The pharmaceutical industry regularly uses Hydrogen Deuterium exchange mass spectrometry (HDX-MS) to inform key decisions in small molecule, antibody, and vaccine R&D. However, the statistical analysis of HDX-MS remains primitive, holding back important - potentially life-changing - discoveries. One key complication is that peptide spectra are manually assessed for quality, and peptide masses are frequently corrected by domain experts. Furthermore, excessive amounts of HDX-MS data are discarded, and inappropriate statistical methods are routinely applied. I develop scalable and extensible software methods to improve reproducibility and interpretation in structural mass spectrometry, along with statistical and machine learning tools for analyzing such data.