Explore the Agenda
9:00 am Registration & Morning Coffee
Workshop A
9:30 am Integrating the Structural Biology Toolbox with Best Practices for Multi-Technique Drug Design
With a variety of techniques available in the structure-based drug design toolbox, each with its own advantages and limitations, optimal drug design requires moving beyond single-technique approaches to cohesive, multi-modal design workflows. This workshop will provide attendees with practical insights into integrating multiple methods, including leveraging the strengths of X-ray crystallography, NMR, Cryo-EM, Cryo-ET, HDX-MS, and computational tools to optimize small molecules, biologics, and complex targets.
Highlights include:
Examine the strengths, limitations, and practical applications of each technique including X-ray crystallography, Cryo-EM, Cryo-ET, NMR, HDX-MS, and biophysical assays, for small molecule and biologics discovery
Learn strategies to integrate multiple structural and computational data sources, manage conformational heterogeneity, and balance experimental versus predictive insights
Explore real-world case studies demonstrating how multi-technique approaches accelerate hit-to-lead optimization, improve candidate selection, and inform beyond potency design
This session is ideal for structural biologists, computational chemists, medicinal chemists, and drug discovery scientists seeking to implement an integrative, technique-agnostic approach to make informed decisions in structure-based drug design.
12:30 pm Lunch Break & Networking
Workshop B
1:30 pm Integrating Artificial Intelligence & Machine Learning into the Structural Biology Toolbox for Enhanced Structure-Based Drug Design
With AI tools like AlphaFold3, Boltz-2, Chai-1, RFdiffusion and generative chemistry platforms moving rapidly into the drug discovery space, the question is no longer if AI belongs in structure-based drug design but how to apply it effectively. This workshop will equip attendees with practical insights on incorporating AI/ML into the structural biology workflow, ensuring models are reliable, interpretable, and complementary to experimental approaches.
Highlights include:
Understand the current capabilities and limitations of AI for protein structure prediction, binding mode analysis, and property optimization
Learn strategies to integrate AI predictions with X-ray crystallography, Cryo-EM, NMR, and biophysical assays to validate and refine outputs
Explore case studies demonstrating AI in action, from accelerating hit-to-lead optimization to enabling novel scaffold and biologics design
Discuss best practices for data quality, avoiding “garbage in, garbage out,” and building trust in AI models across discovery teams
This session is ideal for structural biologists, computational chemists, AI/ML specialists, and drug discovery scientists seeking to unlock the full potential of artificial intelligence as part of a cohesive, structure-driven design strategy.