Explore the Agenda
7:30 am Check-in
Check in & Light Refreshments
8:25 am Chair’s Opening Remarks
Leveraging Experimental Data to Enable Shorter Design & Engineering Timelines For Deeper Biological Understanding & More Informed Drug Design Decisions
8:30 am Harnessing Artificial Intelligence & Cryo-EM to Accelerate Structure-Based Drug Discovery
- Leveraging machine learning–guided construct optimization to improve protein expression, stability, and cryo-EM suitability
- Accelerating structural determination of dynamic and difficult targets such as membrane proteins and large complexes
- Combining AI-assisted protein engineering with cryo-EM structural analysis to generate actionable SBDD insights
9:00 am Session Reserved for Thermo Fisher
9:30 am Aligning Construct Strategy, Stability & Biology for Modern Structure-Based Drug Design
- Designing construct strategies that maximize structural yield and biological fidelity
- Balancing speed and exploration when screening multiple protein variants in parallel
- Demonstrating why high expression yield alone is insufficient, and how prioritising functional competence and conformational relevance have prevented misleading ligand poses and downstream design errors
- Delving into expression, production, solubilisation, and stabilisation strategies to overcome heterogeneity and preserve biologically relevant conformations
10:00 am Speed Networking & Morning Break
This informal session provides the perfect opportunity to connect with your industry colleagues,
from structural biologists to medicinal and computational chemists, protein engineers, and drug
discovery experts. Instigate useful introductions to build upon for the rest of the conference forming
valuable connections.
11:00 am Bridging Structural Biology & Cell Biology: Designing Fusion Protein Therapeutics Using 3D Cell-Surface Models
- Constructing three-dimensional structural models of the red blood cell surface to contextualize drug–target interactions beyond single protein structures
- Using structural insight to design engineered fusion proteins that achieve tissue targeting while minimizing unintended crosslinking and side effects
- Demonstrating how rational protein design, leveraging published cryo-EM structural data, can replace large discovery platforms, enabling small teams to develop novel biologics efficiently
- Leveraging published cryo-EM structural data and integrative modelling to connect molecular structures with cell-level therapeutic mechanisms
Navigating the Reality of Modern Structural Biology Pipelines to Overcome Experimental Constraints & Improve Success Rates
11:30 am Session Reserved for Cryocloud
12:00 pm Panel Discussion: The Reality of Cryo-EM Access, Bottlenecks & Future Directions
- Comparing access, cost, and timelines for large pharma versus biotechs, and when outsourcing or academic partnerships make more sense
- Addressing challenges around data volume, selection, and cloud storage
- How data processing, classification, and refinement workflows impact turnaround time and confidence in results
- Current constraints including size limits, flexibility, heterogeneity, and challenges with smaller molecules or weak interactions
- Emerging hardware, sample preparation strategies, and computational advances aimed at improving resolution, throughput, and applicability
12:45 pm Lunch Break & Networking
1:45 pm Building the Plane While Flying: Embedding Structural Biology in a Platform-Driven Discovery Model
- Operating in a platform-driven discovery model where programs launch before full mechanistic understanding is established
- Using single-particle cryo-EM to resolve divergent mechanisms for distinct chemotypes binding the same pocket
- Identifying alternative binding sites on well-studied targets to enable differentiated pharmacology
2:15 pm Session Reserved for Structura Biotechnology
2:45 pm Evaluating Co-Folding Models Against Experimental & Clinical Reality
- How co-folding models have been employed to guide early design choices and prioritization
- Evaluating how predicting binding modes and interaction interfaces have aligned with functional assays, in vivo behaviour and translational readouts
- Reviewing where predicted interaction surfaces informed mechanism of action understanding, and where experimental structures were required to solve uncertainty
Building Reliable AI-Driven Discovery Workflows that Integrate Prediction, Data & Experimental Validation that Close the Gap Between Computational Models & Biological Reality
3:15 pm Closing the Loop Between Structural Biology & AI: Improving Nanobody Structure Prediction with Targeted Experimental Data
- Identifying low-confidence regions in AI-driven protein structure prediction models for nanobodies through computational analysis
- Designing custom structural biology experiments to generate targeted training data that improves model accuracy
- Showcasing how close collaboration between wet lab structural biology and AI teams can iteratively refine predictive models for biologics discovery
3:45 pm Afternoon Break & Poster Session
Connect with peers in a relaxed atmosphere and continue to forge new and existing relationships
while exploring the latest advancements in structure-based drug design.
To register your poster submission, please contact info@hansonwade.com
4:15 pm Interactive Exchange: Aligning Computational Prediction with Experimental Reality
Following a series of perspectives from structural and computational experts, this interactive session will shift the focus from presentation to participation. Through structured, topic-led roundtable rotations, attendees will explore where predictive modelling delivers real value, where it falls short, and how experimental data quality influences model performance.
Participants will compare workflows, discuss bottlenecks in data generation and validation, and share practical insights on integrating modelling with structural and biophysical evidence to improve confidence in discovery decisions.
4:45 pm Panel Discussion: Combining AI Platform Technologies to Move from Structural Insight to Informed Drug Design Decisions
- Leveraging foundation models to navigate complex protein systems (multi-domain, membrane-embedded, inducedproximity targets) that exceed the limits of single-structure or single-model approaches
- Combining AI-driven protein design with experimental structure generation to resolve ambiguity in binding modes, conformational states, and interaction interfaces
- Closing the loop between AI predictions and wet-lab validation by continuously retraining models on construct performance, structural success, and functional readouts