Integrating predicted and empirical data to address complex datasets in drug discovery

May 1, 2025 17:00 BST / 18:00 CEST / 12:00 EDT / 09:00 PDT

Computational platforms producing molecular dynamics simulations, binding affinity predictions, and experimental validation data are excellent tools for drug discovery development. However, as the volume and complexity of both predicted and empirical data in protein drug discovery increase, the ability to effectively harmonize these diverse data streams is becoming more and more essential to continue accelerating therapeutic development.

Join Congruence Therapeutics' experts Dr. Lee Fader, Dr. Jeremy Dupaul-Chicoine, and Dr. Maximilian Ebert, as they discuss the innovative integrative methods that can manage, analyze and act on various protein data. Discover how capturing biophysical properties of proteins in their full and dynamic states allows for identification of functional pockets and novel chemical matter, creating a bespoke picture to elevate your drug discovery efforts.

Key learning objectives:

• Understand approaches to streamlining the integration of chemistry, biology, and computational data streams in modern drug discovery with knowledge bases such as Congruence’s Revenir^™
• Explore methods for optimizing resource allocation and prioritization between computational predictions and experimental validation
• Discover novel approaches to harmonizing diverse data sources for identification of therapeutic opportunities
• Explore applications of generative AI models in designing for novel targets and binding pockets

Who should attend?

• Scientists in biologics, drug discovery, protein related research and data research.

Certificate of attendance

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