Agility with Complex Data Sets – Integration of Predicted and Empirical Data for Rapid Innovation in Drug Discovery

Join leading experts for an examination of modern approaches to integrating computational predictions with experimental data in protein drug discovery. As the volume and complexity of both predicted and empirical data continue to grow, the ability to effectively harmonize these diverse data streams becomes crucial for accelerating therapeutic development. This webinar will explore cutting-edge methods for managing, analyzing, and acting on complex scientific datasets. 

The session will explore novel methodologies for integrating diverse data types in drug discovery pipelines. We will discuss approaches to harmonizing heterogeneous data streams—including molecular dynamics simulations, binding affinity predictions, and experimental validation data. 

This 45 minute discussion will be followed by 15 minutes of live Q&A with the speakers.

Key Learning Objectives

• Understanding approaches to streamlining the integration of chemistry, biology, and computational data streams in modern drug discovery with knowledge bases such as Congruence’s Revenir™

• Methods for optimizing resource allocation and prioritization between computational predictions and experimental validation

• Novel approaches to harmonizing diverse data sources for identification of therapeutic opportunities

• Applications of generative AI models in designing for novel targets and binding pockets