Recursion OS:
The AI platform to industrialize drug discovery

Decoding biology to radically improve lives

As a top biotech — or TechBio — company, we're harnessing new technologies like advanced AI for drug discovery and drug development, supercomputing power, and automation, to build massive fit-for-purpose datasets and state-of-the-art multimodal machine learning foundation models.Our mission is to decode biology, taking a fundamentally different approach to traditional pharma, to design new drugs more efficiently and effectively.
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The Recursion Operating System (OS), Recursion's proprietary AI drug discovery and drug development platform, spans target identification to clinical trial enrollment. Every data point we collect along the way is fed back into the drug discovery and drug development platform, improving its performance.

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The journey of an experiment

Generating one of the largest relatable data sets in pharma is only possible with Recursion’s automated high throughput labs. We’ve generated ~36 PB of proprietary data across phenomics, transcriptomics, proteomics, and ADME. Click through to follow a single experiment from the start of the process to the end.

Our labs can process up to 2.2 million samples per week.

The Journey of an Experiment

Central to our mission is the Recursion Operating System (OS), a platform powered by one of the world’s largest proprietary biological and chemical datasets. Instead of looking narrowly at a handful of diseases with existing therapeutic hypotheses, we build Maps of Biology and Chemistry that broaden our search and allow us to explore unknown areas of disease biology.

Tissue culture

First, cells are cultured in large batches in our Tissue Culture labs. To focus on scalability, we’ve developed innovative methods for growing, freezing, thawing, and experimenting with these cells in large quantities. Our first maps are in HUVEC and NGN2 neurons; we’ve produced hundreds of billions of each.

We’re one of the largest producers of HUVEC cells in the world. We can create over 100 billion cells per year for our high throughput experiments.

Tissue Culture

First, cells are cultured in large batches in our Tissue Culture labs. To focus on scalability, we’ve developed innovative methods for growing, freezing, thawing, and experimenting with these cells in large quantities. Our first maps are in HUVEC and NGN2 neurons; we’ve produced hundreds of billions of each.

CRISPR at scale

In order to run experiments, we need to intervene in the cell (i.e. perturb it) in order to mimic diseases or to test treatments. The primary way we model diseases on our platform is by knocking out a gene’s function with CRISPR-Cas9 editing. We systematically combine the cells with the programmed CRISPR guide set for each gene, one at a time. We also introduce slight variations in the guides for each gene and we do many instances or replicates of each combination so that we create a more robust experimental signal for each gene.

CRISPR At Scale

In order to run experiments, we need to intervene in the cell (i.e. perturb it) in order to mimic diseases or to test treatments. The primary way we model diseases on our platform is by knocking out a gene’s function with CRISPR-Cas9 editing. We systematically combine the cells with the programmed CRISPR guide set for each gene, one at a time. We also introduce slight variations in the guides for each gene and we do many instances or replicates of each combination so that we create a more robust experimental signal for each gene.

Cell seeding

Cells are seeded into experiment plates. These are essentially grids of miniature test tubes, with each plate containing 1536 miniature test tubes called ‘wells’. Each well will ultimately contain a unique experimental condition with some unique combination of cells and a reagent or condition that we are testing in that well.

Cell Seeding

Cells are seeded into experiment plates. These are essentially grids of miniature test tubes, with each plate containing 1536 miniature test tubes called ‘wells’. Each well will ultimately contain a unique experimental condition with some unique combination of cells and a reagent or condition that we are testing in that well.

Compound management

This automated process is repeated for thousands of plates. In different runs, our platform screens whole genome CRISPR knockout, or we profile millions of compounds in the same manner to compare phenotypes and transcriptomes.

All of the reagents for each experiment are stored in an automated storage system that directly integrates with the liquid transfer and plate automation work cells.

Automated Compound Management

This automated process is repeated for thousands of plates. In different runs, our platform screens whole genome CRISPR knockout, or we profile millions of compounds in the same manner to compare phenotypes and transcriptomes.

All of the reagents for each experiment are stored in an automated storage system that directly integrates with the liquid transfer and plate automation work cells.

Incubation

Over the course of a few days, the plates move through various stages of the assay while the cells take on the effects of the perturbation, including: washing, adding new cell food or ‘media’, and incubation.

Incubation

The plates move through various stages of the assay while the cells take on the effects of the perturbation, getting washed, new cell food or ‘media’ is added, the plates are incubated. This all takes a few days.

Cell imaging

High content microscopes take pictures of each well at different assay points, providing a longitudinal signal for how cells change over time as the reagent coexists in the cell environment. We use Brightfield imaging which gives the most information about the experiment over time.

Our platform produces millions of phenomics images per week.

Cell Imaging

Then high content microscopes take pictures of each well. This can be done at different points along the assay, giving us a longitudinal signal for how the cells change over time as the reagent coexists in the cell environment. We use Brightfield imaging which gives us the most information about the experiment over time.

Trekseq

After imaging, plates are transferred to our next high throughput platform: Transcriptomics, which give us an understanding of gene expression. The plates are processed again with barcodes that bind to each mRNA, giving it a unique identifier. The transcriptomics barcodes can then be ‘read’ by an instrument called a sequencer, resulting in a large data file that is representative of the well’s unique transcriptome

We are one of the largest transcriptomics data producers in the world.

Trekseq

After imaging, plates are transferred to our next high throughput platform: Transcriptomics, which give us an understanding of gene expression. The plates are processed again with barcodes that bind to each mRNA, giving it a unique identifier. The transcriptomics barcodes can then be ‘read’ by an instrument called a sequencer, resulting in a large data file that is representative of the well’s unique transcriptome

Deep learning & AI

The transcriptomics and phenomics data is analyzed through a series of mapping transformations and embedded by our AI models into a mathematical space, allowing us to calculate metrics about and between each perturbation.

Deep Learning & AI

All data from transcriptomics and phenomics are analyzed through a series of mapping transformations where they are embedded by our AI models into a mathematical space, allowing us to calculate metrics about and between each perturbation.

Industrialized workflows

Together these data are used to trigger actions in our industrialized workflows. They build our maps of biology and in those maps we discover relationships which we can further test in our labs.

Industrialized Workflows

Central to our mission is the Recursion Operating System (OS), a platform powered by one of the world’s largest proprietary biological and chemical datasets. Instead of looking narrowly at a handful of diseases with existing therapeutic hypotheses, we build Maps of Biology and Chemistry that broaden our search and allow us to explore unknown areas of disease biology.

Design

From the hits identified, synthesis-aware generative AI workflows are used to design optimized drug candidates to meet target candidate profiles and remove likely liabilities. Models are used to score and rank potential molecules, with active learning used to select panels of the most informative compounds to make.

Design

From the hits identified, synthesis-aware generative AI workflows are used to design optimized drug candidates to meet target candidate profiles and remove likely liabilities. Models are used to score and rank potential molecules, with active learning used to select panels of the most informative compounds to make.

Make

After hits are identified by our industrialized workflows, we select only the very best insights to pursue as potential programs. The molecule is optimized, tested for safety, and developed further into a potential medicine on our translation platform.

Make

After hits are identified by our industrialized workflows, we select only the very best insights to pursue as potential programs. The molecule is optimized, tested for safety, and developed further into a potential medicine on our translation platform.

Test

Next, compounds are tested using our automated biology assays. Molecules automatically pass through panels of increasingly more complex assays to confirm activity against their target and in a range of in vitro and cell-based assays. Multiple other parameters covering ADME, PK, and toxicity are also assessed.

Automating these processes reduces manual handling of experiments, decreasing the time and cost of the majority of biological assays by >75%.

Test

Next, compounds are tested using our automated biology assays. Molecules automatically pass through panels of increasingly more complex assays to confirm activity against their target and in a range of in vitro and cell-based assays. Multiple other parameters covering ADME, PK, and toxicity are also assessed.

Learn

Experimental values obtained from the testing of molecules are automatically fed back into our models to improve them. Iterative design cycles evolve molecules towards our goal, as we learn our way through the project. A final molecule is optimized and designed further into a potential medicine on our translation platform.

Learn

Experimental values obtained from the testing of molecules are automatically fed back into our models to improve them. Iterative design cycles evolve molecules towards our goal, as we learn our way through the project. A final molecule is optimized and designed further into a potential medicine on our translation platform.

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
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