Explore the Agenda
8:00 am Registration & Morning Coffee
8:45 am Chair’s Opening Remarks
Decoding GPCR Conformational Landscapes Through Computational Modeling to Enable Predictive, Functionally Selective & Safer Therapeutics
9:00 am Transforming the Development of Functional Anti-GPCR Antibodies with AI-Driven Design
- Leveraging AI-driven design antibodies against GPCRs without immunization or display, including targeting deep intra-pocket epitopes previously inaccessible to conventional methods
- Generating large panels of GPCR-binding antibodies in a single computational run, achieving high hit rates and experimentally light workflows that validate both binding and functional activity in high relevance settings
- Demonstrating AI-enabled functional design, including agonism, antagonism, and ligand-mimicking behaviours, and highlighting why this represents a step-change for GPCR drug discovery
9:30 am Revealing the Dynamic Pathways of GPCR Bias Through Integrated Modeling & Pharmacology to Accelerate Discovery of Functionally Selective Therapeutics
- Utilising AI and physics-based simulations to reveal conformational signatures behind G-protein vs β-arrestin signaling
- Integrate simulations with pharmacology to turn conformational insight into measurable biological outcomes
- Sharing case studies translating structural insights and pharmacology predictions into the discovery of biased GPCR candidates
10:00 am Morning Break & Networking
10:45 am Leveraging Large-Scale Functional & Developability Data to Generate Novel Antibody GPCR Agonists
- Identifying GPCR antibody agonists from millions of NGS reads by learning sequence-activity maps with fine-tuned protein large language models
- Integrating internal and public datasets to evaluate antibody developability to go beyond activity and increase the probability of success
- Designing novel antibodies with high activity and superior developability by leveraging generative models to manage key trade-offs
Applying AI to Streamline Discovery Pipelines & Generate Functionally Validated, Translationally Relevant Therapeutics
11:15 am Panel Discussion: From Static Pictures to Intelligent Pipelines – The Next Era of AI-Driven GPCR Discovery
- How can dynamic modeling, data integration, and cross-disciplinary collaboration be connected to create a truly cohesive GPCR discovery ecosystem?
- What operational barriers must be overcome to achieve genuine AI-enabled translational science across academia and industry?
- What does the roadmap look like for structure-guided and AI-assisted GPCR discovery to reshape drug development over the next decade?
12:15 pm Lunch Break & Networking
1:15 pm Transforming GPCR Discovery with AI-Driven Feedback Loops to Deliver Faster, More Reliable & Experimentally Confirmed Therapeutic Candidates
- Accelerating GPCR discovery with an AI-driven feedback loop delivers reliable therapeutic candidates from in silico to clinic within two years
- Naturalizing de novo peptides with AI tools creates natural analogues that retain function and improve developability
- Demonstrating an end-to-end case study shows how this AI workflow advances GPCR therapeutics rapidly to first-in-human trials
1:45 pm Accelerating Drug Discovery with Federated Computing: Inside Lilly’s TuneLab Platform
- Highlighting how federated learning enables collaborative model training while preserving data privacy of all participants
- Highlighting how Lilly’s TuneLab platform employs federated computing and AI models to help accelerate breakthrough medicines to patients
- Walking through a user-experience preview that illustrates how researchers design, evaluate, and iterate molecules more rapidly within the platform
2:15 pm GPCR Drug Discovery with NeuralPLexer & Enchant AI: From Dynamic Receptor Structures to Clinically Informed Design
- Introducing Iambic’s NeuralPLexer and Enchant AI technologies and how they integrate into a closed-loop discovery engine to address the structural complexity, conformational dynamics, and challenging pharmacology of GPCRs
- Showing how generative protein–ligand complex predictions for GPCRs (orthosteric, allosteric, and cryptic sites) guide rapid design–make–test cycles to optimize potency, selectivity, and signaling bias
- Highlighting how Enchant AI leverages preclinical data to predict human-relevant PK/ PD, safety, and probability of clinical success, and how combining NeuralPLexer +Enchant reshapes go/no-go decisions for GPCR assets
2:45 pm Accelerating Antibody Discovery for Complex Targets: Digital Platforms & AI Integration for GPCRs
- Digitalization and optimization of biotherapeutics discovery workflows accelerate identification of antibodies against challenging targets such as GPCRs
- Automated platforms leveraging integrated sequence, screening, and informatics data enable efficient selection from large, diverse antibody panels
- These advancements provide a foundation for AI/ML applications to address complex therapeutic targets such as GPCRs