One interesting application of machine learning in chemistry is the ability to understand how underlying variables (x) relate to the output (y). The use of machine learning to understand these variables has been applied to understanding how IR and MS spectra relate to functional groups and how functional groups in organic molecules relate to chemical reactivity . When performing this type of analysis there are two important points to consider. First, machine learning models can only learn from the variables they are given, and second machine learning models can "overfit" to a particular data point set of variables . This second point can be addressed by using cross-validation. Recent works have shown that machine learning models built on small training sets can be successful for both understanding chemistry and for useful predictions. To our knowledge, these techniques have not been used to understand the relationships between storage conditions, chemical stability, and physical stability in drug formulation. In this work, we will outline how an initial design of experiment (DoE) was used to generate training data for both the chemical and physical stability of a peptide drug molecule in formulation matrix.