While foundational tools like DepMap and Open Targets have accelerated precision medicine, they often lack the interpretability and mechanistic insight required to rank ideas with the greatest clinical upside. By harmonizing carefully selected and curated data modalities, this approach allows drug developers to systematically link complex tumor biology to drug response at scale.

This webinar demonstrates how Turbine’s virtual cell models bridge these gaps by simulating ten millions of in silico experiments daily to uncover hidden vulnerabilities. We will review real-world case studies, including the discovery of novel biomarkers for PARP inhibitors and the identification of NEK1 as a viable target where physical CRISPR screens failed to find drivers.

Key learnings:

• Close Interpretability Gaps: Move beyond static data by simulating the specific protein interacions that drive drug response.
• Identify Hidden Vulnerabilities: Uncover synthetic lethal pairs and dependencies that only emerge in complex molecular contexts.
• Increase Chance or Translation to the Clinic: Learn how virtual experiments already identified more than a dozen biomarkers, with two now used in clinical panels.
• Discover “Invisible” Targets: See how virtual screening identified NEK1 as a promising target where traditional in vitro screens failed.
• Accelerate Discovery Intelligence: Explore how investigating millions ideas can deliver a ranked target validation plan in a few months.