Explore the Agenda
8:00 am Check-In & Light Breakfast
Workshop A
9:00 am Closing the Gap Between AI Designed Proteins & Functional Molecules Through High Quality Data Integration, Experimental Validation & Cross Functional Collaboration
This workshop will feature deep dive sessions on:
From Design to Optimization: Building Integrated Platforms for Functionalizing De Novo Proteins
- Addressing the unique challenges of producing and testing designed proteins
- Transitioning from designing for functional exploration to designing for optimization
- Coordinating multidisciplinary teams to measure diverse properties and accelerate design-build-test-learn cycles
Building Curated Databases to Fine-Tune Property-Prediction Models for Designed Proteins
- Bridging the wet- and dry-lab divide to prioritize and plan data generation
- Assessing workflow maturity and data quality before model fine-tuning
- Designing curated datasets that support reliable prediction of protein properties
12:00 pm Lunch Break & Networking
Workshop B
1:00 pm Designing for Developability & Manufacturability from Day One to Improve Stability, Scalability & Reduce Late-Stage Risk in Biologic Development
Late-stage failures driven by aggregation, instability, and manufacturability issues continue to impact biologics pipelines, often because developability is overlooked during early design. As organizations aim to reduce downstream re-engineering, this workshop will explore how to embed developability and CMC considerations earlier through better data strategies, integrated screening, and stronger alignment between discovery and CMC teams to select candidates that are both functional and scalable.
This workshop will focus on:
- How to identify developability risks such as aggregation, viscosity, instability and, poor expression earlier in design workflows using physicochemical and biophysical indicators
- How to integrate developability screening with sequence design and candidate selection to prioritize molecules that are stable at high concentration and suitable for manufacture
- How to improve data quality for developability prediction, including use of representative formulation conditions and more consistent screening assays
- How to embed manufacturability considerations, such as expression purification and, formulation constraints earlier to reduce downstream re-engineering
- How to improve alignment between discovery and CMC teams to enable better decision making and smoother progression from design to development