Business Description

Business Overview Recursion is a clinical-stage TechBio company with a mission to decode biology to radically improve lives. We have advanced a portfolio of differentiated internal programs and strategic partnerships powered by our integrated drug discovery and development platform, the Recursion Operating System (OS). This platform provides end-to-end, AI-native capabilities that span from novel biological ideas through the clinic, integrating multimodal biological data generation, AI-powered small molecule synthesis, and AI-enabled clinical development. All of our technologies are designed to translate complex science into medicines that matter — faster, better, and at scale — for patients who are waiting. Historically, it has taken over ten years and an average capitalized R&D cost of approximately $2-3.5 billion to move a drug discovery project from early discovery to an approved therapeutic, with less than 4% of drug discovery programs initiated resulting in an approved medicine. 1,2,3,4,5,6 Today, we are working to transform the traditional high-attrition, “V-shaped” discovery funnel by pivoting to a ‘T-shaped’ model. By leveraging advanced computational tools across biology, chemistry and clinical development, we aim to rapidly narrow a broad set of potential medicines to the candidates with the highest probabilities of success, with the goal to move programs through development more efficiently and with less attrition. Figure 1. Illustrative. 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. In recent years, advances in artificial intelligence and machine learning (“AI/ML”) have increasingly influenced both the technology and biopharmaceutical industries. Industry reports estimate that a majority of large biopharmaceutical companies now employ AI/ML in some aspect of drug discovery or development, and global investment in AI-enabled drug discovery has grown to several billions of dollars annually. Regulators and policymakers have also engaged more actively in this area, with AI/ML-enabled approaches being applied across multiple stages of drug discovery and development, including target identification, molecular design, chemical synthesis, clinical development, and manufacturing. We believe the increasing adoption of these technologies reflects a growing industry consensus that AI/ML has the potential to improve efficiency, decision-making, and productivity in drug discovery and development — the extent and timing of these benefits remain subject to ongoing validation and focus on proof-of-concept by leading players, including Recursion. 1 Zhou, S. and Johnson, R. (2018). Pharmaceutical Probability of Success. Alacrita Consulting, 1-42. 2 Steedman, M, and Taylor, K. (2024). Measuring the return from pharmaceutical innovation. Deloitte. 1-28. 3 DiMasi et al. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics. 47, 20-33. 4 Paul, et al. (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery. 9,203-214. 5 Martin et al. (2017). Clinical trial cycle times continue to increase despite industry efforts. Nature Reviews Drug Discovery. 16, 157. 6 European Federation of Pharmaceutical Industries and Associations (EFPIA). (2024). The pharmaceutical industry in figures: Key data 2024. 9 Table of Contents Our Strategic Focus Across Three Pillars The Recursion OS provides a common foundation for mapping biology, navigating disease space, designing molecules, and optimizing clinical trials across therapeutic areas and modalities. We deploy these capabilities to meet the specific differentiation and risk-reward needs of each program, tailoring our approach based on medical, market, regulatory, and capital considerations through a combination of internal pipeline development and strategic partnerships. This approach allows us to balance near-term learning and proof generation with longer-term platform innovation, while allocating capital where Recursion has the highest confidence and greatest potential for differentiation. The three strategic pillars described below reflect how we operationalize this model to drive disciplined value creation and impact. Figure 2.…