Recursion Pharmaceuticals, Inc. operates as a biotechnology firm currently in its clinical development phase. The company's mission is to revolutionize drug discovery ...
Recursion Pharmaceuticals, Inc. is a publicly traded clinical-stage biotechnology company headquartered in Salt Lake City, Utah, and listed on the Nasdaq Global Select Market under the symbol RXRX. Founded in 2013, the company was built around the premise that drug discovery can be improved by combining large-scale biological experimentation with ...Recursion Pharmaceuticals, Inc. is a publicly traded clinical-stage biotechnology company headquartered in Salt Lake City, Utah, and listed on the Nasdaq Global Select Market under the symbol RXRX. Founded in 2013, the company was built around the premise that drug discovery can be improved by combining large-scale biological experimentation with advanced computation. Its approach integrates automated laboratory systems, high-content cellular imaging, chemistry, machine learning, artificial intelligence, engineering, and extensive biological datasets. Rather than relying only on manually designed experiments and sequential testing, Recursion seeks to run large numbers of standardized experiments, measure cellular responses, and use computational models to identify relationships between genes, proteins, diseases, and potential drug compounds.
The company describes its platform as a way to decode biology and industrialize drug discovery. A central element is the use of images and other experimental data to characterize how cells change when exposed to genetic or chemical perturbations. These datasets can be used to generate disease signatures, identify potential therapeutic targets, predict compound activity, and prioritize candidates before they enter more expensive stages of development. The objective is to move failure earlier in the research funnel, reduce unnecessary laboratory and clinical spending, and improve the probability that selected programs will eventually produce useful medicines.
Recursion maintains a portfolio of clinical-stage and preclinical candidates. The supplied pipeline includes REC-994, being studied for cerebral cavernous malformation; REC-3599, a Phase I program for GM2 gangliosidosis; REC-2282, associated with neurofibromatosis type 2; and REC-4881, targeting familial adenomatous polyposis. Its preclinical portfolio includes programs addressing Clostridium difficile colitis, neural or systemic inflammation, ovarian cancer, tumor immunity, hepatocellular carcinoma, solid and hematologic malignancies, and immune checkpoint resistance in KRAS/STK11-mutant non-small-cell lung cancer. Program status, clinical outcomes, regulatory decisions, and commercial prospects remain subject to substantial scientific and development risk.
Recursion also operates through partnerships and strategic collaborations. Its reported counterparties include Bayer AG, Takeda Pharmaceutical Company Limited, the University of Utah Research Foundation, the Ohio State Innovation Foundation, and Chromaderm, Inc. Such relationships can provide access to disease expertise, therapeutic assets, research funding, milestone opportunities, and potential commercialization capabilities. Collaboration economics may include upfront payments, research support, milestone payments, royalties, or shared development obligations, depending on the agreement.
The company has approximately 600 full-time employees, placing it in the 501-1000 employee category. As a development-stage biotechnology business, Recursion is heavily dependent on research and development expenditure and has not yet established a broad portfolio of marketed products. The supplied trailing-twelve-month data indicates significant operating losses, negative net income, negative operating cash flow, and negative free cash flow. It also shows a strong current ratio and substantial liquidity relative to short-term obligations, although continued investment in laboratories, personnel, clinical trials, data infrastructure, and technology can consume cash rapidly. The company does not currently pay a dividend. Its principal financial risks include clinical-trial failure, regulatory delays, program discontinuation, competition, dilution from future capital raises, dependence on collaborations, intellectual-property challenges, and the long time required to commercialize successful medicines.
Najat Khan, Ph.D., is identified as the current CEO and president. Christopher Gibson, a co-founder and former CEO, has been associated with the company’s founding and earlier growth and has transitioned toward a board and advisory role. Recursion’s long-term aspiration is to create a scalable technology-enabled drug discovery engine capable of producing multiple successful medicines, while improving the efficiency, predictability, and cost structure of pharmaceutical research.
YoYYoY means Year-over-Year. It compares the latest annual value with the previous annual value to show long-term trend strength.
QoQQoQ means Quarter-over-Quarter. It compares the latest quarter with the immediately previous quarter to show short-term momentum changes.
RevenueThe total money that came through the front door from selling things, before paying a single bill. Think of it as the grand total of every credit card swipe from customers. (YoY compares this year to last year's performance, while QoQ compares the current three months to the previous three).
$74.7M
+26.9%
+18.5%
Net IncomeThe absolute bottom line. If the company paid every single supplier, employee, banker, and tax collector, this is the actual money left in their pocket at the end of the day.
$-644.8M
-39.1%
-11.5%
Gross MarginThe basic markup. If they sell a $100 pair of sneakers, this percentage tells you how much of that price tag is profit right after paying for the rubber and shoelaces, but before paying for things like store rent or TV commercials.
-62.0%
-368.0%
+46.4%
Operating MarginThe 'day job' efficiency score. Out of every dollar a customer spends, this shows how many cents the company keeps after making the product AND paying for all the everyday corporate overhead (like salaries, marketing, and keeping the lights on).
-867.9%
-6.6%
+11.4%
Net MarginThe final take-home percentage. When you strip away every conceivable cost, tax, and interest payment, this is the exact number of cents the company truly gets to keep from every dollar in sales.
-863.4%
-9.6%
+5.9%
Free Cash FlowThe holy grail of corporate cash. It's the spendable, physical money left over after the business pays for its daily operations AND buys the big, expensive upgrades (like new factories or servers) it needs to survive. This is the 'free' money they can use to pay dividends or buy back stock.
$-378.3M
-1.5%
-32.5%
FCF MarginThe ultimate cash conversion rate. It shows how good the company is at turning regular sales directly into cold, hard, spendable cash. A high percentage means the business is an absolute cash-printing machine.
-506.5%
+20.1%
-11.8%
Debt / EquityThe financial risk gauge. It compares how much of the company's empire was built using borrowed money (loans) versus the owners' own money (shareholders). A high number means they are heavily leveraged and playing a riskier game; a low number means they are playing it safe.
6.9%
-34.2%
-4.2%
Current RatioThe 12-month survival check. It simply compares the cash they have right now (plus things they can quickly turn into cash) against the immediate bills they absolutely must pay this year. A score above 1 means they have enough in the wallet to cover the upcoming bills without panicking.
5.50x
+44.4%
-8.0%
Total AssetsThe absolute size of the company's empire. It bundles together absolutely everything of value they own—from the cash in the register and the inventory in the warehouse, to the software patents in the vault and the factories on the ground.
Najat Khan: Good morning, everyone, and thank you for joining us. Before we begin, I'd like to remind everyone that today's discussion will include forward-looking statements. Next slide. Please refer to today's press release and our SEC filings for additional details. At Recursion, our mission is to decode biology to radically improve patient lives. And we do this by building transformational medicines with an AI-native product engine. Over the past year, we have reached an important inflection point. We are no longer just discussing the potential of our platform. We are demonstrating the ability of our AI-native product engine to generate differentiated programs and medicines. Just as a reminder, the engine you see on the left-hand side is built as a continuous learning system. Proprietary multimodal data created in our data factory powers Frontier AI models. And these models can generate new hypotheses where every single prediction is tested experimentally. Each cycle strengthens both the engine and the products it creates. Ultimately, though, the measure of any engine is its output. So let's talk about that. First, our internal pipeline continues to mature. We now have 5 clinical stage programs, including REC-4881 in FAP, where we have generated some of the most promising clinical data in the company's history. Remember, in a disease with no approved therapy and a TAM of almost $10 billion. Second, we continue to make significant progress in our partnerships while learning from the best in the industry and also while validating our engine externally. Together with leading biopharma partners, we have generated more than $500 million in realized inflows while advancing differentiated programs with Sanofi and Roche, Genentech. So today, I'll share how we continue to strengthen our product engine and how we take these advances and are translating it into differentiated medicines, differentiated partnerships and ultimately better outcomes for patients. So the question that naturally comes up, what makes our product engine different? There are many companies applying AI to drug discovery. We believe our advantage isn't AI alone. It's a combination of 3 capabilities that reinforce one another. First, we generate our own proprietary multimodal biological and molecular data at scale. This matters because AI can only learn well from high-quality data and much of the most valuable biology has never been measured systematically. Our 50 petabytes of data is designed specifically to train models, discover new biological relationships and improve over time as new algorithms emerge. Second, we connect these models directly to experimentation through a Lab-in-the-Loop system, spanning biology, design and increasingly the clinic. Every prediction, as I mentioned before, is validated experimentally. Every result feeds back into those models. It is that recursive loop that helps us to move faster, improve our decision quality and systematically build confidence …
