MolecuLern: AI-Driven Precision in Small Molecule Glue Degraders Through a Data-First Approach to De-Risk & Accelerate Drug Discovery
- Demonstrate how data-first AI frameworks in discovery reduce attrition by sharing practical, validated examples of how they are being used to design real, experimentally confirmed small-molecule degraders, showing what works (and what doesn’t) in moving from prediction to proof
- Gain insight into emerging strategies for precision molecular glue design and highlight novel approaches to rationally engineer glue degraders by integrating structural biology, cheminformatics, and predictive modeling offering valuable lessons for anyone working on targeted protein degradation, GLUES or PROTACs, or complex modality discovery
- Discover frameworks to de-risk R&D pipelines and learn how data-centric model validation and feedback loops can reduce late-stage attrition, accelerate candidate selection, and improve return on discovery investment. Ideas that can be applied across therapeutic areas to accelerate the path from concept to candidate