Researchers at Baylor College of Medicine reported an AI- and proteomics-driven approach to discovering molecular glues that degrade VAV1, a signaling protein implicated in blood cancers and autoimmune disease. The team combined high-throughput proteomics screening with GluePlex, a computational workflow integrating protein-structure prediction and physics-based modeling. In Nature Communications, the group described a pathway to identify key degradation determinants—pinpointing a specific VAV1 region used as the ‘degron’ by the glue—then confirmed the mechanism through follow-up experiments dependent on the proteasome and cereblon (CRBN). The work also emphasized that the degron region described differed from canonical CRBN-associated molecular-glue signals. The study matters for the broader field because it demonstrates how structure-aware modeling and unbiased proteomics can be used to accelerate target-to-lead selection for degradation strategies.