licenses out its technological capabilities and custom-built biological maps to industry leaders such as Roche, Genentech, and Sanofi to discover novel targets and accelerate external drug pipelines. Backed by financial stability and multi-year milestone structures, these collaborations validate the efficiency of the platform, which routinely achieves hit-candidate identification in weeks instead of years and slashes the total number of compounds required for physical synthesis by approximately 90% compared to traditional industry averages.

More in Healthcare