Recorded: 27 May 2025

This webinar will address the different approaches to DMPK modeling, comparing data-driven methods that look for trends in existing data with physics-based models that use fundamental equations. It will also cover the limitations in data availability, such as heterogeneity and bias, and the critical importance of understanding chemical space and domain applicability for accurate predictions. DMPK models will be discussed, including examples of good data management practices. We will also dive into different data splitting techniques and how they impact model performance. Finally, we will explore cutting-edge AI applications in DMPK.

Agenda:

  • Understanding the Foundations and Limitations of DMPK Modeling
  • Data Curation and AI Model Evaluation in DMPK
  • Future Directions: Mechanisms, Interpretability, and Physics in DMPK