Join us for a focused demonstration of Turbine’s Payload Selector, an advanced tool built on a foundation model-like architecture and powered by virtual cells. We’ll walk through the user experience and show how the platform enables rapid, simulation-driven selection and optimization of ADC payloads and their combinations across thousands of virtual samples of different preclinical model systems. See how virtual cell simulations help fill the data gaps and predict resistance or efficacy before lab work begins. The session will conclude with a live Q&A to address your specific questions.
Ákos Tarcsay is Product Manager at Turbine, where he leads the development of innovative digital tools for cancer drug discovery. He holds a PhD in Chemical Engineering from Budapest University of Technology and Economics and brings deep experience from roles at Chemaxon and Gedeon Richter. Ákos specializes in translating scientific and customer needs into actionable product features for AI-driven platforms.
Learning Objectives:
- See how you can simulate ADC payload and payload combination response in Turbine’s Virtual Lab
- Learn how to identify and overcome payload resistance across millions of perturbations
- Discover how data powers smarter predictions in novel context through simulations
Experience the future: a seamless, user-friendly Virtual Lab UI in action