rxrx-20221231

Securities registered pursuant to Section 12(b) of the Act:

Title of each class Trading symbol(s) Name of each exchange on which registered

Class A Common Stock, par value $0.00001 RXRX Nasdaq Global Select Market

Large accelerated filer x Non-accelerated filer ☐

Accelerated filer ☐ Smaller reporting company ☐

Emerging growth company ☐

INITIATED 5 CLINICAL TRIALS IN 2022and planning a 6th clinical trial to initiate

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TABLE OF CONTENTS

Page

Part I

Item 1. Business 9

Item 1A. Risk Factors 114

Item 1B. Unresolved Staff Comments 156

Item 2. Properties 156

Item 3. Legal Proceedings 156

Item 4. Mine Safety Disclosures 156

Part II

Item 6. [Reserved] 159

Item 7A. Quantitative and Qualitative Disclosures About Market Risk 171

Item 8. Financial Statements and Supplementary Data 173

Item 9A. Controls and Procedures 203

Item 9B. Other Information 204

Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections 204

Part III

Item 10. Directors, Executive Officers and Corporate Governance 205

Item 11. Executive Compensation 205

Item 14. Principal Accounting Fees and Services 205

Part IV

Item 15. Exhibits and Financial Statement Schedules 206

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PART I

RISK FACTOR SUMMARY

Below is a summary of the principal factors that make an investment in the common stock of Recursion Pharmaceuticals, Inc. (Recursion, the Company, we, us, or our) risky or speculative. This summary does not address all of the risks we face. Additional discussion of the risks summarized below, and other risks that we face, can be found in the section titled “Item 1A. Risk Factors” in this Annual Report on Form 10-K.

•We are a clinical-stage biotechnology company with a limited operating history. We have no products approved for commercial sale and have not generated any revenue from product sales.

•Our drug candidates are in preclinical or clinical development, which are lengthy and expensive processes with uncertain outcomes and the potential for substantial delays.

•We have incurred significant operating losses since our inception, we expect to incur substantial and increasing operating losses for the foreseeable future, and we may not be able to achieve or maintain profitability.

•Our mission is broad and expensive to achieve and we will need to raise substantial additional funding, which may not be available on commercially reasonable terms or at all.

•We expect to finance our cash needs for the foreseeable future potentially through a combination of private and public equity offerings and debt financings, as well as strategic collaborations. If we are unable to raise capital when needed, we would be forced to delay, reduce, or eliminate at least some of our product development programs and other activities, and to possibly cease operations.

•Raising additional capital entails risks, including that it may adversely affect the rights, or dilute the holdings, of our existing stockholders; increase our fixed payment obligations; require us to relinquish rights to our technologies or drug candidates; and/or divert management’s attention from our core business.

•If we are unable to establish additional strategic collaborations on commercially reasonable terms or at all, or if current or future collaborations are not successful, we may have to alter our drug development plans.

•We or our current and future collaborators may never successfully develop and commercialize drug candidates, or the market for approved drug candidates may be less than anticipated, which in either case would materially and adversely affect our financial results and our ability to continue our business operations.

•Our approach to drug discovery is unique and may not lead to successful drug products for various reasons, including potential challenges identifying mechanisms of action for our candidates.

•Although we intend to explore other therapeutic opportunities in addition to the drug candidates we are currently developing, we may fail to identify viable new candidates or we may need to prioritize candidates and, as a result, we may fail to capitalize on profitable market opportunities.

•We may experience delays in initiating and completing clinical trials, including due to difficulties in enrolling patients or maintaining compliance with trial protocols, or our trials may produce inconclusive or negative results.

•If we are unable to obtain or there are delays in obtaining regulatory approvals for our drug candidates in the U.S. or other jurisdictions, or if approval is subject to limitations, we will be unable to commercialize, or delayed or limited in commercializing, the products in that jurisdiction and our ability to generate revenue may be materially impaired.

•Our quarterly and annual operating results may fluctuate significantly due to a variety of factors, a number of which are outside our control or may be difficult to predict, which could cause our stock price to fluctuate or decline.

•If we are not able to develop new solutions and enhancements to our drug discovery platform that keep pace with technological developments, or if we experience breaches or malfunctions affecting our platform, our ability to identify and validate viable drug candidates would be adversely impacted.

•Third parties that provide supplies or equipment, or that manufacture our drug products or drug substances, may not provide sufficient quantities at an acceptable cost or may otherwise fail to perform.

•We or third parties on which we depend may experience system failures, cyber-attacks, and other disruptions to information technology or cloud-based infrastructure, which could harm our business and subject us to liability for disclosure of confidential information.

•Force majeure events, such as the COVID-19 pandemic, a natural disaster, global political instability, or warfare, could materially disrupt our business and the development of our drug candidates.

•If we are unable to adequately protect and enforce our intellectual property rights, including obtaining and maintaining patent protection for our key technology and products that is sufficiently broad, our competitors

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could develop and commercialize technology and products similar or identical to ours and our ability to successfully commercialize our technology and products may be impaired.

•If we are unable to protect the confidentiality of our trade secrets and know-how, our business and competitive position may be harmed.

•If we fail to comply with our obligations in the agreements under which we collaborate with and/or license intellectual property rights from third parties, or otherwise experience disruptions to our business relationships with our partners, we could lose rights that are important to our business.

•We face substantial competition, which may result in others discovering, developing, or commercializing competing products before we do.

•If we are unable to attract and retain key executives, experienced scientists, and other qualified personnel, our ability to discover and develop drug candidates and pursue our growth strategy could be impaired.

•We are subject to comprehensive statutory and regulatory requirements, noncompliance with which may delay or prevent our ability to market our products or result in fines or other liabilities.

Cautionary Note Regarding Forward-Looking Statements

This Annual Report on Form 10-K contains “forward-looking statements” about us and our industry within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. All statements other than statements of historical facts are forward-looking statements. In some cases, you can identify forward-looking statements by terms such as “may,” “will,” “should,” “would,” “expect,” “plan,” “anticipate,” “could,” “intend,” “target,” “project,” “contemplate,” “believe,” “estimate,” “predict,” “potential,” or “continue” or the negative of these terms or other similar expressions. Forward-looking statements contained in this report may include without limitation those regarding:

•our research and development programs

•the initiation, timing, progress, results, and cost of our current and future preclinical and clinical studies, including statements regarding the design of, and the timing of initiation and completion of, studies and related preparatory work, as well as the period during which the results of the studies will become available;

•the ability of our clinical trials to demonstrate the safety and efficacy of our drug candidates, and other positive results;

•the ability and willingness of our collaborators to continue research and development activities relating to our development candidates and investigational medicines;

•future agreements with third parties in connection with the commercialization of our investigational medicines and any other approved product;

•the timing, scope, and likelihood of regulatory filings and approvals, including the timing of Investigational New Drug applications and final approval by the U.S. Food and Drug Administration, or FDA, of our current drug candidates and any other future drug candidates, as well as our ability to maintain any such approvals;

•the timing, scope, or likelihood of foreign regulatory filings and approvals, including our ability to maintain any such approvals;

•the size of the potential market opportunity for our drug candidates, including our estimates of the number of patients who suffer from the diseases we are targeting;

•our ability to identify viable new drug candidates for clinical development and the rate at which we expect to identify such candidates, whether through an inferential approach or otherwise;

•our expectation that the assets that will drive the most value for us are those that we will identify in the future using our datasets and tools;

•our ability to develop and advance our current drug candidates and programs into, and successfully complete, clinical studies;

•our ability to reduce the time or cost or increase the likelihood of success of our research and development relative to the traditional drug discovery paradigm;

•our ability to improve, and the rate of improvement in, our infrastructure, datasets, biology, technology tools, and drug discovery platform, and our ability to realize benefits from such improvements;

•our expectations related to the performance and benefits of our BioHive-1 supercomputer;

•our ability to realize a return on our investment of resources and cash in our drug discovery collaborations;

•our ability to scale like a technology company and to add more programs to our pipeline each year;

•our ability to successfully compete in a highly competitive market;

•our manufacturing, commercialization, and marketing capabilities and strategies;

•our plans relating to commercializing our drug candidates, if approved, including the geographic areas of focus and sales strategy;

•our expectations regarding the approval and use of our drug candidates in combination with other drugs;

•the rate and degree of market acceptance and clinical utility of our current drug candidates, if approved, and other drug candidates we may develop;

•our competitive position and the success of competing approaches that are or may become available;

•our estimates of the number of patients that we will enroll in our clinical trials and the timing of their enrollment;

•the beneficial characteristics, safety, efficacy, and therapeutic effects of our drug candidates;

•our plans for further development of our drug candidates, including additional indications we may pursue;

•our ability to adequately protect and enforce our intellectual property and proprietary technology, including the scope of protection we are able to establish and maintain for intellectual property rights covering our current drug candidates and other drug candidates we may develop, receipt of patent protection, the extensions of existing patent terms where available, the validity of intellectual property rights held by third parties, the protection of our trade secrets, and our ability not to infringe, misappropriate or otherwise violate any third-party intellectual property rights;

•the impact of any intellectual property disputes and our ability to defend against claims of infringement, misappropriation, or other violations of intellectual property rights;

•our ability to keep pace with new technological developments;

•our ability to utilize third-party open source software and cloud-based infrastructure, on which we are dependent;

•the adequacy of our insurance policies and the scope of their coverage;

•the potential impact of a pandemic, epidemic, or outbreak of an infectious disease, such as COVID-19, or natural disaster, global political instability, or warfare, and the effect of such outbreak or natural disaster, global political instability, or warfare on our business and financial results;

•our ability to maintain our technical operations infrastructure to avoid errors, delays, or cybersecurity breaches;

•our continued reliance on third parties to conduct additional clinical trials of our drug candidates, and for the manufacture of our drug candidates for preclinical studies and clinical trials;

•our ability to obtain, and negotiate favorable terms of, any collaboration, licensing or other arrangements that may be necessary or desirable to research, develop, manufacture, or commercialize our platform and drug candidates;

•the pricing and reimbursement of our current drug candidates and other drug candidates we may develop, if approved;

•our estimates regarding expenses, future revenue, capital requirements, and need for additional financing;

•our financial performance;

•the period over which we estimate our existing cash and cash equivalents will be sufficient to fund our future operating expenses and capital expenditure requirements;

•our ability to raise substantial additional funding;

•the impact of current and future laws and regulations, and our ability to comply with all regulations that we are, or may become, subject to;

•the need to hire additional personnel and our ability to attract and retain such personnel;

•the impact of any current or future litigation, which may arise during the ordinary course of business and be costly to defend;

•our expectations regarding the period during which we will qualify as an emerging growth company under the JOBS Act;

•our anticipated use of our existing resources and the net proceeds from our initial public offering; and

•other risks and uncertainties, including those listed in the section titled “Risk Factors.”

We have based these forward-looking statements largely on our current expectations and projections about our business, the industry in which we operate, and financial trends that we believe may affect our business, financial condition, results of operations, and prospects. These forward-looking statements are not guarantees of future performance or development. These statements speak only as of the date of this report and are subject to a number of risks, uncertainties and assumptions described in the section titled “Risk Factors” and elsewhere in this report. Because forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified, you should not rely on these forward-looking statements as predictions of future events. The events and circumstances reflected in our forward-looking statements may not be achieved or occur and actual results could differ materially from those projected in the forward-looking statements. Except as required by applicable law, we undertake no obligation to update or revise any forward-looking statements contained herein, whether as a result of any new information, future events, or otherwise.

In addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These statements are based upon information available to us as of the date of this report. While we believe such information forms a reasonable basis for such statements, the information may be limited or incomplete, and our statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are inherently uncertain and you are cautioned not to unduly rely upon them.

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Item 1. Business.

Business Overview

Recursion is a clinical stage TechBio company leading this burgeoning space by decoding biology to industrialize drug discovery. Central to our mission is the Recursion Operating System (OS), a platform built across diverse technologies that enables us to map and navigate trillions of biological and chemical relationships within the Recursion Data Universe, one of the world’s largest proprietary biological and chemical datasets. We frame this integration of the physical and digital components as iterative loops of atoms and bits. Scaled ‘wet-lab’ biology and chemistry data built in-house (atoms) are organized into virtuous cycles with ‘dry-lab’ computational tools (bits) to rapidly translate in silico hypotheses into validated insights and novel chemistry. Our focus on mapping and navigating the complexities of biology and chemistry beyond the published literature and in a target-agnostic way differentiates us from other companies in our space and leads us to confront a fundamental cause of failure for the majority of clinical-stage programs - the wrong target is chosen due to an incomplete and reductionist view of biology. Our balanced team of life scientists and computational and technical experts creates an environment where empirical data, statistical rigor and creative thinking are brought to bear on our decisions.

We leverage our Recursion OS to enable three key value drivers:

1.An expansive pipeline of internally-developed clinical and preclinical programs focused on genetically-driven rare diseases and oncology with significant unmet need and market opportunities in some cases potentially in excess of $1 billion in annual sales

2.Transformational partnerships with leading biopharma companies to map and navigate intractable areas of biology, identify novel targets and develop potential new medicines that are further developed in resource-heavy clinical trials overseen by our partners

3.Development of one of the largest fit-for-purpose proprietary biological and chemical datasets in the world at a time when advances in AI paired with the right training data are creating disruptive value.

Key Achievements in 2022

Pipeline Delivery

•Initiated five clinical trials including Phase 2 trials in Cerebral Cavernous Malformation (CCM) and Familial Adenomatous Polyposis (FAP), a Phase 2/3 trial in NF2-mutated meningiomas and Phase 1 healthy volunteer trials for REC-4881 and REC-3964

•Received Fast Track Designation from the US FDA and Orphan Drug Designation from the European Commission for REC-4881 for the potential treatment of FAP

•Leveraged our map of biology and chemistry to expand the scope of REC-4881 beyond FAP with plans for a fifth clinical program (Phase 1b/2) being readied to explore the molecule in AXIN1 or APC mutant solid tumors

•Focused our discovery and preclinical pipelines in oncology, with significant advances made in our Target Alpha checkpoint sensitization program and our RBM39 program in homologous recombination proficient ovarian cancer (formerly named Target Gamma) which are now both nearing IND-enabling studies

Partnership Delivery

•Initiated four new programs (for eight total programs initiated to date) in the space of fibrosis with our partners at Bayer and advanced multiple programs towards value inflection points

•Made significant progress against both the gastrointestinal-oncology and neuroscience portions of our collaboration with Roche and Genentech, including cell type evaluation and significant cell scale up in support of initial Phenomap-building efforts which remain on track

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Recursion OS Building

•Industrialized transcriptomics-based validation, including using transcriptomics data to advance programs for one of our partners (at the end of 2022, we had sequenced over 250,000 individual transcriptome samples)

•Industrialized digital tolerability studies using our InVivomics technology to enable better, faster candidate selection

•Industrialized stem cell production (produced over 500 billion neural hiPSC-derived cells in 2022) to enable neurology research at exceptional levels of quality and simultaneously making Recursion one of the largest producers of neural hiPSC-derived cells on earth in the span of a single year

•Advanced several in-house internal digital chemistry applications (two of which we have published on: MolE and Multi-Objective GFlowNets)1

Company Building

•Closed a significant PIPE offering from a cohort of supportive, long-term investors including both new and existing shareholders (Kinnevik, Baillie-Gifford, Mubadala, Laurion, Platinum, Invus)

•Demonstrated commitment to ethical business practices as demonstrated in our inaugural ESG report

•Expanded our laboratory facilities to enable novel technology, partnerships and pipeline

•Evolved as a public company by preparing for SOX and SOC2 compliance

Vision, Mission, People and Culture

Human biology is a highly complex system for which human intelligence alone is insufficient to fully understand. While hundreds of thousands of incredible scientists around the world dedicate themselves to expanding our understanding each day, the extraordinarily high failure rates of human-generated hypotheses in our industry suggest to us that we still understand just a small percentage of biology, chemistry and the interactions between the two.

Simultaneously, our world is currently transiting its next industrial revolution based on extraordinary progress in scaled computation, machine learning (ML) and artificial intelligence (AI). While progress in this field has been steady for decades, the exponential growth trajectory is becoming more apparent to many members of modern society through accessible applications like ChatGPT. While progress is being made using sophisticated computation in virtually every industry, the complexity of biology and the highly regulated nature of the biopharma industry has resulted in a delay in the fruits of technology in our space. However, this means we are in a position to learn from the lessons of the application to technology to many other fields. One of the primary lessons learned across numerous industries is that computational sophistication alone is rarely sufficient to create disruptive change. It is when computational sophistication is paired with the right data, typically in an iterative process of ongoing learning, prediction and refinement, where outsized change is created.

1 Recursion shared preprints at the AI for Accelerated Materials Design workshop and Learning Meaningful Representations of Life workshop: Multi-Objective GFlowNets (https://arxiv.org/pdf/2211.02657.pdf); MolE: A Molecular Foundation Model for Drug Discovery (https://arxiv.org/abs/2210.12765)

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Figure 1. Machine learning native companies across multiple industries create iterative loops of profiling, analysis and inference2. A common theme in the successful application of ML / AI to many industries is the creation of a virtuous loop of learning and iteration. First, real systems (atoms) are profiled in order to create digital representations (bits) which can be analyzed by ML and AI to infer the rules, shapes or values of the real system. For example, digitizing the physical state of the planet using satellite imaging traffic flow, weather and other real-world data allows one to model the real world and predict optimal, real-time and flexible navigation routes.

Recursion was founded in 2013 with a vision to capitalize on the convergence of advancements in computation and machine learning to address the decreasing efficiency of drug discovery and development. We believe that this opportunity represents one of the most positively impactful applications of ML and AI. Our vision is to leverage technology to map and navigate biology and chemistry to discover and develop more, better medicines faster. We believe that neither advanced computational approaches, massive datasets, nor human intelligence alone can fundamentally shift the efficiency curve of drug discovery and development; instead, we believe that those companies that augment their teams with sophisticated computational tools leveraging hard-to-replicate proprietary datasets will have a significant advantage. We believe we are among the companies leading this burgeoning new sector of the biopharma industry that we call TechBio. Our success, and the success of this new sector generally, has the promise to drive better new medicines to patients at higher scale and lower prices. We are working hard to not only lead this space, but define it.

2 Adapted from Rutgers, V and Sniderman, B. (Oct 2018) Around the physical-digital-physical loop - A current look at Industry 4.0 capabilities. Deloitte Insights.

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Figure 2. Eroom’s Law observes that while technology advancements have made many processes faster and less expensive over the years, drug discovery is becoming slower and more expensive.3,4Recursion was created to take advantage of the discontinuity between these fields and harness the power of accelerating technological innovations to improve the efficiency of drug discovery and development.

Our mission at Recursion, Decoding Biology to Radically Improve Lives, flows naturally from our vision. We interpret our mission expansively and believe it to be a durable direction and source of inspiration for our team. While we are best-known for industrializing phenomics (data based on images of cellular structures), we do not feel constrained by that foundation and will use any technology or combination of technologies we see fit to decode biology. Success in decoding biology implies our ability to predict ways to navigate it. The ability to predictably navigate biology may enable us to build a massive pipeline of medicines, either by ourselves, with partners or both. As part of that work, we seek not only to radically improve the lives of patients who could benefit from the medicines we help to deliver, but the lives of those who care for those patients, the lives of our employees and their families, as well as the communities in which we operate our company.

3 Adapted from Scannell, J et al (2012). Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov, 11, 191-200.

4 Adapted from Roser, M et al. (2013). Technological Change. OurWorldInData.org.

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Figure 3: Recursion’s Founding Principles and Values support our ambitious mission. Together, these elements shape Recursion’s culture by guiding our people to high-impact decision-making and behaviors.

Our culture at Recursion is designed with intention to fuel our mission. We believe culture drives delivery. Essential to decoding biology in our context, is a mindset deeply committed to achieving impact at unprecedented scale through pioneering new industrialized approaches. We call it the Recursion Mindset. To embrace this mindset and our ambition, our people must deeply learn what will make them impactful in our context while questioning what made them successful in prior contexts. Sometimes this requires unlearning. Sometimes this requires a professional metamorphosis. For everyone it requires change. To decode biology we intentionally source for an incredible breadth of fields from multiple industries and for all of them Recursion is a new kind of company. The guideposts for teaching our people to successfully transition to TechBio and deliver our mission are our Founding Principles and Values. They are the essential shape of our culture. The Founding Principles direct us in making scientific and technical decisions that further our mission. The Values define the day-to-day behaviors and mindsets that further our mission. Together, along with the brilliance, humility and diversity of our people, our culture comes to life. Together, they are the compass that point our people towards decoding biology.

Figure 4. Recursion’s team requires operating at the interface of many diverse fields. Building a TechBio company requires fluency in operating at the interface of many disciplines and fields not previously attuned to working as closely in traditional BioPharma.

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Business Strategy and Value Drivers

While most small to medium-sized biopharma companies are focused on a narrow slice of biology or therapeutic area, where they believe they have an advantage or insight based on the summed experience of their team, the Recursion OS allows us to discover and translate at scale across biology. However, we are cognizant that building disease-area expertise, especially in clinical development, is essential. And so, we have developed a multi-pronged, capital-efficient business model focused on key value-drivers that enable us to demonstrate our progress over time while continuing to invest in the development of the Recursion OS, which we believe is the engine of value creation in the long-term. While our mapping and navigating tools have the plasticity to be applied across therapeutic areas and modalities, our business model is tailored to maximize value and advance programs cost-effectively based on the nature of market and regulatory dynamics associated with our three value drivers (internal pipeline, transformational partnerships and fit-for-purpose proprietary biological and chemical data).

Figure 5. We harness the value and scale of our maps of biology using a capital efficient business strategy. Our business strategy is segmented into our following value-drivers: (i) internally developed programs in capital-efficient therapeutic areas; (ii) partnered programs in resource-intensive therapeutic areas; and (iii) proprietary, fit-for-purpose training data. *Includes a single oncology indication from our Roche and Genentech collaboration.

Value-Driver 1 - Internally Developed Programs in Capital-Efficient Therapeutic Areas

Recursion is advancing five clinical-stage programs across rare disease and oncology, which we believe are capital-efficient opportunities for our growing clinical development team to focus on. We continue to advance internal preclinical programs focused on oncology to continue building our pipeline.

Value-Driver 2 - Partnered Programs in Resource-Intensive Therapeutic Areas

Recursion has made substantial progress to deliver against two transformational discovery collaborations;first a collaboration in neuroscience and a single gastrointestinal oncology indication with Roche and Genentech signed in late 2021, and second a collaboration in fibrosis with Bayer signed in 2020 and significantly expanded in 2021. We expect to continue making progress towards potential value-accreting program milestones and map-building and data option milestones.

Value-Driver 3 - Proprietary, Fit-for-Purpose Training Data

While we will direct the generation of new data and utilize the latest data in Recursion’s Data Universe to maximize our pipeline and partnership value-drivers, we increasingly see the potential to license subsets of our over 21

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petabytes of proprietary data to a growing universe of collaborators from both the biopharma and technology industries.

The Recursion OS

The creation of virtuous cycles of atoms and bits has been a competitive advantage for leaders in many industries outside of biopharma. This virtuous cycle of profiling the real (atoms) to create digital representations (bits) can be paralleled as an approach to mapping and navigating biology and chemistry as well.

Figure 6. Recursion’s virtuous cycle of atoms and bits. (1) Profile real biological and chemical systems using automation to scale a small number of data-rich assays, including phenomics, transcriptomics, InVivomics and ADMET to generate massive, high quality empirical data; (2) Aggregate and analyze the resultant data using a variety of in-house software tools including proprietary machine learning algorithms in both public and private clouds;and (3) Map and navigate leveraging proprietary software tools to infer relationships between biology and chemistry tested independently. These inferred relationships serve as the basis of our ability to predict how to navigate between biological states using chemical or biological perturbations, which we can then validate in our automated laboratories, completing a virtuous cycle of learning and iteration.

Specifically, at Recursion, automated wet-lab biology and dry-lab computational tools are organized in an iterative loop to rapidly translate in silico hypotheses into testable predictions, which in turn generates more data on which improved predictions can be made. The Recursion OS cycles between the profiling of real systems (atoms) and the aggregation and analysis of data (bits) to infer relationships across biological and chemical systems (mapping and navigating). Collectively, the components of the Recursion OS can be joined together to identify, validate and advance a broad portfolio of novel therapeutic programs quickly, cost-effectively and with minimal human intervention and bias - industrializing drug discovery.We use standardized, automated workflows to identify programs and advance them through key stages of the drug discovery and development pipeline as in the following graphic.

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Figure 7. The Recursion OS is meant to industrialize the drug discovery and development process through multiple cycles of learning and iteration.

Atoms

Our Recursion OS is composed of a broad suite of automated laboratory systems used to conduct standardized, high-dimensional data acquisition at scale. These data span phenomics, transcriptomics, InVivomics and ADME/DMPK assays. Recursion has built a physical library of approximately 1.7 million compounds, including over 1 million new chemical entity (NCE) starting point substances, a large library of known chemical entities which can serve as guideposts, and more than 500 thousand compounds belonging to our collaborators. Further, Recursion has generated a custom whole-genome arrayed CRISPR guide library. Together, these tools allow Recursion to explore millions of different biological perturbations in our own wet-labs. Our tissue culture facility has scaled the production of nearly 50 human cell types and has also enabled work at scale in co-cultures and complex iPSC-derived cell types. In 2022, for example, Recursion generated more than 500 billion human neuronal iPSC-derived cells for our partnered work with Roche and Genentech - a scale achieved by few if any other companies in the world.

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Figure 8. Diverse datasets within the Recursion Data Universe are highly complementary. The Recursion Data Universe consists of complementary datasets spanning multiple data modalities. While phenomics data can be generated cost-effectively and at scale, other datasets such as transcriptomics, DMPK and InVivomics offer increasing insight as we translate programs from early discovery through development.

Bits

The Recursion OS is built on top of a core technology stack that is highly scalable and flexible. This stack is composed of infrastructure components, such as our wholly-owned supercomputer, BioHive-1, where much of our deep learning model training and research happens. In addition, we have built a custom software stack including many proprietary tools integrated into a full-stack data collection, aggregation, storage and analysis pipeline spanning target discovery to digital chemistry.

Flowing through our software infrastructure are more than 21 petabytes of highly relatable biological and chemical data, including: phenomics, transcriptomics, InVivomics, ADMET assays and bespoke bioassay data we call the Recursion Data Universe. We generate, evaluate and analyze this scaled data using our enabling software tool suite, which includes a custom Laboratory Information Management System (LIMS), custom applications to design large experimental layouts consisting of millions of perturbation conditions, tools and dashboards to automatically execute and continuously monitor experimental protocols and a MapApp which enables users to map and navigate core components of our data spanning more than three trillion predicted relationships. Recursion recently released a demo-version of one of our internal tools, MolRec, along with a massive open-source dataset (RxRx3), which allows potential collaborators to get a taste of how Recursion explores relationships among and between potential medicines and genes.

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Figure 9. The MapApp allows our team to simultaneously view multiple relationships between genes and compounds. This proprietary software application enables us to rapidly explore inferred biological and chemical relationships in order to: (i) discover targets, (ii) predict active hits, (iii) optimize for similar or dissimilar relationships and (iv) predict mechanisms of action.

Virtuous Cycles of Atoms and Bits to Advance Programs

As of December 31, 2022, using our highly-automated wet-lab infrastructure, we have executed over 175 million experiments across different biological and chemical contexts in multiple human cell types. Experimental results reside within the Recursion Data Universe, which grows as new experiments are performed. Using this data, we apply sophisticated computational techniques to infer trillions of relationships between biological and pharmacological perturbations in silico and prioritize the most novel and promising candidates for further validation in our wet laboratories. Our mapping and navigating approach to drug discovery means that the ambitious experimental explorations that would have taken us over 1,000 years to physically execute can now be inferred nearly instantaneously due to the relatability of the datasets that we have already constructed. The computational methods at the core of mapping and navigating allow us to turn the output of each experiment from “data exhaust” into a data engine: every compound we profile is analyzed not for its activity against a single target, but for its inferred activity against all possible targets in our arrayed CRISPR library, as well as its similarity to every compound we have run before in its phenomap (digital relationship map of phenomic data) – producing a super-linear growth in biological relationships as we conduct experiments.

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Figure 10. Mapping and navigating enables simultaneous genome-wide screening. Traditional pharma high-throughput screening methods screen thousands to millions of compounds simultaneously against single targets, deriving information about compound activity on that single target, but no information about other targets. Recursion’s mapping and navigating approach in phenomics enables us, in a single experiment, to infer the activity of a compound against all potential targets in our arrayed CRISPR knockout screen.

When building the Recursion OS, we first focused on discovering and validating novel biological targets because we believe that identifying the appropriate target is the most challenging step in the drug discovery process due to bias and limitations associated with the traditional approach to drug discovery. More recently, we have been actively expanding the Recursion OS to more rapidly identify novel chemical starting points, more rapidly drive chemistry optimization through structure-activity-relationships (SAR) and achieve higher success rates in translating our novel target discovery work into IND-enabled programs. In the future, we envision that we will further evolve our approach and incorporate data and techniques that improve our ability to execute clinical programs at scale, including population-scale genomics data and precision medicine tools in order to identify patients for which a potential therapeutic would be beneficial.

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Figure 11. The productionized portions of the Recursion OS today. We use our proprietary software and highly-automated wet laboratory to design and execute up to 2.2 million experiments each week across diverse biological and chemical matter. Complex, high-dimensional data from these experiments are generated at a rate of up to 110 terabytes per week and aggregated and analyzed by proprietary neural networks in either distributed cloud computing environments or on our own supercomputer, BioHive-1. We leverage ML approaches to predict relationships between combinations of biological and chemical perturbations and have made more than 3 trillion such predictions. Our scientists navigate these predictions using proprietary software to discover novel relationships, which we can quickly test either in-house with our Industrialized Program Generation workflow or with clinical research organizations (CROs). As we validate or refute the predictions, our Recursion OS continuously improves.

Demonstrable Impact

Late-stage clinical failures are the primary driver of costs in today’s pharmaceutical R&D model, due in part to inherent uncertainty in the clinical development and regulatory process. Reducing the rate of costly, late-stage failures and accelerating the timeline from hit to a clinical candidate would create a more sustainable R&D model. To achieve this more sustainable model, we believe that in its ideal state, a drug discovery funnel would morph from the being shaped like the letter ‘V’ to being be shaped like the letter ‘T,’ where a broad set of possible therapeutics could be narrowed rapidly to the best candidate, which would advance through subsequent steps of the process quickly and with no attrition. Recursion’s goal is to leverage technology to reshape the typical drug discovery funnel towards its ideal state by moving failure as early as possible to rapidly narrowing the funnel into programs with the highest probability of success.

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Figure 12. Reshaping the drug discovery funnel. Recursion’s goal is to leverage technology to reshape the typical drug discovery funnel towards its ideal state by moving failure as early as possible to rapidly narrowing the funnel into programs with the highest probability of success.

We believe we have made progress in reshaping the traditional drug discovery funnel in the following ways:

•Broaden the funnel of therapeutic starting points. Our flexible and scalable mapping tools and infrastructure enable us to infer trillions of relationships between human cellular disease models and therapeutic candidates based on real empirical data from our own wet-labs, ‘widening the neck’ of the discovery funnel beyond human-hypothesized targets.

•Identify failures earlier when they are relatively inexpensive. Our proprietary navigation tools enable us to explore our massive biological and chemical datasets to validate more and varied hypotheses rapidly. While this strategy results in an increase in early stage attrition, the system is designed to rapidly prioritize programs with a higher likelihood of downstream success based on the exploration of high-dimensional, systems-biology data. Over time, and as our OS improves, we expect that moving failure earlier in the pipeline will result in an overall lower cost of drug development.

•Accelerate delivery of high-potential drug candidates to the clinic. The Recursion OS contains a suite of digital chemistry tools that enable highly efficient exploration of chemical space, including 3D virtual screening as well as translational tools that improve the robustness and utility of in vivo studies.

By leveraging our Recursion OS to explore more than 170 disease programs, we have shown quantifiable improvements in the time, cost and anticipated likelihoods of program success by stage when compared to the traditional drug discovery process. We believe that future iterations of the Recursion OS will enable even greater improvements minimizing the total dollar-weighted failure and maximizing the likelihood of success.

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Figure 13. The trajectory of our drug discovery funnel mirrors the ‘ideal’ pharmaceutical drug discovery funnel. We believe that, compared to industry averages, our approach allows us to: (i) identify low-viability programs earlier in the research cycle, (ii) spend less per program and (iii) rapidly advance programs to a validated lead. Data shown are the averages of all our programs from 2017 through 2022.

Over time, we believe continued successes and improvements in any or all of the dimensions highlighted above will improve overall R&D productivity, allowing us to address targeted patient populations that may otherwise not be commercially viable using traditional drug discovery approaches. Furthermore, we have seen our unbiased approach lead us to novel targets which we believe could enable us to outperform others in highly competitive disease areas where multiple parties often simultaneously pursue a limited number of similar target hypotheses. These advantages potentially significantly expand the total addressable market for our technology. However, the process of clinical development is inherently uncertain, and there can be no guarantee of success.

Pipeline

All of the programs in our internal pipeline are built on unique biological insights surfaced through the Recursion OS where: (i) the disease-causing biology is well defined but the downstream effects of the disease-cause are typically poorly understood, the primary targets are typically considered undruggable, or the primary targets are not well known in the context of a disease and (ii) there is a high unmet medical need, no approved therapies, or significant limitations to existing treatments. Several of our internal pipeline programs could have potential market opportunities in excess of $1.0 billion in annual sales. We currently have four programs in active clinical studies and are preparing for a fifth program to enter a Phase 1b/2 clinical trial in early 2024. In addition to our clinical stage programs, we are actively developing dozens of preclinical and discovery programs.

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Figure 14. The power of our Recursion OS as exemplified by our expansive therapeutic pipeline. All populations defined above are US and EU5 incidence unless otherwise noted. EU5 is defined as France, Germany, Italy, Spain and UK. 1 Prevalence for hereditary and sporadic symptomatic CCM population. 2 Annual US and EU5 incidence for all NF2-driven meningiomas. 3 Our Targets Delta and Alpha programs have the potential to address a number of indications in the immunotherapy space. 4 Our MYC program has the potential to address a number of indications driven by MYC alterations, totaling 54,000 patients in the US and EU5 annually. We have not finalized a target product profile for a specific indication.

Clinical Programs

•REC-994 for the potential treatment of cerebral cavernous malformation, or CCM — a Phase 2, double-blind, placebo-controlled, safety, tolerability and exploratory efficacy study is underway. Orphan Drug Designation has been granted in the US and EU. We expect to share top-line data in 2H 2024.

•REC-2282 for the potential treatment of neurofibromatosis type 2, or NF2 — an adaptive, Phase 2/3, randomized, multicenter study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted. We expect to share a Phase 2 interim safety analysis in 2024.

•REC-4881 for the potential treatment of familial adenomatous polyposis, or FAP — a Phase 2, double-blind, randomized, placebo-controlled study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted.

•REC-4881 for the potential treatment of AXIN1 or APC mutant cancers — a Phase 1b/2 study in select tumor types is expected to initiate in early 2024.

•REC-3964 for the potential treatment of Clostridioides difficile infection — a Phase 1 study in healthy volunteers is underway. We expect to share safety and PK data in 2H 2023.

Preclinical and Discovery Programs

Recursion continues to develop a suite of oncology programs progressing to and in the preclinical space. We believe many of these programs will remain internal at least through early clinical trials, though a subset may be well-positioned for asset-level partnerships at the preclinical or early clinical stages.

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Partnerships

Recursion has made substantial progress to deliver against two large discovery collaborations;first a collaboration in neuroscience and a single gastrointestinal oncology indication with Roche and Genentech signed in late 2021, and second a collaboration in fibrosis with Bayer signed in 2020 and significantly expanded in 2021. We expect to continue to make progress to set up the potential for value-accreting program milestones and map-building and data option milestones.

Roche and Genentech

On December 5, 2021, we entered into a collaboration with Roche and Genentech with the goal to use the Recursion OS to create maps of chemical and whole genome genetic perturbations in multiple cellular contexts of relevance to neuroscience and a single gastrointestinal oncology indication.In addition, together with Roche and Genentech we will create multi-modal models and maps, including significant single-cell sequencing data supplied by our partners, to further expand and refine the number of inferred relationships we uncover. Both approaches will be used to discover and develop up to 40 collaboration programs.In 2022, we made significant progress against both the gastrointestinal-oncology and neuroscience portions of the collaboration, including cell type evaluation and significant cell scale up in support of initial Phenomap-building efforts which remain on track.

Bayer

In August 2020, we entered into a Research Collaboration and Option Agreement with Bayer AG in the field of fibrosis. In December 2021, we significantly expanded this agreement to use our mapping and navigating tools to more efficiently identify biological and chemical insights that can be advanced as therapeutic programs. In 2022, we augmented our existing phenomaps with approximately 500,000 compounds from Bayer’s proprietary chemical library, significantly expanding the chemical diversity within our phenomaps.Additionally, we initiated four (4) new Programs (for a total of eight (8) total Programs initiated to date) and advanced multiple Programs towards value inflection points.Going forward, we expect the use of our mapping and navigating tools to rapidly accelerate the scale and pace at which we can initiate additional Programs.

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Business Detail

Recursion’s Founding

Recursion was founded in November of 2013 as a spin-out from the laboratory of Recursion co-founder Dean Y. Li, then Vice-Dean of Research and Professor of Medicine at the University of Utah (currently President of Merck Research Labs). In Dean’s lab, then MD/PhD student Chris Gibson (currently Recursion CEO) was working with a team to study Cerebral Cavernous Malformation (CCM), a genetic disease for which Recursion now has a drug in human clinical trials.Their research had led them to believe that activation of a protein called RhoA played the central role in the manifestation of CCM pathophysiology in humans. They leveraged an approved drug called simvastatin that is known to inhibit RhoA activation to evaluate their hypothesis in an animal model of CCM disease.The result was the opposite of what they expected; the treatment trended towards making the mice worse, not better.

Figure 15. Modulating a hypothesis highlighted by a traditional approach resulted in a treatment that trended towards doing the opposite of what was expected.5

There were many reasons the experiment could have failed, but the real significance was that the result challenged the team to question the validity of the RhoA hypothesis that they had arrived at using traditional molecular and cellular biology tools. Coming off the failure, the team went back to the drawing board. During their work, they had noticed that human cellular models of CCM in human cells looked very different from healthy cells; that is to say that their cellular morphology was markedly different. That difference sparked an idea to try to unbias the approach by leveraging a phenotypic screen where they applied many different potential treatments to diseased cells, collected images and looked for molecules that reverted the ‘diseased’ cell morphology back to ‘healthy.’

Rather than use a traditional phenotypic screening approach, where people would look at the images by eye or use a very basic measure like the intensity of a single marker from the microscopy images, the team instead used very early ML approaches to make this process much more objective, quantifiable and scalable. It turned out that the ML algorithms had a much higher probability of predicting “hits” - i.e., chemical compounds that demonstrated efficacy in a subsequent completely different experiment where diseased cells were treated and measured to evaluate the level of improvement.

They ran the best molecules from the increasingly complex assay systems through multiple different animal studies, and together with their collaborators ultimately demonstrated that two of those compounds, including what is now REC-994 (a molecule that Recursion is exploring in a phase 2 human safety and exploratory efficacy clinical trial for CCM), rescued multiple aspects of the disease in mice.

5 Gibson, et al. (2015). Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation.Circulation, 131(3), 289-99.

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The early success of leveraging machine learning to explore complex biological data to generate novel hypotheses in a target-agnostic way compelled Chris and Dean, along with a third co-founder, Blake Borgeson to spin-out the technology from the University of Utah. They wanted to test the hypothesis of whether one could use this approach, or similar approaches, to scale drug discovery and development, and thus Recursion was born.

Industrializing the Drug Discovery Process

The traditional drug discovery and development process is characterized by substantial financial risks, with increasing and long-term capital outlays for development programs that often fail to reach patients as marketed products. Historically, it has taken over ten years and an average capitalized R&D cost of approximately $2 billion per approved medicine to move a drug discovery project from early discovery to an approved therapeutic. Such productivity outcomes have culminated in a rapidly declining internal rate of return for the biopharma industry.

Figure 16. Historical biopharma industry R&D metrics. The primary driver of the cost to discover and develop a new medicine is clinical failure. Less than 4% of drug discovery programs that are initiated result in an approved therapeutic, resulting in a risk-adjusted cost of approximately $1.8 to $2.6 billion per new drug launched.6,7,8,9,10

Despite significant investment and brilliant scientists, these metrics point to the need to evolve a more efficient drug discovery process and explore new tools. Traditional drug discovery relies on basic research discoveries from the scientific community to elucidate disease-relevant pathways and targets to interrogate. Coupled with biology’s incredible complexity, this approach has forced the industry to rely on reductionist hypotheses of the critical drivers of complex diseases, which can create a ‘herd mentality’ as multiple parties chase a limited number of therapeutic targets. The situation has been exacerbated by human bias (e.g., confirmation bias and sunk-cost fallacy). Accentuating this problem, the sequential nature of current drug discovery activities and the challenges with aggregation and relatability of data across projects, teams and departments lead to frequent replication of work and long timelines to discharge the scientific risk of such hypotheses. Despite decades of accumulated knowledge, the result is that drug discovery has unintentionally created hurdles for innovation.

Simultaneously, exponential improvements in computational speed and reductions in data storage costs driven by the technology industry, coupled with modern ML tools, have transformed complex industries from media to transportation to e-commerce. The biopharma sector, however, has been slower to embrace such innovations, except in narrow areas.

At Recursion, we are pioneering the integration of innovations across biology, chemistry, automation, data science and engineering to industrialize drug discovery in a full-stack solution built from the bottom-up across dozens of key workflows and processes critical in discovering and developing a drug. For example, by combining advances in high

6 Zhou, S. and Johnson, R. (2018). Pharmaceutical Probability of Success. Alacrita Consulting, 1-42

7 Steedman M, and Taylor K. (2020). Ten years on: Measuring the return from pharmaceutical innovation. Deloitte. 1-44.

8 DiMasi et al. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics. 47, 20-33.

9 Paul, et al. (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery. 9,203-214

10 Martin et al. (2017). Clinical trial cycle times continue to increase despite industry efforts. Nature Reviews Drug Discovery. 16, 157

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content microscopy with arrayed CRISPR genome editing techniques, we can rigorously profile massive, high-dimensional biological and chemical perturbation libraries in multiple human cellular contexts. Leveraging advancements in data storage and computation, we can aggregate and analyze the massive resultant datasets to create digital ‘maps’ of human cellular biology. Finally, we can use modern AI and ML tools to infer relationships within the data, unconstrained by known biology or presumptive hypotheses. We believe that by harnessing advances in technology to industrialize drug discovery, we can derive novel biological insights not previously described by scientific researchers, reduce the effects of human bias inherent in discovery biology and reduce translational risk at the program outset.

Figure 17. Recursion’s approach to drug discovery. We utilize our Founding Principles on the right to build datasets which are scalable, reliable and relatable in order to elucidate novel biological and chemical insights and industrialize the drug discovery process.

We have used our approach to generate one of the largest biological and chemical datasets in the world (over 21 petabytes at the end of 2022) which includes proprietary phenomics, transcriptomics, InVivomics, ADME data and more across a large number of biological and chemical contexts. Additionally, we have built a proprietary suite of software applications within the Recursion OS which has identified over 3 trillion predicted biological and chemical relationships. The following table highlights how Recursion has scaled with respect to experiments, data and biological and chemical relationships. With our approach, we look to turn drug discovery into a search problem where we map and navigate biology in an unbiased manner to discover new insights and translate them into potential new medicines at scale.

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Table 1. Biology and chemistry are complex – data that is scalable and relatable is a differentiator. We are a biotechnology company scaling more like a technology company, as demonstrated by our growth in inputs (experiments as well as biological and chemical contexts) and growth in outputs (data as well as biological and chemical relationships). (1) Includes approximately 500,000 compounds from Bayer’s proprietary library. (2) ‘Predicted Relationships’ refers to the number of unique perturbations that have been predicted using our maps.

Business Strategy and Value Drivers

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Figure 18. We harness the value and scale of our maps of biology using a capital efficient business strategy. Our business strategy is segmented into our following value-drivers: (i) internally developed programs in capital-efficient therapeutic areas; (ii) partnered programs in resource-intensive therapeutic areas; and (iii) proprietary, fit-for-purpose training data. *Includes a single oncology indication from our Roche and Genentech collaboration.

Value Driver 1 - Internally Developed Programs in Capital Efficient Therapeutic Areas

We believe that the primary currency of any biotechnology company today is clinical-stage assets. These programs can be valued using a variety of models by stakeholders in the biopharma ecosystem and most importantly, present the potential to meet critical patient needs. Further, for Recursion, these assets have a variety of additional benefits, including: (i) validation of key elements of the Recursion OS, (ii) growing our expertise in clinical development and (iii) building in-house processes to facilitate smooth interaction with regulatory agencies and advance medicines towards the market. If the Recursion OS evolves in the manner with which it has been designed, then it will improve with more iterations such that future programs could be more novel and potentially more valuable than today’s programs. In this way, operating as a vertically-integrated biopharma company that leverages technology at every step from target discovery through clinical development (and even marketing and distribution) may be the long-term business model with the most upside for our stakeholders, including both investors and patients. Furthermore, we may be opportunistic about selling or licensing assets after they achieve key value-inflection milestones so that we can re-invest in our long-term strategy.

Value Driver 2 - Partnered Programs in Resource Intensive Therapeutic Areas

We believe that in its current form, our Recursion OS is already capable of delivering many more therapeutic insights than we would be able to responsibly shepherd alone today. As such, we have chosen to partner with experienced, top-tier biopharma companies to explore intractable and resource-intensive areas of biology like fibrosis with Bayer and neuroscience with Roche and Genentech. The key advantages of these partnerships are that: (i) we are able to deploy the Recursion OS to turn latent value into tangible value in areas of biology where it would be challenging for us to do so alone; (ii) the clinical development paths for these large therapeutic areas are often resource-intensive and highly complex; and (iii) we are able to learn from our colleagues at these top-tier companies such that it could give us a competitive advantage in the industry over the longer term. This strategy also embeds us in the discovery process of large pharmaceutical companies and gives rise to an alternative long-term business model whereby we become a valued partner of many such companies. Here, Recursion would focus on discovery efforts across a broad set of programs while relying on its partners to develop and market the medicines. Based on how value is ascribed across our industry today, this model alone is not yet feasible to maximize our business impact. However, we feel that due to shifts within the biopharma industry there is some potential for this portion of our business model to accrete notable value over the long-term.

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Value Driver 3 - Proprietary, Fit-for-Purpose Training Data

As has been demonstrated in many other industries, a value drive and competitive advantage can be generated from the creation of a proprietary dataset. At Recursion, we have generated what we believe to be one of the largest fit-for-purpose, relatable biological and chemical datasets on earth. Spanning multiple omics technologies and more than 175 million unique experiments, the over 21 petabyte Recursion Data Universe is of the same rough scale as those generated by some of the oldest and largest pharma companies,11 but is built from scratch in our laboratories with a fundamental purpose to be used as training data for machine learning models. Through intensive internal work, Recursion uses this data and our own algorithms and software to generate and advance our own internal pipeline of medicines (Value Driver 1), as well as in partnership with our collaborators to advance additional discovery programs (Value Driver 2). Our most recent collaboration announcement with Roche and Genentech set an important precedent - there are up to or exceeding $500M in milestones unrelated to specific drug discovery programs, but instead based on successful creation and optioning of collaboration data generated by Recursion. As our field increasingly recognizes the potential for a coming revolution in drug discovery based on virtuous cycles of atoms and bits, these data are themselves becoming a direct value driver. While we will likely direct the generation of new data and exploit the latest data in our data Universe to maximize our pipeline and partnership value-drivers, we increasingly see the potential to license subsets of our data to a growing universe of collaborators for which internal efforts would be minimal, but value could be significant.

Competitive Landscape and Differentiation

There are three key factors that differentiate Recursion from the vast majority of other TechBio companies.

1.Recursion is biology-first, while most other companies are chemistry-first. The highest probability of failure for clinical stage programs in our industry is a lack of efficacy in the intended disease state or an unexpected side-effect. These failure modes are primarily driven by picking the wrong target or not fully understanding the role of that target in broader physiology and not by failure to generate molecules that successfully agonize or antagonize the target of interest. There are of course exceptions to this, but we believe that mapping and navigating biology solves a zero-to-one type problem, while optimizing chemistry is a critical, but insufficient step alone. Because the chemistry problem is more tractable, the vast majority of TechBio companies have started (or remain) here. We believe our biology-first approach is a more critical unlock, and now have the opportunity to build on that success at low cost by adding digital chemistry and related solutions to our solution stack, especially amidst an over-crowded space.

2.Recursion integrates the wet-lab and dry-lab in-house and at scale. With scaled wet-lab (atoms) and dry-lab (bits) creating a virtuous cycle of iteration, Recursion is well positioned compared to those companies of a similar stage either focused more completely on the wet-lab only (traditional biotech or pharma companies) or dry-lab only focused companies who are facing rapidly commoditized algorithms and a challenge differentiating on non-proprietary data.

3.Recursion has achieved a significant scale and stage much earlier than other companies. With five clinical-stage programs, an exciting preclinical pipeline, and two of the largest discovery partnerships in the industry with Roche/Genentech and Bayer, Recursion has achieved a scale, level of integration and stage that few other TechBio companies have. In the context of steep competition for resources amidst challenging capital markets, this position serves Recursion well, especially compared to many of the late stage private companies with significant valuations and burn.

11 Dougherty, E. (2018, October 24). On being and becoming a data science company. Novartis. https://www.novartis.com/stories/being-and-becoming-data-science-company

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Figure 19. Recursion sits at a unique intersection of key advantages. Among TechBio companies, Recursion sits at a unique intersection of a (i) biology vs chemistry-first organization, focused on identifying novel relationships across biological targets and disease areas, which we believe to be the most pressing challenge in our industry; (ii) a virtuous cycle of wet-lab and dry-lab enabling virtuous cycles of iteration and proprietary insight generation; and (iii) with a scaled pipeline and partnerships differentiating from the many early startups and fee-for-service organizations.

While emerging competitors and large, well-resourced incumbents may pursue a similarly differentiated strategy to ours, we have two advantages as a first mover: (i) no amount of resources can compress the time it takes to observe naturally occurring biological processes and (ii) the growing Recursion Data Universe creates compounding network effects that may make it difficult for others to close the competitive gap.

The Recursion OS - Creating Virtuous Cycles of Atoms and Bits

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Figure 20. Recursion’s virtuous cycle of atoms and bits. (1) Profile real biological and chemical systems using automation to scale a small number of data-rich assays, including phenomics, transcriptomics, InVivomics and ADMET to generate massive, high quality empirical data; (2) Aggregate and analyze the resultant data using a variety of in-house software tools including proprietary machine learning algorithms on both public and private clouds; and (3) Map and navigate leveraging proprietary software tools to infer relationships between biology and chemistry tested independently. These inferred relationships serve as the basis of our ability to predict how to navigate between biological states using chemical or biological perturbations, which we can then validate in our automated laboratories, completing a virtuous cycle of learning and iteration.

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Figure 21. The productionized portions of the Recursion OS today. We use our proprietary software and highly-automated wet laboratory to design and execute up to 2.2 million experiments each week across diverse biological and chemical matter. Complex, high-dimensional data from these experiments are generated at a rate of up to 110 terabytes per week and aggregated and analyzed by proprietary neural networks in either distributed cloud computing environments or on our own supercomputer, BioHive-1. We leverage ML approaches to predict relationships between combinations of biological and chemical perturbations and have made more than 3 trillion such predictions. Our scientists navigate these predictions using proprietary software to discover novel relationships, which we can quickly test either in-house with our Industrialized Program Generation workflow or with contract research organizations (CROs). As we validate or refute the predictions, our Recursion OS continuously improves.

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Figure 22. The Recursion OS is meant to industrialize the drug discovery and development process through multiple cycles of learning and iteration. The Recursion OS is built on biologically-native cycles of atoms and bits leveraging phenomics, transcriptomics and InVivomics to drive discovery and validation of targets and compounds, while chemically-native cycles of predictive DMPK (drug metabolism and pharmacokinetics) and automated microsynthesis drive optimization of validated hits towards development candidates suitable for human clinical trials.

Atoms

In order to create large and relatable data sets, standardization and scale are two critical requirements that can be best achieved through automation. Standardization means that the experiment is executed consistently every time, day after day, year after year - and that any deviations can be detected, tracked and quantified. It involves meticulous metadata collection, prospective/retrospective experiment execution analysis, standard results storage, quantitative quality control and more. At the same time, massive scale, with millions of experiments executed per week, requires execution of multi-step assays processed rapidly and in a tightly orchestrated manner. This combination of precise repetition, high speed and massive volumes favors relying on robots over highly trained scientists, whose time is better spent on context-specific problems. In addition, automation of high-dimensional experiment readouts at scale enables cost reductions in the large high-dimensional digital data sets that can underpin today’s cutting edge opportunities in machine learning (bits).

Automation

While we do not consider ourselves to be hardware innovators, we have leveraged a significant team of automation scientists to assemble and synchronize advanced but widely-available robotic components, such as liquid dispensers, plate washers, incubation stations, automated HPLC, mass spectrometry and automated microscopy camera systems, to efficiently execute millions of experiments per week across a variety of data-rich outputs with only a small team overseeing the process at any given time. These robotic systems are modular by design and easily configurable to allow us to create complex and variable workflows. Furthermore, we have recently operationalized a fully integrated system that processes plates continuously through all steps in our primary experimental workflows. This fully integrated system is interoperable with the existing batch processing workcells but provides greater walk-away time for our operators and greater throughput in a smaller footprint.

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Figure 23. Our high-throughput automation platforms make our labs look more like sophisticated manufacturing facilities than biology R&D laboratories. Our high-throughput phenomics platform (top) can execute up to 2.2 million experiments each week with high quality to enable downstream analyses. We are increasingly automating many other of our assays at Recursion.

Phenomics

At the core of the Recursion Data Universe is our proprietary cellular image dataset generated by our automated phenomics platform. While investigating various biological and chemical contexts, the readout remains constant: a fluorescent microscopy image that captures composite changes in cellular morphology; a cellular phenotype. We use our proprietary staining protocol to capture these changes in cellular morphology across our phenomic experiments. This protocol, consisting of six subcellular dyes imaged in six different channels, has been optimized to capture a wide array of biology across nearly any adherent human cell type that can be cultured and perturbed in laboratory conditions. As a result, we can capture the effects of a wide range of biological and pharmacological

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phenomena of interest, including phenotypic changes induced by small molecules, genetic gain- and loss-of-function, toxins, secreted factors, cytokines, infectious agents, or any combination of the above.

Figure 24. Our fluorescent staining protocol images multiple large cellular structures to capture a holistic assessment of cellular state. We use fluorescent dyes to stain a set of common cellular substructures that are subsequently captured using fluorescent microscopy imaging. Combined with tools from the Recursion OS, this complex and rich biological data modality can inform a host of scientific questions. The top image is a composite of the 6 channels, in HUVEC cells. It is followed by faux-colored images of each of the 6 individual channels: nuclei in blue, endoplasmic reticula in green, actin in red, nucleoli in cyan, mitochondria in magenta and Golgi apparatus in yellow. The overlap in channel content is due in part to the lack of complete spectral separation between fluorescent stains.

Cellular morphology is a holistic measure of cellular state that integrates changes from underlying layers of cell biology, including gene expression, protein production and modification and cell signaling, into a single, powerful readout. Image-based -omics can be two to four orders of magnitude more data-dense per dollar than other -omics datasets that focus on these more proximal readouts, enabling us to generate far more data per dollar spent to inform our drug discovery efforts. We currently generate up to 13.2 million images or 110 terabytes of new data to the Recursion Data Universe per week across up to 2.2 million experiments. Lastly, our phenomics approach builds on the recent explosion of powerful computer vision and ML approaches driven by the technology industry over the last decade. Modern ML tools can be trained to identify the most salient features of images without relying on any pre-selected, disease-specific subject matter expertise, even if these features are imperceptible to the human eye. Using these tools, we can capture the aggregate cellular response induced by a disease-causing perturbation or therapeutic and quantify these changes in an unbiased manner, freeing us from human bias. In contrast, traditional drug discovery relies on presumptive target hypotheses and bespoke biological signaling assays that only capture narrow, pre-determined biology and thus limit the scope of biological exploration.

Figure 25. ML algorithms can detect cellular phenotypes that are indistinguishable to the human eye. Most morphological differences within our images are too subtle for the human eye to detect, but ML algorithms like those we deploy in our Recursion OS can readily distinguish between them. The heatmap of similarities shown here between learned embeddings of these images shows clear separation where even well-trained cell biologists or pathologists would be hard-pressed to describe consistent differences.

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Our phenomics laboratory operates approximately 50 weeks each year. We have achieved this level of operational excellence by integrating state-of-the-art technology and adopting lean manufacturing principles. Furthermore, we ensure our lab generates consistent, accurate and precise data through the use of multiple systems: facility controls to prevent contamination of cells, rigorous assay validation and instrument qualification to ensure consistency and routine quality monitoring to automatically capture data and track all critical experiment specifications. Our quality system is designed such that we routinely monitor our performance to identify and implement the appropriate preventive and continuous improvement actions.

Transcriptomics

We have developed an in-house transcriptomics laboratory platform, complete with walk up automation and push button digital data processing, capable of profiling up to 15,000 samples per week covering expression of nearly 20,000 genes from samples drawn from any of our biological modules. At the end of 2022, we had leveraged our transcriptomics platform to sequence over 250,000 individual transcriptome samples to improve our biological understanding of many of our programs. In 2023, we intend to scale and automate this capacity further to enable hits identified from our phenomics platform to be confirmed using an orthogonal, transcriptomic readout as part of our Industrialized Program Generation workflows. This approach of combining high dimensional, large scale data layers from the Recursion OS, across phenomics and transcriptomics, allows us to increase our confidence around which insights to prioritize for scientist follow-up, while at the same time minimizing cost and human effort.

Figure 26. Recursion utilizes an adapted transcriptomic experimental process to leverage industry-standard sequencing systems at vastly reduced cost per sample.

InVivomics

In vivo studies are an important tool for providing an assessment of the efficacy and safety of a compound within the context of a complete, complex whole-organism system. Similar to other steps within the drug discovery and development process, conventional in vivo studies are fraught with human bias and limited in the post-study endpoints that they measure. Using our In Vivo Data Collection Infrastructure, we can collect more holistic measurements of an individual animal’s behavior and physiological state using continuous video feeds and sensor technology (e.g., temperature), surveilling animals in their home environment and analyzing readouts live throughout studies in progress. By automating the process of data collection, we can amass uninterrupted data on animal behavior and physiology across days, weeks, or even months allowing for a more accurate and holistic assessment of the animal’s health state across the entirety of the study. This data can subsequently be used to create more abstract representations of animal behavior, potentially allowing us to rapidly phenotype new animal

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models and identify in vivo disease signatures that may be more relevant for assessing compound efficacy and potential liabilities.

In 2022, our Digital Vivarium consisted of 1,000 total digital cage units and we ran 21 safety (Digital Tolerability Assay) and 14 InVivomic efficacy studies involving our drug candidates. Our Digital Tolerability Assay allows us to non-invasively monitor activity in digital cages and detect meaningful differences between treated and untreated subjects that serve as an early indicator of established disease.

Figure 27. Our proprietary, scalable Smart Housing System for in vivo studies automatically collects and analyzes video and sensor data from all cages continuously.

ADME Data

In 2022, a total of 26 new in vitro pharmacology assays were developed and qualified for validating hits and characterizing molecules. Prioritized assays were optimized with standard operating procedures (SOPs) and quality control (QC) metrics and incorporated into workflows for compound prosecution and program advancement. In addition, three DMPK assays were developed in preparation for on-boarding to a custom-built integrated high-throughput robotic chemistry platform. As this data is generated, it is included in our data warehousing system that connects one-off experimental assays with the rest of the Recursion Data Universe. We have built an analytical laboratory equipped with state-of-the-art liquid chromatography-mass spectrometry equipment. Our analytical chemistry team supports work throughout the lifecycle of our programs, including assessing compound purity and identification for quality control, measuring compound levels in plasma and tissue samples from in vivo ADME and efficacy studies and biomarker identification and validation activities in support of preclinical and clinical translational efforts. To support SAR and the development of predictive models, we have invested in an automated, connected module to process multiple in vitro DMPK assays at scale to evaluate compounds for plasma protein binding, microsomal stability and cell permeability. Using this system we expect an operational capacity of up to 500 compounds per week.

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Figure 28. Recursion’s automated DMPK system allows for automated assay execution across plasma protein binding, microsomal stability and cell permeability studies at scale to advance programs while generating state-of-the-art training data for ML and AI algorithm development. The system has been designed to allow for future potential addition of additional modules into the automated workflow such as addition in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) testing.

Cell Culture and Cell Differentiation Tools

We have built a state-of-the-art cell culture facility to consistently produce high-quality, primary mammalian cells, such as vein, kidney, lung, liver, skin and immune cell subsets, as well as stem cell-derived and cancer cell lines. Approximately 50 cell types have been onboarded to our high-throughput discovery systems, spanning primary cells, cell lines and iPSCs. In 2022, we greatly expanded our cell culture facility footprint to perform work using human induced pluripotent stem cell (hiPSC) lines. Specifically, we have developed protocols using CRISPR genome editing technologies to generate knock-out or knock-in lines. We have developed protocols to differentiate hiPSC cells into several distinct cell types using 3D and 2D differentiation methods. Furthermore, we have developed internal capabilities to characterize these cells using standardized and partly automated methods to molecularly and functionally characterize the differentiated progeny. Lastly, we have developed a scalable platform to produce 50-100 billion cells of interest per week and cryopreserve cells in assay-ready frozen format. In 2022, our team produced over 500 billion hiPSC-derived cells of interest to support various ongoing projects.

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Figure 29. Various cells grown at scale for phenomics assays in-house by Recursion. From top left in clockwise order: iPSC-astrocytes, Bronchial Epithelial Cells, iPSC-neurons, dermal fibroblasts, iPSC-cardiomyocytes and U2OS cells.

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Table 2. Numerous and diverse cell types onboarded to our platform enable us to broadly interrogate biology. Approximately 50 human cell types have been onboarded to our high-throughput discovery systems, spanning primary cells, cell lines and cells derived from iPSCs.

We have on-boarded innovations including large scale, microcarrier-based, suspension culture systems to reduce footprint and increase growth surface for additional scale. We will continue to onboard additional cutting-edge innovations to scale our work further. We maintain a strong track record of quality and consistency in our cell culture facility by implementing facility design and control systems that are uncommon among technology-enabled drug discovery companies. These designs and controls include rigorous process validation and documentation, a personnel training and qualification program and routine quality monitoring. Our quality system is designed such that we routinely monitor our performance to identify and implement the appropriate preventive and continuous improvement actions.

Figure 30. Recursion’s recent facilities expansion has created room for further growth of its specialized high-scale precision tissue culture of diverse and complex human cell types.

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Chemistry Tools

Our in-house chemistry tools include physical compound collections, state-of-the-art compound storage and handling infrastructure and high-precision analytical equipment. Our experienced team of chemists use this equipment and a network of reputable CROs to advance discovery efforts and deliver differentiated drug candidates.

We have a total in-house chemical library of approximately 1.7 million small molecules from a combination of commercial, semi-proprietary and proprietary and partner sources and use this library to identify chemical starting points for discovery campaigns. Over 1 million of these compounds reside within the Recursion’s novel chemical entity library (i.e. these are not compounds belonging to our big-pharma partners), curated by our medicinal chemists and designed for highly druggable chemical properties while avoiding undesirable chemical properties, such as poor solubility and permeability. While this library has been constructed to maximize chemical diversity, we have ensured that several analogs of many compound cores are included to help identify emergent SAR for early hits and enable rapid hit expansion into readily available analogs. Additionally, we have curated a selection of approximately 9,000 clinical-stage and preclinical compounds from public forums or filings, covering approximately 1,000 unique mechanisms of action, for which an abundance of existing data and annotations currently exist. These well-characterized molecules are frequently used as tool compounds within our work and may be advanced as therapeutic programs if the Recursion OS reveals unique and previously undisclosed biological activity. Approximately 500,000 compounds are from Bayer’s NCE library, for which we do not have structural information.

We believe that the scale of our total in-house chemical library is comparable to the scale of chemical libraries curated by some large pharmaceutical companies. We plan to substantially increase the size and diversity of our NCE library over the coming years through a combination of partnerships and investments in automated chemical microsynthesis in order to more fully understand novel biological and chemical relationships. Furthermore, we envision that an automated chemical microsynthesis system would integrate with existing sample management, synthesis and purification capabilities. With the completion of our recent wet-laboratory expansion, we now have the potential capability to store up to more than 60 million compounds (in plated formats) onsite.

Figure 31. Our internal chemical libraries are highly diverse. This visualization of the structural diversity of approximately 1,000,000 of our in-house small molecules, where compounds are clustered based on descriptors using t-distributed stochastic neighbor embedding, demonstrates the evenly distributed and diverse nature of our

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compounds. This diversity increases the probability that we capture useful biochemical interactions across a broad range of biology. Note that red dots indicate known chemical entities.

Bits

Processing and Data Storage Infrastructure

The Recursion OS is built on top of a core technology stack that is highly scalable and flexible. We have adopted a hybrid-cloud strategy, leveraging the benefits of both public and private cloud infrastructure depending on the context and our needs:

•Public Cloud. The public cloud is our default choice for production workloads and applications. The scale, elasticity of compute and storage and economies of scale offered by public cloud computing providers enable us to cost-effectively execute our strategy.

•Private Cloud. The private cloud, or edge computing, is used to integrate our lab data flows, including the upload of data to the public cloud.

•BioHive-1 and High Performance Computing in a Private Cloud. Much of our deep learning model training and research happens with our world-class supercomputer named BioHive-1. BioHive-1 is built on NVIDIA’s DGX SuperPod architecture and is on the TOP500 list of the world’s most powerful supercomputers as of November 2022.

Figure 32. We believe BioHive-1 is one of the most powerful supercomputers dedicated wholly to drug discovery for a single company. BioHive-1 consists of 40 NVIDIA DGX A100 640GB nodes, which further expands our capability to rapidly improve ML models.

Enabling Software Tools

Alongside our infrastructure, we have built a suite of tools that empower our scientists to accurately design, execute and verify the quality of up to 2.2 million diverse experiments each week. Our tools, which take into account real-time onsite reagent supplies, enable consistent control strategies and design standards that make each week’s data relatable across time. Additionally, these tools automatically flag experiments or processes which miss quality

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requirements or stall at some point in the process and notify the appropriate Recursionaut, providing them the tooling needed for manual intervention. Elements of our Enabling Software Tool suite include:

•Experiment Design. The Laboratory Information Management System (LIMS) tracks reagent inventory and enables compound selection from our library. Custom applications design large experimental layouts consisting of millions of perturbation conditions with appropriate randomization and control strategies. Proprietary algorithms design CRISPR gene editing guide RNAs for maximal knockout efficiency.

•Experiment Execution and QC. This suite of tools and dashboards automatically executes and continuously monitors experimental protocols to ensure reliable experiment execution. Custom web applications enable our Recursion scientists to view and interact with microscopy images and associated metadata from our phenomics platform for systematic QC at both the image- and plate-level.

Figure 33. Software tools allow our scientists to design massive experiments while complying with our complex proprietary rules for layout. This graphical interface facilitates experiment plate layout specification for experiments that can span more than 1,000 1536-well plates while ensuring the relatability and appropriate design fit for the purpose of training machine-learning algorithms.

The Recursion Data Universe

The Recursion Data Universe comprises over 21 petabytes of highly relatable biological and chemical data, including: phenomics, transcriptomics, InVivomics, ADMET assays and bespoke bioassay data. These different data modalities are highly complementary as we advance drug discovery and development programs. Phenomic data provides a broad, foundational layer of biological and chemical data, while other datasets provide greater translational insights.

Data Processing Tools

To understand, explore and relate new or existing data in the Recursion Data Universe, we must normalize, transform and analyze the data. Our tools in this layer manage the streaming of our data at scale to the appropriate public and private cloud, the transformation of our images into mathematical representations through our in-house proprietary convolutional neural networks and the standard and custom analyses performed on our data as parameterized and requested by users. Anomalies are flagged to the team for fast resolution.

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Figure 34. We convert raw images into a list of features that allows cross-image comparison. Microscopy images are run through a deep convolutional network with an architecture similar to the one above. The network is trained on our phenomics data so that, layer by layer, each image is transformed into a list of 128 features representing the cellular biology in the image. The resulting features power downstream analysis.

Biological and Chemical Activity Assessment

Our activity assessment tools enable us to evaluate the robustness of diverse disease model phenotypes and subsequently measure the activity of potential therapeutic agents within these disease models. These tools are target-agnostic by design, explore cellular biology holistically and enable the exploration of many disease models and potential therapeutics simultaneously with no significant alteration to the core platform.

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Figure 35. Our proprietary user interface enables our scientists to rapidly identify compounds with maximum positive effect on a disease phenotype while minimizing side effects. The results from our empirical hit identification screens allow drug discovery teams to rapidly explore results and focus on compounds that are believed to be the most promising.

Mapping and Navigating to Drive Outcomes

Our mapping and navigating tools area rapidly growing suite of in-house software applications designed to process and translate data from the Recursion Data Universe into actionable insights for our research and development teams to accelerate programs which fall into two categories: (i) insights into underlying biology and early therapeutic starting points and (ii) insights into the specific chemical substrate of interest.

Recursion’s phenomaps are a massive database of relationships amongst biological and chemical perturbations inferred in silico based on phenotypic similarity. To date, we have built phenomaps consisting of whole genome CRISPR genetic knockouts as well as a large number of small and large molecule-perturbations at multiple concentrations in multiple human cell types. Collectively, these phenomaps contain over 3 trillion inferred biological and chemical relationships generated solely by ML tools without human bias. Our ability to query the relationships between any perturbations in our phenomaps changes drug discovery from an iterative trial-and-error process into a computationally-driven search problem. Furthermore, our teams use phenomaps to understand the mechanisms underpinning disease and how to manipulate them. For example, we can query the similarity (or dissimilarity) created by the CRISPR-engineered knockout of any two genes from our whole-genome arrayed CRISPR screen, revealing both known and novel drug targets never before described in scientific literature. We can also query the similarity between any small molecule in our library and all genetic knockouts, uncovering a compound’s mechanism of action and, most importantly, can infer the activity of such molecules against high-value drug targets. Using automated workflows, we can iteratively add phenotypes for new chemical compounds to our phenomaps on a weekly cadence, further expanding the diversity of chemical space that we can explore and allowing us to optimize individual program chemistry.

The computational methods enabling phenomaps allow us to turn the output of each experiment from “data exhaust” into a data engine. Unlike a traditional high-throughput screen, in which many compounds are profiled for their activity against a single target at a time, in our mapping and navigating approach, every compound we profile is analyzed not for its activity against a single target, but for its inferred activity against all possible targets in our arrayed CRISPR library, as well as its similarity to every compound we have run before in its phenomap – producing a super-linear growth in biological relationships as we conduct experiments.

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Figure 36. Mapping and navigating enables simultaneous genome-wide screening. Traditional pharma high-throughput screening methods (left) screen thousands to millions of compounds simultaneously against single targets, deriving information about compound activity on that single target, but little or no information about other targets. Recursion’s mapping and navigating approach (right) enables us, in a single experiment, to infer the activity of a compound against all potential targets in our arrayed CRISPR knockout screen.

We have invested to create processing pipelines and intuitive user interfaces of our phenomaps that help our scientists navigate the breadth of these relationships and elucidate which insights are most promising. Our flagship user interface, the MapApp, enables users to mine relationships using several complementary visualizations, statistical measurements and data layers including known information about compounds or known relationships between genes and diseases to rapidly distinguish novel insights. We are looking to augment this information further to include predicted data related to physicochemical and structural properties, synthesizable compounds not yet tested on our platform, ADMET assays and in vivo experiments.

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Figure 37. The MapApp allows our team to simultaneously view multiple relationships between genes and compounds. This proprietary software application enables us to rapidly explore inferred biological and chemical relationships in order to: (i) discover targets, (ii) predict active hits, (iii) optimize for similar or dissimilar relationships and (iv) predict mechanisms of action.

Digital Chemistry Platform

The Digital Chemistry Platform is a core part of Recursion’s software ecosystem, comprising an integrated suite of proprietary and commercial tools, enabling our medicinal and computational chemistry team to scalably advance programs from hit to candidate. Key components of the digital chemistry platform include: (i) unified access to and visualization of chemical structures and assay data (including internally-generated high or low-dimensional assay data, externally-generated in vitro or in vivo data and DMPK data); (ii) integrated predictive modeling, chemical search and computational chemistry capabilities; and (iii) molecular design and collaboration. Predictive modeling available in the Digital Chemistry Platform includes both commercially available predictive tools as well as internally developed deep-learning based methods and is applied to both potency and ADMET optimization. We intend to further invest in predictive and digital chemistry capabilities across three domains: (i) chemistry-centric ML model development, (ii) chemistry-centric data generation and (iii) digital and physical chemistry process development to more efficiently drive the Design-Make-Test-Analyze cycle of chemistry optimization, including the roll-out of industrialized workflows that integrate chemistry and biological assay steps autonomously.

InVivomics Research Suite

Our InVivomics Research Suite is a proprietary collection of software tools that enable scientists to monitor and analyze behavioral and physiological data from ongoing and completed in vivo studies. Study data for individual animals or aggregated study groups can be explored in near real-time, better ensuring that the final study data will be reproducible and interpretable and allowing researchers to prepare for follow-on activities prior to final study completion. Continuous monitoring allows researchers to similarly flag unexpected effects that may arise from animal handling, dosing, or compound liabilities and modify or terminate a study as needed. At the end of the study, graphical and tabular data are automatically generated to aid in the evaluation of study results and the design of follow-up in vivo studies.

Additionally, continuous video feeds and our proprietary animal cages enable us to amass uninterrupted data on animal behavior and physiology across days, weeks, or even months. ML tools within our InVivomics Research Suite can use this data to create more comprehensive representations of animal behavior, allowing us to rapidly phenotype new animal models and identify in vivo disease signatures that may be more relevant for assessing potential compound safety and efficacy attributes.

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Figure 38. The InVivomics Research Suite allows our team to track and analyze a broad range of data in ongoing animal studies. These tools enable our in vivo scientists to monitor individual subjects through near real-time video feed and data generation and review study level data.

Bridging from Recursion OS Insights to Program Advancement

The Recursion OS is an integrated, multi-faceted system for iteratively mapping and navigating massive biological and chemical datasets that contain trillions of inferred relationships between disease-causative perturbations and potentially therapeutic compounds. Individually, components of the Recursion OS can be used to build or interrogate one piece of the drug discovery value chain. Collectively, the components of the Recursion OS can be joined together to identify, validate and advance a broad portfolio of novel therapeutic programs quickly, cost-effectively and with minimal human intervention and bias - industrializing drug discovery. We use standardized, automated workflows to identify programs and advance them through key stages of the drug discovery and development pipeline as in Figure 22.

Step 1: Inference

Using our mapping tools and infrastructure, we have profiled diverse biological and pharmacological perturbations, including CRISPR gene knockouts, soluble factors, bacterial toxins and small molecules. Recursion’s phenomaps contain trillions of inferred relationships amongst these perturbations that have been inferred in silico based on phenotypic similarity.

In order to identify novel program starting points, it is important that the Recursion OS accurately predict relationships across diverse domains of biology. To confirm the accuracy of our predictions, we have demonstrated that our approach recapitulates hundreds of well-known biological pathways. In the example below, we illustrate our phenomap predictions for approximately 150 gene knockouts from canonical biological pathways and known agonists or antagonists of these same pathways. By comparing the phenotypes induced by these perturbations to one another using our Recursion OS, we observed that each perturbation creates a unique phenotype and phenotypes form clusters that recapitulate well-understood biological pathways, including genes involved in Bcl-2 signaling, NF-KB signaling, RAS signaling, JAK/STAT signaling and TGFß signaling.

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Figure 39. Inferred relationships between genes and small molecules recapitulate well known biology. Above, we show a visualization of well studied genes and small molecules. Increasingly dark shades of red reflect an increasing degree of phenotypic similarity. Increasingly dark shades of blue reflect an increasing degree of phenotypic oppositeness or anti-similarity (which often suggests inhibitory relationships between genes, though possibly distal).

These findings validate the accuracy of our phenomap relationships and suggest that we can use our approach to identify new drug targets or early therapeutic starting points. We begin de novo programs by searching our phenomaps with respect to (i) well-known biological pathways or (ii) human genetics data and identifying novel gene targets or compound (e.g., small molecule) perturbations that are inferred to have a therapeutic effect above statistically-defined thresholds.

Step 2: Hit Identification

Promising compounds are automatically advanced in our Industrialized Program Generation workflows for further evaluation. First, we physically test candidate compounds in multiple concentrations and replicates using our phenomics assay, including directly in the disease-relevant background, to confirm our predictions. These experiments are designed to confirm predicted relationships of interest from our phenomaps and can be completed rapidly.

Compounds that advance from our phenomics platform are evaluated in one or more high-dimensional, in vitro orthogonal assays to confirm the relationship we observed from our phenomics platform. Today, our transcriptomics platform is used as the primary orthogonal assay within our automated workflows. However, proteomics or metabolomics assays may also be used in the future. Compounds, and related compound series, that confirm and validate in one or more orthogonal assays may be advanced to more bespoke and low-throughput assays as deemed necessary by our scientific teams.

Throughout the early stages of this process, we have intentionally limited human intervention in order to (i) minimize bias and (ii) minimize our dependency on scientists to evaluate and analyze voluminous data packages. Rather, program advancement is automatically triggered if compounds meet pre-specified statistical thresholds. The decision to automate the decision making process, in addition to automating physical experimentation, allows us to advance large numbers of programs simultaneously and efficiently. Data from each assay is summarized in reports which can be reviewed by our scientific teams to assist with program prioritization and advancement as needed.

Step 3: Hit to Lead

After compounds have been empirically confirmed in multiple orthogonal assays, our medicinal chemists work to optimize early chemical starting points into drug-like molecules using our Industrialized Hit-to-Lead (iH2L) workflows. One critical step in this process is to further understand the mechanism by which compounds are demonstrating a therapeutic effect. One way in which our chemists can begin this process, is by using our mapping

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and navigating software tools to compare the phenotype of a candidate molecule to the phenotypes of (i) approximately 9,000 well-characterized clinical-stage and preclinical compounds in our library or (ii) tens of thousands of CRISPR-engineered genetic knockouts in our phenomaps. Novel chemical entities that cluster with annotated compounds and genetic landmarks may share similar mechanistic functions. The below data demonstrates the power of our embeddings to accurately cluster diverse compounds with similar mechanisms of action.

Figure 40. Compounds with the same mechanism cluster together phenotypically. Each dot represents a different compound. Compounds that are phenotypically similar reside closer together and recapitulate mechanistic similarities.

Additionally, while a compound may be active in our screens, most early therapeutic starting points have low potency and undesirable drug properties and must be optimized before advancing into in vivo and, ultimately, human studies. During the hit-to-lead process, our chemists may leverage our phenomics platform to repeatedly measure changes in compound potency and selectivity that result from changes in compound structure. Our chemists also take advantage of our Digital Chemistry Platform to conduct chemical expansion exercises across more than 1 trillion molecules in our in silico library which we can then order for further profiling.

Because this process may extend over several months, it is critical that our platform assay is highly stable over time. To ensure this stability, we test that our assay can reproduce specific measures of compound activity, such as a compound’s EC50 (the concentration of a drug that gives half-maximal response) or max-effect (the maximal response), in experiments run weeks, or even months, apart. In the example below, we ran four separate experiments of a HIF2a inhibitor known to be active against our VHL disease model over a period of three months. Dose-response curves across all four runs demonstrate a high degree of overlap, including highly similar EC50s and max-effect. Our calculated minimum significance ratio from this study, a common industry metric of in vitro

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assay reproducibility over time, is 1.076, which is highly robust by industry benchmarks12. These results demonstrate the stability of our assay and the ability to use our phenomic platform as a basis for SAR.

Figure 41. Compound activity is reproducible across experimental runs. Dose response curves from multiple runs of the same tool compound against our disease model for VHL loss-of-function show high consistency with a minimum significance ratio of 1.076.

Step 4: Candidate Evaluation and IND Preparation

Prior to nominating a clinical candidate, optimized molecules are evaluated in more complex and disease-relevant in vivo models using our InVivomics platform. This process consists of two steps. Firstly, compounds are tested in Digital Tolerability studies, during which we non-invasively monitor subject activity to confirm the most promising compound (e.g. within a series) and identify an optimal dosage. Secondly, we run efficacy studies using our proprietary cage hardware, including continuous sensors and high-resolution video systems, to assess compound effects. Readouts are reported in real time rather than at the end of a study enabling scientists to make informed and impactful decisions regarding study continuation, modification, or termination as well as program advancement.

After optimizing therapeutic drug candidates, we select those compounds that have the best chemical properties to advance through development and ultimately clinical trials. We have built the internal capabilities to drive clinical candidates through IND-enabling studies, regulatory approval processes and human clinical studies. Collectively, members of our team have been involved in hundreds of clinical trials. Additionally, we work closely with a team of external consultants across regulatory, CMC and clinical development to ensure execution success. In the future, we envision that we will evolve the Recursion OS to incorporate data and techniques that improve our ability to execute clinical programs at scale, including population-scale genomics data, industrialized biomarker development and precision medicine tools in order to identify patients for which a potential therapeutic would be beneficial.

The End Result - A Pipeline Designed to Move Failure Early in the Process

Late-stage clinical failures are the primary driver of costs in today’s pharmaceutical R&D model, due in part to inherent uncertainty in the clinical development and regulatory process. Reducing the rate of costly, late-stage failures and accelerating the timeline from hit to a clinical candidate would create a more sustainable R&D model. To achieve this more sustainable model, we believe that in its ideal state a drug discovery funnel would morph from the being shaped like the letter ‘V’ to being shaped like the letter ‘T,’ where a broad set of possible therapeutics could be narrowed rapidly to the best candidate, which would advance through subsequent steps of the process quickly and with no attrition.

12 Haas JV, Eastwood BJ, Iversen PW, et al. (Updated 2017). Minimum Significant Ratio – A Statistic to Assess Assay Variability. Assay Guidance Manual [Internet].

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Figure 42. Reshaping the drug discovery funnel. Recursion’s goal is to leverage technology to reshape the typical drug discovery funnel towards its ideal state by moving failure as early as possible to rapidly narrowing the funnel into programs with the highest probability of success.

We believe we have made progress in reshaping the traditional drug discovery funnel in the following ways:

•Broaden the funnel of therapeutic starting points. Our flexible and scalable mapping tools and Infrastructure enable us to infer trillions of relationships between human cellular disease models and therapeutic candidates based on real empirical data from our own wet-labs, ‘widening the neck’ of the discovery funnel beyond hypothesized and therefore human-biased targets.

•Identify failures earlier when they are relatively inexpensive. Our proprietary navigation tools enable us to explore our massive biological and chemical datasets to validate more and varied hypotheses rapidly. While this strategy results in an increase in early stage attrition, the system is designed to rapidly prioritize programs with a higher likelihood of downstream success because they have been explored in the context of high-dimensional, systems-biology data. Over time, and as our OS improves, we expect that moving failure earlier in the pipeline will result in an overall lower cost of drug development.

We have leveraged our Recursion OS to explore more than 170 disease programs to a depth sufficient to quantify improvements in the time, cost and anticipated likelihoods of program success by stage compared to the traditional drug discovery paradigm. We believe that future iterations of the Recursion OS will enable greater improvements. Ultimately, we look to minimize the total dollar-weighted failure while maximizing the likelihood of success.

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Figure 43. The trajectory of our drug discovery funnel mirrors the ‘ideal’ pharmaceutical drug discovery funnel. We believe that, compared to industry averages, our approach allows us to: (i) identify low-viability programs earlier in the research cycle, (ii) spend less per program and (iii) rapidly advance programs to a validated lead. Data shown are the averages of all our programs from 2017 through 2022.

Investment Roadmap

There are three key factors that differentiate Recursion from the vast majority of other TechBio companies. First, Recursion integrates both wet-lab and dry-lab capabilities in-house in order to create virtuous cycles of learning and iteration. Second, Recursion already functions at significant scale (e.g., five clinical-stage programs, an exciting preclinical pipeline, and two of the largest discovery partnerships in the industry with Roche/Genentech and Bayer, one of the largest biological and chemical datasets in the world, etc.). Third, although Recursion has built significant chemistry capabilities, Recursion was founded as a biology-first company in order to mitigate one of the fundamental causes of failure in drug discovery, choosing the wrong target associated with a disease. While emerging competitors and large, well-resourced incumbents may pursue a similarly differentiated strategy to ours, we have two advantages as a first mover: (i) no amount of resources can compress the time it takes to observe naturally occurring biological processes and (ii) the growing Recursion Data Universe creates compounding network effects that may make it difficult for others to close the competitive gap. In the future, we envision building the following technologies into the Recursion OS.

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Figure 44. We continue to integrate new capabilities into the Recursion OS in order to create additional cycles of learning and iteration that can lead to a more complete understanding of biology and chemistry. In the future, we envision investing in additional digital chemistry capabilities, automated chemical microsynthesis, population-scale genomics data and other technologies.

Our Pipeline

All of the programs in our internal pipeline are built on unique biological insights surfaced through the Recursion OS where: (i) the disease-causing biology is well defined but the downstream effects of the disease-cause are typically poorly understood, the primary targets are typically considered undruggable, or the primary targets are not well known in the context of a disease and (ii) there is a high unmet medical need, no approved therapies, or significant limitations to existing treatments. Several of our internal pipeline programs could have potential market opportunities in excess of $1.0 billion in annual sales. We currently have four programs in active clinical studies and are preparing for a fifth program to enter a Phase 1b/2 clinical study in early 2024. In addition to our clinical stage programs, we are actively developing dozens of preclinical and discovery programs.

Clinical Programs

▪REC-994 for the potential treatment of cerebral cavernous malformation, or CCM — a Phase 2, double-blind, placebo-controlled, safety, tolerability and exploratory efficacy study is underway. Orphan Drug Designation has been granted in the US and EU. We expect to share top-line data in 2H 2024.

▪REC-2282 for the potential treatment of neurofibromatosis type 2, or NF2 — an adaptive, Phase 2/3, randomized, multicenter study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted. We expect to share a Phase 2 interim safety analysis in 2024.

▪REC-4881 for the potential treatment of familial adenomatous polyposis, or FAP — a Phase 2, double-blind, randomized, placebo-controlled study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted.

▪REC-4881 for the potential treatment of AXIN1 or APC mutant cancers — a Phase 1b/2 study in select tumor types is expected to initiate in early 2024.

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▪REC-3964 for the potential treatment of Clostridioides difficile infection — a Phase 1 study in healthy volunteers is underway. We expect to share safety and PK data in 2H 2023.

We believe that the number of potential programs we can generate with our Recursion OS is key to the future of our company, as a greater volume of validated programs has a higher likelihood of creating value. Additionally, we believe that our large number of potential programs makes us an attractive partner for larger pharmaceutical companies. The static or declining level of R&D output at many large pharmaceutical companies means that they have an ongoing need for new projects to fill their pipelines.

Figure 45. The power of our Recursion OS as exemplified by our expansive therapeutic pipeline. All populations defined above are US and EU5 incidence unless otherwise noted. EU5 is defined as France, Germany, Italy, Spain and UK. 1 Prevalence for hereditary and sporadic symptomatic CCM population. 2 Annual US and EU5 incidence for all NF2-driven meningiomas. 3 Our Targets Delta and Alpha programs have the potential to address a number of indications in the immunotherapy space. 4 Our MYC program has the potential to address a number of indications driven by MYC alterations, totaling 54,000 patients in the US and EU5 annually. We have not finalized a target product profile for a specific indication.

REC-994 for Cerebral Cavernous Malformation - Phase 2

REC-994 is an orally bioavailable, superoxide scavenger small molecule being developed for the treatment of symptomatic CCM. In Phase 1 SAD and MAD trials in healthy volunteers directed and executed by us, REC-994 demonstrated excellent tolerability and suitability for chronic dosing. CCM is among the largest rare disease opportunities and has no approved therapies to date. A Phase 2 double-blind, placebo-controlled, safety, tolerability and exploratory efficacy study is underway. Orphan Drug Designation has been granted in the US and EU. We expect to share top-line data in 2H 2024.

Disease Overview

CCM is a disease of the neurovasculature for which approximately 360,000 patients in the US and EU5 are symptomatic. Less than 30% of patients with CCM experience symptoms, resulting in the disease being severely underdiagnosed and suggesting that well more than 1 million patients may have the disease in the US and EU5. CCM and its hallmark vascular malformations are caused by inherited or somatic mutations in any of three genes involved in endothelial function: CCM1, CCM2, or CCM3. Approximately 20% of patients have a familial form of CCM that is inherited in an autosomal dominant pattern. Sporadic disease in the remaining population is caused by somatic mutations that arise in the same genes. CCM manifests as vascular malformations of the spinal cord and brain characterized by abnormally enlarged capillary cavities without intervening brain parenchyma. Patients with CCM lesions are at substantial risk for seizures, headaches, progressive neurological deficits and potentially fatal

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hemorrhagic stroke. Current non-pharmacologic treatments include microsurgical resection and stereotactic radiosurgery. Given the invasive and risky nature of these interventions, these options are reserved for a subset of patients with significant symptomatology and/or easily accessible lesions. Rebleeds and other negative sequelae of treatment further limit the effectiveness of these interventions. There is no approved pharmacological treatment that affects the rate of growth of CCM lesions or their propensity to bleed or otherwise induce symptoms. CCM can be a severe disease resulting in progressive neurologic impairment and a high risk of death due to hemorrhagic stroke.

Figure 46. Vascular malformations (cavernomas) in the brain of a CCM patient.13

Insight from Recursion OS

CCM2 knock-down in human endothelial cells reveal pronounced structural and functional phenotypes that are distinct from healthy cells. We hypothesized that these observed structural changes could be used to enable unbiased drug discovery. Fluorescent microscopy and automated cellular quantification and profiling software enabled high throughput analysis. More than 2,000 commercially available and known chemical entities were rapidly evaluated with this strategy based on the hypothesis that hits from this library could be more quickly translated to the clinic. The novel use of REC-994 for CCM was discovered leveraging this early form of the Recursion OS. The exciting aspect of this novel, unbiased approach was that the drug candidates chosen using automated software analysis outperformed those chosen by human analysis in subsequent orthogonal screens.

13 Cooper, AD. et al. (2008). Susceptibility-weighted imaging in familial cerebral cavernous malformations. Neurology, 71, 382.

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Figure 47: Rescue of structural phenotypes associated with loss of CCM2. Immunofluorescence images of endothelial cells treated with siCTRL, siCCM2, or siCCM2 treated with REC-994 stained for DNA (blue), actin (green) and VE-cadherin (red). According to a machine learning classifier trained on images, REC-994 shows image-based rescue.

REC-994 is a small molecule therapeutic designed to alleviate neurological symptoms associated with CCM and potentially reduce the accumulation of new lesions. REC-994 is an orally bioavailable superoxide scavenger with pharmacokinetics supporting once-daily dosing in humans. The putative mechanism of action of REC-994 is through reduction of reactive oxygen species and decreased oxidative stress that leads to stabilization of endothelial barrier function. In addition, REC-994 exhibits anti-inflammatory properties which could be beneficial in reducing disease-associated pathology.

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Figure 48. REC-994 mechanism of action and proposed potential therapeutic impact.

Preclinical

The activity of REC-994 as a potential treatment for CCM was further confirmed in orthogonal functional assays and in acute and chronic in vivo models. REC-994 demonstrated benefit on acute to subacute disease-relevant hemodynamic parameters such as vascular dynamics and vascular permeability. Chronic administration of REC-994 was also tested in two endothelial-specific knockout mouse models for the two most prevalent genetic causes, CCM1 and CCM2. These mouse models faithfully recapitulate the CNS cavernous malformations of the human disease. Mice treated with REC-994 demonstrated a decrease in lesion number and/or size compared to vehicle treated controls. Notably, 24-hour circulating plasma levels of REC-994 in this in vivo experiment were consistent with exposures seen in humans at a 200 mg daily dose.

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Figure 49. REC-994 rescues acetylcholine-induced vasodilation defect and dermal permeability defect in Ccm2 endothelial specific knockout mice.14

Figure 50. REC-994 reduces lesion severity in chronic mouse models of CCM Disease. Mice treated with REC-994 demonstrated a statistically significant decrease in the number of small-size lesions, with a trend toward a decrease in the number of mid-size lesions.14

Clinical

We conducted a Phase 1 Single Ascending Dose (SAD) study in 32 healthy human volunteers using active pharmaceutical ingredients with no excipients in a powder-in-bottle (PIB) dosage form. Results showed that systemic exposure (Cmax and AUC) generally increased in proportion to REC-994 dose after both single and multiple doses. Median Tmax and t1/2 appeared to be independent of dose. There were no deaths or SAEs reported during this study and no TEAEs that led to the withdrawal of subjects from the study. These data supported a MAD study in healthy human volunteers.

A subsequent Phase 1 Multiple Ascending Dose (MAD) study was conducted in 52 healthy human volunteers and was designed to investigate the safety, tolerability and PK of multiple oral doses of REC-994, to bridge from the PIB

14 Gibson, et al. (2015). Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation, 131(3), 289-99.

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dosage form to a tablet dosage form, as well as to assess the effect of food on PK following a single oral dose. Overall, multiple oral doses of REC-994 were well tolerated in healthy male and female subjects at each dose level administered in this study. There appeared to be no dose-related trends in TEAEs, vital signs, ECGs, pulse oximetry, physical examination findings, or neurological examination findings. Pharmacokinetic results support once-daily oral dosing with the tablet formulation.

Table 3. Summary of Adverse Events from Phase 1 Multiple Ascending Dose Study. AE=adverse event; MAD=multiple ascending dose; SAE=serious adverse event; TEAE=treatment-emergent adverse event.

A two-part Phase 2 study is underway. Part 1 is a randomized, double-blind, placebo-controlled trial to investigate the safety, efficacy and PK of daily doses of REC-994 (200 mg and 400 mg) compared to placebo in participants with symptomatic CCM over a treatment period of 12 months. Part 2 is an optional, double-blind, long-term extension (LTE) study of daily doses of REC-994 (200 mg and 400 mg) for participants completing Part 1 of the study. Currently, there is no development or regulatory precedent or pathway for CCM drug development. Results from the ongoing Phase 2 study are expected to inform a pivotal trial design with guidance from the FDA.

Figure 51. Phase 2 clinical trial schematic for REC-994. Phase 2 trial design to assess the efficacy and safety of REC-994 in patients with symptomatic CCM. Enrollment criteria includes MRI-confirmed lesion(s), diagnosis of familial or sporadic CCM and having symptoms directly related to CCM. Primary outcome measures are safety and

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tolerability. Secondary measures are focused on efficacy, including clinician-measured outcomes, imaging of CCM lesions, acute stroke scales and patient reported outcomes.

Competitors

To our knowledge, the REC-994 program is the only industry-sponsored therapeutic program in clinical trials for CCM. If approved, REC-994 would be the first pharmacologic disease-modifying treatment for CCM, one of the largest areas of unmet need in the rare disease space. Other ongoing research includes an investigator-initiated study of a marketed therapeutic and a preclinical industry-sponsored program.

•Investigators at the University of Chicago are evaluating the efficacy of atorvastatin, or Lipitor, on reduction in hemorrhage rate in patients with CCM. As of February 2023, the phase 1/2 randomized, placebo-controlled, double-blinded, single-site clinical trial is ongoing with an estimated study completion date of June, 2025.

•Neurelis is currently in preclinical development of NRL-1049, a repurposed ROCKi to potentially reduce the accumulation of new lesions and alleviate neurological symptoms in patients with CCM.

REC-2282 for Neurofibromatosis Type 2 - Phase 2/3

REC-2282 is a small molecule HDAC inhibitor being developed for the treatment of NF2-mutant meningiomas. In previous clinical studies, the molecule has been well tolerated, including in patients dosed for multiple years, and potentially reduced cardiac toxicity that differentiates it from other HDAC inhibitors. In contrast to approved HDAC inhibitors, REC-2282 is both CNS-penetrant and orally bioavailable. An adaptive, Phase 2/3, randomized, multicenter study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted. We expect to share a Phase 2 interim safety analysis in 2024.

Disease Overview

Neurofibromatosis type 2 (NF2) is an autosomal dominant, inherited, rare, tumor syndrome that predisposes affected individuals to multiple nervous system tumors, the most common of which are bilateral vestibular schwannomas, intracranial meningiomas, spinal meningiomas and other spine tumors such as ependymomas.

Approximately one-half of individuals with NF2 have meningiomas and most of these individuals will have multiple meningiomas. In patients with NF2 the incidence of meningiomas increases with age, and lifetime risk may be as high as 75%. Combined, we believe NF2-driven meningiomas occur in approximately 33,000 patients per year in the US and EU5. Patients with NF2 are diagnosed typically in their late teens or early 20s and present with hearing loss which is usually unilateral at the time of onset, focal neurological deficits and symptoms relating to increasing intracranial pressure.

Although most meningiomas are benign their location often makes complete resection untenable, and subsequently patients with NF2 experience loss of hearing, facial paralysis, poor balance and visual difficulty. Spinal tumors can result in weakness and disability and some patients become wheelchair bound. Many patients with multi-tumor disease die in early adulthood. Due to the catastrophic nature of the disease and lack of non-surgical options for management, new approaches to treatment are needed, particularly those directed toward shrinking tumor burden.

Insight from Recursion OS

To select REC-2282 for our NF2 program, we employed our brute-force approach by developing a high content phenotypic screen to identify cellular and structural changes associated with the genetic knockdown of NF2 by siRNA in HUVEC cells. Transfected NF2-deficient cells were treated with thousands of compounds to discover molecules that restored the structural defects associated with loss of NF2. REC-2282 reversed this complex cellular phenotype back to a healthy state (wildtype) in four independent screens at concentrations between 0.1 to 1 μM, in line with efficacious concentration levels in our preclinical experiments. Additionally, REC-2282 failed to exhibit the same level of dose dependent rescue in the evaluation of hundreds of other tumor suppressor or oncogene knockdown models, providing further evidence of a selective effect in the specific context of NF2 loss of function. Together, these experiments demonstrated robust and reproducible activity in disease relevant settings suggesting the therapeutic potential of REC-2282 in treating NF2-mutant tumors.

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Figure 52. REC-2282 rescued the loss of NF2. A) Immunofluorescent images of human endothelial cells treated with siRNA control or siRNA NF2. B) REC-2282 rescued the high-dimensional disease phenotype as evidenced with a left shift from the disease to the healthy state. HUVEC, human umbilical vein endothelial cells; NF2, neurofibromatosis type 2; siRNA, small interfering RNA.

REC-2282, is an orally bioavailable, CNS-penetrating, pan-histone deacetylase, or HDAC, inhibitor with PI3K/AKT/mTOR pathway modulatory activity. By comparison to marketed HDAC inhibitors, REC-2282 is uniquely suited for patients with NF2 and NF2-mutant CNS tumors, due to its oral bioavailability, CNS-exposure and lack of cardiovascular liabilities.

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Figure 53. REC-2282 would be a first-in-class HDAC inhibitor for the potential treatment of NF2 meningiomas. We believe REC-2282 is well suited for NF2 vs other HDAC inhibitors due to its oral bioavailability and CNS-exposure.15,16,17

NF2 disease is driven by mutations in the NF2 gene, which encodes an important cell signaling modulator, merlin. Loss of merlin results in activation of multiple signaling pathways converging on PI3K/AKT/mTOR among others and results in enhanced cell proliferation. Anti-neoplastic effects of HDAC inhibitors, like REC-2282, are thought to derive primarily via disruption of the protein phosphatase 1 (PP1)-HDAC interaction, and the subsequent inhibition of PI3K/AKT signaling leading to growth arrest and apoptosis of cancer cells.

We obtained a global license for REC-2282 from the Ohio State Innovation Foundation in December 2018. Orphan drug designation for REC-2282 in NF2 has been granted in the US and EU. Fast Track Designation for REC-2282 in NF2-mutated meningioma was granted in the US in 2021.

15 Sborov, D.W et al. (2017) A phase 1 trial of the HDAC inhibitor AR-42 in patients with multiple myeloma and T- and B-cell lymphomas. Leuk Lymphoma, 58(10), 2310-2318.

16 Collier KA, et al. (2021). A phase 1 trial of the histone deacetylase inhibitor AR-42 in patients with neurofibromatosis type 2-associated tumors and advanced solid malignancies. Cancer Chemother Pharmacol. 87(5), 599-611.

17 Prescribing Information of Vorinostat/Belinostat/Romidepsin respectively.

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Figure 54. REC-2282 acts on an important pathway in tumor development to inhibit the growth of tumor cells. A potential mechanism of action of REC-2282 in NF2.18

Preclinical

After we discovered the novel use of REC-2282 for NF2 using our platform, we performed a literature search to better understand the molecule and validate disease models. REC-2282 has been shown to be pharmacologically active in various cancer cell lines and mouse xenograft models. REC-2282 had been shown to inhibit in vitro proliferation of vestibular schwannoma, or VS, and meningioma cells by inducing cell cycle arrest and apoptosis at doses that correlate with AKT inactivation. In preclinical models, REC-2282 inhibited the growth of primary human VS and Nf2-deficient mouse schwannoma cells, as well as primary patient-derived meningioma cells and the benign meningioma cell line, Ben-Men-1.

In animal models of NF2, REC-2282 suppressed in vivo tumor growth of an Nf2-deficient mouse vestibular schwannoma allograft. In addition, REC-2282 suppressed in vivo tumor growth of human vestibular schwannoma xenograft models in mice fed chow formulated to deliver 25 mg/kg/day REC-2282 for 45 days. REC-2282 also suppressed the growth of meningioma cells in an orthotopic mouse model of NF2-deficient meningioma that contained luciferase-expressing Ben-Men-1 meningioma cells. These animal data served as a functional and orthogonal validation of our platform findings.

18 Adapted from Petrilli and Fernández-Valle. (2016). Role of Merlin/NF2 inactivation in tumor biology. Oncogene, 35(5), 537-48.

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Figure 55. REC-2282 shrinks vestibular schwannoma xenografts in SCID-ICR mice and prevents growth & regrowth of tumors in the NF2-deficient meningioma mouse model. (A) Change in VS tumor volume for each control mouse, demonstrating a mean 6% increase. (B) REC-2282 significantly reduces the mean size of VS tumor volume by ~28% across SCID-ICR mice implanted with VS xenografts. Error bars shown are the 95% CI. P=0.006. C) REC-2282 also suppressed the growth of Ben-Men-1-LucB tumor xenografts as measured by tumor bioluminescence.19,20

Clinical

Four Investigator-Sponsored Trials (ISTs) of REC-2282 (previously referred to as AR-42) have been completed. In study AR-42-001, REC-2282 was administered as monotherapy. In the other 3 trials, REC-2282 was administered in combination with anti-neoplastic agents: decitabine (AR-42-002), pazopanib (AR-42-003) and pomalidomide (AR 42 004), respectively. In these studies, REC-2282 was given to 77 patients with solid or hematological malignancies in doses ranging from 20 mg to 80 mg three times a week for three weeks followed by one week off-treatment in four-week cycles. Multiple patients were treated for multiple years using this dosing regimen at the 60 mg dose and the longest recorded treatment duration is 4.4 years at the 40 mg dose. The majority of adverse events were transient cytopenia that did not result in dose reduction or stoppage. The MTD in patients with solid tumors was determined to be 60 mg. The REC-2282 plasma exposure in patients with hematological malignancies and solid tumors generally increased with increasing doses. There were no consistent signs of plasma REC-2282 accumulation across a 19-day administration period nor obvious differences in PK between hematologic and solid tumor patients.

In another early Phase 1 pharmacodynamic IST conducted by Ohio State University, it appeared that REC-2282 suppressed aberrant activation of ERK, AKT and S6 pathways in vestibular schwannomas from adult patients undergoing tumor resection. These results may be difficult to achieve with single pathway inhibitors of ALK or MEK.

Recursion is currently conducting an adaptive, Phase 2/3, randomized, multicenter study to evaluate the efficacy and safety of REC-2282 in patients with progressive NF2-mutated meningiomas with underlying NF2 disease and sporadic meningiomas with documented NF2 mutations. The study is designed to accelerate the path to potential product registration by allowing for initiation of a confirmatory Phase 3 study prior to full completion of Phase 2. It is a combined Phase 2/3 study design, beginning with a Proof-of-Concept Phase 2 portion in which 20 adult subjects and up to nine adolescent subjects will begin treatment on two active dose arms. Subject safety will be monitored by an independent Data Monitoring Committee, which will apply dose modification and stopping rules as indicated. After all 20 adult subjects have completed six months of treatment, an interim analysis will be performed for the purpose of 1) determination of go/no-go criteria for Phase 3 portion of the study, 2) selection of the dose(s) to carry forward, 3) re-estimation of sample size for the planned Phase 3 and 4) agreement from FDA to initiate Phase 3. Subjects in the Phase 2 portion will continue treatment for up to 26 months total and then have the option to enroll in an Extension study. The Phase 3 portion currently requires recruitment of an additional 60 subjects (adult and potentially adolescent subjects), who will receive treatment for up to 26 months. The planned primary endpoint is Progression-Free Survival (PFS).

19 Adapted from Jacob A, et al. (2012). Triological Society Thesis Preclinical Validation of AR42, a Novel Histone Deacetylase Inhibitor, as Treatment for Vestibular Schwannomas. Laryngoscope, 122(1), 174-189.

20 Burns SS, et al. (2013). Histone Deacetylase Inhibitor AR-42 Differentially Affects Cell-cycle Transit in Meningeal and Meningioma Cells, Potently Inhibiting NF2-Deficient Meningioma Growth. Cancer Res; 73(2), 792-803.

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Figure 56. Phase 2/3 clinical study for REC-2282. Phase 2/3 study design to assess the efficacy and safety of REC-2282 in patients with progressive NF2-mutated meningiomas. Enrollment criteria include MRI-confirmed progressive meningioma and either (1) sporadic meningiomas with confirmed NF2 mutation or (2) confirmed diagnosis of NF2 disease. The primary outcome measure for the phase 2 portion of the study is safety and tolerability. Primary endpoint for the phase 3 portion of the study is Progression-Free Survival (PFS).

Competitors

There are currently five active programs in clinical development targeting NF2-driven brain tumors.

•Brigatinib, an approved ALK inhibitor for NSCLC from Takeda Pharmaceuticals, is in Phase 2 for NF2 disease meningioma, vestibular schwannoma and ependymoma.

•Crizotinib, an ALK/ROS1 inhibitor, is being studied in an investigator sponsored Phase 2 study in progressive vestibular schwannoma in NF2 patients.

•Selumetinib, a MEK inhibitor from AstraZeneca, is being studied in a Phase 2 study for NF2 related tumors.

•GSK2256098, a FAK inhibitor from GlaxoSmithKline, is being studied in a basket Phase 2 for meningiomas with a variety of targeted therapies and genetic alterations, including NF2 mutation.

•IK-930, a TEAD inhibitor from Ikena Oncology, is being studied in a basket Phase 1 for advanced solid tumors driven by hippo signaling, including patients with NF2 mutations.

REC-4881 for Familial Adenomatous Polyposis (FAP) - Phase 2

REC-4881 is an orally bioavailable, non-ATP-competitive allosteric small molecule inhibitor of MEK1 and MEK2 being developed to reduce polyp burden and progression to adenocarcinoma in FAP patients. REC-4881 has been

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well tolerated in prior clinical studies. A Phase 2, double-blind, randomized, placebo-controlled study is underway. Orphan Drug Designation in the US and EU as well as Fast Track Designation in the US have been granted.

Disease Overview

FAP is a rare tumor predisposition syndrome affecting approximately 50,000 patients in the US and EU5 with no approved therapies. FAP is a genetic disorder resulting from a heterogeneous spectrum of point mutations in the adenomatous polyposis coli (APC) gene. The APC gene is a tumor suppressor gene which encodes a negative regulator of the Wnt signaling pathway.

FAP is characterized by progressive development of hundreds to thousands of adenomatous polyps in the lower gastrointestinal tract, mainly in the colon and rectum, and is associated with up to a 100% lifetime risk of colorectal cancer before age 40, if left untreated. Standard of care for patients with FAP is colectomy in late teenage years. Without surgical intervention, affected patients will progress to colorectal cancer by early adulthood. Post-colectomy, patients receive endoscopic surveillance every 6-12 months to monitor disease progression given the ongoing risk of malignant transformation.

Despite removing the main at-risk organ, approximately 50% of patients will develop adenomatous lesions in the neo-rectum. Once endoscopic management is no longer sufficient, additional surgical procedures are required. Similarly, these patients also develop duodenal (particularly ampullary) adenomas which also require endoscopic management. In the presence of larger adenomas and evidence of carcinoma, patients require additional localized surgery, including radical Whipple procedures. There are currently no approved therapies for FAP.

Insights from Recursion OS

The novel use of REC-4881 for FAP was discovered by leveraging knock-down of the FAP disease gene APC in human cells using the Recursion OS. To select REC-4881 as a potential therapeutic for FAP, Recursion developed a high content phenotypic screen to identify cellular and structural changes associated with knockdown of APC using small interfering RNA (siRNA) in osteosarcoma U2OS cells. Using machine vision and automated analysis software, Recursion quantified hundreds of cellular parameters associated with APC knockdown. This complex phenotype was used as the basis for a chemical screen of more than 3,000 known drugs and bioactive compounds, revealing several RAF and MEK inhibitors, including REC-4881, which reversed the structural defects associated with loss of APC. REC-4881 exhibited highly specific and potent reversal of cellular phenotypes when compared to the MEK inhibitors selumetinib and binimetinib.

Figure 57. REC-4881 rescued phenotypic defects of cells with APC knockdown. Compared to thousands of other molecules tested, REC-4881 rescued phenotypic defects substantially better (including better rescue than other MEK inhibitors) for APC-specific knockdown.

REC-4881 is an orally bioavailable, non-ATP-competitive allosteric small molecule inhibitor of MEK1 and MEK2 (IC50 2-3 nM and 3-5 nM, respectively) that is being developed to reduce polyp burden and progression to adenocarcinoma in FAP patients. We obtained a global license for REC-4881 from Takeda Pharmaceuticals

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(TAK-733) in May 2020. Orphan drug designation for REC-4881 in FAP and APC-driven tumors was granted by the FDA in 2021.

FAP is driven by loss of function of APC, which is a critical component of the β-catenin destruction complex, leading to aberrant activation of the Wnt pathway. This Wnt-on state can lead to RAS stabilization, activation of the RAS/ERK pathway and the activation of MYC, leading to cell proliferation and survival - including the growth of adenomas seen in FAP. REC-4881 inhibits MEK1/2 thereby inhibiting ERK activation, decreasing MYC activity, restoring cells back to a Wnt-off state and inhibiting cell proliferation.

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