We present a multimodal AI framework that fuses radical footprinting data with deep learning to more accurately model antibody–antigen complexes, including disordered and hypervariable interfaces. Using Immuto’s high-throughput radical footprinting platform, large-scale structural datasets, comprising hundreds of protein complexes, are generated to train AI systems, providing solution-state, dynamic data across all protein classes, including multipass transmembrane proteins. These experimental constraints enable advanced, actionable models for antibody engineering, affinity maturation, and specificity design.