RECURSION PHARMACEUTICALS, INC.RXRX

Recursion highlights anticipated benefits of its agentic AI tools for drug discovery productivity.

“Forward Looking Information This presentation contains, and any accompanying discussion may contain, statements that are not historical facts may be considered forward-looking statements under federal securities laws. Such statements may be identified by words such as “anticipates,” “believes,” “estimates,” “expects,” “intends,” “plans,” “potential,” “predicts,” “projects,” “seeks,” “should,” “will,” or words of similar meaning and include, but are not limited to, statements regarding the impact of the acceptance of the target validated option milestone by Genentech on future developments and potential treatments; the possibility of targets and development candidates to be optioned by program partners; timing of future clinical and preclinical trials; the impact of REC-4881 trial on the Recursion OS and other clinical and preclinical programs; the anticipated benefits of our agentic AI tools, including expected improvements in scientific productivity, design and analysis cycle times, and clinical trial enrollment rates; financial position, cash runway, and ability to reduce our cash operating expense; our ability to use AI to translate complex science into medicines faster and better; Recursion’s OS industrializing first- and best-in-class drug discovery; our ability to industrialize clinical development and the effect of doing so on clinical trial outcomes; the occurrence or realization of potential milestones and their potential timing or amounts; current and future preclin”

Recursion states agentic AI is compounding its advantage across biology, design, and ClinTech.

“Agentic AI is compounding Recursion's advantage across Biology, Design, and ClinTech:”

Recursion lists forward-looking statements on its agentic AI tools and platform.

“This document contains information that includes or is based upon “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, including, without limitation, those regarding the occurrence or realization of potential milestones; the timing of data readouts and other milestones; the impact of Genentech’s option of the neuroscience target on future development of that or other potential targets; the impact of preclinical data from our programs on the future success of those programs or trials; the timing and outcome of anticipated engagement with the FDA, including the definition of a registrational path for REC-4881; financial position, cash runway, cash burn, and cash operating expense guidance; Recursion’s ability to translate platform insights into validated targets and optimized development candidates; the reusability of our maps of biology and the potential to generate and experimentally validate additional therapeutic hypotheses, including through multi-modal maps combining our phenomics data with partner-provided data; Recursion’s future as a leader in TechBio and ability to deliver better treatments to patients faster; expectations relating to early and late stage discovery, preclinical, and clinical programs, including timelines for commencement of and enrollment in studies, data readouts, meetings with regulators, and progression toward IND-enabling studies; expectations and developments with respect to licenses and collabo”

Recursion lists forward-looking statements on anticipated benefits of its agentic AI tools.

“This presentation of Recursion Pharmaceuticals, Inc. (“Recursion,” “we,” “us,” or “our”) and any accompanying discussion contain statements that are not historical facts may be considered forward-looking statements under federal securities laws and may be identified by words such as “anticipates,” “believes,” “estimates,” “expects,” “intends,” “plans,” “potential,” “predicts,” “projects,” “seeks,” “should,” “will,” or words of similar meaning and include, but are not limited to, statements regarding the impact of the acceptance of the target validated option milestone by Genentech on future developments and potential treatments; the impact of REC-4881 trial on the Recursion OS and other clinical and preclinical programs; the anticipated benefits of our agentic AI tools, including expected improvements in scientific productivity, design and analysis cycle times, and clinical trial enrollment rates; financial position, cash runway, and ability to reduce our cash expense; our ability to use AI to translate complex science into medicines faster and better; Recursion’s OS industrializing first- and best-in-class drug discovery; our ability to industrialize clinical development and the effect of doing so on clinical trial outcomes; the occurrence or realization of potential milestones and their potential timing or amounts; current and future preclinical and clinical studies, including timelines for enrollment in studies, data readouts, progression toward IND-enabling and other pote”

Recursion has implemented multiple agentic AI tools that are driving design cycles and increasing enrollment rates.

“28 Today's agents are an early foundation. As frontier AI advances, the value of Recursion's proprietary data and infrastructure compounds Select examples of early agent implementations Frontier AI and agents amplifies the value of Recursion's proprietary data and product engine Pairs frontier reasoning with proprietary multimodal maps to surface novel targets Target discovery agent Enabling scientists to mine and extract insights from proprietary maps in hours rather than weeks Design agent reasons over SAR & structural data to identify what to solve next and how Drug design agents Reduce structural analysis from 4h -> 30m Agent-generated hypotheses now driving design cycles in active programs Agentic decision layer coordinates patient, site, operational, CMC, and biometrics data Clinical strategy orchestration agent Agent-supported enrollment driving a 1.3 to 1.6x increase in enrollment rates vs. historical benchmarks”

Recursion discusses anticipated benefits of its agentic AI tools for productivity and clinical trials.

“This presentation of Recursion Pharmaceuticals, Inc. (“Recursion,” “we,” “us,” or “our”) and any accompanying discussion contain statements that are not historical facts may be considered forward-looking statements under federal securities laws and may be identified by words such as “anticipates,” “believes,” “estimates,” “expects,” “intends,” “plans,” “potential,” “predicts,” “projects,” “seeks,” “should,” “will,” or words of similar meaning and include, but are not limited to, statements regarding the impact of the acceptance of the target validated option milestone by Genentech on future developments and potential treatments; the impact of REC-4881 trial on the Recursion OS and other clinical and preclinical programs; the anticipated benefits of our agentic AI tools, including expected improvements in scientific productivity, design and analysis cycle times, and clinical trial enrollment rates;financial position, cash runway, and ability to reduce our cash expense; our ability to use AI to translate complex science into medicines faster and better;Recursion’s OS industrializing first- and best-in-class drug discovery; our ability to industrialize clinical development and the effect of doing so on clinical trial outcomes;the occurrence or realization of potential milestones and their potential timing or amounts; current and future preclinical and clinical studies, including timelines for enrollment in studies, data readouts, progression toward IND-enabling and other potenti”

Recursion has implemented target discovery, design, and clinical strategy orchestration agents with measurable gains.

“10 Today's agents are an early foundation. As frontier AI advances, the value of Recursion's proprietary data and infrastructure compounds Select examples of early agent implementations Frontier AI and agents amplifies the value of Recursion's proprietary data and product engine Pairs frontier reasoning with proprietary multimodal maps to surface novel targets Target discovery agent Enabling scientists to mine and extract insights from proprietary maps in hours rather than weeks Design agent reasons over SAR & structural data to identify what to solve next and how Drug design agents Reduce structural analysis from 4h -> 30m Agent-generated hypotheses now driving design cycles in active programs Agentic decision layer coordinates patient, site, operational, CMC, and biometrics data Clinical strategy orchestration agent Agent-supported enrollment driving a 1.3 to 1.6x increase in enrollment rates vs. historical benchmarks”

Recursion uses automation and agentic orchestration to generate and prioritize AI-designed molecules.

“Recursion OS: Precision molecular design to deliver higher-quality drug candidates to the clinic • 100 million+ molecules generated using synthetically aware design per year • ~90% of synthesized molecules are AI-generated, scored, and prioritized – all patentable • ~330 compounds synthesized to an advanced candidate in ~17 months (on average)1 • >10 development candidates designed across programs 1. Compared to industry average of over 2,500 compounds and 48 months Insight to Molecule Automated Testing Synthesis Aware Design Physics-based Models ML Property Prediction Generative Chemistry Automation & Agentic Orchestration 51”

Agentic AI compounds Recursion's advantage across biology, design, and ClinTech.

“Agentic AI is compounding Recursion's advantage across Biology, Design, and ClinTech:”

Recursion OS platform uses automation and agentic orchestration for drug discovery.

“Recursion OS: An AI-native, full stack platform for drug discovery and clinical development 5 In Silico Binding Affinity – Frontier Models Automated Testing Synthesis Aware Design Physics-based Models ML Property Prediction Transcriptomics Data Foundation Model Virtual Cell Target Validation Phenomics Data & Foundation Model Causal AI Patient Selection & RWD Insight to MoleculeBiology to Insight Molecule to Patient Generative Chemistry Automation & Agentic Orchestration Automation & Agentic Orchestration Automation & Agentic Orchestration AI-powered Recruitment Clinical Trial Design Patient Data & Causal Models”

Recursion plans next-gen models and agentic systems for generative drug design.

“Recursion – Momentum with discipline 8 W h e re w e ’r e g o in g R e ce n t w in s ✓ Precision AI-molecule design delivering differentiated assets • ~90% fewer compounds synthesized and 2x faster candidate advancement vs industry ✓ Roche and Genentech: First-in-class CRISPR phenomap • Whole genome human neuronal and microglial biology maps ✓ ClinTech: Contextualized FAP efficacy signal • High quality registry + AI-enabled RWE strengthened interpretation of single-arm data Mature, actionable portfolio of high-quality targets • Integrate omics and patient data with purpose-built models Generative drug design at scale • Next-gen models and agentic systems for design AI-powered Clinical Development at scale • Increased automation and in silico trial design and execution Focused innovation, grounded in clear impact 2”

Recursion will deploy AI agents to compress timelines and reduce cost.

“Recursion – Momentum with discipline 9 ✓ ~35% reduction (~$200M) in pro forma operating expenses YoY • Driven by sharper portfolio focus, G&A optimization, improved platform efficiency ✓ Runway extended to early 2028 W h e re w e ’r e g o in g R e ce n t w in s Pair bold ambition with disciplined execution Relentless capital discipline • Portfolio decisions anchored in automated Target Product Profiles • Objective, data-driven go/no-go gates • Platform investment tied directly to measurable impact Operational leverage at scale • Deploy AI agents to compress timelines and reduce cost • Embed automation across workflows 3”

Recursion OS applies automation and agentic orchestration to biological data for insights.

“Recursion OS: Translating complex biological signals into actionable insights 35 Biology to Insight • >50 petabytes of high-quality multimodal data - proprietary and publicly available data (wet and dry lab) • State of the art foundation models • ~1.9B-parameter phenomics foundation model • ~394M-parameter transcriptomics foundation model • Virtual cell efforts ongoing • 10+ biology maps created for partnered and internal discovery to fuel insights across the pipelineIn Silico Binding Affinity – Frontier Models Transcriptomics Data Foundation Model Virtual Cell Target Validation Phenomics Data & Foundation Model Automation & Agentic Orchestration Patient Data & Causal Models”

Recursion OS uses generative chemistry, ML models, and automation for molecular design and synthesis.

“Recursion OS: Precision molecular design to deliver higher-quality drug candidates to the clinic 36 • 100 million+ molecules generated using synthetically aware design per year • ~90% AI-generated, scored, and prioritized – all patentable • ~330 compounds synthesized to an advanced candidate in ~17 months (on average)1 • >10 development candidates designed across programs 1. Compared to industry average of over 2,500 compounds and 42 months Insight to Molecule Automated Testing Synthesis Aware Design Physics-based Models ML Property Prediction Generative Chemistry Automation & Agentic Orchestration”

Causal AI patient selection deployed across programs with real-world data improvements in trials.

“Recursion OS: Designed to improve trial efficiency, patient access, and probability of clinical success 37 Causal AI Patient Selection & RWD Automation & Agentic Orchestration AI-powered Recruitment Clinical Trial Design Molecule to Patient Real-World Scale: • ~300M real-world lives accessible through integrated data partnerships Clinical Execution Improvements (Early Results): • 10–40% increase in eligible patient population • ~1.3–1.6× improvement in enrollment rates • Site & country selection reduced from months to hours • Study startup reductions of up to 3 months Precision & Evidence Integration: • Causal AI-driven patient selection deployed across programs • Natural history and EHR analyses contextualizing trial results with RWD”

Recursion deploys AI agents and automated systems to orchestrate wet-lab and dry-lab work.

“Rather than optimizing isolated steps, the Recursion OS improves decision-making across the entire R&D value chain—from decoding unknown biology and generating first-in-class targets, to designing synthetically feasible molecules, to selecting the right patients and executing trials more efficiently. By systematically generating, integrating, and analyzing high-dimensional experimental and real-world data, we train purpose-built machine learning and foundation models that translate complex biology into actionable insights across discovery and development. Throughout these processes, we are deploying AI agents and automated systems to help orchestrate our wet-lab experimentation and dry-lab modeling, standardizing workflows, coordinating data generation and analysis, and enabling faster, more consistent, and higher-confidence decisions at scale.”

Recursion integrates agentic and automated systems to accelerate R&D learning and decisions.

“Across all three stages, Recursion integrates automated wet-lab experimentation with in-house computational analysis. Our laboratories generate large volumes of standardized, high-quality biological and chemical data, while our dry-lab capabilities apply machine learning, physics-based modeling, and statistical inference to extract actionable insights from those data. This tight integration allows experimental results to directly inform computational models, and model outputs to guide subsequent experiments, enabling faster iteration and more consistent decision-making across programs. Additionally, the integration of agentic and automated systems underpins our approach at Recursion and enables us to accelerate learning and decision-making throughout the R&D process.”

Recursion's ClinTech platform uses agentic automation for clinical trial strategy and execution.

“These capabilities form Recursion’s Clinical Development Technology (ClinTech) platform, an AI-enabled clinical development system that unifies molecule-to-patient decisions within an integrated workflow. It combines global site intelligence data, real-world patient data—including ~300 million real world lives and 1 million molecularly profiled lives—with causal inference, simulation, and agentic automation to continuously inform trial strategy and execution. By embedding patient relevance and operational feasibility earlier in development, ClinTech supports more disciplined trial design and execution at portfolio scale.”

Recursion deployed AI coding tools used by 91% of tech employees authoring 35% of code.

“In 2025, we made significant progress to deploy agentic and automated AI solutions across the workforce to enhance enterprise productivity and efficiency. In our technology organization, approximately 91% of employees are actively using AI coding tools to accelerate their work. As a result, 35% of our code is authored by AI, allowing our teams to focus on solving the problems and trusting AI to accelerate creating the solution. This saves an average of 4.3 hours of work per week, per employee.”

Recursion's platform includes AI agents and depends on continuous reliable operation of its tools.

“Our drug discovery platform is central to our mission to decode biology by integrating technological innovations across biology, chemistry, automation, data science, and engineering. The platform includes the Recursion Operating System, which combines an advanced infrastructure layer to generate proprietary biological and chemical datasets, a suite of custom software, algorithms, AI models, AI agents, and machine learning tools. Our platform depends upon the continuous, effective, and reliable operation of our software, hardware, databases, and related tools and functions, as well as the integrity of our data. Our ability to develop drug candidates and increase revenue depends in large part on our ability to enhance and improve our platform. The success of any enhancement to our platform depends on several factors, including (i) innovation in hardware solutions; (ii) increased computational storage and processing capacity; (iii) development of more advanced algorithms; and (iv) generation of additional biological and chemical data, such as that which is necessary to our ability to identify important and emerging use cases and quickly develop new and effective innovations to address those use cases.”

Recursion has deployed AI-powered clinical development across programs.

“9 Ongoing momentum Where we’re going Unprecedented scale in phenomics • At-scale cellular imaging uncovering novel biology Mature, actionable portfolio of high-quality targets • Integrate omics and patient data with purpose-built models (e.g. transcriptional Foundational Models) 2. Focused innovation, grounded in clear impact Generative drug design with predictive precision • AI-multi-parameter optimization paired with structure- based systems AI-powered Clinical Development – initiated • Natural history (e.g. FAP) and biomarker (e.g. CDK7) analysis • AI-driven site selection and recruitment Generative drug design at scale • Next-gen models and agentic systems for design • Design drives differentiation & speed for programs AI-powered Clinical Development at scale • Deployed across programs and stages • Increased automation and in silico trial design and execution”

Recursion plans generative AI design and clinical development AI at scale.

“Expected upcoming milestones 2026 and 2027 pipeline and partnership catalysts 31 1. Data-driven decision for potential Phase 1 initiation 2. Cash, cash equivalents and restricted cash (unaudited) as of December 31, 2025 3. Year-end 2027 runway guidance includes risk-adjusted cash inflows from partnerships 4. Cash burn—defined as operating cash flow less capital expenditures, excluding partnership and financing inflows, transaction expenses—is a non-GAAP financial measure. See Appendix for reconciliation of non-GAAP financial measures. Translate insights ➔ proof points ➔ new medicines Pair bold ambition with disciplined execution Focused innovation, grounded in clear impact 1H 2026 REC-4881 (MEK1/2i) Engage with FDA REC-1245 (RBM39 degrader) Mono - early safety and PK 2H 2027 REC-4539 (LSD1i) Mono - Early safety and PK 1H 2027 REC-4881 (MEK1/2i) Additional clinical data REC-617 (CDK7i) Combo - early safety and PK REC-3565 (MALT1i) Mono - early safety and PK • $755M in YE 2025 cash2 with expected runway through YE 20273 • Expected 2026 cash burn4 of <$390 million • Biology foundation models and patient data enabling scalable, high- quality target discovery • Generative AI design at scale (next-gen models and agentic systems) • Clinical Development AI at scale Partner catalysts – 2026 & 2027 Later-stage discovery milestones Advancing maps to early-stage programs Anticipated multiple new project initiations 2H 2026 Go/no-go decision1 REC-7735 (PI3Kα H1047Ri) REC-102 (ENPP1i)”