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Pharmaceutical Analysis Track

Panel Discussion

Tuesday, July 14, 2026
11:30 AM - 12:00 PM East Coast USA Time
Location: Gina Knee
The discussion may be articulated around diffrent points including:
1- Making Retention Prediction Reliable: Can Physics‑Informed AI Deliver for Industry?
2- From Black Boxes to Predictable Outcomes: Improving Retention Modeling with Molecular Dynamics?
3- Why AI Alone Isn’t Enough for Retention Time Prediction in Pharmaceutical LC?
4- Reducing Risk in Retention Prediction: Integrating Molecular Dynamics with AI?
5-Toward Trustworthy Retention Models: Bridging Molecular Simulation and AI for LC?

Learning Objectives:

  • Explain what makes retention prediction challenging in LC
  • Justify some limits of AI/ML tools today for RT prediction
  • Elaborate and justify alternative Physics-based solution
  • Launch the idea of combining Physics-based and AI/ML tools for improving RT prediction in LC