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Morning Session 9:30 am – 12:30 pm Afternoon Session 1:30 pm – 4:30 pm Welcome State of Recursion Chris Gibson PhD – Co-Founder & CEO Recursion OS Lina Nilsson PhD – Senior VP of Inception Labs Preclinical Laura Schaevitz PhD – Senior VP and Head of Research Fireside Chat with Deepak Nijhawan, MD, PhD David Mauro MD PhD – Chief Medical Officer Deepak Nijhawan MD PhD – UT Southwestern, Distinguished Chair in Biomedical Science Tours & Demos Senior Management Afternoon Convocation Najat Khan PhD – Chief R&D Officer & Chief Commercial Officer Partnerships Matt Kinn – Senior VP of Business Development & Corporate Initiatives John Marioni PhD – Genentech, Senior VP and Head of Computational Sciences Clinical Programs David Mauro MD PhD – Chief Medical Officer Company & Milestones Michael Secora PhD – Chief Financial Officer Break Fireside Chat with Jensen Huang Chris Gibson PhD – Co-Founder & CEO Jensen Huang – NVIDIA, Founder & CEO Closing Remarks Chris Gibson PhD – Co-Founder & CEO Breakfast & Arrival at Recursion (Upper Level) 8:30 am – 9:30 am Lunch & Break (Upper Level, High Throughput Feeding) 12:30 – 1:30 pm Dinner — Mar Muntanya (Hyatt Regency) 5:00 – 7:00 pm

Welcome State of the Company

This presentation and any accompanying discussion and documents contain information that includes or is based upon "forward-looking statements" within the meaning of the Securities Litigation Reform Act of 1995. These forward-looking statements are based on our current expectations, estimates and projections about our industry and our company, management's beliefs and certain assumptions we have made. The words “plan,” “anticipate,” “believe,” “continue,” “estimate,” “expect,” “intend,” “may,” “will” and similar expressions are intended to identify forward-looking statements. Forward-looking statements made in this presentation include outcomes and benefits expected from the Tempus partnership, including our ability to leverage the datasets acquired through the license agreement into increased machine learning capabilities and accelerate clinical trial enrollment; outcomes and benefits expected from the Enamine partnership, including the generating and co-branding of new chemical libraries; outcomes and expected benefits from the Helix partnership, including the development of causal AI models and biomarker and patient stratification strategies; expected BioHive supercomputer capabilities; outcomes and benefits from licenses, partnerships and collaborations, including option exercises by partners, additional partnerships, and the ability to house tools on the BioNeMo Marketplace; the potential for additional partnerships and making data and tools available to third parties; advancements of our Recursion OS, including augmentation of our dataset; outcomes and benefits expected from the Large Language Model-Orchestrated Workflow Engine (LOWE); the occurrence or realization of any near- or medium-term potential milestones; the initiation, timing, progress, results, and cost of our research and development programs and our current and future preclinical and clinical studies, including timelines for enrollment in studies, data readouts, and progression toward IND- enabling studies; the potential size of the market opportunity for our drug candidates; our ability to identify viable new drug candidates for clinical development and the accelerating rate at which we expect to identify such candidates; 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, and many others. Forward-looking statements made in this presentation are neither historical facts nor assurances of future performance, are subject to significant risks and uncertainties, and may not occur as actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. For a discussion of factors that could affect our business, please refer to the "Risk Factors" sections in our filings with the U.S. Securities and Exchange Commission, including our Annual Report for the Fiscal Year ended December 31, 2023, on Form 10-K and our most recent Quarterly Report on Form 10-Q. This presentation does not purport to contain all the information that may be required to make a full analysis of the subject matter. We undertake no obligation to correct or update any forward-looking statements, whether as a result of new information, future events or otherwise. Certain information contained in this presentation relates to or is based on studies, publications, surveys and other data obtained from third-party sources and the company’s own internal estimates and research. While the company believes these third-party sources to be reliable as of the date of this presentation, it has not independently verified, and makes no representation as to the adequacy, fairness, accuracy or completeness of, any information obtained from third-party sources. In addition, all of the market data included in this presentation involves a number of assumptions and limitations, and there can be no guarantee as to the accuracy or reliability of such assumptions. Finally, while the company believes its own internal research is reliable, such research has not been verified by any independent source. Cross-trial or cross-candidate comparisons against other clinical trials and other drug candidates are not based on head-to-head studies and are presented for informational purposes; comparisons are based on publicly available information for other clinical trials and other drug candidates. Any non-Recursion logos or trademarks included herein are the property of the owners thereof and are used for reference purposes only.

Help define what we view as a tipping point moment as BioTech transitions to TechBio and understand why Recursion is uniquely positioned to take advantage of this Share details and updates on our: • Pipeline – with 7 clinical trial readouts expected in the next ~18 months • Partnerships - with potential near term options on both maps and programs • Platform - with industry-leading data generation and compute Let you get a feel for Recursion and hear from expert partners from outside Recursion about the current and potential future impact of our work

The Moment: A Tale of Two Cities

Scannell et al., Nature Reviews Drug Discovery 11:191, 2012

Data, compute, and automation are shifting the speed, cost, and quality of novel insights today, and we are nearing the stage where we can harvest the earliest of this jump forward New types of companies have emerged that are truly “bilingual” in tech and science We believe the transformation of BioPharma through AI is inevitable, just as we are seeing in so many industries — we believe it is a matter of who, how and when

Most BioTech companies have built a point solution - they’ve developed a tool, process, model or analysis to accomplish an important step in drug discovery. This is how we started too. But discovering and developing medicines requires hundreds of steps… Phenomics

More data and compute enables more generalizable Models Fit for Purpose Data is Critical and the primary bottleneck More Compute is needed and few are investing at our scale

Pre-Industrialization Post-Industrialization Team of 40 people 30 program hypotheses explored Team of 7 people 115 program hypotheses explored ~1 FTE equivalent 201 program hypotheses initiated 2022 2023 Half of 2024 Continued Improvement

CapitalCompute DataPeople

Four ingredients needed to continue leading TechBio in an industry at the tipping point People

Four ingredients needed to continue leading TechBio in an industry at the tipping point Data

Four ingredients needed to continue leading TechBio in an industry at the tipping point Compute

Four ingredients needed to continue leading TechBio in an industry at the tipping point Capital

We exist to run an experiment... ….An experiment to determine if there might be a better way to discover and develop drugs... ...We need this sort of ambition in BioTech if we hope to have a chance of transforming our ability to impact patients and drive down the cost of medicines.

Platform • Internal programs now initiated by LLM with multiple hit nominations for LLM-generated programs with more on the way • Moving towards large-scale multi-omics and generalizable foundation models with first genome-scale transcriptomics map and patient data • Data and tools available to biopharma and commercial users: Bayer will be 1st beta-user of LOWE for drug discovery and development Pipeline • 7 clinical trial readouts expect over the next ~18 months with new programs embracing our tools to drive novel chemistry against novel targets advancing quickly Partnership • Roche & Genentech: program optioned in oncology continues to progress with potential additional near-term program & very near-term map options • Bayer: On track to complete 25 unique multi-modal data packages in Q3 2024 with first joint Project now advancing rapidly towards Lead Series nomination

The Recursion Operating System

Phenomics Data

Phenomics Data

Kn ow le dg e AI Strategy Experiments Randomized Experiments Experiments

BioHive-1: • 320 NVIDIA A100's • #84 on TOP500 list when built Nvidia invests $50 million in biotech company Recursion for A.I. drug discovery BioHive-2: • 504 NVIDIA H100's, 4X BioHive-1 • Top ranked supercomputer in pharma • #35 on TOP500 list 2020 Initial discussions began 2021 2023 2024

0.48 0.49 0.47 0.46 0.45 0.44 0.43 0.42 10⁸ 10⁹ Parameters Bi ol og ic al re ca ll DL-2 Phenom-Beta Phenom-1 0.50 Recall of biological relationships vs model size

Brightfield to capture dynamics DATA GENERATION MODELS increase in expressed gene knock-outs detected >250 million experiments >50 human cell types >1 trillion neurons generated 2 w eeks Parameters Bi ol og ic al re ca ll Recall of biological relationships vs model size 0.48 0.50 0.46 0.44 0.42 10⁸ 10⁹ DL-2 Phenom-Beta Phenom-1 Phenom-2 candidate 6 m onths 2 weeks of rapid iteration on Biohive-2 enabled

>1M samples sequenced First genome-scale transcriptomic map DATA GENERATION MODELS Ability to predict compounds that failed later disease-relevant assays in internal tests Ability to predict compounds that passed later disease-relevant assays in internal tests Replaced time-consuming, disease-specific validation assays with portfolio-wide multimodal model workflow IL-6 pathway Transcriptomics Phenomics

1.25 1.00 0.75 0.50 0.25 0.00 -0.25 10⁶ 10⁷ 10⁸ 10⁹ Generalized performance over all 22 TDC ADMET datasets Number of parameters N or m al ize d pe rf or m an ce DATA GENERATION MODELS Estimated 90x throughput over manual approach >750 compounds per week Our single generalizable model improves with multimodal data and model size External best model 2024 MolGPS with Phenomics MolGPS Average of all TDC models

DATA GENERATION MODELS • Machine learning enables scale by extracting signals from video and temperature sensors • Applied across breadth of Recursion portfolio • Designed to select the right molecule at the right dose before entering studies >1,000 digital mouse cages 150 digital rat cages in 2024 Social housing increases relevance

DATA GENERATION MODELS >20 PB of real-world multi-modal oncology data Hundreds of thousands of unique de-identified patient records across diverse therapeutic areas Combining Recursion maps of biology with patient clinical data unlocks causal modeling to find novel targets

Phenom-Beta, available on NVIDIA BioNeMo, outperforms open-source "gold standard" CellProfiler CellProfiler Phenom-Beta Phenom-1 Phenom-2 candidate Re la tiv e Re ca ll Im pr ov em en t 1.00 1.05 1.10 1.15

Turning drug discovery into a search problem

Turning drug discovery into a search problem

Preclinical: The Power of Prediction

Novel opportunity 1-2 years Millions of $ Disease relevance ~One potential program

Novel opportunity + Disease relevance + Predicted chemical properties Weeks vs. years Thousands of $ vs. millions Hundreds of potential programs evaluated, we choose the best

Rapidly narrowing the funnel through a standardized approach

Accelerate the development of high potential drug candidates

1-2 years Millions of $ Industry Recursion Weeks vs. years Thousands of $ vs. millions

Identifying novel targets and optimizing novel chemistry

PBMC-derived fibrocyte assay • Human-PBMCs are differentiated to fibrocytes • Treatment with a control peptide gives desired impact on fibrocytes (control state) Promising hits from phenotypic screen Control State: 10 µg/mL control peptide Disease State: Undisclosed treatment Concentration (µM)

CONFIDENTIAL INFORMATION OF RECURSION REC-A 50% Rescue 1.5 µM Representative concentration response curves (CRC) from the phenotypic assay Healthy Control State Disease State Core N H R1 R4 R5 Core N H R2 H R5 REC-B 50% Rescue ~10 µM Significant reduction of disease modifying activity

Target Epsilon KO Target Epsilon IC50 = 31.5 nM Target Epsilon IC50 = 3600 nM Target Epsilon IC50 = 106 nMCore N H R1 R4 R5 Core N H R3 R4 R5 Epsilon biochemical confirmation 2.5 µM 2.5 µM 2.5 µM n/a Core N H R7 R5 R2 REC-C REC-A REC-D REC-D REC-A REC-C Target Epsilon KO GeneCompounds

Compounds with <10 µM tubulin IC50 Tubulin Related Genes Compounds with >30 µM tubulin IC50 Tubulin Related Genes REC-C Core N H R3 R4 R5 REC-D Core N H R7 R5 R2 Phenomap correlates with tubulin polymerization inhibition assay

Accelerating to IND enabling studies through in silico novel target prediction

CDK12RBM39CDK13 CDK12 RBM39 CDK13 Similar Opposite

CDK12RBM392.5 μM1.0 μM0.25 μM0.1 μMCDK13 Similar Opposite CDK12 RBM39 2.5 μM 1.0 μM 0.25 μM 0.1 μM CDK13 REC-E REC-E

0 2 4 6 REC-E REC-F SR-4835 THZ531 CDK8 CDK2 CDK20 CDK9 CDK12 CDK13 CDK4 CDK6 CDK7 In si lic o – bi nd in g z- sc or e Probability of CDK Binding as predicted by MatchMaker {Positive control} {Positive control}

REC-E REC-F SR-4835 THZ531 100 75 50 25 0 0.001 0.01 0.1 1 10 % CD K1 2 Ac tiv ity (n or m . t o ve hi cl e) CDK12 inhibitors Hit Compounds CDK12 Activity Concentration (µM) {Positive control} {Positive control}

Connecting data layers end-to-end improves quality and speed of insights

Target 1 Target 1 REC-G and REC-H at 1, 3, 10 µM REC-G and REC-H at 1, 3, 10 µM

Phenomics Confirmation Screen Transcriptomics Confirmation Screen Disease Score Disease O ff Ta rg et O ff Ta rg et Disea Score Target 1 KO Healthy REC-G 1 µM REC-G 2.5 µM REC-G 10 µM

Vehicle REC-G 30 mg/kg REC-G 100 mg/kg REC-G 300 mg/kg 100 mg/kg b.i.d REC-G TGI: 66% 50 mg/kg b.i.d REC-G TGI: 16% Tumor Volume m m 3 Days Vehicle Sunitinib:pos control TGI: 105% Daily Motion

Identifying novel molecules for a previously undruggable target

Target KO REC-J 0.1 µM REC-J 0.25 µM REC-J 1.0 µM REC-J 2.5 µM REC-J 2.5 µM REC-J 1.0 µM REC-J 0.25 µM REC-J 0.1 µM Target KO

REC-J EC50 = 100 µM REC-K EC50 = >10 µM 3 4 2 1 0 0.01 0.1 1 10 100 Concentration (µM) Am ou nt o f S ta bi liz ed P ro te in REC-J REC-K

High potential, thoroughly explored

High potential, unexplored Low potential, thoroughly explored Low potential, unexplored

Our focus

1-2 years Millions of $ Industry Recursion Weeks vs. years Thousands of $ vs. millions

Fireside Chat: Dr Deepak Nijhawan Associate Professor in the Departments of Internal Medicine and Biochemistry at UT Southwestern Medical Center

Afternoon Convocation

Patient AI impact in healthcare Patients are waiting

Prediction Diagnosis Therapeutic Generation Therapeutic Development Personalization & Access Organizational Productivity Achieve earlier diagnosis through AI-enabled diagnostic methods, biomarkers, clinical decision support and more Generate and optimize new molecules, biologics, cell therapies, etc. leveraging generative AI Optimize clinical development with precision medicine, digital endpoints, real-world evidence, and AI-driven execution of clinical trials. Predict risk factors and holistic understanding disease causality using multiomics and clinical insights Boost productivity through automated operations and elevate the quality of care with virtual assistants Achieve precision medicine by ensuring access and adoption, delivering the right treatment to the right patient at the right time

To From Industrialized, AI- powered discovery Multiomics-based, image-based, holistic ML, generative AI and physics- based design, optimization, predictive models Precision medicine, adaptive trial design RWE and ML-driven enrollment, improved likelihood of success, and faster Personalized medicine, AI- enabled patient and provider engagement Pathway-specific research, genomics Heuristics-based design, molecular modeling, immunization Lengthy, linear clinical processes, imprecise disease definition, high failure rates Broad, one-size- fits-all marketing Molecular invention Precision development Digital commercialization Therapeutic hypothesis

Time Pr og re ss Relentlessly outcomes focused vs. point solutions Investing to innovate where it matters the most Agility to adopt new waves of tech + bio innovation Bilingual talent and culture (science + AI)

AI and multiomics to identify novel targets and comprehensive biological pathways Dry-wet lab combo - Industrialized workflows Cutting edge AI/Machine Learning Connected, high quality data at scale (proprietary + public) World class compute Complementary partnerships Bilingual talent (AI + science) AI-based personalized sales, marketing AI/RWE based DTC, market access, and adoption Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Therapeutic hypothesis Molecular invention Precision development Digital commercialization Gen AI-driven novel drug-like molecular entities1 ML-based precision medicine AI-based trial design and execution 1. with optimized PK/PD profiles Potential to create & deliver transformational medicines to radically improve patient lives

Dry-wet lab - Industrialized workflows Cutting edge AI/ML Connected, high quality data World class compute Complementary partnerships Bilingual talent (AI + science) Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n AI & multiomic Digital In Vivo Gen 1.0: Repurposed molecules Gen 2.0: New drug-like molecules Data & AI software AI & ultio ics ML-based precision medicine AI-based trial design and execution Potential to create & deliver transformational medicines to radically improve patient lives Therapeutic hypothesis Molecular invention Precision development Digital commercialization

Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Novel biology: ü RBM39, 12+ in early and late discovery • Roche – GI Onc, Neuroscience ü Bayer – Oncology Repurposed molecules to novel molecules: ü RBM39, Epsilon, 12+ in early and late discovery, 60% using Gen AI ü Roche – RG1 ü 2.5x lower cost to IND ü 3x faster time to lead Pipeline: 7 programs • 5 in clinical stage Phenom-1 LOWE (LLM to orchestrated workflows) 50 PB+ high quality data (biological, chemical, clinical) BioHive-2 Roche/Genentech, Bayer, Nvidia, Enamine, Tempus, Helix, and more MatchMaker (Hit ID) MolE (ADME) GFlowNets (Lead Op) Causal AI AI-based trial execution • AXIN1/APC Ph 2 Phenom-Beta Potential to create & deliver transformational medicines to radically improve patient lives Therapeutic hypothesis Molecular invention Precision development Digital commercialization

• Novel biology: RBM39 ML-enabled phenomics insights Gen 2.0 NCE: • RBM39 • ~2.5x lower cost to IND • ~3x faster time to lead Therapeutic hypothesis Molecular invention Precision dev. Commercial- ization Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Phenom-1 LOWE 50 PB+ BioHive-2 Cross-industry partnerships – Enamine, Tempus Precision medicine – Causal AI Phenom-Beta ~18 m onths: N ew biology to developm ent candidate (vs. 45 m onths for industry) Sp ee d & C os t MatchMaker MolE GFlowNets Pipeline: & programs • 5 in clinical stage Just-in-time recruitment: • AXN1/APC

Co st to IN D ($ M ) Ti m e to V al id at ed L ea d (m o) 0 5 10 15 20 25 30 Industry Recursion 0 5 10 15 20 25 30 35 Industry Recursion ~2.5x lower cost ~3x faster • Novel biology: RBM39 Gen 2.0 NCE: • RBM39 • ~2.5x lower cost to IND • ~3x faster time to lead Therapeutic hypothesis Molecular invention Precision dev. Commercial- ization Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Phenom-1 50 PB+ BioHive-2 Cross-industry partnerships – Enamine, Tempus MatchMaker MolE GFlowNets Precision medicine – Causal AI Phenom-Beta Sp ee d & C os t LOWE Pipeline: & programs • 5 in clinical stage Just-in-time recruitment: • AXN1/APC

• Novel biology: RBM39 Gen 2.0 NCE: • RBM39 • ~2.5x lower cost to IND • ~3x faster time to lead Therapeutic hypothesis Molecular invention Precision dev. Commercial- ization Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Phenom-1 50 PB+ BioHive-2 Cross-industry partnerships – Enamine, Tempus MatchMaker MolE GFlowNets Precision medicine – Causal AI Phenom-Beta LOWE Fa st er re cr ui tm en t Traditional à 4-6 months AXIN1/APC Ph2 enrollment Data-driven, just-in- time à 4-6 weeks Site Partnerships + Master Agreements + Central IRBs Genomics & RWD + AI Sites/participants engaged in real time at point of care Pipeline: & programs • 5 in clinical stage Just-in-time recruitment: • AXN1/APC

Therapeutic hypothesis Molecular invention Precision development

Phenom-Beta Hosted on Nvidia BioNeMo platform 75% Masked 25% of all images 86M parameter model 50% of all images 307M parameter model Original Image Fo un da tio na l m od el s f or e xt er na l u se • Novel biology: RBM39 Gen 2.0 NCE: • RBM39 • ~2.5x lower cost to IND • ~3x faster time to lead Therapeutic hypothesis Molecular invention Precision dev. Commercial- ization Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Phenom-1 50 PB+ BioHive-2 Cross-industry partnerships – Enamine, Tempus MatchMaker MolE GFlowNets Precision medicine – Causal AI Phenom-Beta LOWE Pipeline: & programs • 5 in clinical stage Just-in-time recruitment: • AXN1/APC

AI & multiomics- based novel targets and pathways Dry-wet lab combo - Industrialized workflows Cutting edge AI/ML Connected, high quality data World class compute Complementary partnerships Bilingual talent (AI + science) Pipeline: Primarily Gen AI based new drug-like molecules Digital twins optimized mfg AI based DTC market access, adoption AI personalized sales, marketing Pi pe lin e (in te rn al & e xt er na l) Fo un da tio n Potential to create & deliver transformational medicines to radically improve patient lives Preclinical Clinical Manufacturing Commercialization Data & AI-based software solutions AI-based pharma- cology ML-based precision medicine AI-based trial design & execution

Enhance AI-Driven Chemistry Advance Preclinical and Clinical Stage Programs Innovate with AI across Clinical Development Continue Investment in Scalable Infrastructure – wet and dry lab Deliver on Strategic Partnerships Create additional SaaS opportunities to advance the creation of medicines

Partnerships

Build internal pipeline in indications with potential for accelerated path to approval Pipeline Strategy Precision Oncology Rare Disease Partnership Strategy Partner in complex therapeutic areas requiring large financial commitment or competitive arbitrage Leverage partner knowledge and clinical development capabilities Undruggable Oncology Other large, intractable areas of biology (e.g., CV/Met) Neuroscience* Pipeline Partnerships License subsets of data and key tools Direct generation of new data internally to maximize pipeline and partnership value-drivers Data Strategy Licensing Augment Recursion OS Data *Includes a single oncology indication from our Roche and Genentech collaboration.

Partnership Strategy Partner in complex therapeutic areas requiring large financial commitment or competitive arbitrage Leverage partner knowledge and clinical development capabilities Undruggable Oncology Other large, intractable areas of biology (e.g., CV/Met) Neuroscience* Pipeline Partnerships Data *Includes a single oncology indication from our Roche and Genentech collaboration.

Trademarks are the property of their respective owners and used for informational purposes only. Undruggable oncology targets Announced Sept 2020 Amended Nov 2023 Neuroscience (and single oncology indication) Announced Dec 2021

Computational Sciences in Drug Development John Marioni, PhD FMedSci Senior Vice President & Head of Computational Sciences, gRED

AI IN COMPUTATIONAL SCIENCES: PAST, PRESENT, AND FUTURE 117

AI IN COMPUTATIONAL SCIENCES: PAST, PRESENT, AND FUTURE 118

Pijuan-Sala, Griffiths, Guibentif, et al., Nature, 2019

We can generate huge amounts of data—from both healthy and perturbed conditions… but how will we make sense of these data and make predictions about perturbations we have not seen?

AI IN COMPUTATIONAL SCIENCES: PAST, PRESENT, AND FUTURE 121 CELL TYPES AND STATES 104 GENES 2x104 VARIANT COMBINATIONS GENE COMBINATIONS 101342.4x10 8

This is one example where computational models, especially foundation models and generative AI can transform how we discover and develop medicines

gRED Computational Sciences (gCS) seeks to make this vision a reality This is one example where computational models, especially foundation models and generative AI can transform how we discover and develop medicines

HOW?

EXPERIMENT AI/ML DATA LAB LOOP IN A

AI IN COMPUTATIONAL SCIENCES: PAST, PRESENT, AND FUTURE 126

EXPERIMENT AI/ML DATA TRANSFORMER BASED ORACLE

Our AI strategy for R&D Scale & resolutionLab in a Loop, integrated QUANTITY QUALITY Full stack, across all aspects of R&D; up to “self drive” Maximize benefit of large size: proprietary legacy data and data generation capacity Key partnerships: Strategic partnerships Partnership around unique data generation, AI/ML model development and hardware

RIGHT TARGET OR CHEMICAL MATTER FOR THE DISEASE

In collaboration with

RIGHT MOLECULE

Cells Treatments scRNA-seq Multi-Modal Model Development Pheno-transcripto map Multi-modal embedding Cell painting Multi-modal data collection Cell painting embedding Cell painting Pheno-map scRNA-seq scRNA-seq embedding Transcripto-map Data generation 132 1 2 Validation + Discovery

MODELS ARE ONLY AS GOOD AS THE DATA

Challenges Data management, metadata and access Integrating expertise from multiple disciplines Access to scalable scientific computing for fitting/fine-tuning models Democratizing access and ensuring use of data and models

Challenges... But already driving to solutions Data management, metadata and access: modernizing our data stack and exploiting the cloud and associated tools Integrating expertise from multiple disciplines: internal organizational structure and external partners Access to scalable scientific computing for fitting/fine-tuning models: partnering with outstanding companies in the industry Democratizing access and ensuring use of data and models: Autonomous agents as the next-generation scientific assistant

THANK YOU!

Neuroscience (and single oncology indication) Announced Dec 2021 Undruggable oncology targets Collaboration announced Sept 2020 Amended Nov 2023 Trademarks are the property of their respective owners and used for informational purposes only.

Q1 Work initiated Initiated 1st joint Project which will now be advancing rapidly towards Lead Series nomination On track to complete 25 unique multi-modal data packages in Q3 Nov 2023 pivot to Oncology Q2 Q3 Bayer expected to be the first beta-user of our LOWE LLM- orchestrated workflow software

Trademarks are the property of their respective owners and used for informational purposes only. Computation and ML/AI Real-world data access (oncology) Announced Nov 2023 Announced July 2023 Announced Dec 2023 Cheminformatics and chemical synthesis Announced May 2024 Real-world data access (non-oncology)

• Multi-site network protocol continuously aggregating in various therapeutic areas • Geographically and demographically diverse population consented for re-contact • Whole exome sequencing paired with rich, longitudinal clinical data for all consenting patients • Access to hundreds of thousands of unique records each year

Partnerships DataPipeline

Clinical

144 More than a dozen discovery and research programs in oncology or with our partners – first program optioned by Roche-Genentech in GI-oncology 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 population. (2) Annual US and EU5 incidence for all NF2-driven meningiomas. (3) Prevalence for adult and pediatric population. (4) Our program has the potential to address several indications. (5) We have not finalized a target product profile for a specific indication. (6) Incidence for US only. (7) 2L+ drug-treatable population. (8) 2L+ drug-treatable population comprising ovarian, prostate, breast, and pancreatic cancers. Program Indication Target Patient Population Preclinical Phase 1 Phase 2 Phase 3 Anticipated Near-Term Milestones REC-994 Cerebral Cavernous Malformation Superoxide ~ 360K1 Topline readout in September 2024 REC-2282 Neurofibromatosis Type 2 HDAC ~ 33K2 Preliminary data readout in Q4 2024 REC-4881 Familial Adenomatous Polyposis MEK ~ 50K3 Preliminary data readout in H1 2025 REC-3964 Clostridioides difficile Infection TcdB ~730K Ph2 initiation in Q4 2024 Epsilon Fibrotic Diseases Undisclosed ~ 50K4,5,6 IND submission in early 2025 REC-4881 Advanced AXIN1/APC-mutant Cancers MEK ~ 104K7 Preliminary data readout in H1 2025 RBM39 Advanced HR-Proficient Cancers RBM39 ~ 220K8 IND submission in Q3 2024, Ph 1/2 initiation in Q4 2024 Ra re & O th er O nc ol og y LILAC SYCAMORE POPLAR ALDER TUPELO

Target / MOA Superoxide Scavenger Molecule Type Small Molecule Lead Indication(s) Cerebral Cavernous Malformations Status Phase 2 Designation(s) US & EU Orphan Drug Source of Insight Recursion OS

PATHOPHYSIOLOGY & REASON TO BELIEVE PREVALENCE & STANDARD OF CARE CAUSE KEY ELEMENTS Symptomatic US + EU5, >1 million patients worldwide live with these lesions today LOF mutations in genes CCM1, CCM2 & CCM3, key for maintaining the structural integrity of the vasculature due to unknown mechanisms Efficacy signal in Recursion OS as well as functional validation via scavenging of massive superoxide accumulation in cellular models; reduction in lesion number with chronic administration in mice • Targeting sporadic and familial symptomatic CCM patients with CCM1, CCM2, and CCM3 mutations • Superoxide scavenger, small molecule • Phase 2 readout expected September 2024 • US & EU Orphan Drug Designation Vascular malformations of the CNS leading to focal neurological deficits, hemorrhage and other symptoms No approved therapy • Most patients receive no treatment or only symptomatic therapy • Surgical resection or stereotactic radiosurgery not always feasible because of location and is not curative >5x larger US patient population than other rare diseases like Cystic Fibrosis (>31k patients) Vascular malformations (cavernomas) Julia – living with CCM Clinical: CCM

147 Source: Data above from Gibson, et al. Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation, 2015 or Recursion internal data (Ccm1 mouse model) Preclinical Studies: REC-994 reduces lesion burden and ameliorates vascular defects in genetic mouse models of CCM • REC-994 stabilizes the integrity of vasculature against challenges to permeability • Altered vascular permeability is a clinically relevant feature of CCM lesions Reduces lesion number & size in Ccm1 and Ccm2 LOF mouse models1 Rescues acetylcholine-induced vasodilation defect2 Lesion size (mm2) Ccm1 LOF Model ecKO + REC-994 WT ecKO % V as od ila tio n Acetylcholine [Log M] Lesion size (mm2) Ccm2 LOF Model * * * Rescues dermal permeability defect in CCM2 mice3 DMSO control REC-994 Ccm2 WT Ccm2 ecKO De rm al P er m ea bi lit y (A bs or pt io n, A U ) * Clinical: CCM

Source : The Symptomatic Cerebral Cavernous Malformation Trial of REC-994 (SYCAMORE) Screening & Randomization 1:1:1 Treatment Follow-up Outcome Measures Enrollment Criteria • MRI-confirmed CCM lesion(s) • Familial or sporadic • Symptoms directly related to CCM • Primary: Safety and tolerability • Secondary: Efficacy • Exploratory: Biomarkers Trial Update 400mg 200mg Placebo Visits: Days 1 & 2 Months 1, 3, 6, 9 & 12 Enroll ~60 Extension Study 12 Months Treatment Period Topline Data Expected September 2024 Clinical: CCM

Outcome Measures • Primary: Safety and Tolerability • Adverse events & symptoms • Secondary & Exploratory: • Efficacy § Clinician-measured outcomes (CGI, PGI) § MRI Imaging § Impact of acute stroke (mRS, NIHSS) § Patient and Investigator reported outcomes (SMSS, PROMIS-29, CCM-HI, symptom questionnaires) • Enrollment is complete • Vast majority of participants who completed 12 months of treatment continue to enter long-term extension • Analysis o Identification of trends across multiple endpoints o Changes in vascular permeability o E.g., hemosiderin deposition o Change in lesion burden o Subgroup Trial Update Source: https://clinicaltrials.gov/study/NCT05085561 Clinical: CCM

Target / MOA HDAC Inhibitor Molecule Type Small Molecule Lead Indication(s) NF2 Mutated Meningiomas Status Phase 2/3 Designation(s) Fast Track; US and EU Orphan Drug Source of Insight Recursion OS

PATHOPHYSIOLOGY & REASON TO BELIEVE PREVALENCE & STANDARD OF CARE CAUSE KEY ELEMENTS LOF mutations in NF2 tumor suppressor gene, leading to deficiencies in the tumor suppressor protein merlin Efficacy signal in Recursion OS, cellular, and animal models; suppression of aberrant ERK, AKT, and S6 pathway activation in a Phase 1 PD Study in NF2 patient tumors • Targeting familial & sporadic NF2 meningioma patients • HDAC inhibitor, small molecule • Oral dosing • Preliminary readout expected Q4 2024 • Fast-Track and US & EU Orphan Drug Designation Inherited rare CNS tumor syndrome leading to loss of hearing and mobility, other focal neurologic deficits No approved therapy • Surgery/RT is standard of care (when feasible) • Location may make complete resection untenable, leading to hearing loss, facial paralysis, poor balance and visual difficulty • Stasis or shrinkage of tumor could improve prognosis Treatable US + EU Ricki – living with NF2 Intracranial meningiomas Clinical: NF2

152 NF2 knockdown cells Healthy Cells REC-2282 HUVEC, human umbilical vein endothelial cells; NF2, neurofibromatosis type 2; siRNA, small interfering RNA. Prevents growth & regrowth of NF2- deficient meningioma model in mice + REC-2282 (months) + Normal diet (months) Clinical: NF2

Phase 2/3 trial initiated in Q2 2022 Outcome Measures Key Enrollment Criteria • MRI-confirmed progressive meningioma • Sporadic meningioma with confirmed NF2 mutation • Familial NF2 meningioma • Have documented progression with past 24 months • Primary: PFS6 defined as proportion of patients who are alive or progression free after • Secondary: ORR, Safety, PK/PD Source : Efficacy and Safety of REC-2282 in Patients With Progressive Neurofibromatosis Type 2 (NF2) Mutated Meningiomas (POPLAR-NF2) Phase 2 portion Preliminary Phase 2 readout (safety & preliminary efficacy) expected in Q4 2024 FDA Mtg 6-month PFS (Futility Analysis) § Go/No-go to Ph3 § Safety/Tolerability § PK § PFS 40 mg TIW ~6 Sporadic ~6 Familial 60 mg TIW ~6 Sporadic ~6 Familial Clinical: NF2

Target / MOA MEK Inhibitor Molecule Type Small Molecule Lead Indication(s) Familial Adenomatous Polyposis Status Phase 2 Designation(s) Fast Track; US and EU Orphan Drug Source of Insight Recursion OS

Polyps Found in Colon and Upper GI Tract PATHOPHYSIOLOGY & REASON TO BELIEVE PREVALENCE & STANDARD OF CARE CAUSE KEY ELEMENTS Inactivating mutations in the tumor suppressor gene APC • Targeting classical FAP patients (with APC mutation) • MEK inhibitor, small molecule • Oral dosing • Preliminary readout expected H1 2025 • Fast-Track and US & EU Orphan Drug Designation Polyps throughout the GI tract with extremely high risk of malignant transformation Efficacy signal in the Recursion OS showed specific MEK 1/2 inhibitors had an effect in context of APC LOF. Subsequent APCmin mouse model showed potent reduction in polyps and dysplastic adenomas No approved therapy • Colectomy during adolescence (with or without removal of rectum) is standard of care • Post-colectomy, patients still at significant risk of polyps progressing to GI cancer • Significant decrease in quality-of-life post-colectomy (continued endoscopies, surgical intervention) Diagnosed US + EU Clinical: FAP

Clinical: FAP

157 Source : Evaluate REC-4881 in Patients With FAP (TUPELO) Part 2 Enrollment Commenced Outcome Measures Key Enrollment Criteria • Confirmed APC mutation • ≥ 55 years old • Post-colectomy/proctocolectomy • No cancer present • Polyps in either duodenum (including ampulla of vater) or rectum/pouch • Primary: • Safety & Tolerability • Change from baseline in polyp burden at 12 weeks • RP2D • Secondary: • PK/PD Part 2 Dose Expansion at RP2D • Futility Assessment • Go/No-Go Single agent REC-4881 Dose Escalation • Safety • Tolerability • PK/PD Screening & Treatment 4 mg QD (n ≤ 6) 8 mg QD (n ≤ 6) 12 mg QD (n ≤ 6) Recommended Phase 2 Dose Clinical: FAP Phase 2 initiated preliminary readout expected by H1 2025 Trial Update

Target / MOA MEK Inhibitor Molecule Type Small Molecule Lead Indication(s) Solid Tumors with AXIN1 or APC Mutations Status Phase 2 Source of Insight Recursion OS

PREVALENCE & STANDARD OF CARE CAUSE LOF mutations in AXIN1 or APC tumor suppressor genes PATHOPHYSIOLOGY & REASON TO BELIEVE Alterations in the WNT pathway are found in a wide variety of tumors and confer poor prognosis and resistance to standard of care Efficacy signal in the Recursion OS and favorable results in PDX models harboring AXIN1 or APC mutations vs wild-type leading to a significant PFS benefit only in mutant models KEY ELEMENTS • Targeting AXIN1 or APC mutant cancers • MEK inhibitor, small molecule • Oral dosing • Enrollment ongoing • Phase 2 initial readout expected H1 2025 Substantial need for developing therapeutics for patients harboring mutations in AXIN1 or APC, as these mutations are considered undruggable Treatable US + EU5 To our knowledge, REC-4881 is the only industry sponsored small molecule therapeutic designed to enroll solid tumor patients harboring mutations in AXIN1 or APC Clinical: AXIN1 or APC AXIN1/APC regulate WNT pathway

160 REC-4881 is phenotypically opposite to the genetic KO of APC and AXIN1 providing a novel mechanism that may restore the disease state modeled by the loss of these genes 0.1 µm 0.25 µm 1.0 µm 2.5 µm AXIN1 AXIN 1 APC APC 2.5 µm 1.0 µm 0.25 µm 0.1 µm REC 4881 Clinical: AXIN1 or APC Significantly greater antitumor activity in mutant models led to significant PFS benefit

FPI achieved Q1 2024 Outcome Measures Enrollment Criteria • Unresectable, locally advanced, or metastatic cancers • ≥ 55 years old • AXIN1 or APC mutation confirmed by NGS (tissue or blood) • CRC patients must be RAS / RAF wildtype • No MEK inhibitor treatment within 2 months of initial dose • ≥ 1 prior line of therapy • ECOG PS 0-1 • Primary • Safety/tolerability • ORR (RECIST 1.1) • Secondary • PK • Additional efficacy parameters • Utilizing genomics & RWD data for patient/site matching • Phase 2 initial readout expected H1 2025 Trial Update Source : A Study of REC-4881 in Participants With Cancers Which Have an AXIN1 or APC Mutation Screening & Treatment Safety Assessment 12 mg REC-4881 QD R P 2 D AXIN1 (n=10) APC (n=10) Futility Assessment AXIN1 (n=10) APC (n=10) Once 10 pts enrolled in each cohort with ≥ 1 scan post-baseline Futility Assessment Part 1 Part 2 Clinical: AXIN1 or APC

Target / MOA Selective C. difficile Toxin Inhibitor Molecule Type Small Molecule Lead Indication(s) Prevention of CDI Status Phase 2 Source of Insight Recursion OS

163 PATHOPHYSIOLOGY & REASON TO BELIEVE Diagnosed US + EU5 patients • Severity of infection varies and can range from mild to severe, requiring colectomy • >29,000 patients die in the US each year from CDI • Cost burden of up to $4.8bn annually TREATMENT PARADIGM • Standard of care for 1st occurrence: Antibiotics alone • Recurrence (20-30% of patients) treated with antibiotics ± adjunct therapy (bezlotoxumab IV or fecal transplant) • REC3964 inhibits the C. difficile toxins and is a non-antibiotic therapy • Selective Inhibitor of C. difficile Toxins • Recursion's 1st Small Molecule NCE to Reach the Clinic • Binds and blocks catalytic activity of the toxin's innate glucosyltransferase, but not the host’s PATHOPHYSIOLOGY & REASON TO BELIEVE PREVALENCE & STANDARD OF CARE Clinical: C. difficile

Disease State Healthy Cells REC-3964 0.1 µM Clinical: C. difficile

Screening Randomization & Treatment • Phase 1 and DDI studies completed • Phase 2 initiation expected in Q4 2024, preliminary readout expected by end of 2025 Trial Updates REC-3964 250 mg orally BID REC-3964 500 mg orally BID Observational R 2:1:1 N=80 High Risk of Recurrence Patients with confirmed CDI Vancomycin Orally for 14 days Follow Up Patients with symptom resolution Outcome Measures • Patients at high risk of recurrence • ≥3 bowel movements in 24 hours • Confirm CDI using EIA (toxin) • No fulminant CDI • No history of chronic diarrheal illness due to other causes • Primary • Rate of recurrence • Secondary • Additional efficacy measures • Safety / tolerability • PK Enrollment Criteria Clinical: C. difficile

Target / MOA RBM39 Molecular Glue Degrader Molecule Type Small Molecule Lead Indication(s) TBD Status IND submission in Q3 2024, Phase 1/2 initiation in Q4 2024 Source of Insight Recursion OS

Planned Phase 1/2 study of RBM39 degrader in Biomarker Selected Relapsed Refractory HR-Proficient Solid Tumors Phase 1A Dose Finding (Accelerated Titration BOIN) N=30 Dose Level 1 Dose Level 2 Dose Level 3 Dose Level 4+ Single Patient, Accelerated Titrations until Pharmacologically Active Dose (PAD) or minor toxicity / 3 patient cohorts if DLT N = 3-6 Using BOIN design Phase 1B Dose Confirmation N=20 Dose #2 N=10 Dose #1 N=10 Indication #1 Phase 2 N=10-30 Indication #1 • IND submission expected in Q3 2024 • Phase 1/2 initiation expected in Q4 2024 • Phase 1 dose-escalation readout by end of 2025

to truly industrialize drug discovery, data and AI solutions must be integrated as modules across many steps Exciting scientific collaborations span biopharma, tech & data Clinical Development

Treatments begin Clinical Development Good Lab Practices (GLP) & Toxicity Chemistry, Manufacturing, and Controls (CMC) Patient Selection/ Biomarker Strategy Trial Design/ Statistical Approach Trial Start-up Enrollment Collectively we believe these processes could significantly reduce time from IND enabling studies to 1st patient dosed Through the use of RWD, we will be able to actively identify potential patients months before study start Establishing a Recursion trial network will dramatically reduce the time to study start up Better utilization of RWD to design inclusion, exclusion, schedule of assessments, and synthetic controls will allow shorter time to POC • Better utilizing PD biomarker o identify dose thereby getting to RP2D faster • Incorporating patient enrichment strategies at the initiation of studies will expedite GO/NO GO decisions Adopting small scal philosophy and ata hubs enables manufacturing and release timelin s to be reduced Evaluating ways to improve and reduce reliance on animal testing while augmenting with in silico predictions or cel - based assays

Company & Milestones

~43% Female Male ~55% ~1% Non-Binary Parity Pledge Signer gender parity and people of color parity Data shown reflective of Q1 2024, gender statistics include participating individuals have advanced degrees >50% Technology – data science, software engineering, automation, etc. Life Sciences – biology, chemistry, development, etc. Strategic Operations >500 employees Functional Breakdown Locations San Francisco, California Salt Lake City, Utah Toronto, Ontario Montréal, Québec London, England Headquarters in Salt Lake City, Utah with additional locations in: • San Francisco, California • Toronto, Ontario • Montréal, Québec • London, England

Inception / Valence De-risks novel capabilities to expand the Recursion OS to the point of minimum viable product Accelerator Drives company-wide efficiencies and paves the way for achieving the Recursion mission OS Develops, deploys, and improves the platform and workflows that make up the Recursion OS Value Translates data and insights into value for our patients, partners, investors, and others

Pipeline • CCM: Ph2 readout expected in September 2024 • NF2: Ph2 safety & preliminary efficacy expected in Q4 2024 • FAP: Ph2 safety & preliminary efficacy expected in H1 2025 • AXIN1 or APC Mutant Cancers: Ph2 FPI achieved in Q1 2024 with safety & preliminary efficacy expected in H1 2025 • C. difficile Infection: Ph2 initiation expected in Q4 2024 with preliminary readout expected by end of 2025 • Target RBM39 / HR-Proficient Cancers: IND submission expected in Q3 2024 and Ph1/2 initiation expected in Q4 2024 with Ph1 dose-escalation readout by end of 2025 • Target Epsilon (novel target in fibrotic diseases): IND submission expected in early 2025 with Ph1 healthy volunteer readout by end of 2025 • Dozens of internal & partner programs in early stages with first LLM & causal model driven programs entering pipeline

Partnerships • Roche & Genentech: validation program option exercised for 1st validated hit series in oncology, potential program & map options on the near or very near-term • Bayer: delivered multiple oncology data packages, on track to complete 25 unique data packages in Q3 2024, initiated and advancing 1st joint project towards lead series nomination, potential near-term program options, agreed to be 1st beta-user of LOWE for drug discovery and development • Tempus & Helix: building large-scale causal AI models to generate target hypotheses across cancer and other disease areas, exploring novel NSCLC targets • Potential for additional partnership(s) in large, intractable areas of biology Platform • Built our 1st genome-scale transcriptomics KO map, moving towards multiomics foundation models • Active learning and exploration of proteomics, organoids, spheroids, & automated synthesis • Potential to make some data and tools available to biopharma and commercial users • OS moving towards autonomous discovery Strong Financial Position ~$296M in cash Q1 2024 Cash refers to cash and cash equivalents at the end of Q1 2024

Fireside Chat with Jensen Huang

Closing Remarks