We first compiled a sensitivity property dataset comprised of 386 energetic materials synthesized by the research groups of Klapötke and Shreeve. The structural diversity contained within our dataset is extensive, featuring compounds representative of nearly every major explosophore class in numbers sufficiently large for the application of MLR modeling. For each of the HEMs in our dataset, sensitivity properties (i.e., Tdec and IS) and experimental crystal structures were obtained from literature sources. Notably, HEMs containing metal centers and ionic structures with associated energetic counterions and oxidizers (perchlorate, guanidinium, etc.) were excluded as the presence of these auxiliary components substantially alters the sensitivity properties of HEMs, precluding efforts to study the decomposition mechanisms of explosophores on organic molecules. Consistency between the methods used to collect experimental HEM sensitivity properties is also a requirement for the compilation of high-quality datasets for predictive modeling. Differential scanning calorimetry (DSC) is typically employed to measure the decomposition temperature of energetic materials, the precision of which is dependent on the rate at which a sample is heated. As such, our dataset contains compounds subjected to the same DSC procedure. Impact sensitivity measurements are typically conducted using mechanical tests and are often not reported with a high degree of precision. In our study, we transformed experimental IS measurements to a logarithmic scale (log(IS)). The diversity of the compounds in our dataset required several simple yet important filtering steps prior to the generation of data subsets appropriate for MLR modeling. Perhaps intuitively, different explosophores impart different levels of sensitivity to a given HEM. Therefore, it is challenging to develop an interpretable MLR model using a training set that includes molecules spanning several different explosophore classes. To address this issue, we utilized Python scripts to sort and categorize HEMs using SMARTS queries and SMILES keys. The result of this selection was the creation of a number of subsets (i.e., monoazides, polyazides, tetrazoles, ditetrazoles) that maintained structural diversity about the constraint of a common explosophore. Statistical models generated from these datasets could then be used to extract chemical insight characteristic of a specific explosophore class.