Recursion Pharmaceuticals' Presentation at the 53rd Annual JPMorgan Global Technology, Media and Communications Conference: Advancements in TechBio and AI-Driven Drug Discovery

Key Takeaways

TL;DR: Recursion Pharmaceuticals (RXRX) leverages its AI/ML-driven TechBio platform to tackle the biopharma industry's 95% drug failure rate, achieving 80-90% reductions in time/cost and identifying novel targets (e.g., RBM39, FDA-cleared in 2024). With $450M+ in partnership rev., upcoming $300M+/program milestones, and upcoming clinical data (CDK7, FAP, and 3 oncology programs), RXRX aims to redefine drug discovery efficiency and patient outcomes. Critical NVIDIA and Sanofi/Roche collaborations validate its platform, while its simulated clinical trials and real-world data integrations address trial bottlenecks.

1. TechBio Platform & AI/ML Impact

• Core Mission: Transform biopharma’s 95% failure rate by integrating AI-driven predictive modeling across drug discovery/development.
  • Ben Taylor: “We create complex simulations weighing thousands of variables to balance risks holistically.”
  • Platform Reductions: 80-90% less cost/time in drug discovery; 50% lower IND-enabling study costs.
• Recursion OS 2.0 (post-Exscientia merger):
  • Combines multi-omic data (phenomics, transcriptomics, patient data) with GenAI tools (e.g., GFlowNets) for end-to-end drug design.
  • Enamine REAL Partnership: Screened tens of Bs compounds in silico → selected top candidates, slashing physical testing costs.

2. Partnerships & Revenue Drivers

• Pharma Collaborations:
  • Sanofi: 4 programs advanced in <2 yrs vs. historic decade-long timelines → $300M+/program milestones (15 programs total).
  • Roche: $30M milestone paid for target map delivery; 40 programs potential.
• Tech Alliances:
  • NVIDIA: Critical for scaling AI on 65PB proprietary datasets (Jensen Huang views healthcare as “highest-impact AI field”).
  • Tempus/Patient Data: Enhances clinical trial design/patient selection via real-world evidence.

3. Clinical Trial Optimization

• Patient Enrichment: Merges in-house data with anonymized patient cohorts to boost signal-to-noise ratios.
• Simulated Trials: Predict failure risks (e.g., drug-drug interactions, side effects) → minimize costly late-stage failures.
  • Lina Nilsson: “We model clinical outcomes to avoid hyperbilirubinemia risks seen in competitor drugs.”

4. Pipeline Catalysts (Next 12-18M)

• Oncology Focus:
  • CDK7 & FAP: Early positive clinical data (Dec-2024) → efficacy updates and combination trial results expected.
  • 3 New Programs: Clear endpoints (e.g., tumor response rates) to validate platform’s predictive accuracy.
• Platform Validation: Success here de-risks pipeline and partnerships → key for royalty-driven LT rev.

5. Regulatory & Macro Positioning

• FDA Alignment: RXRX’s human-based models (vs. animal testing) align with FDA’s recent guidance → competitive edge.
• Biotech Downturn: Positioned as “biz model vs. binary bet” via diversified partnerships/platform validation.