Schrödinger (SDGR) investor relations material

Platform innovation and scientific advancements

• Developed a computational platform to design and predict properties of molecules using first principles physics, enabling accurate simulation in complex environments.

• Integrated machine learning and AI methods trained on physics to scale exploration of chemical space, allowing analysis of trillions of molecules.

• Built robust enterprise systems for managing and analyzing massive data generated by simulations.

• Launched Bunsen, an agent leveraging LLMs to automate and scale complex workflows, positioning for future technology shifts.

• Platform is applicable beyond drug discovery, including materials science and electronics design.

Business model, customer engagement, and growth

• Licensing platform to pharma and biotech, with strong adoption among top 20 global pharma and growing traction in biotech as market conditions improve.

• High customer retention supported by new product rollouts like Bunsen, Predictive Tox, and Crystal Structure Prediction, expanding reach and budgets.

• Transitioning from on-prem to hosted solutions over three years, targeting 75% hosted revenue for cleaner financials and improved customer support.

• Q2 saw 27% ACV growth year-over-year, broad gains across pharma, biotech, and materials, and increased annual guidance by $10 million.

• Operational efficiencies achieved through headcount reductions and reduced CRO spending, with new spin-outs like Tectora transferring costs and retaining equity stakes.

AI, technology differentiation, and industry impact

• AI alone cannot replace physics-based simulation due to the vastness of chemical space; physics-driven data generation is essential for novel drug discovery.

• Coding tools and AI accelerate development but require rigorous validation, making full automation challenging.

• Bunsen enables customers to scale expertise, acting as a co-scientist and unlocking AI budgets for adoption.

• Predictive Tox module allows earlier de-risking of drug candidates, with positive beta testing and real-world success in identifying and resolving toxicity issues.

• Collaborations with major pharma (e.g., BMS, Novartis, Eli Lilly) drive platform adoption and validation, with ongoing high demand for partnerships.