Explore the Agenda
8:00 am Check-In & Light Breakfast
9:00 am Chair’s Opening Remarks
Accelerating Bispecific, Trispecific & T-Cell Engager Design & Optimization Through Hybrid In Silico & Experimental Strategies to Enhance Developability, Function & Translation
9:10 am Target Selection & Selectivity Engineering in a 2+1 T-Cell Engager Format Can Drive Positive Clinical Outcomes
- How do target properties, affinity, and format interplay during early research?
- What molecular properties can lead to successful T-cell engager platforms at multiple levels of development?
- What opportunities are available upon initial positive clinical signals?
9:40 am Round Table: Closing the Design-Build-Test-Learn Loop for Multispecific Antibody Discovery & Candidate Selection
- Combining computational and experimental workflows to identify and refine bispecific candidates
- Determining what data is most valuable for informing and improving protein design models
- Building effective feedback loops between in silico prediction and experimental validation
- Key challenges in translating engineered multispecific antibodies from discovery through candidate selection
10:40 am Morning Break & Networking
Benchmark Hybrid Computational & Wet-Lab Workflows to Improve Design-Build-Test Iteration Cycles & Improve Candidate Success
11:40 am Biological Proximity to Therapeutic Design: Integrating Computational Approaches with High-Throughput Experimentation Quality
- Combining computational analysis with experimental interrogation to uncover biologically relevant protein relationships and guide therapeutic hypotheses
- Using rapid probe generation and high-throughput testing to inform target selection, molecular design, and functional optimization
- Applying iterative design-build-test workflows to translate biological insights into differentiated therapeutic concepts
12:10 pm Round Table: Optimizing Design-Build-Test-Learn Workflows to Improve Iteration Speed Decision-Making & Data Value
- Where do bottlenecks occur in design-build-test-learn workflows and how do differences in timing between computational and experimental teams’ slow iteration cycles?
- How can closer integration between wet-lab and data science improve data handoffs, reduce delays and enable faster iteration?
- How can teams prioritize experiments that generate high value data to improve decisions and maximize learning in each cycle?
1:10 pm Lunch Break & Networking
Integrate Structural Biology, Biophysics, & High-Throughput Experimental Validation for Improved Predictability & Translational Success
2:10 pm Panel Discussion: Applying Structural Biology Insights to Improve Functional Prediction & Translational Success in Protein Design
- Assessing the limitations of structure prediction methods such as AlphaFold and exploring how moving beyond static models to dynamic systems can improve protein design outcomes
- Examining how cryo-EM data and co-folding models can be combined to enhance prediction of multi-component interactions in complex biologics
- Understanding what drives the gap between structural accuracy and functional performance, and how to better translate in silico designs into biological activity
3:10 pm Protein Design to Enable Structural Biology of Membrane Proteins & GPCRs: From Soluble Analogues to Conformationally Stabilized Receptors
- Surveying the protein engineering objectives that enable structural studies of membrane proteins and GPCRs, including the use of soluble analogues and stabilized receptor constructs
- Prioritizing design strategies that deliver structural biology value by improving construct tractability, conformational control, and downstream drug discovery impact