A Baylor College of Medicine-led team reported an AI-assisted approach to discover and optimize “molecular glue” compounds that degrade VAV1, an immune protein implicated in blood cancers and autoimmune disease. The work combined high-throughput proteomics with AI-based structural prediction to identify a new class of degradation agents. Molecular glues are small molecules that induce proximity between a target protein and cellular degradation machinery, leading to selective degradation rather than inhibition. In this case, VAV1 degradation is positioned as a potential strategy for therapeutic intervention in diseases driven by aberrant immune signaling. The study matters for the biotech pipeline because it shows a pragmatic integration of proteomics screening and structure-informed AI to accelerate hit-to-lead progression in protein-degrader discovery—an area where target validation and mechanism clarity are major bottlenecks. While the report is discovery-focused, the identification of a VAV1-directed glue class sets up subsequent preclinical optimization and lead selection efforts.