Day One

• Overcoming resolution and flexibility challenges in small molecule/protein complexes
• Leveraging cryo-EM for conformational heterogeneity and dynamic systems
• Integrating cryo-EM data with complementary approaches (crystallography, NMR, MD) for a well-rounded picture

• Demonstrating how Cryo-ET complements Cryo-EM and computational approaches to resolve heterogeneity in large molecular complexes
• Case studies where Cryo-ET combined with modeling (e.g. AlphaFold) enables structural insights into viruses and challenging biological assemblies
• Practical considerations: sample prep, overcoming technical hurdles, and leveraging maps for better modelling of complex assemblies
• Exploring the role of Cryo-ET within an integrated workflow to accelerate mechanism-driven drug discovery

This informal session provides the perfect opportunity to connect with your industry colleagues, from structural biologists to medicinal and computational chemists, protein engineers, and even drug discovery experts. Instigate useful introductions to build upon for the rest of the conference forming valuable connections.

• Showcasing emerging AI-based methods transforming cryo-EM and cryo-ET workflows
• Exploring how AI enables more accurate determination of molecular and cellular structures in native environments
• Highlighting opportunities where AI integration overcomes limitations of traditional approaches
• Discussing future applications of AI-enhanced cryo-EM/ET for structural biology and drug discovery

• Leveraging structural insights to engineer large molecules and fusion proteins with enhanced potency and selectivity
• Applying structure-guided approaches to minimize liabilities such as immunogenicity, instability, or off-target effects
• Exploring strategies to optimize molecular architecture for improved developability, safety, and therapeutic efficacy

• Illustrate how structural modeling informed the transition from a conventional bispecific format to a 2+1 configuration, boosting receptor activation from 30% to 70%
• Share optimization strategies across linker design, Fab vs. scFv formats, and IgG subclass backbones to enhance clustering efficiency and functional activity
• Highlight the iterative design process, including Fc modifications to balance effector function, and lessons learned for applying structure-based design to complex antibody engineering challenges

Despite rapid advances in structural techniques, computational models, and biophysical tools, many discovery programs still operate in disciplinary silos. This panel will explore how to weave together structural biology, biophysics, computational chemistry, and CADD to generate a holistic, data-driven view of targets and ligands, ultimately enabling more potent, selective, and developable candidates.

• How can structural insights, computational modeling, and experimental validation be better integrated into a single design loop?
• What are the practical barriers, technical, organizational, cultural, that prevent true cross-functional integration, and how can we overcome them?
• Where has integration already delivered measurable improvements in candidate quality (potency, selectivity, developability)?
• How can AI and machine learning act as a “bridge” between disciplines, and where are the limits of that approach?
• What does an ideal, multidisciplinary discovery team look like in practice?