Recursion Pharmaceuticals (Augmented Perception – Profile) Post

Recursion Pharmaceuticals uses augmented perception to speed up the initial stages of drug discovery.

 The problem. Matching thousands of drugs to thousands of diseases:

There are thousands of rare diseases and thousands of FDA approved drugs that may have a positive impact on those diseases, but those drugs, both singly and in combination, on the rare diseases is time-consuming. Rare diseases are diseases that affect less than 200,000 people.  Pharma companies are less likely to pursue those smaller markets. Despite the small market of any single disease, it’s estimated that rare diseases affect 10% of Americans. If Recursion’s platform can help access this large market of 10% of Americans, it could be massively valuable.

Recursion was founded by an experiment to find a drug that impacts a rare disease which weak blood vessels lead blood into the brain and cause strokes. The experiment applied 2,000 different drugs to a diseased cell sample. Then, a pair of cell biologists looked at the 2,000 experiments to evaluated the phenotypic impact – or how the drugs appeared upon visual inspection to impact the cell sample. A computer algorithm developed by Anne Carpenter’s group at the Broad Institute also looked at the images from the experiments. The human team selected 39 drugs that appeared to have a positive impact on the diseased cells.

Here’s the interesting par: the computer program also selected 39 drugs – but the computer-selected and human-selected sets didn’t overlap at all. The computer selected 39 different drugs than the people did.

And, after closer study, only one of the people-selected drugs continued to appear to have an impact while 7 of the computer-selected drugs were impactful enough to merit further study.

The solution. Recursion’s automated phenotypic drug discovery platform:

Phenotypic drug discovery involves testing a drug in vitro (in a test tube) by applying a drug compound to a diseased cell sample. Researchers can judge the impact of the test by observing phenotypic changes in the cell sample.

Recursion’s platform has automated microscopes that sends thousands of images of in vitro tests each week to image recognition software. The software, armed with image data of healthy tissue, looks at the images and determines if the tested drug makes the cells look healthier.

Initial results. Recursion has made significant progress toward their goal of treating 100 genetic diseases by 2025:

Thus far, Recursion has identified promising compounds for 34 different rare diseases. Seven have progressed to in vivo tests and two are nearing applying to enter FDA trials. Their success has generated investor interest; Recursion has raised nearly $80M since February 2017.



Critique: the pharma value chain is challenging: 

The main issue that I can see with Recursion’s platform is that, because of the nature of the value chain in drug discovery, it fails to extract much value. Simply identifying that an approved drug has in vitro phenotypic impacts on a cell sample isn’t worth much. From there, millions of dollars must be spent to test, optimize, and characterize the lead compound in animal models. Then the drug must be tested in people in FDA clinical trials, which takes many years and many $Ms.

Recursion simply produces the early lead. Now, that drug is FDA-approved, so safety tests would often (but not always) less intensive. And, many pharma companies that own these drugs just have them sitting on the shelf, not being used or sold. So it could be a compelling tool for large pharma companies that have patented compounds that are failing to generate good data in the clinic – sort of a second-life option for those drugs.


But the dataset could be valuable in the long run:

However, Recursion also seems focused on generating a massive dataset of cellular models, which grows by 20 TB each week. This data could be a valuable tool for a large pharma company that develops its own drugs, such as Recursion partner, Sanofi. I would suggest focusing on collecting that data. Perhaps the company could develop a portable version of their platform and partner with CROs (contract research organizations) to collect data from clinical trials.


However, in the long run, competition from AI drug design companies may be problematic:

One general category of competitors are general AI-enabled drug development companies, such as Insilico Medicine and Atomwise. These companies promise to perform rational design of drugs from computer models. However, they haven’t yet produced compelling results.




NIH profile:

NIH rare diseases:

Anne Carpenter’s group at the Broad:

Investor blog post about Recursion:

Tech Crunch article about $60M Series B:

FierceBiotech profile:



Team Members: Brentt Baltimore, Moises Numa, Corey Ritter, Mitchell Stubbs

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