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Drug discovery teams face siloed data and manual workflows that slow pipeline decisions. Learn how a connected scientific environment structures work as AI-ready knowledge, enabling simulation-driven discovery with full traceability from design to decision.
This expert panel from Hannover Messe 2026 explores how a continuous digital thread breaks down silos, the true cost of disconnected data and why now is the time for transformation. Learn how Siemens' expanded portfolio, including Dotmatics, delivers end-to-end solutions for faster, more efficient drug development.
Drug discovery and development structures scientific work as AI-ready knowledge from the start. Continuous data capture, lab automation and in silico modeling provide full traceability from design to decision, accelerating pipeline decisions and bringing therapies to patients faster.
Real-time data capture across modalities with lab automation structures experimental knowledge at the source, advancing collaboration and evolving experiments into dynamic, reusable models.
Multiscale in silico modeling brings digital twin power into early development, reducing reliance on physical experiments, expanding design space, derisking candidate selection and delivering stronger first-in-human outcomes.
Knowledge graphs connect distributed scientific and preclinical data, unifying intelligence so AI surfaces patterns sooner, recommends next-best experiments and drives faster, fully informed decisions that advance programs.
Learn how Dotmatics' Luma scientific intelligence platform and Siemens digital enterprise solutions create a seamless digital thread from discovery through manufacturing. This approach enables real-time data continuity, structured workflows and synchronized execution to reduce cycle times, minimize deviations and accelerate time-to-market.
Siemens and Dotmatics work cohesively to create a continuous digital thread from discovery through manufacturing. Dotmatics Luma turns experimental data into structured, AI-ready knowledge and Siemens digital twin and Industrial AI Fabric enable real-time traceability, reduce rework and accelerate time-to-market for life-saving therapies.
Siemens Digital Process Twin helped Johnson & Johnson Innovative Medicine cut pharmaceutical production time and costs by 35%. Using gPROMS FormulatedProducts process modeling, Johnson & Johnson optimized solvent switches with 30% less chemical consumption. The virtual model guides production in real-time, reduces experimental burden and accelerates development from lab scale to commercial manufacturing.
Computational tools and lab automation streamline drug development by predicting molecular properties, automating data collection and accelerating candidate selection.
At Johnson & Johnson, we try to minimize the environmental impact of our production processes. That’s why we set up a pilot project with the Digital Process Twin from Siemens.
