We have investigated the relationship between the storage conditions, physical and chemical stability of Peptide A using interpretive machine learning techniques. These machine learning models trained only on the storage conditions of the peptide are able to reliably predict the total degradation (TD) and percent label claim (%LC, a measure of potency) with R 2 values greater than 0.90 and a mean absolute error less than 0.5 for the prediction of TD and less than 1.5 for the prediction of %LC. These models were then analyzed to understand how the underlying variables relate to the chemical degradation of Peptide A to show that the pH and %EB combined with the timepoint of the experiment are important features of the model while the usage of histidine does not appear to be important for modeling the chemical degradation of the peptide. We believe that these modeling efforts are useful for understanding the degradation of Peptide A as a function of its storage conditions. In conclusion, we provide a framework to develop the relationships between chemical and physical degradation for future studies in drug development and in chemical sciences. ).