Pre-Conference Day

• Exploring the complementary roles of NeuralPLexer and Enchant to combine proteinligand structural modeling with multimodal property prediction in GPCR drug discovery
• Applying predictions, uncertainty estimates and experimental feedback across the design-make-test-analyze cycle to prioritize compounds and experiments while balancing potency, selectivity and ADME properties
• Sharing lessons learned from connecting AI-guided design with experimental pharmacology and iterative compound optimization to support GPCR discovery programs

• Demonstrating how Biagon’s machine learning platform integrates structural, experimental and computational data to predict GPCR signaling outcomes and guide the rational design of biased agonists
• Showcasing results in studies from obesity, pain and oncology programs demonstrating successful translation from computational prediction to experimental validation
• Exploring how predictive biased agonism can improve therapeutic selectivity and accelerate the development of next-generation GPCR-targeted medicines

• Where has AI demonstrably delivered measurable impact on GPCR hit identification, optimization and candidate selection?
• What benchmarks and validation frameworks do the field need to credibly assess AI platform performance against traditional methods?
• As AI capabilities expand, where does biological expertise and experimental validation remain non negotiable in the decision-making process?

• Leveraging AI-driven structural comparisons to identify previously unrecognized GPCR-like proteins hidden beyond sequence similarity
• Applying experimental biology to validate computational predictions and establish novel receptor functions
• Expanding the future GPCR target landscape by illuminating “dark” and “super-dark” biology inaccessible through conventional discovery approaches

• Developing functional nanobody binders to enable mechanistic studies of orphan GPCRs that remain difficult to interrogate using conventional pharmacological tools
• Using computational protein design to target receptors lacking experimental structures
• Integrating cryo-EM, microscopy, surface plasmon resonance, and cell-based assays to validate nanobody binding, define mechanism, and establish receptor pharmacology

• Applying structure-guided design principles to optimize potency, selectivity and druglike properties
• Overcoming medicinal chemistry challenges associated with targeting the Orexin 2 receptor
• Translating receptor biology and pharmacology into a development candidate for narcolepsy

Roundtable A: Comparing AI-Enabled & Experimental Screening Approaches to Improve GPCR Hit Identification

1. Where does AI outperform traditional screening workflows today?
2. When does experimental screening still provide superior biological insight?
3. What combination of computational and experimental approaches creates the highest-quality hits?

Roundtable B: Reconciling Structural, Computational & Functional Approaches to Characterize GPCR Conformational States

1. Where does Cryo-EM deliver the clearest actionable insight, and where does it fall short?
2. When does computational/ dynamic modelling genuinely outperform a static structure?
3. How do functional assay signals get reconciled with structural predictions when they disagree?