To the extent of our knowledge, this is the first work that uncovers a quantitative global explanatory framework from chemistry's deep learning models, such as GNN models. In the past, explainability has been done locally, using extrinsic tools, and observing how the model responds to limited examples. However, our linear analogies structure, and perturbational replications sets the framework for a global explanation to the latent space of GNN chemistry. While the main frame is set, there is still much room to explore. One direction is to map out the trajectories of reactions in the latent space, and uncover what is happening between end-points of reactions, i.e. transition states. Another direction is to study how this global explanatory framework reduces to a local one and thus can give us explanations on a case-by-case basis. For example, we can study how the replicates improve prediction of chemical quantities (or changes in chemical quantities) based on finely tuning the neighborhood composition. This also sets the stage for a global evaluation of model generalizability and transferability. Lastly, there is room for improving the replicates of the GNN model. For instance, the perturbational replicate model uses a neighborhood composition feature space that ignores the exact layout of the graph, by incorporating the exact layout, and using the same geometry optimization as the GNN model, can lead to a finer replicate. Nonetheless, the findings in this paper set the stage for global explorations in GNN modeling of chemistry in a single coherent framework.