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Modern protein drug discovery relies on successfully integrating computational predictions and experimental data. As the volume and complexity of both predicted and empirical data continues to grow, the ability to effectively harmonize these diverse data streams becomes crucial for accelerating therapeutic development and maximizing resource use. And if that process is not optimized, discovery costs can quickly escalate.
In this GEN webinar, our subject matter experts, Dr. Lee Fader, Dr. Jeremy Dupaul-Chicoine, and Dr. Maximilian Ebert, will provide an overview of current methodologies and technological innovations for harmonizing heterogeneous data streams including molecular dynamics simulations, binding affinity predictions, and experimental validation data to drive breakthroughs in protein therapeutics. Key learning objectives include:
- 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 for designing for novel targets and binding pockets
A live Q&A session will follow the presentation, offering you a chance to pose questions to our expert panelists.
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