The presence of relatively little experimental data (13 proteolytic stability data points, 3 dimerization data points) means that it is difficult to make firm statistical conclusions about these screening metrics. It is, however, the difficulty in generating experimental data that motivates the development of computational screening techniques. Our framework presented here is widely applicable and allows easy screening of large numbers of insulin glycoforms. To further validate this approach, we will in the future apply this framework in a blind challenge to more complicated insulin glycoforms that are under experimental investigation. The results presented in this paper suggest that we are likely to be able to differentiate the structures with high proteolytic stability and low dimerization propensity from others, as long as the configurational ensembles of the investigated structures are sufficiently sampled. Building more complicated statistical learning models at the present time, such as decisions trees or multiple linear predictors is likely not viable, as the relative paucity of experimental data would cause high uncertainty of these weights and decision cuts. However, clearer metrics could possibly be identified in more sophisticated machine-learning approaches that essentially combine 3D coordinates of the systems of interest in a non-linear fashion. For example, recent deep learning frameworks like DiffNets appear capable of identifying subtle structural signatures that predict biophysical properties in future studies.