With often thousands of non-covalent interactions present in a standard MD simulation, identifying the key interactions responsible for regulating a given process of interest can be challenging affair, especially in the case of interactions between residues distant from the site of interest. By building on and interfacing with well-established tools within the biomolecular simulation community, our package provides a largely automated approach for a user to identify and rank these key interactions for further investigation. We have applied our package to three different exemplar systems to demonstrate potential use cases and evaluate how the methods within perform relative to one another. Overall, and in all three cases, we were able to gain detailed insight into each conformational change studied. This includes both large scale conformational changes (PTP1B's WPD-loop conformations) and much more subtle the different classes/values of the target variable, we would recommend turning to the statistical analysis package for both classification and regression. One possible application of the ML package in KIF would be to identify a minimal set of interactions needed to describe a conformational change of interest. The interactions identified from this approach could then be described by something quick and easy to measure such as a set of distances and used as collective variable(s) for enhanced sampling simulations (such as metadynamics simulations) to rapidly simulate the conformational change of interest. This approach could also benefit from approaches such as RAVE (Reweighted autoencoded variational Bayes for enhanced sampling), which would enable the further reduction of the number of collective variables.